Rithe,_Gayatri_final.ipynb

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        "<a href=\"https://colab.research.google.com/github/gayatririthe/2-git-workflow-practice-XinranTaoAngela/blob/main/Rithe%2C_Gayatri_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Predicting Diamond Prices\n",
        "\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "## Introduction to Problem & Data"
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        "#### Problem Statement:\n",
        "Since diamonds have historically been linked to luxury and worth, precise pricing is crucial for investors, buyers, and sellers in the jewelry industry. My goal for my final project is to create a predictive model that uses physical characteristics and categorical features, including cut, color, and clarity, to determine a diamond's price. Diamond pricing is a complex process influenced by numerous factors, including carat weight, dimensions, and qualitative grading. This can lead to significant variability in price points.\n",
        "\n",
        "Professionals in the field may find this model to be a useful tool that helps appraisers give more reliable assessments, clients make educated judgments about what to buy, and jewelers set competitive prices for diamonds. The program can also offer insights into the main factors by precisely forecasting diamond prices. By accurately predicting diamond prices, the model can also provide insights into the key drivers of value in the diamond market, helping stakeholders optimize their strategies.\n",
        "\n",
        "The successful implementation of this project will not only enhance predictive accuracy regarding dimaond pricing but also enhancing transparency in a market that is otherwise unclear and provide data-driven insights to members of diamond industry.  "
      ],
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        "#### Dataset Description:\n",
        "\n",
        "The dataset for this project is sourced from Kaggle and comes in CSV format, providing information on a large and diverse collection of diamonds. It includes 53,940 rows, with each row being an individual diamond, and 10 columns that have a mix of physical measurements, qualitative features, and pricing data. Key attributes include carat weight, dimensions (x, y, z), depth, and qualitative grades for cut, color, and clarity, making this dataset comprehensive for analyzing diamond pricing.\n",
        "\n",
        "To prepare the data for modeling, I will need to do some preprocessing. This will involve cleaning missing or inconsistent values, handling any redundant columns, and engineering additional features to enhance the predictive power of the model. For example, creating features like volume and depth-to-table ratio can provide new insights into how physical attributes correlate with price. One potential challenge is the variability in the dataset—diamond pricing is influenced by both objective and subjective properties, which can introduce noise and make modeling more complex.\n",
        "\n",
        "Despite these challenges, the dataset provides a strong foundation for developing a regression model to predict diamond prices accurately. By analyzing the data, the project will be able to predict the prices of the diamonds."
      ],
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      "source": [
        "#### Data Pre-Processing & Preliminary Examination:"
      ],
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      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from IPython.display import HTML\n",
        "import kagglehub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#load dataset\n",
        "diamonds = pd.read_csv('sample_data/diamonds.csv')\n",
        "diamonds.head()"
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              "   Unnamed: 0  carat      cut color clarity  depth  table  price     x     y  \\\n",
              "0           1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
              "1           2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
              "2           3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
              "3           4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
              "4           5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
              "\n",
              "      z  \n",
              "0  2.43  \n",
              "1  2.31  \n",
              "2  2.31  \n",
              "3  2.63  \n",
              "4  2.75  "
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          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5sYPM144Or2",
        "outputId": "4644b33b-7e62-4035-9228-dbdd14b1db44"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 53920 entries, 0 to 53939\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   carat              53920 non-null  float64\n",
            " 1   cut                53920 non-null  object \n",
            " 2   color              53920 non-null  object \n",
            " 3   clarity            53920 non-null  object \n",
            " 4   depth              53920 non-null  float64\n",
            " 5   table              53920 non-null  float64\n",
            " 6   price              53920 non-null  int64  \n",
            " 7   x                  53920 non-null  float64\n",
            " 8   y                  53920 non-null  float64\n",
            " 9   z                  53920 non-null  float64\n",
            " 10  volume             53920 non-null  float64\n",
            " 11  price_per_carat    53920 non-null  float64\n",
            " 12  depth_table_ratio  53920 non-null  float64\n",
            "dtypes: float64(9), int64(1), object(3)\n",
            "memory usage: 5.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop unnecessary columns\n",
        "diamonds = diamonds.dropna()\n",
        "\n",
        "# Filter out rows with invalid values in dimensions\n",
        "diamonds = diamonds[(diamonds['x'] > 0) & (diamonds['y'] > 0) & (diamonds['z'] > 0)]\n"
      ],
      "metadata": {
        "id": "2boFbch84V-X"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Engineering for volume, price_per_carat, depth_table_ratio\n",
        "diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z']\n",
        "diamonds['price_per_carat'] = diamonds['price'] / diamonds['carat']\n",
        "diamonds['depth_table_ratio'] = diamonds['depth'] / diamonds['table']"
      ],
      "metadata": {
        "id": "UPfTPt7M6ng8"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZmiVwxcR5Q2U",
        "outputId": "449ff8ab-13b5-481f-d162-1f36087c7e34"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 53920 entries, 0 to 53939\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   carat              53920 non-null  float64\n",
            " 1   cut                53920 non-null  object \n",
            " 2   color              53920 non-null  object \n",
            " 3   clarity            53920 non-null  object \n",
            " 4   depth              53920 non-null  float64\n",
            " 5   table              53920 non-null  float64\n",
            " 6   price              53920 non-null  int64  \n",
            " 7   x                  53920 non-null  float64\n",
            " 8   y                  53920 non-null  float64\n",
            " 9   z                  53920 non-null  float64\n",
            " 10  volume             53920 non-null  float64\n",
            " 11  price_per_carat    53920 non-null  float64\n",
            " 12  depth_table_ratio  53920 non-null  float64\n",
            "dtypes: float64(9), int64(1), object(3)\n",
            "memory usage: 5.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "a-VHXv8Y6veF",
        "outputId": "ed07be8c-87b7-412e-e408-a182f0e3a921"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              carat         depth         table         price             x  \\\n",
              "count  53920.000000  53920.000000  53920.000000  53920.000000  53920.000000   \n",
              "mean       0.797698     61.749514     57.456834   3930.993231      5.731627   \n",
              "std        0.473795      1.432331      2.234064   3987.280446      1.119423   \n",
              "min        0.200000     43.000000     43.000000    326.000000      3.730000   \n",
              "25%        0.400000     61.000000     56.000000    949.000000      4.710000   \n",
              "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
              "75%        1.040000     62.500000     59.000000   5323.250000      6.540000   \n",
              "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
              "\n",
              "                  y             z        volume  price_per_carat  \\\n",
              "count  53920.000000  53920.000000  53920.000000     53920.000000   \n",
              "mean       5.734887      3.540046    129.897567      4007.833685   \n",
              "std        1.140126      0.702530     78.219789      2012.176020   \n",
              "min        3.680000      1.070000     31.707984      1051.162791   \n",
              "25%        4.720000      2.910000     65.189759      2477.666667   \n",
              "50%        5.710000      3.530000    114.840180      3494.633257   \n",
              "75%        6.540000      4.040000    170.846415      4949.019608   \n",
              "max       58.900000     31.800000   3840.598060     17828.846154   \n",
              "\n",
              "       depth_table_ratio  \n",
              "count       53920.000000  \n",
              "mean            1.076575  \n",
              "std             0.053360  \n",
              "min             0.616842  \n",
              "25%             1.045867  \n",
              "50%             1.082456  \n",
              "75%             1.112500  \n",
              "max             1.462791  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>carat</th>\n",
              "      <th>depth</th>\n",
              "      <th>table</th>\n",
              "      <th>price</th>\n",
              "      <th>x</th>\n",
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              "      <th>depth_table_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>53920.000000</td>\n",
              "      <td>53920.000000</td>\n",
              "      <td>53920.000000</td>\n",
              "      <td>53920.000000</td>\n",
              "      <td>53920.000000</td>\n",
              "      <td>53920.000000</td>\n",
              "      <td>53920.000000</td>\n",
              "      <td>53920.000000</td>\n",
              "      <td>53920.000000</td>\n",
              "      <td>53920.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.797698</td>\n",
              "      <td>61.749514</td>\n",
              "      <td>57.456834</td>\n",
              "      <td>3930.993231</td>\n",
              "      <td>5.731627</td>\n",
              "      <td>5.734887</td>\n",
              "      <td>3.540046</td>\n",
              "      <td>129.897567</td>\n",
              "      <td>4007.833685</td>\n",
              "      <td>1.076575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.473795</td>\n",
              "      <td>1.432331</td>\n",
              "      <td>2.234064</td>\n",
              "      <td>3987.280446</td>\n",
              "      <td>1.119423</td>\n",
              "      <td>1.140126</td>\n",
              "      <td>0.702530</td>\n",
              "      <td>78.219789</td>\n",
              "      <td>2012.176020</td>\n",
              "      <td>0.053360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>326.000000</td>\n",
              "      <td>3.730000</td>\n",
              "      <td>3.680000</td>\n",
              "      <td>1.070000</td>\n",
              "      <td>31.707984</td>\n",
              "      <td>1051.162791</td>\n",
              "      <td>0.616842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>949.000000</td>\n",
              "      <td>4.710000</td>\n",
              "      <td>4.720000</td>\n",
              "      <td>2.910000</td>\n",
              "      <td>65.189759</td>\n",
              "      <td>2477.666667</td>\n",
              "      <td>1.045867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.700000</td>\n",
              "      <td>61.800000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>2401.000000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>5.710000</td>\n",
              "      <td>3.530000</td>\n",
              "      <td>114.840180</td>\n",
              "      <td>3494.633257</td>\n",
              "      <td>1.082456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.040000</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>5323.250000</td>\n",
              "      <td>6.540000</td>\n",
              "      <td>6.540000</td>\n",
              "      <td>4.040000</td>\n",
              "      <td>170.846415</td>\n",
              "      <td>4949.019608</td>\n",
              "      <td>1.112500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.010000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>18823.000000</td>\n",
              "      <td>10.740000</td>\n",
              "      <td>58.900000</td>\n",
              "      <td>31.800000</td>\n",
              "      <td>3840.598060</td>\n",
              "      <td>17828.846154</td>\n",
              "      <td>1.462791</td>\n",
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              "summary": "{\n  \"name\": \"diamonds\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19063.163433731836,\n        \"min\": 0.2,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.7976982566765577,\n          0.7,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"depth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19044.900701730687,\n        \"min\": 1.4323310748192533,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          61.749514094955494,\n          61.8,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19044.94356983822,\n        \"min\": 2.2340642090637437,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          57.456834198813056,\n          57.0,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18217.947675104948,\n        \"min\": 326.0,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3930.993230712166,\n          2401.0,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19061.66603413026,\n        \"min\": 1.119422825791933,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.731626854599407,\n          5.7,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19059.2432459639,\n        \"min\": 1.1401257950842199,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.734887054896142,\n          5.71,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19061.19780224914,\n        \"min\": 0.7025303439302039,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.540046364985163,\n          3.53,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18885.285065995973,\n        \"min\": 31.707984,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          129.89756706199924,\n          114.84018,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_per_carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18057.86847905444,\n        \"min\": 1051.1627906976744,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4007.8336849674456,\n          3494.633256528418,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"depth_table_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19063.273031505716,\n        \"min\": 0.053360366824654906,\n        \"max\": 53920.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.0765746014920012,\n          1.0824561403508772,\n          53920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bCJV8TfA67uE",
        "outputId": "37261f46-6351-459f-829b-d798aa1973a8"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   carat      cut color clarity  depth  table  price     x     y     z  \\\n",
              "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43   \n",
              "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31   \n",
              "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31   \n",
              "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63   \n",
              "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75   \n",
              "\n",
              "      volume  price_per_carat  depth_table_ratio  \n",
              "0  38.202030      1417.391304           1.118182  \n",
              "1  34.505856      1552.380952           0.980328  \n",
              "2  38.076885      1421.739130           0.875385  \n",
              "3  46.724580      1151.724138           1.075862  \n",
              "4  51.917250      1080.645161           1.091379  "
            ],
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              "      <th>carat</th>\n",
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              "      <th>clarity</th>\n",
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              "      <td>0.23</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.29</td>\n",
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              "      <td>1151.724138</td>\n",
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              "      <td>0.31</td>\n",
              "      <td>Good</td>\n",
              "      <td>J</td>\n",
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              "      <td>63.3</td>\n",
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              "      <td>4.34</td>\n",
              "      <td>4.35</td>\n",
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              "      <td>1080.645161</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "diamonds",
              "summary": "{\n  \"name\": \"diamonds\",\n  \"rows\": 53920,\n  \"fields\": [\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4737952923906269,\n        \"min\": 0.2,\n        \"max\": 5.01,\n        \"num_unique_values\": 273,\n        \"samples\": [\n          0.77,\n          1.58,\n          0.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n    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\"dtype\": \"number\",\n        \"std\": 1.1401257950842199,\n        \"min\": 3.68,\n        \"max\": 58.9,\n        \"num_unique_values\": 550,\n        \"samples\": [\n          6.48,\n          5.69,\n          8.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7025303439302039,\n        \"min\": 1.07,\n        \"max\": 31.8,\n        \"num_unique_values\": 374,\n        \"samples\": [\n          5.62,\n          2.45,\n          2.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.21978923213327,\n        \"min\": 31.707984,\n        \"max\": 3840.5980600000003,\n        \"num_unique_values\": 28464,\n        \"samples\": [\n          70.93998,\n          54.605564,\n          69.32016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_per_carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2012.1760203900808,\n        \"min\": 1051.1627906976744,\n        \"max\": 17828.846153846152,\n        \"num_unique_values\": 26684,\n        \"samples\": [\n          4938.9312977099235,\n          7725.333333333333,\n          3290.1960784313724\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"depth_table_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.053360366824654906,\n        \"min\": 0.6168421052631579,\n        \"max\": 1.4627906976744185,\n        \"num_unique_values\": 2112,\n        \"samples\": [\n          1.0278688524590165,\n          0.9933884297520661,\n          1.157699443413729\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 40
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              "count    53920.000000\n",
              "mean      3930.993231\n",
              "std       3987.280446\n",
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              "25%        949.000000\n",
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        "diamonds['depth_table_ratio'].describe()"
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      "outputs": [
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              "count    53920.000000\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The cleaned and enhanced dataset I’m working with includes 53,920 entries, each representing an individual diamond. The dataset captures a wide range of features, both physical and qualitative, alongside pricing information. The diamonds have carat weights ranging from 0.2 to 5.01, with prices from $326 to $18,823. The average price is $3,931, with most diamonds priced between $949 (25th percentile) and $5,323 (75th percentile).\n",
        "\n",
        "To better understand the factors driving diamond prices, I engineered three additional features: **volume** (calculated as the product of the x, y, and z dimensions), **price per carat** (price divided by carat weight), and **depth-to-table ratio** (depth divided by table). The volume of the diamonds ranges from 31.7 to 3,840.6 cubic millimeters, while the price per carat varies from $1,051 to $17,828. The depth-to-table ratio has a mean of 1.08, with most diamonds falling between 1.05 and 1.11, which provides insight into the diamonds’ proportions.\n",
        "\n",
        "With these features and the dataset's range of physical dimensions and categorical attributes, such as cut, color, and clarity, I am will be able to develop a predictive model for diamond pricing."
      ],
      "metadata": {
        "id": "2pM38BwX6V9h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "udj-AkDr-pon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "metadata": {
        "id": "hCo2So7K-B9w"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#scatterplot of carat vs price by cut\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.scatterplot(data=diamonds, x='carat', y='price')\n",
        "plt.title('Carat vs Price by Cut')\n",
        "plt.xlabel('Carat')\n",
        "plt.ylabel('Price ($)')\n",
        "plt.legend(title='Cut')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "ZUBNQUU48iJc",
        "outputId": "cc089647-56db-436c-e6ce-1c14f9b6573c"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This scatterplot shows the relationship between diamond carat weight and price, with points colored according to the diamond's cut. It's clear that there's a strong positive correlation between carat weight and price—larger diamonds generally lead to higher prices. However, we also see noticeable clustering of prices, especially for diamonds with lower carat weights (around 0 to 1 carat). These clusters suggest that diamonds with the same carat weight can vary in price due to other factors, such as cut, clarity, and color. The coloring by cut shows some variation, but the overall trend between carat and price remains the dominant factor. Additionally, there are a few high-price outliers, particularly for diamonds weighing over 2 carats, which are priced much higher than others. This indicates that diamonds with exceptional qualities may be priced far above the average for their carat size. Overall, the plot highlights the strong link between carat weight and price while also showing the complexity behind diamond pricing being influenced by multiple characteristics."
      ],
      "metadata": {
        "id": "yFCjCLdt9U-V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#boxplot price per carat by cut\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=diamonds, x='cut', y='price_per_carat')\n",
        "plt.title('Price per Carat by Cut')\n",
        "plt.xlabel('Cut')\n",
        "plt.ylabel('Price per Carat ($)')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "1xZjLUyQ80Z8",
        "outputId": "3c544b71-a318-4e5a-893d-dc6b2d792fe8"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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Pi/bIYhm0SE5KSkLv3r3h6ekJmUyGjRs3SrbLZLJyfz766CMx5umnny6zfd68eZL9pKamomPHjrCzs4OXlxfmz59fJpf169ejadOmsLOzg7+/P7Zu3Volz5mIiMhYhISEICYmBpcuXUJERAR69OiBiIgIpKenIyYmhn2SyaIZdDKR+/fvo2XLlhg1ahRefvnlMtszMzMly7/88gtGjx6N/v37S9bPmjULr7/+urhcs2ZN8XZOTg66du2KLl26YNmyZTh58iRGjRoFFxcXjB07FgCwf/9+DB48GHPnzkWvXr2wZs0a9OvXD8eOHUOLFi30+ZSJiIiMSkhICIKDgznjHpEWo+mTLJPJ8OOPP6Jfv36PjOnXrx9yc3Oxa9cucd3TTz+NSZMmYdKkSeXeZ+nSpZg+fTqysrJga2sLAHj//fexceNGnD17FgAwaNAg3L9/H5s3bxbv165dOwQGBmLZsmWVyp99komIiIiMm1n2Sb5+/Tq2bNmC0aNHl9k2b9481KpVC0FBQfjoo49QXFwsbktOTkZISIhYIANAaGgozp07hzt37ogxXbp0kewzNDQUycnJj8ynoKAAOTk5kh8iIiIiMg8GHW6hi6+//ho1a9YsMyzjzTffRKtWreDm5ob9+/dj2rRpyMzMxMKFCwEAWVlZZXpAuru7i9tcXV2RlZUlrisdU3oee21z585FTEyMPp4aERERERkZkymSv/rqK4SFhcHOzk6yfsqUKeLtgIAA2Nra4o033sDcuXOhUCiqLJ9p06ZJHjsnJwdeXl5V9nhEREREVH1Mokjet28fzp07h3Xr1j02tm3btiguLsbly5fRpEkTKJVKXL9+XRKjWVYqleK/5cVotpdHoVBUaRFORERERIZjEmOSv/zyS7Ru3RotW7Z8bGxKSgqsrKxQt25dAIBKpUJSUhKKiorEmB07dqBJkyZwdXUVY0pfDKiJUalUenwWRERERGQqDFok37t3DykpKUhJSQHwcArMlJQUZGRkiDE5OTlYv349xowZU+b+ycnJ+OSTT3DixAlcunQJiYmJmDx5MoYOHSoWwEOGDIGtrS1Gjx6NtLQ0rFu3DosXL5YMlXjrrbewbds2LFiwAGfPnsXMmTNx5MgRTJgwoWp/AURERERklAzaAm7Pnj147rnnyqwfMWIEVq5cCQBYvnw5Jk2ahMzMTDg7O0vijh07hvDwcJw9exYFBQXw8fHBsGHDMGXKFMlQiNTUVERERODw4cOoXbs2Jk6ciKlTp0r2tX79ekRGRuLy5cto1KgR5s+fjx49elT6ubAFHJk7tVrNPqpERGTSdKnXjKZPsqmzxCKZRZPlSEpKQkJCgqTji1KpRHh4OGfkIiIik6FLvWYSF+6R8WHRZDmSkpIQHR0NlUqFGTNmwMfHB+np6UhMTER0dDSnriUiIrPEI8l6YklHkksXTWFhYZKiKTk5mUWTGVGr1QgLC4Ovry9iY2NhZfXvZQwlJSWIjIxEeno6Vq9ezbMIRERk9Mxyxj0yDmq1GgkJCVCpVIiNjYWfnx8cHBzg5+eH2NhYqFQqLF26FGq12tCpkh6kpqYiKysLYWFhkgIZAKysrBAWFobMzEykpqYaKEMi0ge1Wo3jx49j165dOH78OP+GE4HDLUhHmqJpxowZjyyaIiIikJqaiqCgIANlSfpy+/ZtACgza6WGZr0mjohMD4fPEZWPR5JJJyyaLIubmxuAh+0Zy6NZr4kjItOiGT7n6+uL+Ph4bN26FfHx8fD19UV0dDSSkpIMnSKRwbBIJp2waLIsAQEBUCqVSExMRElJiWRbSUkJEhMT4eHhgYCAAANlSERPisPniCrGIpl0wqLJssjlcoSHhyM5ORmRkZFIS0tDXl4e0tLSEBkZieTkZIwfP54X7RGZoNLXHAiCIBmTLAgCrzkgi8cxyaQTTdEUHR2NyMjIR3a3YNFkPkJCQhATE4OEhARERESI6z08PNjJhMiEaYbFXbt2DTExMbhx44a4rW7dunj99dclcUSWhi3g9MSSWsAB5V/o4eHhgfHjx7NoMlOcPIbIvBw/fhyTJ09+bNyiRYt4ITaZDU4mQlUuJCQEwcHBLJosiFwu5wclkRnx8/ODTCaDIAhwdXXF6NGjoVKpkJycjC+//BJ37tyBTCaDn5+foVMlMggWyfTEWDQREZmuEydOQHMyuUmTJvDx8YG9vT18fHzQpEkTHDhwAIIg4MSJE3jmmWcMnC1R9WORTEREZIG2b98OAOjRoweOHTtW5pqD7t2745dffsH27dtZJJNFYpFMRERkgR48eAAA6NChA95+++0yw+cOHDiAX375RYwjsjRsAUdERGSB/P39AQBffvklZDIZgoKC8MILLyAoKAgymQxffvmlJI7I0rBIpiemVqslfTXZcJ6IyHS89NJLsLKywsWLF/HBBx9I+qB/8MEHuHTpEqysrPDSSy8ZOlUig+BwC3oi5bWAUyqVCA8PZws4IiITYGtri4EDB2Lt2rU4dOgQDhw4IG6zsnp4DG3gwIGwtbU1VIpEBsUjyaSzpKQkREdHw9fXF/Hx8di6dSvi4+Ph6+uL6OhoJCUlGTpFIiKqhHHjxuHVV1+FTCaTrJfJZHj11Vcxbtw4A2VGZHicTERPLGUyEbVajbCwMPj6+iI2NlY82gA8nJY6MjIS6enpWL16NXsmExGZiMLCQmzatAnXrl2Dp6cn+vbtyyPIZJY4mQhVmdTUVGRlZWHGjBmSAhl4eHouLCwMERERSE1NZQ9lIiITYWtriwEDBhg6DSKjwuEWpJPbt28DAHx8fMrdrlmviSMiIiIyRSySSSdubm4AgPT09HK3a9Zr4oiIiIhMEYtk0klAQACUSiUSExNRUlIi2VZSUoLExER4eHggICDAQBkSERER/Xcskkkncrkc4eHhSE5ORmRkpKSvZmRkJJKTkzF+/HhetEdEREQmjd0t9MRSultolNcn2cPDA+PHj2efZCIiIjJKutRrLJL1xNKKZOBhO7jU1FTcvn0bbm5uCAgI4BFkM8bXm8h88f1NloIt4KhayOVytnmzEJxhkch88f1NVD6OSSaiCnGGRSLzxfc30aNxuIWecLgFT8+ZI86waLn4/jZ/pd/fMTExOHXqlPh6t2jRAtHR0Xx/k9nhcAuqcjw9Zxk4w6Jl4vvbMmje371798bQoUNx/fp1cZu7uzv69OmD/fv38/1NFovDLUhnPD1nOTjDouXh+9tyaN63n3/+ObKzsyXbsrOz8fnnn0viiCwNi2TSiVqtRkJCAlQqFWJjY+Hn5wcHBwf4+fkhNjYWKpUKS5cuhVqtNnSqpAecYdGy8P1tWVxcXMTbrVq1knwpatWqVblxRJaERTLpRHN6Liws7JGn3zMzM5GammqgDEmfOMOiZeH727Jo3tM1a9bE7NmzJV+KZs+ejZo1a0riiCwNi2TSCU+/WxbOsGhZ+P62LJovO7m5uYiKipK8v6OiopCbmyuJI7I0vHCPdFL69Lufn1+Z7Tz9bn5CQkIQExODhIQEREREiOs9PDwQExPDC7nMCN/flmnkyJHYtm1bmff38OHD8c033xgwMyLDYpFMOil9+r28lmA8/W6eQkJCEBwczJZgZo7vb8sSGBiIVatW4ejRo1i1alWZFnBTpkwR44gsEYdbkE54+t1yaWZYfOGFFxAUFMTX2Azx/W1ZAgMD4eLigpMnTyIqKgq2trZQqVSwtbVFVFQUTp48CVdXVxbJZLE4mYieWNpkIuX1UfXw8MD48eN5+p3IxPH9bTk0Lf9sbW1RUFAgrlcoFCgsLOSQKjI7utRrLJL1xNKKZIAzchGZM76/LQe/FJElYZFsAJZYJBMRkXnglyKyFJyWmoiIiCpNc80BEf2LF+4REREREWlhkUxEREREpIXDLYiIqAyOUSUiS8cimYiIJMrrdqBUKhEeHs5uB0RkMTjcgp6YWq3G8ePHsWvXLhw/fhxqtdrQKRHRf6Tpm+vr64v4+Hhs3boV8fHx8PX1RXR0NJKSkgydIhFRtWALOD2xtBZwPNJEZH7UajXCwsLg6+tb7rTUkZGRSE9Px+rVqzn0gohMki71Go8kk854pInIPKWmpiIrKwthYWGSAhkArKysEBYWhszMTKSmphooQyKi6sMimXSiVquRkJAAlUqF2NhY+Pn5wcHBAX5+foiNjYVKpcLSpUs59ILIBN2+fRsA4OPjU+52zXpNHBGROWORTDrhkSYi8+Xm5gYASE9PL3e7Zr0mjojInLG7BemER5osV2FhITZt2oRr167B09MTffv2ha2traHTIj0KCAiAUqlEYmIiYmJicOrUKbEFXIsWLZCYmAgPDw8EBAQYOlXSM7b8IyqLRTLppPSRJj8/vzLbeaTJPC1btgzr16+XDKNZtmwZBgwYgHHjxhkwM9InuVyO8PBwREdHo1evXigoKBC3KRQKFBYWIiYmhsWTmUlKSkJ8fDyuX78urnN3d0dERAQvxCaLxuEWpJPSR5pKSkok20pKSnikyQwtW7YMa9euhZOTE9555x1s2LAB77zzDpycnLB27VosW7bM0CmSnj2q6RGbIZmfpKQkREVFITs7W7I+OzsbUVFRvBCbLJpBi+SkpCT07t0bnp6ekMlk2Lhxo2T7yJEjIZPJJD/dunWTxNy+fRthYWFwcnKCi4sLRo8ejXv37kliUlNT0bFjR9jZ2cHLywvz588vk8v69evRtGlT2NnZwd/fH1u3btX78zUHmiNNycnJiIyMRFpaGvLy8pCWlobIyEgkJydj/PjxPNJkJgoLC7F+/Xq4urpi7dq1qFevHlJSUlCvXj2sXbsWrq6uWL9+PQoLCw2dKumB5sLc9u3b44cffkC/fv3Qpk0b9OvXDz/88APat2/PC3PNiFqtxsKFCwEArVq1knQratWqFQBg4cKFfL3JYhl0uMX9+/fRsmVLjBo1Ci+//HK5Md26dcOKFSvEZYVCIdmuuVBsx44dKCoqwmuvvYaxY8dizZo1AB72w+vatSu6dOmCZcuW4eTJkxg1ahRcXFwwduxYAMD+/fsxePBgzJ07F7169cKaNWvQr18/HDt2DC1atKiiZ2+6QkJCEBMTg/j4eERERIjrlUolYmJieHrOjGzatAlqtRodO3bEiBEjyvTF7tChA37++Wds2rQJAwYMMGCmpA+aC3NbtGiBPn36iMXRkSNH8PPPP+O5554TL8wNCgoycLb0X6WkpCA7Oxv+/v6Ii4sTL8b28/NDXFwc3nrrLZw8eRIpKSlo3bq1gbMlqn4GLZK7d++O7t27VxijUCigVCrL3XbmzBls27YNhw8fRps2bQAAn376KXr06IGPP/4Ynp6eSExMRGFhIb766ivY2trCz88PKSkpWLhwoVgkL168GN26dcO7774LAJg9ezZ27NiBzz77jKeSKyCTyQydAlWxa9euAQB++ukntG/fHjNmzICPjw/S09ORmJiIn3/+WRJHpk1zwe3OnTvh6uqK0aNHQ6VSITk5GV9++SV27twpiSPTlpKSAgB47bXXyu1WNHLkSLz99tssksliGf2Y5D179qBu3bpo0qQJxo8fj1u3bonbkpOT4eLiIhbIANClSxdYWVnh4MGDYkxISIjkKvzQ0FCcO3cOd+7cEWO6dOkiedzQ0FAkJyc/Mq+CggLk5ORIfiwFJxOxHJovqA0aNCi3L7avr68kjkybZvapmjVrYv369ejVqxdq1aqFXr16Yf369ahZs6YkjswDx5oTlc+oi+Ru3brhm2++wa5du/Dhhx9i79696N69u3gKMCsrC3Xr1pXcx9raGm5ubuJp4aysLLi7u0tiNMuPiyl9alnb3Llz4ezsLP54eXn9tydrIjiZiGXRFME3btwo90LNmzdvSuLItF26dAkAULdu3XKPLNapU0cSR6YtMDAQALBy5cpy398rV66UxBFZGqMukl999VX06dMH/v7+6NevHzZv3ozDhw9jz549hk4N06ZNw927d8Wfq1evGjqlasHJRCyL5gxJbm4uBgwYgJ9//hn//PMPfv75ZwwYMAC5ubmSODJtmgMDly5dKvfCXE2Lx4oOIJDpCAwMhIuLC06ePInp06dLXu/p06fj5MmTcHV1ZZFMFsuk+iT7+vqidu3auHDhAl544QUolUrcuHFDElNcXIzbt2+Lp3+VSqWk9yMAcflxMRWdQlYoFGUuIrQEnEzEsmj6XXfp0gW//fYbFixYIG6Ty+Xo0qULdu7cyb7YZsLT0xMA0Lt3bxw6dEhyYa6Hhwd69+6Nn376SYwj0yaXyzFlyhRER0fj2LFjkiGGCoUCMpkMkydPZrcislhGfSRZ219//YVbt27Bw8MDAKBSqZCdnY2jR4+KMbt370ZJSQnatm0rxiQlJaGoqEiM2bFjB5o0aQJXV1cxZteuXZLH2rFjB1QqVVU/JZPDaWsti6Yvdl5eHrZs2YKIiAi89NJLiIiIwJYtW5CXl8e+2Gakb9++kMvl2LdvH77++mssWrQIM2bMwKJFi7By5Urs27cPcrkcffv2NXSqpCeabkWaz0MNNzc3disiiycTDDhi/969e7hw4QIAICgoCAsXLsRzzz0HNzc38Q3av39/KJVKXLx4Ee+99x5yc3Nx8uRJ8Shu9+7dcf36dSxbtkxsAdemTRuxBdzdu3fRpEkTdO3aFVOnTsWpU6cwatQoLFq0SNICrlOnTpg3bx569uyJtWvXYs6cOTq1gMvJyYGzszPu3r1r1he1qNVqhIWFwdfXt9xpa6Ojo5Geno7Vq1fz6IOZ0FyoqVKpEBYWJulukZyczA9SM6OZPMbV1RWjRo0Su1t89dVXuHPnDl599VXOsmiGOC01WQpd6jWDFsl79uzBc889V2b9iBEjsHTpUvTr1w/Hjx9HdnY2PD090bVrV8yePVtykd3t27cxYcIE/Pzzz7CyskL//v2xZMkS1KhRQ4xJTU1FREQEDh8+jNq1a2PixImYOnWq5DHXr1+PyMhIXL58GY0aNcL8+fPRo0ePSj8XSymSgX9naFIoFGWmrS0oKMCsWbNYNJmZpKQkJCQkSMaienh4YPz48XytzVB505DL5XJOQ05EJs9kimRzYmlFcnR0NGxtbcsUyYWFhTyyaKZ4pMmyFBYWYtOmTbh27Ro8PT3Rt29fSStNIiJTxCLZACylSC493CIqKgo///yz+CHau3dvzJo1i8MtiIiIyCjpUq+ZVHcLMjxNC7jevXtj5MiRktPvGzZsQK9evbB//35OW0tEREQmjUUy6UTT2u3zzz8vd5riL774QhJHREREZIpMqgUcGZ6LiwsAwN/fv9wZ9/z9/SVxRERERKaIR5LpianVapw4cULSAo6IiIjIHLBIJp1kZ2cDAE6ePIlevXqV2wKudBwRERGRKeJwC9JJZWfS44x7REREZMp4JJl04ufnB7lcDicnJ6xduxanT58Wh1s0b94cr776KnJycuDn52foVEnP2CeZiIgsCYtk0klaWhrUajXu3LmDmJgYhIWFQaVSIT09HTExMbhz544YxxZw5qO8GfeUSiXCw8M5cQwREZklDrcgnWhau02fPh2XLl1CREQEevTogYiICKSnp2P69OmSODJ9mhkWfX19ER8fj61btyI+Ph6+vr6Ijo5GUlKSoVMkIiLSOx5JJp1oxhp7enoiMTGxzOn3s2fPSuLItKnVaiQkJEClUiE2NhZWVg+/V2ta/kVGRmLp0qUIDg7m0AsiIjIrPJJMOgkICIBSqURiYiJKSkok20pKSpCYmAgPDw8EBAQYKEPSJ80Mi2FhYWKBrGFlZYWwsDBkZmYiNTXVQBkSERFVDR5JJp3I5XKEh4cjOjq63BZwhYWFiImJ4VFFM6EZNuPj41Puds16Dq8hIiJzwyPJ9EQEQdBpPZkmzbCZ9PT0crdr1nN4DRERmRsWyaQTzRjV9u3bY/369QgODoaPjw+Cg4Oxfv16tG/fHkuXLoVarTZ0qqQHHF5DRESWikUy6UQzRvXevXvo06cP/vjjD6Snp+OPP/5Anz59kJubyzGqZkQzvCY5ORmRkZFIS0tDXl4e0tLSEBkZieTkZIwfP57Da4hMnFqtxvHjx7Fr1y4cP36cBzqIwDHJpCPN2NPU1FRYW1ujU6dOaNKkCc6dO4e9e/fi5MmTkjgyfSEhIYiJiUFCQgIiIiLE9R4eHoiJiWGfZCITxz7oROVjkUw6qVGjBoCHnQ1q1aqFXbt2YdeuXQAAd3d33Lx5EyUlJWIcmYeQkBAEBwdzxj0iM6Ppg65SqTBjxgz4+PggPT0diYmJiI6O5hdhsmgskkknf/zxB4CH41F9fX0RFRUl/lFdvXo1rl+/Lsa1bdvWkKmSnsnlcs6iSGRG2AedqGIck0w6+fvvv/UaR0REhsE+6EQV45Fk0omdnR2Ah10P0tPTy4xRDQgIQGpqqhhHRETGiX3QiSrGIpl00qFDB/zxxx84ffo0fv75Z5w9e1Yco9q0aVP07t1bjCMiIuNVug+6n59fme3sg06WjsMtSCdKpRIAUFxcjD59+uDQoUNo3LgxDh06hD59+qC4uFgSR0RExol90IkqJhM4RZpe5OTkwNnZGXfv3oWTk5Oh06kyarUaYWFhKCgowJ07d8psd3V1hZ2dHVavXs0LPYiIjFzp7hZhYWGS7hbJycnsbkFmR5d6jUWynlhKkQz8+0f1mWeewYMHD5CTkwMnJyfY29vj8OHD/KNKRGRCyuuT7OHhgfHjx/NvOZkdFskGYElFMgAsW7YM69evl8zKJJfLMWDAAIwbN86AmRERka7UajX7oJNF0KVe44V7pLOkpCSsW7cObdu2Rb169VBQUACFQoG///4b69atQ/PmzXn0gYjIhLAPOlFZPJKsJ5ZyJFkzJlnzXLWnMXV2dkZOTg7HJBMREZHR4ZFkqjKa5vNZWVlQKBSSbXfu3BGL5tTUVB6VICIiIpPFIpl08s8//4i3W7VqhaFDh0qmpU5OTi4TR+aBYxaJiMiSsEgmnWhmXmrQoAHi4uLEqUz9/PwQFxeHMWPG4NKlS5yhycyUd/W7UqlEeHg4x58TEZFZ4mQipJOcnBwAKDPUQkMzHbUmjkyfpuWfr68v4uPjsXXrVsTHx8PX1xfR0dFISkoydIpE9B+p1WocP34cu3btwvHjxyWdi4gsFY8kk040R45Pnz6NDz74oEx3i9OnT0viyLSp1WokJCRApVIhNjZWcuYgNjYWkZGRWLp0KYKDgzn0gshE8UwRUflYJJNOAgMDsWrVKtSsWRMHDhwos71mzZrIzc1FYGBg9SdHeqe5UHPGjBkQBAHHjx+XjEkOCwtDREQEL9QkMlGaM0Xt2rXDoEGDYGdnh/z8fBw6dAjR0dGcHIosGotk0klgYCBsbW2Rm5sLuVwOHx8f8Y9qeno6cnNzoVAoWCSbCc3Y8mvXrmH27NlljjSNHj1aEkdEpkNzpqhx48a4dOmSeOE1ALi7u6Nx48Y8U0QWjUUy6UStVqO4uFi8feHChTIxRUVFUKvV/KNqBtzc3AAAc+bMgUqlwowZM8RuJomJiZgzZ44kjohMR+mWnu3bt0dUVJTk/b1//34xjmeKyBJx4CjpZNOmTSgpKQEAyGQyyTbNcklJCTZt2lTtuZH++fn5QS6Xw8XFBbNmzYKfnx8cHBzg5+eHWbNmwcXFBXK5HH5+foZOlYh0pGnV2bZtW8TGxkre37GxsWjbtq0kjsjS8Egy6eSvv/4CADg7O+O7777D6dOnxTGqzZs3x8CBA3H37l0xjkxbWloa1Go17ty5g6ioKISFhUmONN25c0eM45EmItOSnZ0NAOjYsWOZi62trKzQoUMHHDx4UIwjsjQ8kkw6KX3kwdpa+h3L2toazz77rCSOTJtmrPH06dNx6dIlREREoEePHoiIiEB6ejqmT58uiSMi0+Hi4gIA2Ldvn3iGUKOkpAS///67JI7I0vBIMumkVq1aAIDff/8dKSkpuHHjhritbt26uHfvniSOTJtmrLGnpycSExPLzLh39uxZSRwRmY7atWsDAA4dOoTIyMgyZ4oOHTokiSOyNCySSSdeXl4AgLy8POTl5Um2lS6YNXFk2gICAqBUKpGYmIjY2FjJkIqSkhIkJibCw8MDAQEBBsySiJ6E5v3t7OyMixcvIiIiQtymVCrRuHFj5OTk8P1NFovDLUgnvXr10mscGTe5XI7w8HAkJycjMjISaWlpyMvLQ1paGiIjI5GcnIzx48ezkwmRCdK8v8+dO1dm3PGdO3dw7tw5vr/JovFIMulEM6NeZeJat25dxdlQdQgJCUFMTAwSEhIkR5o8PDw40QCRGdDuVPS49USWgkUy6SQlJaXScSySzUdISAiCg4PLjEnmESYi01V62vmYmBicOnVKfH+3aNEC0dHRnEyELBqHW5BOioqKxNtyuRwNGzZEixYt0LBhQ8kf0dJxZB7kcjmCgoLwwgsvICgoiB+aRCZOM5lIWFhYuS3gwsLCkJmZidTUVANlSGRYPJJMOklPTxdvu7q6Smbcq127ttj6rXQcEREZH047T1QxFsmkk4sXL4q3i4uL0blzZ9jZ2SE/P18yFKN0HBERGR9OO09UMRbJ9MSys7OxZ88eQ6dBRERPQDPtvJOTE2bNmiVOEKWZdn7AgAHIycnhtPNksTgmmXTSsGFDvcYREZFhaE87X7rFY1RUFO7cuQO1Wo20tDRDp0pkECySSSfBwcF6jSMiIsPgtPNEFeNwC9LJ+fPn9RpHRESGwWnniSpm0CPJSUlJ6N27Nzw9PSGTybBx40ZxW1FREaZOnQp/f384OjrC09MTw4cPx7Vr1yT7ePrppyGTySQ/8+bNk8SkpqaiY8eOsLOzg5eXF+bPn18ml/Xr16Np06aws7ODv78/tm7dWiXP2dTdvHlTr3FERGQYpaedLyoqwoULF3Dq1ClcuHABRUVFnHaeLJ5BjyTfv38fLVu2xKhRo/Dyyy9LtuXl5eHYsWOYMWMGWrZsiTt37uCtt95Cnz59cOTIEUnsrFmz8Prrr4vLNWvWFG/n5OSga9eu6NKlC5YtW4aTJ09i1KhRcHFxwdixYwEA+/fvx+DBgzF37lz06tULa9asQb9+/XDs2DG0aNGiCn8DpufWrVvibSsrK5SUlJS7XDqOiIiMj2Za6qioKHTr1k2yLT4+HsDDz1f2RCdLZdAiuXv37ujevXu525ydnbFjxw7Jus8++wzPPvssMjIy4O3tLa6vWbMmlEpluftJTExEYWEhvvrqK9ja2sLPzw8pKSlYuHChWCQvXrwY3bp1w7vvvgsAmD17Nnbs2IHPPvsMy5Yt08dTNRu1atUSb5cukLWXS8cREZFxOn369GO3c+p5slQmdeHe3bt3IZPJ4OLiIlk/b9481KpVC0FBQfjoo49QXFwsbktOTkZISAhsbW3FdaGhoTh37hzu3LkjxnTp0kWyz9DQUCQnJz8yl4KCAuTk5Eh+LEHt2rX1GkemQ61W4/jx49i1axeOHz8OtVpt6JSI6D8oLCzEd999BwCSz8jSy9999x0KCwurPTciY2AyF+7l5+dj6tSpGDx4MJycnMT1b775Jlq1agU3Nzfs378f06ZNQ2ZmJhYuXAgAyMrKgo+Pj2Rf7u7u4jZXV1dkZWWJ60rHlJ59SNvcuXMRExOjr6dnMmrUqKHXODINSUlJSEhIKDMjV3h4OI8yEZmoH3/8UTwD2Lp1awwdOlScTGT16tVITk5GSUkJfvzxRwwaNMjA2RJVP5MokouKijBw4EAIgoClS5dKtk2ZMkW8HRAQAFtbW7zxxhuYO3cuFApFleU0bdo0yWPn5OTAy8uryh7PWPDCPcuTlJSE6OhotGvXDoMGDRJnWDx06BCio6MRExPDQpnIBKWmpgIAmjZtiri4OFhZPTy57Ofnh7i4OISHh+Ps2bNITU1lkUwWyeiLZE2BfOXKFezevVtyFLk8bdu2RXFxMS5fvowmTZpAqVTi+vXrkhjNsmYc86NiHjXOGQAUCkWVFuHGShAEAA8v+CjvdLtmvSaOTJtarUZCQgIaN26MixcvSoYg1a1bF40bN8bSpUsRHBzMi3uITExBQQEAwN/fXyyQNaysrNCiRQucPXtWjCOyNEY9JllTIJ8/fx47d+6s1MVgKSkpsLKyQt26dQEAKpUKSUlJKCoqEmN27NiBJk2awNXVVYzZtWuXZD87duyASqXS47MxDzKZDMDD4snGxkayzcbGRiycNXFk2lJTU5GVlYVz587h7t27km13797FuXPnkJmZKR6RIiLT0bhxYwDAL7/8IrmWBwCKi4uxbds2SRyRpTFokXzv3j2kpKQgJSUFAJCeno6UlBRkZGSgqKgIr7zyCo4cOYLExESo1WpkZWUhKytLvIggOTkZn3zyCU6cOIFLly4hMTERkydPxtChQ8UCeMiQIbC1tcXo0aORlpaGdevWYfHixZKhEm+99Ra2bduGBQsW4OzZs5g5cyaOHDmCCRMmVPvvxNiVPrqufSS59HJFR+HJdPzzzz/i7VatWiE+Ph5bt25FfHw8WrVqVW4cEZmG1q1bA3j4WfzKK6/g559/xj///IOff/4Zr7zyCu7duyeJI7I0Bh1uceTIETz33HPisqZwHTFiBGbOnImffvoJABAYGCi532+//YbOnTtDoVBg7dq1mDlzJgoKCuDj44PJkydLCmBnZ2ds374dERERaN26NWrXro2oqCix/RsAtG/fHmvWrEFkZCQ++OADNGrUCBs3bmSP5HK0bNkSq1evBgBYW1tLrnouvdyyZUuD5Ef6pZmOtkGDBuWOWRwzZgwuXbrEaWuJTFBgYCBcXFyQnZ2N7OxsLFiwoEyMi4tLmc9gIkth0CK5c+fOFY5dfdy41latWuHAgQOPfZyAgADs27evwpgBAwZgwIABj92XpSs9bk27LVDpZe3xbWSacnNzATwcg69Wq3HixAlx2toWLVrAzs5OEkdEpkMul2PKlCmIiop6ZMyUKVN4vQFZLKO/cI+MS3Z2tl7jyLhpxpafPn0avXr1klzAo1AoxGWOQScybba2tpIDHdrLRJaIRTLpxMHBQa9xZNwCAwOxatUqAGXP7JRe5ulYItOj6V7Tvn17xMTE4NSpU5IzRdHR0exeQxaN58RJJ5s3b9ZrHBm30q2htI8Wa5atrKzg7+9f7bkR0X+j6V4TFhZWbgu4sLAwdq8hi8YjyaSTa9eu6TWOjFtaWpo4I1fpNoqll0tKSpCWloagoKBqz4+Inpzmgttr165h9uzZZWbUHD16tCSOyNLwSDLppLKThHAyEfOgae3WqFGjMn3Ka9eujUaNGkniiMh0uLm5AQDi4uLg6+srafHo6+uLuLg4SRyRpWGRTDrRTNKirzgybpoLMF1cXHDr1i3Jtn/++QfOzs6SOCIyHX5+fpDL5XB1dcWsWbPg5+cHBwcH+Pn5YdasWXB1dYVcLoefn5+hUyUyCA63IJ38/fffeo0j4+bi4gIAOHz4cJkxyYIg4MiRI5I4IjIdaWlpUKvVyM7OxowZM/Dss8+KXWsOHTqE7OxsCILA4VRksVgkk0443MKyVLb4ZZFMZHo0Y41ffvllbNy4EcnJyeI2uVyOl19+GRs2bOCYZLJYOhXJZ86cwdq1a7Fv3z5cuXIFeXl5qFOnDoKCghAaGor+/ftDoVBUVa5EVM0uXLgg3ra2tpZcvGdjYyP2Ub1w4QKeeeaZas+PiJ6cZqzxhg0b0K5dO9SrVw8FBQVQKBT4+++/sWHDBkkckaWpVJF87NgxvPfee/j9998RHByMtm3b4qWXXoK9vT1u376NU6dOYfr06Zg4cSLee+89TJo0icWymbK2rtz3qsrGkXE7deqUeLtVq1Z46qmnxA/Rv/76CwcPHiwTR0SmQTMm2c7ODpcvX5bMYKtUKuHo6Ij8/HyOSSaLValKpn///nj33Xfx/fffV3haNTk5GYsXL8aCBQvwwQcf6CtHMiIPHjzQaxwZt/z8fABA48aNceTIEbEoBh6ejm3cuDH+/PNPMY7Mh1qtRmpqqji5REBAACeUMDOaMcn379+Hra0t3nnnHahUKiQnJ+PLL7/E/fv3xTiOSSZLVKki+c8//4SNjc1j41QqFVQqVZl+qmQ+7t27p9c4Mm6NGzfG0aNH8eeff5Y5OyCTyfDnn3+KcWQ+kpKSkJCQUKZvbnh4OEJCQgyYGelT6RaPd+/exccffyxuc3d3R6NGjXD+/Hm2eCSLVakWcJUpkP9LPJmOgoICvcaRcWvVqpV4u7i4WLKt9HLpODJtSUlJiI6OLrdvbnR0NJKSkgydIumJpnVjs2bNysy4J5PJ0KxZM0kckaX5zwNHd+7ciX379qFNmzbo3bu3PnIiI6aZfU1fcURkPNRqNRISEqBSqRAbGysWTn5+foiNjUVkZCSWLl2K4OBgDr0wA5rhkz/99BPatWuHQYMGiS3gDh48iJ9++kkSR2RpdJpMJDw8HDNmzBCXN2zYgG7dumHLli0YNGgQFi5cqPcEichwUlJSxNvawy1KL5eOI9OVmpqKrKwshIWFlTmyaGVlhbCwMGRmZiI1NdVAGZI+le5acezYMSxevBjz58/H4sWLcezYsXLjiCyJTkXyb7/9JhmPtnDhQsyZMwdHjhzB6tWrkZCQoPcEybg4OjrqNY6M240bNwAAPj4+5Q638PHxkcSRadP0w9W8rto069k31/xo2jk+apnIElVquEVMTAwAICMjA5s2bUJycjIEQcDhw4fRsmVLzJo1C/n5+cjIyMCsWbMAAFFRUVWXNRkMW8BZFs304unp6ZDJZJJJYmQyGdLT0yVxZNo0RwzT09PLbfuleb15ZNE8lP6yU977W7PML0VkqSpVyYwcORIAsGzZMrz44osIDAzEvn37oFQq8f7770MQBNy/fx9LlizByJEjOduaGdM+Bftf48i4BQQEIDExEUDZWRRLLwcEBFRrXlQ1AgICoFQqkZiYiJiYGJw6dUpsAdeiRQskJibCw8ODr7eZKF38VvT+ZpFMlqpSRXL9+vUBAO3atcNHH32EiIgIfPrpp3jppZfg7e0NADh8+DB8fHzEZTJPlb1Yhxf1mAfNkcPKxLVt27aKs6GqJpfLER4ejujoaPTq1UvSpUahUKCwsBAxMTF8f5uJu3fvirdtbGzKzKipWS4dR2RJdDrct2jRIshkMowdOxZubm6Ijo4Wt/3vf/9jdwsLUNn2fmwDaB5OnDjxyG0ymaxScWR6HnU2kGcJzUvpawkqavHIaw7IUuk0cPTpp5/Gvn37yt32xRdf6CUhMm7sk2xZrl69CuDh8Bnttn6CIIjrNXFk2jQt4Nq3b1/ucIvo6Gi2gDMjpb/0VDTcgl+OyFJx4CjpRDNNqb7iyLhpCmPNv/Xq1cNTTz2FevXqlbudTFvpFnA2NjYICgrCCy+8gKCgINjY2LAFHBFZlEoVyfPmzcODBw8qtcODBw9iy5Yt/ykpMl48kmxZtLtW/P333/jrr7/w999/VxhHpokt4IiI/lWpIvn06dPw9vZGeHg4fvnlF9y8eVPcVlxcjNTUVPEU3aBBg1CzZs0qS5iIqo+dnZ1e48i4lW4BVx62gDMvnEGVqGKVKpK/+eYb7Ny5E0VFRRgyZAiUSiVsbW1Rs2ZNKBQKBAUF4auvvsLw4cNx9uxZyYQjRGS6eObAspRuAaddGJWUlLAFnJmp7LUEvOaALFWlL9xr2bIlPv/8c/zvf/9Damoqrly5ggcPHqB27doIDAxE7dq1qzJPIjKAO3fu6DWOjFvpFnCRkZEICwuDj48P0tPTkZiYiOTkZLaAMyOVHTbD4TVkqXSeFs3KygqBgYEIDAysgnSIyJg4OzvrNY6MX0hICGJiYpCQkICIiAhxvYeHB2JiYnim0IxUduppTlFNlopzBxPRI1V2EgFONmBeQkJCEBwcjNTUVLEFXEBAAI8gmxlbW1u9xhGZGxbJpJPy+uU+Ko5MX40aNfQaR6ZDLpcjKCjI0GlQFeKFuZZLrVbzS3AlsEgmnVhbW1fq1Ju1Nf9rmYNLly7pNY5MBz9EzV9lX0++7uYlKSkJCQkJyMrKEtcplUqEh4dzOJUWVjKkk8rOvMQZmsxDZfujVzaOTAM/RC1Dbm6uXuPI+CUlJSE6OhoqlQozZsyQXJgbHR3N6w606HxOfNSoUeW+Ye7fv49Ro0bpJSkyXkVFRXqNI+PGL0WWR/Mh6uvri/j4eGzduhXx8fHw9fVFdHQ0kpKSDJ0i6Qn7JFsWzbTzKpUKsbGx8PPzg4ODA/z8/BAbGwuVSoWlS5dCrVYbOlWjoXOR/PXXX5d71OjBgwf45ptv9JIUERFVP36IWhYnJye9xpFxKz3tvCAIOH78OHbt2oXjx49DEAROO1+OSg+3yMnJgSAIEAQBubm5koH8arUaW7du5dS0REQmTPMhOmPGjDIX31pZWSEsLAwRERFITU3lRX1mID8/X69xZNw0/a6vXbuG2bNnlxlONXr0aEkc6VAku7i4QCaTQSaToXHjxmW2y2QyxMTE6DU5IiKqPpoPRx8fn3K3a9bzQ9Q85OTk6DWOjJtmOvm4uDi0b9++zJjkuLg4SRzpUCT/9ttvEAQBzz//PDZs2CD5Jdra2qJ+/frw9PSskiSJiKjqaf6up6enw8/Pr8z29PR0SRyZNl5zYFn8/Pwgl8vh5OSEWbNmiV2o/Pz8MGvWLAwYMAA5OTnlvvctVaWL5E6dOgF4+EfSy8uLfXCJiMxMQEAAlEolEhMTERMTg1OnTokt4Fq0aIHExER4eHggICDA0KmSHlhbW1fqImu29DQPaWlpUKvVyM7ORlRUVJlp57OzsyEIAtLS0jic6v/p/D+/fv36AIC8vDxkZGSU6ZnLP55E5kMul1fqIi32UTUPcrkc4eHhiI6ORs+ePSV/321tbVFUVISYmBi+3maioKBAr3Fk3DTDpD744AN8+eWXZaad/+CDDxAXF8fhVKXoXCTfvHkTr732Gn755Zdyt/OqZyLzIZPJ9BpHpkEQhDIHQCoziRCZFraAsyyaYVI3btwoM4SmpKQE169fl8TRExTJkyZNQnZ2Ng4ePIjOnTvjxx9/xPXr1xEbG4sFCxZURY5EZCAski2LWq3GwoULAQBt27bFU089hcLCQtja2uKvv/7CwYMHsXDhQgQHB/NoshmQyWSVGm/M97d5CAgIgIuLCz7//HOoVCpERUWJwy1Wr16NL774Aq6urhwRUIrORfLu3buxadMmtGnTBlZWVqhfvz5efPFFODk5Ye7cuejZs2dV5ElEBqBQKCo1ZlGhUFRDNlTVUlJSkJ2dDW9vb1y+fBkHDx4Ut7m7u8Pb2xsZGRlISUlB69atDZgp6QOLZNLGizSldC6S79+/L/ZDdnV1xc2bN9G4cWP4+/vj2LFjek+QiAzH0dER9+7dq1Qcmb6UlBQAQEZGBtq1a4cOHTqgoKAACoUCf//9Nw4cOCDGsUg2fTxTZFlSU1ORnZ2N119/HT/99JNkTLJSqcTrr7+Ozz//nH3QS9G5SG7SpAnOnTuHp59+Gi1btsT//vc/PP3001i2bBk8PDyqIkciMhD2UbUsmrGntWrVwqFDhyRjUa2srFCrVi3cunWLY1SJTJDmgry6deuW+8VHcwCUF+79S+ci+a233kJmZiYAIDo6Gt26dUNiYiJsbW2xcuVKfedHRAbEC3ssi2b64Vu3bpXb5vPWrVuSODJtVlZWlbrYni1fzYPmgrw5c+ZApVKVmUxkzpw5kjh6giJ56NCh4u3WrVvjypUrOHv2LLy9vVG7dm29JkdEhuXq6iqZurSiODJ9pYtf7S8+pZdZJJsHDrewLJxMRHc6fT0sKipCgwYNcObMGXGdg4MDWrVqxQKZyAzxQ9SynD17Vq9xZNwq29aP7f/Mg2YykTt37iAqKgppaWnIy8tDWloaoqKicOfOHajVaqSlpRk6VaOh05FkGxsb5OfnV1UuRGRkKnPRni5xZNxu3Lih1zgiMh6ascbTp08vdzKR6dOnczIRLToPt4iIiMCHH36IL774glNVEpk5FsmW5Z9//tFrHBEZD81YY09PTyQmJiI1NVWcdj4gIEA8Q8Qxyf/Suco9fPgwdu3ahe3bt8Pf379M66cffvhBb8kRkWFVtmcme2uah9LTD9vY2Eh6ZNva2oqn3TlNMZHpCQgIgFKpRGJiImJjYyVt3kpKSpCYmAgPDw9OJlKKzkWyi4sL+vfvXxW5EBGRAZVu5efg4ICgoCDY2dkhPz8fx48fF4tktvwzD3K5vFLdLTi7onmQy+UIDw9HdHQ0IiMjERYWJulukZycjJiYGL7epehcJK9YsaIq8iAiIgMrPYTu7t272LNnz2PjyHTZ2NhUqki2sbGphmyoOoSEhCAmJgYJCQllxiTHxMQgJCTEgNkZHzY/JCIiAIC3t7de44jIOGkPkWOv+/I9UZH8/fffY+DAgWjXrh1atWol+dFFUlISevfuDU9PT8hkMmzcuFGyXRAEREVFwcPDA/b29ujSpQvOnz8vibl9+zbCwsLg5OQEFxcXjB49usxFRKmpqejYsSPs7Ozg5eWF+fPnl8ll/fr1aNq0Kezs7ODv74+tW7fq9FyIiEzdK6+8otc4Mm6lx5zrI46MX1JSEqKjo9GgQQPEx8dj69atiI+PR4MGDRAdHY2kpCRDp2hUdC6SlyxZgtdeew3u7u44fvw4nn32WdSqVQuXLl1C9+7dddrX/fv30bJlS8THx5e7ff78+ViyZAmWLVuGgwcPwtHREaGhoZI2dGFhYUhLS8OOHTuwefNmJCUlYezYseL2nJwcdO3aFfXr18fRo0fx0UcfYebMmVi+fLkYs3//fgwePBijR4/G8ePH0a9fP/Tr1w+nTp3S8bdDRGS6FAqFXuPIuHFGTcuiVquRkJAAlUqFmJgYFBYWIjk5GYWFhYiJiYFKpcLSpUsrNQTHUug8sCwhIQHLly/H4MGDsXLlSrz33nvw9fVFVFSUzr31unfv/sjCWhAEfPLJJ4iMjETfvn0BAN988w3c3d2xceNGvPrqqzhz5gy2bduGw4cPo02bNgCATz/9FD169MDHH38stjkpLCzEV199BVtbW/j5+SElJQULFy4Ui+nFixejW7duePfddwEAs2fPxo4dO/DZZ59h2bJluv6KiIhMUmX/hrOPqnlg9xrLkpqaiqysLPTu3RvDhg2TzKaqVCrRq1cv7N+/H6mpqZLOF5ZM5yPJGRkZaN++PQDA3t4eubm5AIBhw4bh22+/1Vti6enpyMrKQpcuXcR1zs7OaNu2LZKTkwEAycnJcHFxEQtkAOjSpQusrKxw8OBBMSYkJAS2trZiTGhoKM6dO4c7d+6IMaUfRxOjeZzyFBQUICcnR/JDRGTKsrOzAQBt2rSBlZX048HKykr8W6uJI9PGGTUti+bL7eeffw5fX1/JcAtfX1988cUXkjh6giPJSqUSt2/fRv369eHt7Y0DBw6gZcuWSE9P1+u3Tc03HHd3d8l6d3d3cVtWVhbq1q0r2W5tbQ03NzdJjI+PT5l9aLa5uroiKyurwscpz9y5cxETE/MEz4yIyDi5uLgAAI4cOYJ27dqhbdu2UCgUKCgowMGDB3HgwAFJHJk2uVyO4uLiSsWR6dO8b/39/REbGyt+Efbz80NsbCzeeustnDx5ku/vUnQ+kvz888/jp59+AgC89tprmDx5Ml588UUMGjQIL730kt4TNFbTpk3D3bt3xZ+rV68aOiUiov+ksjNtcUYu81D6DKs+4ojMjc5HkpcvXy4O4o+IiECtWrWwf/9+9OnTB2+88YbeElMqlQCA69evw8PDQ1x//fp1BAYGijE3btyQ3K+4uBi3b98W769UKnH9+nVJjGb5cTGa7eVRKBS8eIWIzFLt2rVx6NAh8cgx8HC4Re3atTkltRnJy8vTaxwZN80wqVOnTpU7mYimWQGHU/1L5yPJVlZWkkbyr776KpYsWYKJEyfq9dumj48PlEoldu3aJa7LycnBwYMHoVKpAAAqlQrZ2dk4evSoGLN7926UlJSgbdu2YkxSUpKkhc2OHTvQpEkTuLq6ijGlH0cTo3kcIiJLoPlw/Oeff8p0NCgpKRELZH6IEpkezRmgMWPG4NKlS4iIiECPHj0QERGB9PR0jBkzRhJHOhTJ58+fx+DBg8u9QO3u3bsYMmQILl26pNOD37t3DykpKUhJSQHw8GK9lJQUZGRkQCaTYdKkSYiNjcVPP/2EkydPYvjw4fD09ES/fv0AAM2aNUO3bt3w+uuv49ChQ/jjjz8wYcIEvPrqq/D09AQADBkyBLa2thg9ejTS0tKwbt06LF68GFOmTBHzeOutt7Bt2zYsWLAAZ8+excyZM3HkyBFMmDBBp+dDRGTKKjsWkWMWiUxPQEAAlEol0tLSsGrVKixatAgzZszAokWL8M033yAtLQ0eHh4ICAgwdKpGo9LDLT766CN4eXnBycmpzDZnZ2d4eXnho48+wtKlSyv94EeOHMFzzz0nLmsK1xEjRojt5e7fv4+xY8ciOzsbHTp0wLZt22BnZyfeJzExERMmTMALL7wAKysr9O/fH0uWLJHktn37dkRERKB169aoXbs2oqKiJL2U27dvjzVr1iAyMhIffPABGjVqhI0bN6JFixaVfi5ERKauoKBAvP3MM8+gffv24oV7+/fvx+HDh8vEEZFpkMvlCA8PR3R0NKKiovDss89CoVDg8uXL+O6773DgwAHExMTwQs1SKl0k7927F6tXr37k9oEDB2LIkCE6PXjnzp0r7Ighk8kwa9YszJo165Exbm5uWLNmTYWPExAQgH379lUYM2DAAAwYMKDihImIzNj3338v3ra2tkbjxo3FMYuHDh2SxGlagRKR6QgJCcGgQYOwfv16SZtbuVyOQYMGISQkxIDZGZ9KF8kZGRll2q2VVrt2bXZ4ICIyYTdv3gQAvPDCC0hLS0NERIS4zcPDA88//zx2794txhGRaUlKSsK6devQrl07PPvss7Czs0N+fj4OHTqEdevWoXnz5iyUS6l0kezs7IyLFy+ifv365W6/cOFCuUMxiIjINNSpUwdXr17F6dOnsWrVKpw6dQq3b9+Gm5sbWrRogeHDh4txRGRaSk9LXbpPMgD07dsXkZGRWLp0KYKDgznk4v9V+sK9kJAQfPrpp4/cvmTJEnTs2FEvSRERUfUbNGgQACAzMxMzZsyAra0tVCoVbG1tMWPGDGRmZkriiMh0aKalDgsLK3dGzbCwMGRmZiI1NdVAGRqfSh9JnjZtGlQqFV555RW89957aNKkCQDg7NmzmD9/Pn799Vfs37+/yhIlIqKq1aZNG9ja2qKwsBAHDhyQ9EnWsLW1FaenJiLToZluWnsWYg3Nek5L/a9KH0kOCgrC999/j6SkJKhUKri5ucHNzQ3t27fHvn378N1336FVq1ZVmSsREVUhuVyOyMjICmMiIyN5KpbIBGn6H6enp5e7XbOefZL/pdOMe7169cKVK1ewbds2XLhwAYIgoHHjxujatSscHByqKkciIiIi+g80fZITExMRExNT5pqDxMRE9knWovO01Pb29njppZeqIhciIjIgtVqNuLi4CmPi4uJ4YQ+RCdL0SY6KikLPnj1RWFgobtMMs5o1axbf26XoXCQTkXHKz89HRkaGwR7/zz//1Ov+vL29JRMHUdU7cuTIYycKKSgowJEjR9C2bdtqyoqI9K10gVzeMj3EIpnITGRkZEhmkqxu+n7s5cuXo3HjxnrdJ1Vs7dq1lY5jkUxkWtRqNRYuXFhhzMKFC3mmqBQWyURmwtvbG8uXL9frPvPy8jBp0qTHxn3yySd6vy7B29tbr/ujx7ty5Ype44jIeKSkpCA7OxvAw5ZvJSUl4jbNcnZ2NlJSUtC6dWsDZWlcdCqSi4uLsWbNGoSGhsLd3b2qciKiJ2BnZ1clR17r1auHv//+u8LtgYGBen9cqn75+fl6jSMi43H06FHx9rPPPothw4aJ086vWrVKbPl49OhRFsn/r9It4ADA2toa48aN4x9IIguSmJiIevXqlbutXr16SExMrOaMqKqo1Wq9xhGR8Th79iyAh1PMz5kzB35+fnBwcICfnx/mzJkDDw8PSRw9wXCLZ599FikpKY+cnpqIzE9iYiLu3r2LKVOm4OLFi2jQoAEWLlwIZ2dnQ6dGelT69Ks+4ojIeGguzrOxsYFarcaJEyckLeBsbGwkcfQERXJ4eDimTJmCq1evonXr1nB0dJRsZ389IvPk7OyMqVOnYuzYsZg6dSoLZDNUVFSk1zgiMh5KpRKnTp1CRkbGI1vAaeLoIZ2L5FdffRUA8Oabb4rrZDIZBEGATCbjaTgiIiIiIxMaGoqdO3cCKPtFt/RyaGhoteZlzHQukh81nSERERERGaeWLVuKBzUfRSaToWXLltWYlXHTuUjmWGQiIqLqxcmC6L9KS0sTC2TtQrn0+rS0NAQFBVV7fsboifokr1q1CsuWLUN6ejqSk5NRv359fPLJJ/Dx8UHfvn31nSMREVUDuVxeqSFznGig+nGyIPqvbt++Ld7WPqJcerl0nKXTuUheunQpoqKiMGnSJMTFxYl/UF1cXPDJJ5+wSCYiMlEVnYZ9kjjSn6qYLGj79u34/vvvHxv3yiuvoGvXrnp9bE4WVP2cnJwAADVr1sT333+P06dPi90tmjdvjldeeQW5ubliHD1Bkfzpp5/i888/R79+/TBv3jxxfZs2bfDOO+/oNTkiIqo+bAFnvKpisqAGDRpUqkgeP348zx6YgUuXLgEA6tatCysr6TQZVlZWqFOnDnJzc3Hp0iU888wzhkjR6DzRhXvljVVRKBS4f/++XpIiIiKiqiWXyzFr1ixERUU9MmbWrFkskM1EVlYWAODixYvo1asXCgoKxG0KhUJc1sSRjjPuAYCPjw9SUlLKrN+2bRuaNWumj5yIiIioGoSEhGDWrFlwcHCQrHd0dMSsWbMQEhJioMxI3zw9PfUaZwl0PpI8ZcoUREREID8/H4Ig4NChQ/j2228xd+5cfPHFF1WRIxEREVWRkJAQBAcHY+vWrViwYAHefvtt9OjRg0eQzUyvXr0QHx8PGxsbbNiwAb/88guuXbsGT09PdO/eHS+99BKKiorQq1cvQ6dqNHQukseMGQN7e3tERkYiLy8PQ4YMgaenJxYvXixONEJERKbHwcEBeXl5lYoj8yKXy9GkSRMAQJMmTVggm6GzZ88CeDhxSO/evSUX4CYkJIjLZ8+eZQu4//dELeDCwsIQFhaGvLw83Lt3D3Xr1tV3XkREVM0qO2MqZ1YlMj2lW7s9qk+ydpyle6IiGQBu3LiBc+fOAXjYX69OnTp6S4qIiKofi2Qi8+Xi4iLe1u6JXnq5dJyl0/nCvdzcXAwbNgyenp7o1KkTOnXqBE9PTwwdOhR3796tihyJiKgasE8ykfkq3bpR+4tu6WW2ePyXzkXymDFjcPDgQWzZsgXZ2dnIzs7G5s2bceTIEbzxxhtVkSMREVUDe3t7vcYRkfE4ceKEeFsmk6F169Z4/fXX0bp1a8hksnLjLJ3Owy02b96MX3/9FR06dBDXhYaG4vPPP0e3bt30mhwREVWfGjVq4N69e5WKIyLTcu3aNcny0aNHcfToUQCQFMnacZZM5yPJtWrVgrOzc5n1zs7OcHV11UtSRERU/R48eKDXOCIyHlevXhVv29jYSLaVXi4dZ+l0LpIjIyMxZcoUyYwsWVlZePfddzFjxgy9JkdERNWnsLBQr3FEZJwcHBzwzjvvYMOGDXjnnXfY1vERdB5usXTpUly4cAHe3t7w9vYGAGRkZEChUODmzZv43//+J8YeO3ZMf5kSEVGV4oV7ROardu3aOH/+PAAgLy8PH3/8sbhNoVBI4ughnYvkfv36VUEaRESki/z8fGRkZOh1nzVq1EB+fn6l4v7880+9Pra3tzfs7Oz0uk8i+pe3tzeSk5PL3Vb6i6/mACg9QZEcHR1dFXkQEZEOMjIyMHbsWIM89j///KP3x16+fDkaN26s130S0b9sbW3F29pDpkovl46zdE88mQgRERmOt7c3li9frtd9FhYWYsKECY+N++yzz/T+QcqjV0RVKzAwEKtWrULNmjWRm5tbZrtmfWBgYPUnZ6RYJBMRmSA7O7sqOfIaHByMP/74o8LtLVq00PvjElHVCgwMhIuLC7Kzs2FlZSWZNMTKygq5ublwdXVlkVwKi2QzVhVjFnXBMYtEpicuLg7Tp08vt1AODg5GXFycAbIiov9KLpejW7duWLt2bZlZ9TTLoaGhkMvlhkjPKLFINmOGHLMIgGMWiUxUXFwcHjx4gA8//BB79uxB586dMXXqVM60R2TC1Go1tm3bBuDhuGPtcciFhYXYtm0bXn/9dRbK/++Ji+TCwkKkp6ejQYMGsLZmrW2MqmLMoi6Fr74fm2MWiaqPvb09hgwZgj179mDIkCEskIlMXEpKCrKzs+Ht7Y38/HzcuHFD3Obi4gI7OztkZGQgJSUFrVu3NmCmxkPn6jYvLw8TJ07E119/DeDhKXVfX19MnDgR9erVw/vvv6/3JOnJVMWYxXnz5lXqNZ43bx6P+hIRERmJlJQUAP/ObVHa3bt3xaKZRfK/dJ5xb9q0aThx4gT27NkjGR/apUsXrFu3Tq/JkfFp166dXuOIiIio6pUeh9yyZUuEhIQgKCgIISEhaNmyZblxlk7nI8kbN27EunXr0K5dO8hkMnG9n58fLl68qNfkyDhpxihWtJ2IiIiMR82aNQE87GRx6NChMts1HS80cfQER5Jv3ryJunXrlll///59SdFM5m3Pnj2YN2+eZN28efNYIBMRERkhTW/kRx0p1qwvr4eypdK5SG7Tpg22bNkiLmsK4y+++AIqlUp/mZHRa9eunXhx3vLlyznEgoiIyEhVdhgFh1v8S+fhFnPmzEH37t1x+vRpFBcXY/HixTh9+jT279+PvXv3VkWORERERPQfpKen6zXOEuh8JLlDhw5ISUlBcXEx/P39sX37dtStWxfJycm8GpKIiIjICF29elWvcZbgiRocN2jQAJ9//rm+cyEiIiKiKlBcXKzXOEug85HkrVu34tdffy2z/tdff8Uvv/yil6SIiIiIiAxJ5yL5/fffh1qtLrNeEAROJEJERERkhG7fvi1Zdnd3x4ABA+Du7l5hnCXTebjF+fPn0bx58zLrmzZtigsXLuglKSIiIiLSHysr6XHR69evY/369Y+Ns2Q6/yacnZ1x6dKlMusvXLgAR0dHvSRFRERERPpjbV2546KVjbMEOhfJffv2xaRJkySz6124cAFvv/02+vTpo9fkAODpp5+GTCYr8xMREQEA6Ny5c5lt48aNk+wjIyMDPXv2hIODA+rWrYt33323zMD0PXv2oFWrVlAoFGjYsCFWrlyp9+dCREREZAiurq56jbMEOhfJ8+fPh6OjI5o2bQofHx/4+PigWbNmqFWrFj7++GO9J3j48GFkZmaKPzt27AAADBgwQIx5/fXXJTHz588Xt6nVavTs2ROFhYXYv38/vv76a6xcuRJRUVFiTHp6Onr27InnnnsOKSkpmDRpEsaMGVPuBYpEREREpsbLy0uvcZZA52Pqzs7O2L9/P3bs2IETJ07A3t4eAQEBCAkJqYr8UKdOHcnyvHnz0KBBA3Tq1Elc5+DgAKVSWe79t2/fjtOnT2Pnzp1wd3dHYGAgZs+ejalTp2LmzJmwtbXFsmXL4OPjgwULFgAAmjVrht9//x2LFi1CaGholTwvIiIiourSokULJCcnVyqOHnqi0dkymQxdu3bFu+++iwkTJlRZgaytsLAQq1evxqhRo8TpsAEgMTERtWvXRosWLTBt2jTk5eWJ25KTk+Hv7y+5ejM0NBQ5OTlIS0sTY7p06SJ5rNDQ0Ar/MxUUFCAnJ0fyQ0RERGSMbt68qdc4S1CpI8lLlizB2LFjYWdnhyVLllQY++abb+olsfJs3LgR2dnZGDlypLhuyJAhqF+/Pjw9PZGamoqpU6fi3Llz+OGHHwAAWVlZZdqbaJazsrIqjMnJycGDBw9gb29fJpe5c+ciJiZGn0+PiIiIqEpwMhHdVapIXrRoEcLCwmBnZ4dFixY9Mk4mk1Vpkfzll1+ie/fu8PT0FNeNHTtWvO3v7w8PDw+88MILuHjxIho0aFBluUybNg1TpkwRl3NycjiOh4iIiIzS33//rdc4S1CpIjk9Pb3c29XpypUr2Llzp3iE+FHatm0L4GHHjQYNGkCpVOLQoUOSmOvXrwOAOI5ZqVSK60rHODk5lXsUGQAUCgUUCsUTPRciIiKi6nTjxg3xtrW1teQM+vXr18UjyKXjLJ1OY5KLiorQoEEDnDlzpqryeaQVK1agbt266NmzZ4VxKSkpAAAPDw8AgEqlwsmTJyUv+o4dO+Dk5CROiqJSqbBr1y7Jfnbs2AGVSqXHZ0BERERkGPn5+eLt4uJi/P333+JP6SEWpeMsnU5Fso2NjUF+eSUlJVixYgVGjBghaXJ98eJFzJ49G0ePHsXly5fx008/Yfjw4QgJCUFAQAAAoGvXrmjevDmGDRuGEydO4Ndff0VkZCQiIiLEI8Hjxo3DpUuX8N577+Hs2bNISEjAd999h8mTJ1f7cyUiIiLSN+2z315eXggJCSkzVJRnyf+lc3eLiIgIfPjhh9U6sHvnzp3IyMjAqFGjJOttbW2xc+dOdO3aFU2bNsXbb7+N/v374+effxZj5HI5Nm/eDLlcDpVKhaFDh2L48OGYNWuWGOPj44MtW7Zgx44daNmyJRYsWIAvvviC7d+IiIjILGi31L169SqSkpJw9erVCuMsmc59kg8fPoxdu3Zh+/bt8Pf3LzMV9ePGDD+Jrl27QhCEMuu9vLywd+/ex96/fv362Lp1a4UxnTt3xvHjx584RyIiIiJjVVhYqNc4S6Bzkezi4oL+/ftXRS5EREREVAUePHig1zhLoHORvGLFiqrIg4iIiIjw8OK5jIwMve7T2dm50nF//vmnXh/b29sbdnZ2et1ndah0kVxSUoKPPvoIP/30EwoLC/HCCy8gOjr6kS3SiIiIiEh3GRkZknkgqtOJEyf0/tjLly9H48aN9brP6lDpIjkuLg4zZ85Ely5dYG9vj8WLF+PGjRv46quvqjI/IiIiIovi7e2N5cuX632/cXFxuHLlCgDg6aefxuXLl8V/gYfXcE2fPl3vj+vt7a33fVaHShfJ33zzDRISEvDGG28AeNhxomfPnvjiiy9gZaVzkwwiIiIiKoednV2VHHn9+uuvMW7cOJw9e1YsjDX/Nm3aFMuWLdP7Y5qySle3GRkZ6NGjh7jcpUsXyGQyXLt2rUoSIyIiIiL9WrZsGTZv3oyWLVsCAFq2bInNmzezQC5HpYvk4uLiMoOubWxsUFRUpPekiIiIiKhq1KhRAxEREQAezn9Ro0YNA2dknCo93EIQBIwcOVIyE0t+fj7GjRsn6ZVcFX2SiYiIiIiqU6WL5BEjRpRZN3ToUL0mQ0RERERkDCpdJLM/MhERERFZCralICIiIiLSwiKZiIiIiEgLi2QiIiIiIi0skomIiIiItLBIJiIiIiLSwiKZiIiIiEgLi2QiIiIiIi0skomIiIiItLBIJiIiIiLSwiKZiIiIiEgLi2QiIiIiIi0skomIiIiItLBIJiIiIiLSwiKZiIiIiEgLi2QiIiIiIi0skomIiIiItLBIJiIiIiLSwiKZiIiIiEgLi2QiIiIiIi0skomIiIiItLBIJiIiIiLSwiKZiIiIiEgLi2QiIiIiIi0skomIiIiItLBIJiIiIiLSwiKZiIiIiEgLi2QiIiIiIi0skomIiIiItLBIJiIiIiLSwiKZiIiIiEgLi2QiIiIiIi0skomIiIiItLBIJiIiIiLSwiKZiIiIiEgLi2QiIiIiIi0skomIiIiItLBIJiIiIiLSYm3oBIgsyfXr13H37l1Dp/HErly5IvnXlDk7O8Pd3d3QaRARkZFikUxUTa5fv46hw4ajqLDA0Kn8Z3FxcYZO4T+zsVVg9apvWCgTEVG5WCQTVZO7d++iqLAAD3w7ocTO2dDpWDSr/LvApb24e/cui2QiIioXi2SialZi54wSx9qGToOIiIgqwAv3iIiIiIi0GHWRPHPmTMhkMslP06ZNxe35+fmIiIhArVq1UKNGDfTv3x/Xr1+X7CMjIwM9e/aEg4MD6tati3fffRfFxcWSmD179qBVq1ZQKBRo2LAhVq5cWR1Pj4iIiIiMlFEXyQDg5+eHzMxM8ef3338Xt02ePBk///wz1q9fj7179+LatWt4+eWXxe1qtRo9e/ZEYWEh9u/fj6+//horV65EVFSUGJOeno6ePXviueeeQ0pKCiZNmoQxY8bg119/rdbnSURERETGw+jHJFtbW0OpVJZZf/fuXXz55ZdYs2YNnn/+eQDAihUr0KxZMxw4cADt2rXD9u3bcfr0aezcuRPu7u4IDAzE7NmzMXXqVMycORO2trZYtmwZfHx8sGDBAgBAs2bN8Pvvv2PRokUIDQ2t1udKRESmiy0ejQdbPJI+GH2RfP78eXh6esLOzg4qlQpz586Ft7c3jh49iqKiInTp0kWMbdq0Kby9vZGcnIx27dohOTkZ/v7+kjdKaGgoxo8fj7S0NAQFBSE5OVmyD03MpEmTKsyroKAABQX/tvLKycnRzxMmIiKTwxaPxoUtHkkfjLpIbtu2LVauXIkmTZogMzMTMTEx6NixI06dOoWsrCzY2trCxcVFch93d3dkZWUBALKyssq8QTTLj4vJycnBgwcPYG9vX25uc+fORUxMjD6eJhERmTi2eDQebPFI+mLURXL37t3F2wEBAWjbti3q16+P77777pHFa3WZNm0apkyZIi7n5OTAy8vLgBkREZGhscUjkfkw+gv3SnNxcUHjxo1x4cIFKJVKFBYWIjs7WxJz/fp1cQyzUqks0+1Cs/y4GCcnpwoLcYVCAScnJ8kPEREREZkHkyqS7927h4sXL8LDwwOtW7eGjY0Ndu3aJW4/d+4cMjIyoFKpAAAqlQonT57EjRs3xJgdO3bAyckJzZs3F2NK70MTo9kHEREREVkeox5u8c4776B3796oX78+rl27hujoaMjlcgwePBjOzs4YPXo0pkyZAjc3Nzg5OWHixIlQqVRo164dAKBr165o3rw5hg0bhvnz5yMrKwuRkZGIiIiAQqEAAIwbNw6fffYZ3nvvPYwaNQq7d+/Gd999hy1bthjyqRORGWC3A+PBbgdEpCujLpL/+usvDB48GLdu3UKdOnXQoUMHHDhwAHXq1AEALFq0CFZWVujfvz8KCgoQGhqKhIQE8f5yuRybN2/G+PHjoVKp4OjoiBEjRmDWrFlijI+PD7Zs2YLJkydj8eLFeOqpp/DFF19UW/s3fogaD36Ikj6x24FxYbcDItKVURfJa9eurXC7nZ0d4uPjER8f/8iY+vXrY+vWrRXup3Pnzjh+/PgT5fhf8EPUuPBDlPSJ3Q6MB7sdENGTMOoi2dzxQ9R48EOUqgq7HRARmSYWyUaAH6JERERExsWkulsQEREREVUHFslERERERFo43IKIiIhIR+xOZTyqqjsVi2QiIiIiHbA7lXGpqu5ULJKJiIiIdMDuVMajKrtTsUgmIiIiegLsTmXeeOEeEREREZEWFslERERERFpYJBMRERERaWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERaWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERaWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERabE2dAJElsbqQbahU7B4fA2IiOhxWCQTVTP79CRDp0BERESPwSKZqJo98AlBib2LodOwaFYPsvllhYiIKsQimaialdi7oMSxtqHTICIiogqwSCYiqkIc/2x41fka8PU2PL4GpC8skomIqhCHdVgWvt5E5oNFMhFRFeIYdMOrzjHofL0Nj9cckL6wSCYiqkIcg25Z+HoTmQ8WyURERERPgOOfDa8qXwMWyURERERPgMM6zBuLZCIiIqInwDHohleVY9BZJBsBnq4xPL4GRESkK45BN28sko0AT9cQERERGRcWyUaAp2sMjy2DiIiIqDQWyUaAp2uIiIiIjIuVoRMgIiIiIjI2LJKJiIiIiLSwSCYiIiIi0sIimYiIiIhIC4tkIiIiIiItLJKJiIiIiLSwSCYiIiIi0sIimYiIiIhIC4tkIiIiIiItLJKJiIiIiLRwWmoiIiKiJ2CVf9fQKVi8qnwNWCQTERER6cDZ2Rk2tgrg0l5Dp0IAbGwVcHZ21vt+WSQTERER6cDd3R2rV32Du3dN90jylStXEBcXh+nTp6N+/fqGTuc/cXZ2hru7u973yyKZiIiISEfu7u5VUphVt/r166Nx48aGTsMoGXWRPHfuXPzwww84e/Ys7O3t0b59e3z44Ydo0qSJGNO5c2fs3Ss93fHGG29g2bJl4nJGRgbGjx+P3377DTVq1MCIESMwd+5cWFv/+/T37NmDKVOmIC0tDV5eXoiMjMTIkSOr/DmS5eEYNsPja0BERI9j1EXy3r17ERERgWeeeQbFxcX44IMP0LVrV5w+fRqOjo5i3Ouvv45Zs2aJyw4ODuJttVqNnj17QqlUYv/+/cjMzMTw4cNhY2ODOXPmAADS09PRs2dPjBs3DomJidi1axfGjBkDDw8PhIaGVt8TJrPGMWzGparGsBERkXkw6iJ527ZtkuWVK1eibt26OHr0KEJCQsT1Dg4OUCqV5e5j+/btOH36NHbu3Al3d3cEBgZi9uzZmDp1KmbOnAlbW1ssW7YMPj4+WLBgAQCgWbNm+P3337Fo0aJHFskFBQUoKCgQl3Nycv7r0yUzxzFsxqWqxrAREZF5MOoiWZumuHBzc5OsT0xMxOrVq6FUKtG7d2/MmDFDPJqcnJwMf39/yYdhaGgoxo8fj7S0NAQFBSE5ORldunSR7DM0NBSTJk16ZC5z585FTEyMnp4ZWQqOYSMiIjINJlMkl5SUYNKkSQgODkaLFi3E9UOGDEH9+vXh6emJ1NRUTJ06FefOncMPP/wAAMjKyipTlGiWs7KyKozJycnBgwcPYG9vXyafadOmYcqUKeJyTk4OvLy8nui5cXyk4fE1ICIiotJMpkiOiIjAqVOn8Pvvv0vWjx07Vrzt7+8PDw8PvPDCC7h48SIaNGhQZfkoFAooFIr/tA+OUTUuHKNKVYFfwAyPrwERPQmTKJInTJiAzZs3IykpCU899VSFsW3btgUAXLhwAQ0aNIBSqcShQ4ckMdevXwcAcRyzUqkU15WOcXJyKvcosr5wjKpx4RhV0id+CTYu/BJMRLoy6iJZEARMnDgRP/74I/bs2QMfH5/H3iclJQUA4OHhAQBQqVSIi4vDjRs3ULduXQDAjh074OTkhObNm4sxW7dulexnx44dUKlUenw25eMYVSLzxC/BxqW6vgTzqLXh8TUgfTHqIjkiIgJr1qzBpk2bULNmTXEMsbOzM+zt7XHx4kWsWbMGPXr0QK1atZCamorJkycjJCQEAQEBAICuXbuiefPmGDZsGObPn4+srCxERkYiIiJCHC4xbtw4fPbZZ3jvvfcwatQo7N69G9999x22bNlisOdORKaPX4ItB88cGBeeOSB9MOoieenSpQAeThhS2ooVKzBy5EjY2tpi586d+OSTT3D//n14eXmhf//+iIyMFGPlcjk2b96M8ePHQ6VSwdHRESNGjJD0Vfbx8cGWLVswefJkLF68GE899RS++OIL9kgmIqJK4ZkD48Lhc6QPRl0kC4JQ4XYvL68ys+2Vp379+mWGU2jr3Lkzjh8/rlN+REREGjxzQGRerAydABERERGRsWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERaWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERaWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERaWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERaWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERaWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERabE2dAJERERE9K/8/HxkZGRU6WNcuXJF8m9V8vb2hp2dXZU/jr6xSDZjfJMREZkH/j23LBkZGRg7dmy1PFZcXFyVP8by5cvRuHHjKn8cfWORbMb4JiMyXyyaLAv/nlsWb29vLF++3NBp6I23t7ehU3giLJLNGN9kloVFk2Vh0WRZ+PfcstjZ2fH9YARkgiAIhk7CHOTk5MDZ2Rl3796Fk5OTodMhC/Tnn39WW9FUHVg0Vaw6vhRVJ34pIqLqoEu9xiJZT1gkk6GxaCIiIqqYLvUah1sQmQmeniMiItIf9kkmIiIiItLCIpmIiIiISAuLZCIiIiIiLSySiYiIiIi0sEgmIiIiItLCIpmIiIiISAuLZCIiIiIiLSySiYiIiIi0sEgmIiIiItLCIpmIiIiISAuLZCIiIiIiLSySiYiIiIi0sEgmIiIiItLCIpmIiIiISAuLZCIiIiIiLSySiYiIiIi0sEgmIiIiItJibegEzIUgCACAnJwcA2dCREREROXR1Gmauq0iLJL1JDc3FwDg5eVl4EyIiIiIqCK5ublwdnauMEYmVKaUpscqKSnBtWvXULNmTchkMkOnU21ycnLg5eWFq1evwsnJydDpUBXj621Z+HpbFr7elsVSX29BEJCbmwtPT09YWVU86phHkvXEysoKTz31lKHTMBgnJyeLepNZOr7eloWvt2Xh621ZLPH1ftwRZA1euEdEREREpIVFMhERERGRFhbJ9J8oFApER0dDoVAYOhWqBny9LQtfb8vC19uy8PV+PF64R0RERESkhUeSiYiIiIi0sEgmIiIiItLCIpmIiIiISAuLZKqUzp07Y9KkSUa/TzKMy5cvQyaTISUlxdCpkJGRyWTYuHGjodMgE8LPBsNYuXIlXFxcDJ2GUWGRTACAkSNHol+/foZOg3Q0cuRIyGQyyGQy2NraomHDhpg1axaKi4urNQ8vLy9kZmaiRYsW1fq4VDlZWVl466230LBhQ9jZ2cHd3R3BwcFYunQp8vLyDJ2eRevduze6detW7rZ9+/ZBJpMhNTW1WnMqLCzERx99hFatWsHR0RHOzs5o2bIlIiMjce3atWrNhXRX+nOh9M+FCxcqvN+gQYPw559/VlOWpoEz7hGZuG7dumHFihUoKCjA1q1bERERARsbG0ybNk0SV1hYCFtb2yrJQS6XQ6lUVsm+6b+5dOkSgoOD4eLigjlz5sDf3x8KhQInT57E8uXLUa9ePfTp08fQaVqs0aNHo3///vjrr7/KzNq6YsUKtGnTBgEBATrv90nf7wUFBejatStSU1MRExOD4OBg1KlTB+np6fj222/x6aefYu7cuTrvl6qX5nOhtDp16lR4H3t7e9jb2z9ye1V+hhgrHkmmMu7fv4/hw4ejRo0a8PDwwIIFC8rEFBQU4J133kG9evXg6OiItm3bYs+ePeL2W7duYfDgwahXrx4cHBzg7++Pb7/9thqfheVQKBRQKpWoX78+xo8fjy5duuCnn34Szw7ExcXB09MTTZo0AQBcvXoVAwcOhIuLC9zc3NC3b19cvnxZ3J/mfnPmzIG7uztcXFzEo9Pvvvsu3Nzc8NRTT0n+AGsPtyjvtN3GjRshk8nE5ZkzZyIwMBBfffUVvL29UaNGDYSHh0OtVmP+/PlQKpWoW7cu4uLiqux3ZwnCw8NhbW2NI0eOYODAgWjWrBl8fX3Rt29fbNmyBb179wYAZGRkoG/fvqhRowacnJwwcOBAXL9+XbKvpUuXokGDBrC1tUWTJk2watUqyfbz588jJCQEdnZ2aN68OXbs2FFtz9NU9erVC3Xq1MHKlSsl6+/du4f169dj9OjRAIDff/8dHTt2hL29Pby8vPDmm2/i/v37YvzTTz+N2bNnY/jw4XBycsLYsWPx/PPPY8KECZL93rx5E7a2tti1a1e5+SxatAi///47du/ejTfffBOtW7eGt7c3OnXqhGXLlmHOnDlibEFBAd58803UrVsXdnZ26NChAw4fPizZ3969e/Hss89CoVDAw8MD77//vuRMV2U+b0h3ms+F0j+LFy+Gv78/HB0d4eXlhfDwcNy7d0+8j/bfbc3f6C+++AI+Pj6ws7MzwDMxLBbJVMa7776LvXv3YtOmTdi+fTv27NmDY8eOSWImTJiA5ORkrF27FqmpqRgwYAC6deuG8+fPAwDy8/PRunVrbNmyBadOncLYsWMxbNgwHDp0yBBPyaLY29ujsLAQALBr1y6cO3cOO3bswObNm1FUVITQ0FDUrFkT+/btwx9//IEaNWqgW7du4n0AYPfu3bh27RqSkpKwcOFCREdHo1evXnB1dcXBgwcxbtw4vPHGG/jrr7/+U64XL17EL7/8gm3btuHbb7/Fl19+iZ49e+Kvv/7C3r178eGHHyIyMhIHDx78T49jqW7duoXt27cjIiICjo6O5cbIZDKUlJSgb9++uH37Nvbu3YsdO3bg0qVLGDRokBj3448/4q233sLbb7+NU6dO4Y033sBrr72G3377DQBQUlKCl19+Gba2tjh48CCWLVuGqVOnVsvzNGXW1tYYPnw4Vq5cidLTFqxfvx5qtRqDBw/GxYsX0a1bN/Tv3x+pqalYt24dfv/99zIF8Mcff4yWLVvi+PHjmDFjBsaMGYM1a9agoKBAjFm9ejXq1auH559/vtx8vv32W7z44osICgoqd3vpL7rvvfceNmzYgK+//hrHjh1Dw4YNERoaitu3bwMA/v77b/To0QPPPPMMTpw4gaVLl+LLL79EbGysuI/KfN6QflhZWWHJkiVIS0vD119/jd27d+O9996r8D4XLlzAhg0b8MMPP1jmNScCkSAII0aMEPr27Svk5uYKtra2wnfffSduu3XrlmBvby+89dZbgiAIwpUrVwS5XC78/fffkn288MILwrRp0x75GD179hTefvttcblTp07iPunJaF43QRCEkpISYceOHYJCoRDeeecdYcSIEYK7u7tQUFAgxq9atUpo0qSJUFJSIq4rKCgQ7O3thV9//VXcZ/369QW1Wi3GNGnSROjYsaO4XFxcLDg6OgrffvutIAiCkJ6eLgAQjh8/LgiCIKxYsUJwdnaW5Prjjz8Kpf/kREdHCw4ODkJOTo64LjQ0VHj66afLPPbcuXOf8Ddk2Q4cOCAAEH744QfJ+lq1agmOjo6Co6Oj8N577wnbt28X5HK5kJGRIcakpaUJAIRDhw4JgiAI7du3F15//XXJfgYMGCD06NFDEARB+PXXXwVra2vJ34VffvlFACD8+OOPVfQMzcOZM2cEAMJvv/0mruvYsaMwdOhQQRAEYfTo0cLYsWMl99m3b59gZWUlPHjwQBAEQahfv77Qr18/ScyDBw8EV1dXYd26deK6gIAAYebMmY/Mxc7OTnjzzTcl6/r16yf+f1GpVIIgCMK9e/cEGxsbITExUYwrLCwUPD09hfnz5wuCIAgffPBBmb838fHxQo0aNQS1Wl2pzxvS3YgRIwS5XC6+Zo6OjsIrr7xSJm79+vVCrVq1xGXtv9vR0dGCjY2NcOPGjepI2yhxTDJJXLx4EYWFhWjbtq24zs3NTTxVDwAnT56EWq1G48aNJfctKChArVq1AABqtRpz5szBd999h7///huFhYUoKCiAg4ND9TwRC7J582bUqFEDRUVFKCkpwZAhQzBz5kxERETA399fMobsxIkTuHDhAmrWrCnZR35+Pi5evCgu+/n5wcrq3xNN7u7ukovy5HI5atWqhRs3bvyn3J9++mlJLu7u7pDL5WUe+78+DkkdOnQIJSUlCAsLQ0FBAc6cOQMvLy94eXmJMc2bN4eLiwvOnDmDZ555BmfOnMHYsWMl+wkODsbixYsBQNyHp6enuF2lUlXPEzJxTZs2Rfv27fHVV1+hc+fOuHDhAvbt24dZs2YBePi+TU1NRWJiongfQRBQUlKC9PR0NGvWDADQpk0byX7t7OwwbNgwfPXVVxg4cCCOHTuGU6dO4aefftIpv4SEBNy/fx9LlixBUlISgIefFUVFRQgODhbjbGxs8Oyzz+LMmTMAHv6fUKlUkqPPwcHBuHfvHv766y/cuXPnsZ839GSee+45LF26VFx2dHTEzp07MXfuXJw9exY5OTkoLi5Gfn4+8vLyHvnZXL9+/ceOZTZnLJJJZ/fu3YNcLsfRo0chl8sl22rUqAEA+Oijj7B48WJ88skn4hioSZMmSU7pk35o/hja2trC09MT1tb/vq21T7Hfu3cPrVu3lnzYapT+Q2hjYyPZJpPJyl1XUlJSbk5WVlaSU8cAUFRUVCbuvz4OVaxhw4aQyWQ4d+6cZL2vry8AVHiRDlWv0aNHY+LEiYiPj8eKFSvQoEEDdOrUCcDD9+0bb7yBN998s8z9vL29xdvlDakZM2YMAgMD8ddff2HFihV4/vnnUb9+/Ufm0ahRozL/Xzw8PAA8LGDJNDg6OqJhw4bi8uXLl9GrVy+MHz8ecXFxcHNzw++//47Ro0ejsLDwkUXyo4ZpWQqOSSaJBg0awMbGRjIG9M6dO5K2MEFBQVCr1bhx4wYaNmwo+dF0OPjjjz/Qt29fDB06FC1btoSvry9by1QRzR9Db29vSYFcnlatWuH8+fOoW7dumdfO2dlZbznVqVMHubm5kguLLHI8m4HVqlULL774Ij777DPJa6GtWbNmuHr1Kq5evSquO336NLKzs9G8eXMx5o8//pDc748//pBsv3r1KjIzM8XtBw4c0OfTMWsDBw6ElZUV1qxZg2+++QajRo0Sj8C2atUKp0+fLvOebdiw4WO7Dfj7+6NNmzb4/PPPsWbNGowaNarC+MGDB2PHjh04fvx4hXGaCzhL/58oKirC4cOHJf8nkpOTJV+Y//jjD9SsWRNPPfVUpT5vSD+OHj2KkpISLFiwAO3atUPjxo3Zzq8SWCSTRI0aNTB69Gi8++672L17N06dOoWRI0dKTn83btwYYWFhGD58OH744Qekp6fj0KFDmDt3LrZs2QLg4dGIHTt2YP/+/Thz5gzeeOONMlfKU/ULCwtD7dq10bdvX+zbtw/p6enYs2cP3nzzzf98EV5pbdu2hYODAz744ANcvHgRa9asKXP1PlWPhIQEFBcXo02bNli3bh3OnDmDc+fOYfXq1Th79izkcjm6dOkCf39/hIWF4dixYzh06BCGDx+OTp06iafw3333XaxcuRJLly7F+fPnsXDhQvzwww945513AABdunRB48aNMWLECJw4cQL79u3D9OnTDfnUTUqNGjUwaNAgTJs2DZmZmRg5cqS4berUqdi/fz8mTJiAlJQUnD9/Hps2bSpz4d6jjBkzBvPmzYMgCHjppZcqjJ08eTJUKhVeeOEFLF68GMeOHUN6ejp+/fVX/PLLL+LZQ0dHR4wfPx7vvvsutm3bhtOnT+P1119HXl6e2JEjPDwcV69excSJE3H27Fls2rQJ0dHRmDJlCqysrCr1eUP60bBhQxQVFeHTTz/FpUuXsGrVKixbtszQaRk9/k+kMj766CN07NgRvXv3RpcuXdChQwe0bt1aErNixQoMHz4cb7/9Npo0aYJ+/frh8OHD4qm/yMhItGrVCqGhoejcuTOUSiUnKzECDg4OSEpKgre3N15++WU0a9YMo0ePRn5+PpycnPT2OG5ubli9ejW2bt0qtv+bOXOm3vZPldegQQMcP34cXbp0wbRp09CyZUu0adMGn376Kd555x3Mnj0bMpkMmzZtgqurK0JCQtClSxf4+vpi3bp14n769euHxYsX4+OPP4afnx/+97//YcWKFejcuTOAh0NsfvzxRzx48ADPPvssxowZw/Z9Oho9ejTu3LmD0NBQydjugIAA7N27F3/++Sc6duyIoKAgREVFSWIqMnjwYFhbW2Pw4MGPbeNlZ2eHXbt2YerUqVixYgU6dOiAZs2aYdKkSQgODpbMnjhv3jz0798fw4YNQ6tWrXDhwgX8+uuvcHV1BQDUq1cPW7duxaFDh9CyZUuMGzcOo0ePRmRkpLiPynze0H/XsmVLLFy4EB9++CFatGiBxMRE9ruuBJmgPXCQiIiIzMbly5fRoEEDHD58GK1atTJ0OkQmg0UyERGRGSoqKsKtW7fwzjvvID09vcyYciKqGIdbEBERmaE//vgDHh4eOHz4MMefEj0BHkkmIiIiItLCI8lERERERFpYJBMRERERaWGRTERERESkhUUyEREREZEWFslERERERFpYJBMRERERaWGRTERkobKysjBx4kT4+vpCoVDAy8sLvXv3xq5duyp1/z179kAmkyE7O7tqEyUiMgBrQydARETV7/LlywgODoaLiws++ugj+Pv7o6ioCL/++isiIiJw9uxZQ6dIRGRQnEyEiMgC9ejRA6mpqTh37hwcHR0l27Kzs5GdnQ0fHx8cP34cgYGB4npXV1f89ttvePrpp+Hj4yO534gRI7By5cpqegZERFWLwy2IiCzM7du3sW3bNkRERJQpkAHAxcXlsfvw8vLChg0bAADnzp1DZmYmFi9erO9UiYgMhsMtiIgszIULFyAIApo2bfrE+5DL5XBzcwMA1K1bt1KFNRGRKeGRZCIiC8NRdkREj8cimYjIwjRq1AgymazCi/OsrB5+PJQuqIuKiqo8NyIiY8EimYjIwri5uSE0NBTx8fG4f/9+me3Z2dmoU6cOACAzM1Ncn5KSIomztbUFAKjV6qpLlojIQFgkExFZoPj4eKjVajz77LPYsGEDzp8/jzNnzmDJkiVQqVSwt7dHu3btMG/ePJw5cwZ79+5FZGSkZB/169eHTCbD5s2bcfPmTdy7d89Az4aISP9YJBMRWSBfX18cO3YMzz33HN5++220aNECL774Inbt2oWlS5cCAL766isUFxejdevWmDRpEmJjYyX7qFevHmJiYvD+++/D3d0dEyZMMMRTISKqEuyTTERERESkhUeSiYiIiIi0sEgmIiIiItLCIpmIiIiISAuLZCIiIiIiLSySiYiIiIi0sEgmIiIiItLCIpmIiIiISAuLZCIiIiIiLSySiYiIiIi0sEgmIiIiItLCIpmIiIiISMv/Ac8kbF0YRWfFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This box plot shows the relationship between the price per carat of diamonds and their cut quality. Each box shows a different cut category: Ideal, Premium, Good, Very Good, and Fair. From the plot, we can observe that diamonds with an \"Ideal\" or \"Premium\" cut tend to have a slightly higher median price per carat compared to diamonds with lower-quality cuts. The \"Fair\" cut category has the lowest median price per carat, while \"Very Good\" and \"Good\" cuts fall somewhere in between. There are also noticeable outliers in each category, particularly for \"Ideal\" and \"Premium\" cuts, where a few diamonds are priced significantly higher. The interquartile range (the box part of the plot) for all categories appears relatively consistent, indicating that the variability in price per carat is similar across all cut qualities. However, the spread of values for \"Fair\" and \"Good\" cuts seems slightly narrower compared to the others. This suggests that diamonds with these cuts generally have more consistent pricing, though still influenced by factors such as carat weight and other characteristics. Overall, the plot emphasizes the importance of cut quality in determining diamond pricing, with better cuts generally leading to higher price per carat."
      ],
      "metadata": {
        "id": "JpReGrnN9gdk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#histogram distribution of diamond prices\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.histplot(diamonds['price'], kde=True)\n",
        "plt.title('Distribution of Prices')\n",
        "plt.xlabel('Price')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "sqN87Kkt8Ual",
        "outputId": "574d44f7-18e5-4134-b239-e981e14171f3"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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AAAAAiGAEewAAAAAAIhjBHgAAAACACEawBwAAAAAgghHsAQAAAACIYAR7AAAAAAAiGMEeAAAAAIAIRrAHAAAAACCCEewBAAAAAIhg1nAXgPDx+/0KBAL1amuxWGS18s8FAAAAAJobklor5ff7lZmVrfzdu+rVPi09Q9u3bSXcAwAAAEAzQ0prpQKBgPJ379Jj7y+V1WY/Ylu/z6v7LumrQCBAsAcAAACAZoaU1spZbXZZ7UcO9gAAAACA5ovB8wAAAAAAiGAEewAAAAAAIhjBHgAAAACACEawBwAAAAAgghHsAQAAAACIYAR7AAAAAAAiGMEeAAAAAIAIRrAHAAAAACCCEewBAAAAAIhgBHsAAAAAACIYwR4AAAAAgAhGsAcAAAAAIIIR7AEAAAAAiGAEewAAAAAAIhjBHgAAAACACEawBwAAAAAgghHsAQAAAACIYAR7AAAAAAAiGMEeAAAAAIAIRrAHAAAAACCCEewBAAAAAIhgBHsAAAAAACIYwR4AAAAAgAgW9mC/c+dOXX311UpOTlZUVJR69uypb7/9NrTcMAyNHz9e6enpioqK0uDBg/Xjjz/WWce+ffs0YsQIxcfHKyEhQSNHjlRZWVmdNitWrNAZZ5whp9OpzMxMPfHEE02yfwAAAAAANKawBvv9+/frtNNOk81m02effaY1a9bo6aefVmJiYqjNE088oeeff14vv/yyFi9erJiYGOXl5amqqirUZsSIEVq9erVmzZqladOm6YsvvtCoUaNCy91ut4YMGaLs7GwtXbpUTz75pB5++GH97W9/a9L9BQAAAACgoZkMwzDCtfE//vGP+vrrr/Xll18ecrlhGMrIyNBdd92lP/zhD5KkkpISpaamaurUqbryyiu1du1ade/eXUuWLFG/fv0kSdOnT9f555+vHTt2KCMjQ5MnT9b999+v/Px82e320LY/+OADrVu37qh1ut1uuVwulZSUKD4+voH2Prw8Ho+cTqeemLZS1pqfyeH4vV7dc0FPVVVVyeFwNFGFAAAAANB6HUsODesV+48++kj9+vXT//zP/yglJUUnn3yy/v73v4eWb968Wfn5+Ro8eHBonsvl0oABA7Rw4UJJ0sKFC5WQkBAK9ZI0ePBgmc1mLV68ONRm0KBBoVAvSXl5eVq/fr32799/UF0ej0dut7vOBAAAAABAcxTWYL9p0yZNnjxZnTt31owZM3TLLbfotttu02uvvSZJys/PlySlpqbWeV9qampoWX5+vlJSUuost1qtSkpKqtPmUOs4cBsHmjhxolwuV2jKzMxsgL0FAAAAAKDhhTXYB4NBnXLKKXrsscd08skna9SoUbrpppv08ssvh7MsjRs3TiUlJaFp+/btYa0HAAAAAIDDCWuwT09PV/fu3evM69atm7Zt2yZJSktLkyQVFBTUaVNQUBBalpaWpsLCwjrL/X6/9u3bV6fNodZx4DYO5HA4FB8fX2cCAAAAAKA5CmuwP+2007R+/fo683744QdlZ2dLknJycpSWlqbZs2eHlrvdbi1evFi5ubmSpNzcXBUXF2vp0qWhNnPmzFEwGNSAAQNCbb744gv5fL5Qm1mzZqlLly51RuAHAAAAACDShDXY33HHHVq0aJEee+wxbdiwQW+88Yb+9re/afTo0ZIkk8mksWPH6k9/+pM++ugjrVy5Utdee60yMjJ08cUXS6q+wn/eeefppptu0jfffKOvv/5aY8aM0ZVXXqmMjAxJ0m9/+1vZ7XaNHDlSq1ev1ltvvaXnnntOd955Z7h2HQAAAACABmEN58b79++v999/X+PGjdOECROUk5OjZ599ViNGjAi1ueeee1ReXq5Ro0apuLhYp59+uqZPny6n0xlq8+9//1tjxozRueeeK7PZrOHDh+v5558PLXe5XJo5c6ZGjx6tvn37qk2bNho/fnydZ90DAAAAABCJwvoc+0jBc+x5jj0AAAAANKWIeY49AAAAAAD4ZQj2AAAAAABEMII9AAAAAAARjGAPAAAAAEAEI9gDAAAAABDBCPYAAAAAAEQwgj0AAAAAABGMYA8AAAAAQAQj2AMAAAAAEMEI9gAAAAAARDCCPQAAAAAAEYxgDwAAAABABCPYAwAAAAAQwQj2AAAAAABEMII9AAAAAAARjGAPAAAAAEAEI9gDAAAAABDBCPYAAAAAAEQwgj0AAAAAABGMYA8AAAAAQAQj2AMAAAAAEMEI9gAAAAAARDCCPQAAAAAAEYxgDwAAAABABCPYAwAAAAAQwQj2AAAAAABEMII9AAAAAAARzBruAhA5PB7PUdtYLBZZrfyzAgAAAICmQgLDUQUCfslklsvlOmrbtPQMbd+2lXAPAAAAAE2E9IWjMoJByQjq0XcXy+GMPmw7v8+r+y7pq0AgQLAHAAAAgCZC+kK9WW12We32cJcBAAAAADgAg+cBAAAAABDBCPYAAAAAAEQwgj0AAAAAABGMYA8AAAAAQAQj2AMAAAAAEMEI9gAAAAAARDCCPQAAAAAAEYxgDwAAAABABCPYAwAAAAAQwQj2AAAAAABEMII9AAAAAAARjGAPAAAAAEAEI9gDAAAAABDBCPYAAAAAAEQwgj0AAAAAABGMYA8AAAAAQAQj2AMAAAAAEMEI9gAAAAAARDCCPQAAAAAAEYxgDwAAAABABCPYAwAAAAAQwQj2AAAAAABEMII9AAAAAAARjGAPAAAAAEAEI9gDAAAAABDBCPYAAAAAAESwsAb7hx9+WCaTqc7UtWvX0PKqqiqNHj1aycnJio2N1fDhw1VQUFBnHdu2bdOwYcMUHR2tlJQU3X333fL7/XXazJs3T6eccoocDoc6deqkqVOnNsXuAQAAAADQ6MJ+xf6kk07S7t27Q9NXX30VWnbHHXfo448/1jvvvKP58+dr165duvTSS0PLA4GAhg0bJq/XqwULFui1117T1KlTNX78+FCbzZs3a9iwYTr77LO1bNkyjR07VjfeeKNmzJjRpPsJAAAAAEBjsIa9AKtVaWlpB80vKSnRK6+8ojfeeEPnnHOOJGnKlCnq1q2bFi1apIEDB2rmzJlas2aNPv/8c6WmpqpPnz569NFHde+99+rhhx+W3W7Xyy+/rJycHD399NOSpG7duumrr77SpEmTlJeX16T7CgAAAABAQwv7Ffsff/xRGRkZ6tChg0aMGKFt27ZJkpYuXSqfz6fBgweH2nbt2lVZWVlauHChJGnhwoXq2bOnUlNTQ23y8vLkdru1evXqUJsD11HbpnYdh+LxeOR2u+tMAAAAAAA0R2EN9gMGDNDUqVM1ffp0TZ48WZs3b9YZZ5yh0tJS5efny263KyEhoc57UlNTlZ+fL0nKz8+vE+prl9cuO1Ibt9utysrKQ9Y1ceJEuVyu0JSZmdkQuwsAAAAAQIMLa1f8oUOHhr7v1auXBgwYoOzsbL399tuKiooKW13jxo3TnXfeGXrtdrsJ9wAAAACAZinsXfEPlJCQoBNPPFEbNmxQWlqavF6viouL67QpKCgI3ZOflpZ20Cj5ta+P1iY+Pv6wJw8cDofi4+PrTAAAAAAANEfNKtiXlZVp48aNSk9PV9++fWWz2TR79uzQ8vXr12vbtm3Kzc2VJOXm5mrlypUqLCwMtZk1a5bi4+PVvXv3UJsD11HbpnYdAAAAAABEsrAG+z/84Q+aP3++tmzZogULFuiSSy6RxWLRVVddJZfLpZEjR+rOO+/U3LlztXTpUt1www3Kzc3VwIEDJUlDhgxR9+7ddc0112j58uWaMWOGHnjgAY0ePVoOh0OSdPPNN2vTpk265557tG7dOr300kt6++23dccdd4Rz1wEAAAAAaBBhvcd+x44duuqqq1RUVKS2bdvq9NNP16JFi9S2bVtJ0qRJk2Q2mzV8+HB5PB7l5eXppZdeCr3fYrFo2rRpuuWWW5Sbm6uYmBhdd911mjBhQqhNTk6OPvnkE91xxx167rnn1K5dO/3jH//gUXcAAAAAgBbBZBiGEe4imju32y2Xy6WSkpIWc7+9x+OR0+nUE9NWymq3H7FtVXmZ7rukryZ++L0cUdGHbef3enXPBT1VVVUV6jEBAAAAADh2x5JDm9U99gAAAAAA4NgQ7AEAAAAAiGAEewAAAAAAIhjBHgAAAACACEawBwAAAAAgghHsAQAAAACIYAR7AAAAAAAiGMEeAAAAAIAIRrAHAAAAACCCEewBAAAAAIhgBHsAAAAAACIYwR4AAAAAgAhGsAcAAAAAIIIR7AEAAAAAiGAEewAAAAAAIhjBHgAAAACACEawBwAAAAAgghHsAQAAAACIYAR7AAAAAAAiGMEeAAAAAIAIRrAHAAAAACCCEewBAAAAAIhgBHsAAAAAACIYwR4AAAAAgAhGsAcAAAAAIIIR7AEAAAAAiGAEewAAAAAAIhjBHgAAAACACEawBwAAAAAgghHsAQAAAACIYAR7AAAAAAAiGMEeAAAAAIAIRrAHAAAAACCCEewBAAAAAIhgBHsAAAAAACIYwR4AAAAAgAhGsAcAAAAAIIIR7AEAAAAAiGAEewAAAAAAIhjBHgAAAACACEawBwAAAAAgghHsAQAAAACIYAR7AAAAAAAiGMEeAAAAAIAIRrAHAAAAACCCEewBAAAAAIhgBHsAAAAAACIYwR4AAAAAgAhGsAcAAAAAIIIR7AEAAAAAiGAEewAAAAAAIhjBHgAAAACACEawBwAAAAAgghHsAQAAAACIYAR7AAAAAAAiGMEeAAAAAIAIRrAHAAAAACCCEewBAAAAAIhgzSbYP/744zKZTBo7dmxoXlVVlUaPHq3k5GTFxsZq+PDhKigoqPO+bdu2adiwYYqOjlZKSoruvvtu+f3+Om3mzZunU045RQ6HQ506ddLUqVObYI8AAAAAAGh8zSLYL1myRH/961/Vq1evOvPvuOMOffzxx3rnnXc0f/587dq1S5deemloeSAQ0LBhw+T1erVgwQK99tprmjp1qsaPHx9qs3nzZg0bNkxnn322li1bprFjx+rGG2/UjBkzmmz/AAAAAABoLGEP9mVlZRoxYoT+/ve/KzExMTS/pKREr7zyip555hmdc8456tu3r6ZMmaIFCxZo0aJFkqSZM2dqzZo1ev3119WnTx8NHTpUjz76qF588UV5vV5J0ssvv6ycnBw9/fTT6tatm8aMGaPLLrtMkyZNOmxNHo9Hbre7zgQAAAAAQHMU9mA/evRoDRs2TIMHD64zf+nSpfL5fHXmd+3aVVlZWVq4cKEkaeHCherZs6dSU1NDbfLy8uR2u7V69epQm5+vOy8vL7SOQ5k4caJcLldoyszM/MX7CQAAAABAYwhrsH/zzTf13XffaeLEiQcty8/Pl91uV0JCQp35qampys/PD7U5MNTXLq9ddqQ2brdblZWVh6xr3LhxKikpCU3bt28/rv0DAAAAAKCxWcO14e3bt+v222/XrFmz5HQ6w1XGITkcDjkcjnCXAQAAAADAUYXtiv3SpUtVWFioU045RVarVVarVfPnz9fzzz8vq9Wq1NRUeb1eFRcX13lfQUGB0tLSJElpaWkHjZJf+/pobeLj4xUVFdVIewcAAAAAQNMIW7A/99xztXLlSi1btiw09evXTyNGjAh9b7PZNHv27NB71q9fr23btik3N1eSlJubq5UrV6qwsDDUZtasWYqPj1f37t1DbQ5cR22b2nUAAAAAABDJwtYVPy4uTj169KgzLyYmRsnJyaH5I0eO1J133qmkpCTFx8fr1ltvVW5urgYOHChJGjJkiLp3765rrrlGTzzxhPLz8/XAAw9o9OjRoa70N998s/7yl7/onnvu0e9+9zvNmTNHb7/9tj755JOm3WEAAAAAABpB2IJ9fUyaNElms1nDhw+Xx+NRXl6eXnrppdByi8WiadOm6ZZbblFubq5iYmJ03XXXacKECaE2OTk5+uSTT3THHXfoueeeU7t27fSPf/xDeXl54dglAAAAAAAalMkwDCPcRTR3brdbLpdLJSUlio+PD3c5DcLj8cjpdOqJaStltduP2LaqvEz3XdJXEz/8Xo6o6MO283u9uueCnqqqqmLwQQAAAAD4BY4lh4b9OfYAAAAAAOD4EewBAAAAAIhgBHsAAAAAACIYwR4AAAAAgAhGsAcAAAAAIIIR7AEAAAAAiGAEewAAAAAAIhjBHgAAAACACEawBwAAAAAgghHsAQAAAACIYAR7AAAAAAAiGMEeAAAAAIAIRrAHAAAAACCCEewBAAAAAIhgBHsAAAAAACIYwR4AAAAAgAhGsMcxMQxDG/eUacWOYgWDRrjLAQAAAIBW77iCfYcOHVRUVHTQ/OLiYnXo0OEXF4Xmafv+Kr25ZLumrdituev36OMVu+T1B8NdFgAAAAC0ascV7Lds2aJAIHDQfI/Ho507d/7iotC8FJZ5lXrVRH2ytkiFpR7ZLCZZzCZtKarQu9/tUJnHH+4SAQAAAKDVsh5L448++ij0/YwZM+RyuUKvA4GAZs+erfbt2zdYcQi//RVefbi6SM6snrKYpF6ZCeqXnSh3pV8fLd+lPaUevbVkuy7qk6EEuync5QIAAABAq3NMwf7iiy+WJJlMJl133XV1ltlsNrVv315PP/10gxWH8FuyeZ+ChlS1fbVuvPhcJSfESZKi7VZd0T9THy7bqf0VPr3z7Q5d3Cs1zNUCAAAAQOtzTF3xg8GggsGgsrKyVFhYGHodDAbl8Xi0fv16XXDBBY1VK5pYcYVX6wpKJUn75/xdsQ5LneWuKJsu75epDJdT3kBQCzfvD0eZAAAAANCqHdc99ps3b1abNm0auhY0M99s2SfDkLISHPLmbzhkG6fNoryT0mQ2SduLq2TP6NLEVQIAAABA63ZMXfEPNHv2bM2ePTt05f5Ar7766i8uDOFVXOHVuvzqq/V928XqyyO0jY+yqWtavNbsdsv1qyubpkAAAAAAgKTjvGL/yCOPaMiQIZo9e7b27t2r/fv315kQ+ZZs2S/DkLKTo5USaz9q+/7tE2WSFN2xv1btcjd+gQAAAAAAScd5xf7ll1/W1KlTdc011zR0PWgGSip9WpdfHc4H5CRJOvjRhj+XEG3XiSkxWl9Yrpfmb9YrOW0buUoAAAAAgHScV+y9Xq9+9atfNXQtaCa+3VI9En5WUrTSXVH1fl/fTJcMI6jZ6/Zo9a6SRqwQAAAAAFDruIL9jTfeqDfeeKOha0Ez4K70ac3uA6/W119itE0Va6vvxv/LnEMPtgcAAAAAaFjH1RW/qqpKf/vb3/T555+rV69estlsdZY/88wzDVIcmt66glIFDaldQpQyEup/tb5WycK3FdP9TH22Kl/r80vVJS2uEaoEAAAAANQ6rmC/YsUK9enTR5K0atWqOstMJtMvLgrhs2VvuSSpc2rscb3ft3er8rqnaMaaQv11/kY9c0WfBqwOAAAAAPBzxxXs586d29B1oBmo8gWUX1IlSWqfHHPc67nhV1masaZQ01fn68/egKLsloYqEQAAAADwM8d1jz1apq1FFTIkJcfYFR9lO2r7w+nTzqV2iVGq8AY0e11BwxUIAAAAADjIcV2xP/vss4/Y5X7OnDnHXRDCZ0tRdTf8X3K1Xqq+HeM3vTM0ed5GfbRsly7oldEQ5QEAAAAADuG4gn3t/fW1fD6fli1bplWrVum6665riLrQxAzD0NaiCklS+zbRv3h9F9YE+3nr98hd5VO88/h7AAAAAAAADu+4gv2kSZMOOf/hhx9WWVnZLyoI4VHg9qjSF5DdYj6mZ9cfTte0OHVKidWGwjLNWJWv/+mX2QBVAgAAAAB+rkHvsb/66qv16quvNuQq0URqu+FnJUXLYv7lTzYwmUy6sHd1F/yPV+z+xesDAAAAABxagwb7hQsXyul0NuQq0URC99c3QDf8Wr+pCfZfb9irojJPg60XAAAAAPCT4+qKf+mll9Z5bRiGdu/erW+//VYPPvhggxSGplPu8avAXR28s3/hwHkHymkTo54nuLRyZ4k+Xblb1+S2b7B1AwAAAACqHVewd7lcdV6bzWZ16dJFEyZM0JAhQxqkMDSdbfuqB81rG+dQrOO4/kkc1oW9M7RyZ4k+Xk6wBwAAAIDGcFwpbsqUKQ1dB8Joy97ax9w1XDf8WsN6pevPn67VN1v2aVdxpTISfvnAfAAAAACAn/yie+yXLl2q119/Xa+//rq+//77hqoJTShoGNpac8X+lz6//lAyEqJ0avskSdInDKIHAAAAAA3uuK7YFxYW6sorr9S8efOUkJAgSSouLtbZZ5+tN998U23btm3IGtGI8t0eefxBOa1mpbkaZ+DD3/RO1zdb9umj5bt006AOjbINAAAAAGitjuuK/a233qrS0lKtXr1a+/bt0759+7Rq1Sq53W7ddtttDV0jGtHW/ZWSqgfNM5t++WPuDmVoz3SZTNLKnSXaXVLZKNsAAAAAgNbquIL99OnT9dJLL6lbt26hed27d9eLL76ozz77rMGKQ+PLL6keDT8zqfHufW8T61CfzARJ0px1hY22HQAAAABojY4r2AeDQdlstoPm22w2BYPBX1wUmojJrMIyryQpLb5xuuHXOrdriiRpLsEeAAAAABrUcQX7c845R7fffrt27doVmrdz507dcccdOvfccxusODQuW5ts+YOG7BazEmPsjbqts2uC/dcbilTlCzTqtgAAAACgNTmuYP+Xv/xFbrdb7du3V8eOHdWxY0fl5OTI7XbrhRdeaOga0UgcGSdKklLiHY12f32t7unxSot3qtIX0KJNRY26LQAAAABoTY5rVPzMzEx99913+vzzz7Vu3TpJUrdu3TR48OAGLQ6Ny55eHewbuxu+JJlMJp3dta3+8812zV1XqLO6pDT6NgEAAACgNTimK/Zz5sxR9+7d5Xa7ZTKZ9Otf/1q33nqrbr31VvXv318nnXSSvvzyy8aqFQ3MURvsG/gxdx6P55DTGR2rn2f/+doCVVVVye/3N+h2AQAAAKA1OqZg/+yzz+qmm25SfHz8QctcLpd+//vf65lnnmmw4tB4yj1+2dpkSZJSG+iKfSDgl0xmuVwuOZ3Og6YL+neS4fdqZ3GV4tudqMysbMI9AAAAAPxCx9QVf/ny5fq///u/wy4fMmSInnrqqV9cFBrf6t2lMpktirVbFOs4rjsyDmIEg5IR1KPvLpbDGX3INh+vKtC2/VW6ZMLrenv0mQoEArJaG2b7AAAAANAaHVOiKigoOORj7kIrs1q1Z8+eX1wUGt/yHSWSpJQ4R4Ov22qzy2o/9Cj7HdrGadv+Km0v8Tb4dgEAAACgNTqmrvgnnHCCVq1addjlK1asUHp6+i8uCo1v5U63JCk1rnEfc/dz7dvESJJ2l3hkcsQ06bYBAAAAoCU6pmB//vnn68EHH1RVVdVByyorK/XQQw/pggsuaLDi0Hhqr9inNsIV+yNxRdmUFG2XISkq55Qm3TYAAAAAtETH1BX/gQce0HvvvacTTzxRY8aMUZcuXSRJ69at04svvqhAIKD777+/UQpFwylwVynf7ZERDKhtE1+xl6T2baK1b5tXUR37N/m2AQAAAKClOaZgn5qaqgULFuiWW27RuHHjZBiGpOpnlOfl5enFF19UampqoxSKhrNse7EkyVe0XXZLhybffk6bGH23rVhRHfoqEDSafPsAAAAA0JIc83Dk2dnZ+vTTT7V//35t2LBBhmGoc+fOSkxMbIz60AiW1wR7z671ks5s8u2nu6Jkt5jkjXZp5U63BnRqmMftAQAAAEBrdEz32B8oMTFR/fv316mnnnrcoX7y5Mnq1auX4uPjFR8fr9zcXH322Weh5VVVVRo9erSSk5MVGxur4cOHq6CgoM46tm3bpmHDhik6OlopKSm6++67D3o2+rx583TKKafI4XCoU6dOmjp16nHV21LUXrH37v4hLNu3mE3KTIySJH25YW9YagAAAACAluK4g31DaNeunR5//HEtXbpU3377rc455xxddNFFWr16tSTpjjvu0Mcff6x33nlH8+fP165du3TppZeG3h8IBDRs2DB5vV4tWLBAr732mqZOnarx48eH2mzevFnDhg3T2WefrWXLlmns2LG68cYbNWPGjCbf36bi9/vl8XgOOVVWVmn5jmJJkmdXeIK9JGWFgn1R2GoAAAAAgJbgmLviN6Tf/OY3dV7/+c9/1uTJk7Vo0SK1a9dOr7zyit544w2dc845kqQpU6aoW7duWrRokQYOHKiZM2dqzZo1+vzzz5Wamqo+ffro0Ucf1b333quHH35YdrtdL7/8snJycvT0009Lkrp166avvvpKkyZNUl5eXpPvc2Pz+/3KzMpW/u5dh1xuS85Uxo2TFfRWybd3q4LBYBNXWC0rsbr7/Yqdbu0v9yoxpukH8QMAAACAliCswf5AgUBA77zzjsrLy5Wbm6ulS5fK5/Np8ODBoTZdu3ZVVlaWFi5cqIEDB2rhwoXq2bNnnQH78vLydMstt2j16tU6+eSTtXDhwjrrqG0zduzYw9ZSe3W7ltvtbrgdbWSBQED5u3fpsfeXymo7OCyvLSjTnB+KlJ4Uq+1GUIYRnmAf67DKW7hZ9pQcffHjHl3U54Sw1AEAAAAAkS6sXfElaeXKlYqNjZXD4dDNN9+s999/X927d1d+fr7sdrsSEhLqtE9NTVV+fr4kKT8//6BR+GtfH62N2+1WZWXlIWuaOHGiXC5XaMrMzGyIXW1SVptdVvvB057y6vEHmvr59YdSuWmpJGn+D3vCXAkAAAAARK6wB/suXbpo2bJlWrx4sW655RZdd911WrNmTVhrGjdunEpKSkLT9u3bw1pPQypwV0mSUmJtYa5EqtxcHey/+GGPgjz2DgAAAACOS9i74tvtdnXq1EmS1LdvXy1ZskTPPfecrrjiCnm9XhUXF9e5al9QUKC0tDRJUlpamr755ps666sdNf/ANj8fSb+goEDx8fGKioo6ZE0Oh0MOR/ivaDe0QNBQUZlXktQ2JvzB3rNjraLtFu0t82rNbrd6nOAKd0kAAAAAEHHCfsX+54LBoDwej/r27SubzabZs2eHlq1fv17btm1Tbm6uJCk3N1crV65UYWFhqM2sWbMUHx+v7t27h9ocuI7aNrXraE2KK7wKGIZsFpPiHJZwlyMF/crtkCSJ7vgAAAAAcLzCGuzHjRunL774Qlu2bNHKlSs1btw4zZs3TyNGjJDL5dLIkSN15513au7cuVq6dKluuOEG5ebmauDAgZKkIUOGqHv37rrmmmu0fPlyzZgxQw888IBGjx4duuJ+8803a9OmTbrnnnu0bt06vfTSS3r77bd1xx13hHPXw2JvzdX65BiHTCZTmKupdkanZEnSvPWFR2kJAAAAADiUsHbFLyws1LXXXqvdu3fL5XKpV69emjFjhn79619LkiZNmiSz2azhw4fL4/EoLy9PL730Uuj9FotF06ZN0y233KLc3FzFxMTouuuu04QJE0JtcnJy9Mknn+iOO+7Qc889p3bt2ukf//hHi3zU3dEUlVeP9N8mtvk8Wq422H+3rVgllT65osJ/iwAAAAAARJKwBvtXXnnliMudTqdefPFFvfjii4dtk52drU8//fSI6znrrLP0/fffH1eNLUntFfs2sc1n/IB2iVHq2DZGG/eUa8GGvRraMz3cJQEAAABARGl299ij8ewtq71i33yCvSSd1SVFkjRvPffZAwAAAMCxIti3Eh5/QKVV1c+wT25GXfEl6cwT20qqHkDPMHjsHQAAAAAcC4J9K1HbDT/WYZXT1gxGxD/AqTlJctrMyndXaX1BabjLAQAAAICIQrBvJYrKmt/AebWcNotyO1QPojef7vgAAAAAcEwI9q1Ecxw470AHdscHAAAAANQfwb6VqB04r7ndX1/rzJoB9JZs2acyjz/M1QAAAABA5CDYtwKGYaiomV+xz2kTo+zkaPkChhZuLAp3OQAAAAAQMQj2rUBplV/eQFBmk5QY3Tyv2EsHdscvDHMlAAAAABA5CPatQG03/KQYuyxmU5irObzaYD9vPY+9AwAAAID6Iti3AnvLm3c3/Fq5HZNlt5i1Y3+lNu0tD3c5AAAAABARCPatQFFp8x44r1a03apTc5Ik8dg7AAAAAKgvgn0rEClX7CXprC413fF57B0AAAAA1AvBvoXzB4PaX1ET7GOaf7Cvvc9+8aYiVfkCYa4GAAAAAJo/gn0Lt7/cJ8OQnFazYhyWcJdzVJ1SYpXhcsrjD2rhJh57BwAAAABHQ7Bv4WpHxG8T65DJ1HxHxK9lMpl0ZpcUSdxnDwAAAAD1QbBv4WqDfXMfOO9Atd3xv+A+ewAAAAA4KoJ9C7e3LHIGzqt1WqdkWc0mbdpbrm1FFeEuBwAAAACaNYJ9C3dgV/xIEee0qW92oiRp/g+FYa4GAAAAAJo3gn0LVukNqMJbPbJ8UkzkdMWXpDNrHns3l/vsAQAAAOCICPYt2L6a59fHO62yWyPrUA/ulipJ+mrDXpV7/GGuBgAAAACar8hKezgmReXV3fAj7Wq9JHVOiVV2crS8/iCD6AEAAADAERDsW7DaK/bJMZFzf30tk8mkId2rr9rPXFMQ5moAAAAAoPki2Ldg+yqqg31ijC3MlRyfISelSZJmry2QLxAMczUAAAAA0DwR7FuwSL5iL0mnZCUqOcYud5VfSzbvC3c5AAAAANAsEexbKI8/qHJP9Yj4kXrF3mI26dxuKZLojg8AAAAAh0Owb6H2V/gkSbEOqxxWS5irOX5Duld3x5+5Ol+GYYS5GgAAAABofgj2LdS+mmAfiSPiH+j0zm0UZbNoV0mVVu9yh7scAAAAAGh2CPYt1P4WEuydNovOPLGtJLrjAwAAAMChEOxbqFCwj47sYC9Jv6597N3q/DBXAgAAAADND8G+hap91F2kX7GXpHO6pshiNmldfqm2FVWEuxwAAAAAaFYI9i2QyeZQac2I+EmxkR/sE2PsOrV9kiRp5hqu2gMAAADAgQj2LZAtqZ0kKcpmUZQtckfEP9CQk2q643OfPQAAAADUQbBvgWzJmZJaRjf8WrX32S/Zsk8F7qowVwMAAAAAzQfBvgWytcmS1LKCfbvEaPXNTpRhSB8v3xXucgAAAACg2SDYt0At8Yq9JF3UJ0OS9OEygj0AAAAA1CLYt0AtNdgP65kui9mklTtLtHFPWbjLAQAAAIBmgWDfwnj9QVkT0yVJyS0s2CfHOjSocxtJ0off7wxzNQAAAADQPBDsW5jNRRUymS2yW0yKtreMEfEPdPHJJ0iSPli2S4ZhhLkaAAAAAAg/gn0Ls3FPuSQpKdouk8kU5moa3q+7pyrabtG2fRX6fntxuMsBAAAAgLAj2LcwtfeeJ0bbwlxJ44i2WzWk5tF3HzGIHgAAAAAQ7FuaDTVX7FtqsJeki/pUd8eftmKX/IFgmKsBAAAAgPAi2Lcwm0Jd8VtusD+9cxslxdi1t8yrrzbsDXc5AAAAABBWBPsWxB8IanNRhaSWfcXeZjHrgl7VI//zTHsAAAAArR3BvgXZuq9CvoChoLdKcY6WNyL+gWq7489Yna8Krz/M1QAAAABA+BDsW5ANhdUD5/mKtrfIEfEPdEpWgrKTo1XhDWja8t3hLgcAAAAAwoZg34KUVPoU67DIV7Q93KU0OpPJpKtOzZIk/XPRFp5pDwAAAKDVIti3IJf3y9S3487SvhkvhruUJnF5v0zZrWat2unmmfYAAAAAWi2CfQtjMplk+D3hLqNJJMXY9ZteGZKkfy3cGuZqAAAAACA8CPaIaNfmZkuSPlmxW3vLWscJDQAAAAA4EMEeEa13ZoJ6t3PJGwjq7W9b/tgCAAAAAPBzBHtEvKsHVl+1//eibQoEGUQPAAAAQOtCsEfE+03vDCVE27SzuFJz1hWGuxwAAAAAaFIEe0Q8p82iK/plSpJeW7BZHo+nXpPf7w9z5QAAAADwyxHs0SJcPTBbJpP01YYixaZ3kNPpPOqUmZVNuAcAAAAQ8azhLgBoCJlJ0TqzcxvN+2GvBj/wus7p0vaI7f0+r+67pK8CgYCsVj4GAAAAACIXV+zRYtx0evUgeuv3VKgiYJLVbj/8ZLOHuVoAAAAAaBgEe7QY/bITVbllmYKGtGTLvnCXAwAAAABNIqzBfuLEierfv7/i4uKUkpKiiy++WOvXr6/TpqqqSqNHj1ZycrJiY2M1fPhwFRQU1Gmzbds2DRs2TNHR0UpJSdHdd9990L3T8+bN0ymnnCKHw6FOnTpp6tSpjb17CIOSr/8jSVqz2y13pS/M1QAAAABA4wtrsJ8/f75Gjx6tRYsWadasWfL5fBoyZIjKy8tDbe644w59/PHHeueddzR//nzt2rVLl156aWh5IBDQsGHD5PV6tWDBAr322muaOnWqxo8fH2qzefNmDRs2TGeffbaWLVumsWPH6sYbb9SMGTOadH/R+Dw7VqtdgpOr9gAAAABajbCOGjZ9+vQ6r6dOnaqUlBQtXbpUgwYNUklJiV555RW98cYbOueccyRJU6ZMUbdu3bRo0SINHDhQM2fO1Jo1a/T5558rNTVVffr00aOPPqp7771XDz/8sOx2u15++WXl5OTo6aefliR169ZNX331lSZNmqS8vLwm3280rv5ZLu0ortKa3W71b5+k+ChbuEsCAAAAgEbTrO6xLykpkSQlJSVJkpYuXSqfz6fBgweH2nTt2lVZWVlauHChJGnhwoXq2bOnUlNTQ23y8vLkdru1evXqUJsD11HbpnYdP+fxeOR2u+tMiBwZLqcyE6O4ag8AAACgVWg2wT4YDGrs2LE67bTT1KNHD0lSfn6+7Ha7EhIS6rRNTU1Vfn5+qM2Bob52ee2yI7Vxu92qrKw8qJaJEyfK5XKFpszMzAbZRzSdATnJkrjXHgAAAEDL12yC/ejRo7Vq1Sq9+eab4S5F48aNU0lJSWjavn17uEvCMTohMSp01X7xZq7aAwAAAGi5mkWwHzNmjKZNm6a5c+eqXbt2oflpaWnyer0qLi6u076goEBpaWmhNj8fJb/29dHaxMfHKyoq6qB6HA6H4uPj60yIPLkdf7pqn19SFeZqAAAAAKBxhDXYG4ahMWPG6P3339ecOXOUk5NTZ3nfvn1ls9k0e/bs0Lz169dr27Ztys3NlSTl5uZq5cqVKiwsDLWZNWuW4uPj1b1791CbA9dR26Z2HWiZ0l1R6pYWJ0mau75QQcMIc0UAAAAA0PDCGuxHjx6t119/XW+88Ybi4uKUn5+v/Pz80H3vLpdLI0eO1J133qm5c+dq6dKluuGGG5Sbm6uBAwdKkoYMGaLu3bvrmmuu0fLlyzVjxgw98MADGj16tBwOhyTp5ptv1qZNm3TPPfdo3bp1eumll/T222/rjjvuCNu+o2mc1qmN7BazCks9WrWzJNzlAAAAAECDC2uwnzx5skpKSnTWWWcpPT09NL311luhNpMmTdIFF1yg4cOHa9CgQUpLS9N7770XWm6xWDRt2jRZLBbl5ubq6quv1rXXXqsJEyaE2uTk5OiTTz7RrFmz1Lt3bz399NP6xz/+waPuWoEYhzXUJX/BxiJVeP1hrggAAAAAGlZYn2Nv1KNrtNPp1IsvvqgXX3zxsG2ys7P16aefHnE9Z511lr7//vtjrhGRr9cJLq3eVaK9ZV4t2Fikwd1Sj/4mAAAAAIgQzWLwPLReHo+nXpPff/xX2s1mk87ukiJJWr3Lrd0lBz/iEAAAAAAiFcEeYREI+CWTWS6XS06n86hTZlb2Lwr3GQlR6pZePZDenHWFCgQZSA8AAABAyxDWrvhovYxgUDKCevTdxXI4o4/Y1u/z6r5L+ioQCMhqPf5/sqd3aqPNe8q1t8yrxVuLj3s9AAAAANCccMUeYWW12WW1H2Wy2RtkW9F2q86tub/++x1uObN6Nch6AQAAACCcCPZoVTqlxKpHRrwkKfmCu7S/whvmigAAAADglyHYo9UZdGJbJURZZY1L1oMfra3X0xkAAAAAoLki2KPVsVnMGtK1jYyAT7PW7tGbS7aHuyQAAAAAOG4Ee7RKbWMdKv7iX5KkCR+v0drd7jBXBAAAAADHh2CPVsv9zfs6rWOSKn0B3fjatyoq84S7JAAAAAA4ZgR7tGKGnrmsp7KTo7WzuFI3v75UXn8w3EUBAAAAwDEh2KNVS4i26ZXr+inOYdWSLfv14AerGEwPAAAAQEQh2KPV65QSp+d/e7LMJumtb7drytdbwl0SAAAAANQbwR6QdHaXFN13fjdJ0p8+WaPZawvCXBEAAAAA1A/BHqgx8vQcXd6vnYKG9L///k6LNxWFuyQAAAAAOCpruAsA6svjOfKo9UdbfjQmk0l/vqSnisq8mr2uUDe+9q3+M2qguqbGKBAI1GsdFotFVisfKwAAAABNhyv2aPYCAb9kMsvlcsnpdB52crlckqRg8PhHtrdZzHpxxCk6NSdJpR6/rn31G2Wd1P+I2z1wyszKlt/vb6hdBwAAAICj4tIimj0jGJSMoB59d7EczujDtquqKNP4y3NlGL/skXVOm0X/uK6ffvv3RVq10y3zubfpvgk9lBAbdcT3+X1e3XdJXwUCAa7aAwAAAGgyXLFHxLDa7LLajzDZ7A22rXinTa/dcKpy2kTLGp+ij9fuU2XQ1GTbBwAAAID6ItgDh5Ec69CUa0+Rrzhf7iq//vvdTrmrfOEuCwAAAADqINgDR5DucqrgjXGKd1pVUunTf5fukLuScA8AAACg+SDYA0cRKN2jS3qlyhVlk7vKr3e/I9wDAAAAaD4I9kA9xDqsuuyUdkqIsqm0yq93lu7QvnJvuMsCAAAAAII9UF+xTquG922nxGibyjx+vbN0u/JLqsJdFgAAAIBWjmAPHINYh1X/0zdTqfEOVfmCeu/7Hdq2ryLcZQEAAABoxQj2aNU8Hs9Rp5+Lslt06cntlJkUJV/A0IfLdurHgtIwVA8AAAAAkjXcBQDhEAj4JZNZLperXu2DwWCd13arWRf2ztDM1QX6sbBMn67K15mdkhqjVAAAAAA4IoI9WiUjGJSMoB59d7EczujDtquqKNP4y3NlGMGDllnNZp3XI03O9Xu0cmeJ5m/YJ9evrpRhGI1ZOgAAAADUQVd8tGpWm11W+xEmm/2I7zebTDq7S1ud2r76an3CGVfrz5/9oGCQcA8AAACgaRDsgV/IZDIpt2OyTu+QKEn61+LtuuPtZfL6D77KDwAAAAANjWAPNJDeJ8Rr78dPyWo26cNlu3TD1G9UUukLd1kAAAAAWjiCPdCAytfM00u/7a1ou0VfbyjSZZMXaDuPwwMAAADQiAj2QAM7s3MbvXNzrlLjHfqxsEyXvLRAy7cXh7ssAAAAAC0UwR5oBCdluPTB6NPULT1ee8s8uuJvCzVjdX64ywIAAADQAhHsgUaS7orSOzfn6qwubVXlC+rm15fqH19u4nF4AAAAABoUwR5oRLEOq/5xbT+NGJAlw5D+9MlaPfTRavkDjJgPAAAAoGEQ7IFGZrWY9aeLe+j+87vJZJL+uXCrRv1rqco9/nCXBgAAAKAFINgDTcBkMummQR300m9PkcNq1px1hbps8tfaXFAij8dzxMnv5wQAAAAAgMMj2AMN7Egh/ZwTkzT12j4yKt1am1+mQX/6WK6OJ8vpdB52yszKJtwDAAAAOCxruAsAWopAwC+ZzHK5XEdta4lrq95/+JeKlKiMa57UWZ2S1S0t9qB2fp9X913SV4FAQFYrH1cAAAAAByMpAA3ECAYlI6hH310shzP6sO2qKso0/vJcXdyzreZvLtWGwjLN+bFI+6oCOqNTG5nNpiasGgAAAECkoys+0MCsNrus9iNMNrskyWYx6/weaRqQkyRJWra9WO8v26kKL93uAQAAANQfwR4II5PJpIEdkjWsZ7psFpN27K/Uf77ZrvySqnCXBgAAACBCEOyBZqBTSqyu7J+lxGibyjx+vbt0h1btLAl3WQAAAAAiAMEeaCaSYuy6on+mOraNUcAwNHtdoWav3yuTzRHu0gAAAAA0YwR7oBlxWC0a1jNdv+qYLJOkdYXlSr/uWa0vKAt3aQAAAACaKYI90MyYTCb1b5+kS085QTF2i2zJmfqfv32j/3yzTYZhhLs8AAAAAM0MwR5optolRuuKk9NVuelbefxBjXtvpUa/8Z32lXvDXRoAAACAZoRgDzRjUXaLCt95RH/4dSdZzCZ9ujJfec9+oTnrCsJdGgAAAIBmgmAPNHuGbjq9vT7439PUKSVWe0o9+t3Ub/XH/65QmYdn3gMAAACtHcEeiBA927k07dbTdePpOTKZpDeXbNeQZ+Zr5ur8cJfWaPx+vzweT70mv5+THAAAAGidCPZABHHaLHrggu76z00DlZkUpV0lVRr1r6W68bUl2r6vItzlNSi/36/MrGw5nc56TZlZ2YR7AAAAtErWcBcA4NgN7JCsmWPP1F/m/qi/fbFJn68t1Fcb9urWczpr5Ok5ctos4S7xiPx+vwKBwBHbeDwe5e/epcfeXyqrzX7k9fm8uu+SvgoEArJa+W8NAAAArQtX7IEIFWW36O68rvrs9jM0ICdJVb6gnpyxXmc9OU9vfrNN/kAw3CUeUn2vxLtcLkmS2WKV1W4/8nSU4A8AAAC0ZFzaAiJcp5Q4vTlqoD5YtlNPzfhBO4sr9cf3VupvX2zSXUO6aGiPNJnNpnCXGRIIBOp1Jb6qokzjL8+VYTTPExQAAABAc0GwB1oAk8mkS05up6E90vXvxdv04twN2rS3XKPf+E7tk6N1w2k5uqxvO8U4ms9H3mqrvtp+2OW+Y78K7/F4jtrGYrHQXR8AAAAtCl3xgRbEabNo5Ok5mn/3Wbr93M6Kc1q1pahCD320WgMnztafP1mjDYVl4S6zwQUCfslklsvlYpA9AAAAtDpctgIiwLFeiY5z2nTHr0/UqEEd9N53OzTl6y3atLdcf/9ys/7+5WZ1TYvTBb3SdUGvDLVvE9PY5R+TQNCQNxBUhTcgS2yyyjwB+Ux+mUzVJy4sh7itwAgGJSOoR99dLIcz+rDrZpA9AAAAtET8ZQs0YwdeiT6atPQMbd+2tU5gjXFYdU1ue40YkK35P+zRvxZt1Rc/7NG6/FKtyy/VUzN/UKeUWPVvn6RTcxLVv32S2iUePhgfr2DQ0L4Kr/JLqrS9qFSxfYZq8ZZiVfgNlXn8Kvf4VekLyOsPyh80Qu9rN/o1vb40v866bBaTnDaLomwWxTttSoyxKdYSlD39RBlm6xG79wMAAAAtUViD/RdffKEnn3xSS5cu1e7du/X+++/r4osvDi03DEMPPfSQ/v73v6u4uFinnXaaJk+erM6dO4fa7Nu3T7feeqs+/vhjmc1mDR8+XM8995xiY2NDbVasWKHRo0dryZIlatu2rW699Vbdc889TbmrwHFpqCvRZrNJZ3dN0dldU1Rc4dWM1fmatmK3Fmws0obCMm0oLNN/vtkmSWoT61CHNjFq3yZa2ckxykyKVrzTqjinVbEOm6Lt1Y/S8wWqQ7gvEFRplV/FFV7tr/Bpf4VXe0u9KnBXKd9dpfySKhWWVskX+CmwJ+eN1rfbS46472aTFPD7ZLHaJEm1ed8XMOQL+FVa5VdhqUfaUz0//dpn9Ori3UqJdyjDFaWMhChlJDgVbef8JQAAAFq2sP7FW15ert69e+t3v/udLr300oOWP/HEE3r++ef12muvKScnRw8++KDy8vK0Zs0aOZ1OSdKIESO0e/duzZo1Sz6fTzfccINGjRqlN954Q5Lkdrs1ZMgQDR48WC+//LJWrlyp3/3ud0pISNCoUaOadH+B43W0geZq1afLfpRFGn5yhq7on6X95V59s2WflmzepyVb9mnVLrf2lnm0t8yjb7bsa4jSQ0ym6pMGKXF2LZk/S6cOGqy4aIdiHVbFOqyKsllkt5rlsJplt5jlrSzXfZf01cQPv5cjKlqGYcjjD6rKF1CVL6gKn18lFT7tr/CpqLRS23blyxqbpAK3RwVuj77fXixJSnc51bFtrDq0jVEcGR8AAAAtUFj/zB06dKiGDh16yGWGYejZZ5/VAw88oIsuukiS9M9//lOpqan64IMPdOWVV2rt2rWaPn26lixZon79+kmSXnjhBZ1//vl66qmnlJGRoX//+9/yer169dVXZbfbddJJJ2nZsmV65plnDhvsPR5PnYDkdrsbeM+BhnUsXfaln7rtJ8bYlXdSmvJOSpMklXv82lBYpi1F5dqyt0Jbi8q1o7hSZVV+lXv9Kqvyq8zjl9lkktViks1iltVsUqzDqoRomxKj7UqItqtNrF2p8U6luZxKjXcq3eVU2ziHbBazPB6PnLefqTN/d8UxdZs3maq74DttloOWVZWX6b77h+i+t5dob5W0q7hKu4orVVTu1e6SKu0uqdJXG/YqMdqm+AHDVeD2KKuto97bBgAAAJqzZnv9avPmzcrPz9fgwYND81wulwYMGKCFCxfqyiuv1MKFC5WQkBAK9ZI0ePBgmc1mLV68WJdccokWLlyoQYMGyX5AgMjLy9P//d//af/+/UpMTDxo2xMnTtQjjzzSuDsINKD6dtmXjtxtP8ZhVe/MBPXOTGjEahtPnMOqNgnR6poWL0kqrfJp055ybdpbrh37K7S/wqfEs27QWc98qTNPbKv/6Zepwd1SZbfygBAAAABErmYb7PPzqwfMSk1NrTM/NTU1tCw/P18pKSl1llutViUlJdVpk5OTc9A6apcdKtiPGzdOd955Z+i12+1WZmbmL9wjoPHVt8v+sfD7/QoEAkdt1xyfDx/ntIVOVHh8Aa3fXaLP5nwhZ+ZJmrt+j+au36OUOIeuGZit3w7IUnIsV/EBAAAQeZrXX+HNhMPhkMPBH/iA3+9XZla28nfvOmrb1LR0bdzw41HDfX3GAWgMDptF3dNiNfWNe7V2R5E+Wlmod5buUGGpR0/P+kEvzN2gi3pn6MYzOqhLWlxYagQAAACOR7MN9mlp1ff8FhQUKD09PTS/oKBAffr0CbUpLCys8z6/3699+/aF3p+WlqaCgoI6bWpf17YBcGiBQED5u3fpsfeXymo7fE8AT1WFHvyf3DpPoziaYDDYECUel4w4q24/O0e3nJGt6WsK9M9F27Vyp1vvLN2hd5bu0JBubfW/Z3ZQj3YJza4XAgAAAPBzzfYv1pycHKWlpWn27NmhIO92u7V48WLdcsstkqTc3FwVFxdr6dKl6tu3ryRpzpw5CgaDGjBgQKjN/fffL5/PJ5ut+rFZs2bNUpcuXQ7ZDR9oLepz5by2zdG6+Pt93nrf419VUabxl+fKMJo+2B9pkEF7RlfF979Y0V1+pZlr92jm2j0KbF+mjx67WSdnJzd5rQAAAEB9hTXYl5WVacOGDaHXmzdv1rJly5SUlKSsrCyNHTtWf/rTn9S5c+fQ4+4yMjJCz7rv1q2bzjvvPN100016+eWX5fP5NGbMGF155ZXKyMiQJP32t7/VI488opEjR+ree+/VqlWr9Nxzz2nSpEnh2GUg7I51BH2p/lfX63OPv9XXsGMAHIv6DDJYVO7V0u0l+nFPhSyZfXTJ5EU6u0tb3XZuZ52cxclAAAAAND9hDfbffvutzj777NDr2gHrrrvuOk2dOlX33HOPysvLNWrUKBUXF+v000/X9OnTQ8+wl6R///vfGjNmjM4991yZzWYNHz5czz//fGi5y+XSzJkzNXr0aPXt21dt2rTR+PHjeYY9Wq1jGUE/nFfXG9ORTkCk2u06PzFWe4rL9bfXXld8z3NCA+2d3jFJ/3tWB/XNSjjofc1x8EAAAAC0DmH9K/Sss86SYRiHXW4ymTRhwgRNmDDhsG2SkpL0xhtvHHE7vXr10pdffnncdQItUXO/uh5u8Q6Tij59ViVf/0eu3MsV0+McfbVxn77auE+Vm5aq+Mt/yZv/U4+jtPQMbd+2lXAPAACAJsdfoABwCLU9Gx76xwdyOKNVUunTdzvcWldQpqgOfRXVoa86JEfp1OwEuezSfZf0VSAQINgDAACgyfEXKAAcQW3PhmS7Xb92xah/jleLN+/TuvxSbSqq1KaiSnVuGy1rQvrRVwYAAAA0AnO4CwCASJIQbVfeSWm6ekCWOrWtfrzfj3sqlHHTy3rgwzXaVVwZ5goBAADQ2hDsAeA4JMc6NKxXuq7qn6msRKdMZove+W6Xznpynh7+aLX2lB79cYIAAABAQyDYA8AvkBLv1G96pCr/9bvVL8slbyCoqQu2aNATc/TYtNUq2F8mj8cTmvx+f7hLBgAAQAtDsAeAXygQ8Muza73+O/oMFbz1gDy71qvSF9Tfvtqi/hM+Veo51yvKlSyn06nMrGzCPQAAABoUg+cBwC9UO4L+o+8ulsMZLcMwtGVfpRZvKVaRYpRwxtVKOesanZQarY/+eBGj5wMAAKBBccUeABpI7Qj6NodDndMTNGJgtob2SFNyjF3egKHvd5XrhJtf1cTpP6jAXRXucgEAANBCcMkIABqJyWTSialx6pwSq017y7V4U5H2lElTF27TG9/s0P/0a6ebz+yozKTocJcKAACACMYVewBoZCaTSR3bxup/+qSp4O3x6puVIG8gqH8v3qaznpqnu95erh8LSsNdJgAAACIUwR4AmojJZFLV5u/0xsh+emvUQJ3RuY0CQUP//W6Hfj3pC13zymLNW18owzDCXSoAAAAiCF3xASAMBnRI1oAOyVq2vViT523QzDUF+vLHvfryx73qlBKr63KzdfHJJyjOaQt3qQAAAGjmuGIPAGHUJzNBf72mn+b/4WyNPD1HsQ6rNhSW6cEPV2vgY7N13/srtXpXSbjLBAAAQDPGFXsAaGIej+egeamxFt3z64763zOy9d/vd+nNb3do094KvbF4m95YvE2927l0Wb9MXdgrQ65oruIDAADgJwR7AGgigYBfMpnlcrnq1T69z1m65A9PaeaaAi3fUaLlO0r06LQ1+nX3VF12Sjud3rmNbBY6XgEAALR2BHsAaCJGMCgZQT367mI5nEd+xJ3f59V9l/TVM5f1UKmvhz74fqfeXbpD6/JL9cmK3fpkxW4lRtt0Xo90/aZXugZ0SJbFbGqiPQEAAEBzQrAHgCZmtdlltdvr1dbj8SjO4dA1p56gq/tnaG1+qd77frc+XVWgonKv/vPNNv3nm21qG+fQ4G6p+nX3FP2qYxs5bZZG3gsAAAA0FwR7AGiGjtpt32SWM6unorsNUmzX07VHCoX8KJtFp3duo8HdUnR21xSlxDmbtHYAAAA0LYI9ADRD9e227/d5dd/wAZq5fIu+2LBfn68t0O6SKs1aU6BZawokVY+8P7hbis7pmqquaXEy02UfAACgRSHYA0AzVq9u+0G/BmTFaVDnNrr/vE5al1+m2ev3aO76vVq1y61l24u1bHuxnpr5g5Jj7DqtUxud3qmNTuvcRickRDXNjgAAAKDREOwBIIIdrcu+JTZJUR37K6rTADmzeqpI0kfLd+mj5bskSe2To/WrDkn6VcckDWifqPio6kfpWSwWWa38igAAAIgE/NUGABHsWEbar6go18S7bpIzu5ei2p8se3pnbSmq0JaiCr2xZIeMYEDegk3y7FitqLKdWj7rXbWJP/I6AQAAEH4EewBoAerTZd/u88qzfaUemPQPOZzR8viD2llSpR37q7S9uFLFlZIjvbMc6Z0lSf0nzlWX1DgNyEnSgA7JOjUnSW1iHU2xOwAAADgGBHsAaGVqTwJY7dKJ0U6dmF49v6zKrx3FFdpRVK5lq9fKlpypdfmlWpdfqtcWbpUkdWwbo37ZCTq1faL6ZycqNd5Bt30AAIAw4y8xAIAkKdZpVde0eOW4rPr0ztEyR8XLmXmSHJk95MzsIXtKjjbuKdfGPeV669udkiTf/l0y7d2kJ+66UQM7Jh91MD5OAgAAADQ8/roCANRRe9/+I/+cUee+/SpfQLvcHu0qqdKuEo/2lnllS8yQEjN030frJEn+kkJ5dqxR1c418uxcK9+erZIRDK0jLT1D27dtJdwDAAA0IP6yAgAc0s/v24+1SyfGRIW67nv8AW3N3683X/u7Op17pfaW+2V1pcjqSlHMSWdJkmwWk9LiHEqLdygl2qLJvz9XgUCAYA8AANCA+MsKAHBcHFaLshKdKp4/VcPvvF0mm1P57irtKq7U7pIq5ZdUyRsIantxlbYXV0mSMm9/UxdNXqR+7ZPULztJfbMT1S4xSiaTKcx7AwAAELkI9gCABmG3mpWVFK2spOru+0HDUFGZV7tLKrWrpEq7iyvlrpLW5ZdpXX6ZXl+0TZKUEudQr3YJ6tXOpZ7tXOp5govR9wEAAI4BwR4A0CjMJpPaxjnUNs6hXu0kv9ercVcM0ntfLtPynWVaunW/Vu8qUWGpR5+vLdDnawtC781wOdUjI049MuLV44R49ciIlyvKdsjtMCAfAABo7fhLCADQZALl+5XXPVUXnpwlqXpAvlU7S7RyZ4lW7CjRih3F2rS3vGaAvirNXLsn9F7f/l3y5m+QN/9HefM3ylu4WcGqUgbkAwAArR5/BQEAwsZps1Tfb98+KTSvqKRcJ/QcqIv/+KKKKgMqLPWqpMovW2KGbIkZiuk2KNQ2xm7WnrWL9cT09eqRmaju6XFqnxwjq8Ucjt0BAAAIC4I9AKBJeTyeIy63mQLybF+lvtlJoVH5q3wBFZZ6VOiuUoHboz1lHpVU+lTuDSq6Y3/97astkrZIkhxWs05MjVO39Dh1S4+vntLi5Yo+dFd+AACASEewBwA0iUDAL5nMcrlc9WofDAZD3zttljoD80mS1x9UQXGZ/v7EeN38x0f1455K/VBYpgpvQCtruvcfKD3eoa5pceqSFqsuqXHqmharrMSoQ17d5759AAAQSfirBQDQJIxgUDKCevTdxXI4ow/brqqiTOMvz5VhBA/bRqoehT8lxqqy5TP01JWf1cw1yZqQJntKjmwpObKn5Mjetr2sCWna7fZot9ujuT/s/akmv1e+ou3y7tkq396t8u3dJu+erWobY9P2bVsI9wAAICLwFwsAoElZbfZQF/tDLvcdftnP1fdkgbvUrSfvHqVLxr2oEq+0t9yrfeU++a122VM7yp7asU77oKdCw19eqK5pLp2YFqcuqXE6MS1WbWMdMplM9a4PAACgKRDsAQAR72gnC6KdTnl2rFbvdi45oqpPABiGIXeVX0VlHu0t96qozKOicq/2l3slR7SW73Br+Q53nfXEO61qnxwdmjqmxKpjSrxy2sQoxsGvVAAAEB78FQIAaJVMJpNcUTa5omzq0Pan+RXl5Xrkpktka5MpW5ss2dpky94mW9bEdLmrpBU73Vqx033Q+lLjHcpOjtEJCVHKSHAq3VX9NSMhSumuKMU7rVztBwAAjYJgDwDAAcwy5Nu7VeNffrtO935/IKiSKr+KK/0qrvRpf7lHy7//Vhld+mhfuU8Fbo8K3Icf8T/GblF6QpRS4hxKjLErKdpe89VW/TXGrsSaebF2q2IclqM+ts/v9ysQCNRrvxgQEACAlovf8AAAHMLPu/dbJTmjpNTE6td+r1cz/3CvfigpUVXQrK1FFdq6r1K7S6qU767SrpIq5ZdUabfbo+IKn8q9AW0oLNOGwrJ61+CwmhXrsCqmZop1WELfR9vM+s+/XlPZ/j0yvBUKemomb4UMz89eeyuVlpam7du2Eu4BAGiB+O0OAMBxOJbH95msDqV17K7/fPCZiqv82ldzL/++Cq/2l/tUVO7R/nKf9lV4VVzhlS9gSJI8/qA8fq+Kyr2HXK+1+2Al1LPeoKdCv3p8rmKdVsU6rHJF2ZQYbavpKWCrmexKjnWobXyUEmPsSoiyHbXXAAAACD+CPQAAx6G+I/JLkt/n1X2X9NXJGVFyOBxHbuv3q1OX7ircVyyzPUome9TPvkbLfMC8X118nQIyy+sPyusPyhMIhr73BoIKBKtPEpgd0Sos86qw7NAnCQ7HFWWtvkWgJvwn19w2kBRjV5tYh1JcUaHvE6Ptsls5EQAAQFMj2AMA8AscbUR+6diu7tf603+XyO5wHnZ5VUWZxl+eqzPuvD000v+h+INBlbpLNXHUhRr93LuSxSFvIKgqf1BVvoAqfT99rfD6tHPbFpmj4mWJipcklVT6VVLp15ai+tUd77QqOdahpBi7kmPsSo61Kzmm5nXN99Vfq8cTsNEjAACAX4xgDwBAIzuWq/u1gd1itR7xhIHVd+STCaF2ZrOibBb5i/OVlhBzxJMAVeVluu/B8/Tou4tlc0TJ4wuq8oATAJW+QM0UVIXHp1VLF6tn/1wVV1bfXhA0JHeVX+4qvzbvLa9XfXEOq+KjbIqPsskVZQ09qSDeWf3VFf3T6/iaZbEOq6IdFsXYrbKYedIAAAAEewAAmkh9ru7XN7A3JqvNLrvDIbtDijtMG09lhebc+6BmvxWsmWOS2Rkrc4xLliiXLNEumaOrv8a2zdAFl16hfRV+7a+oHjOguMKnoCGVevwq9fi1s7jyuGp12syKsf8U9GMcVkXbLdXhv+bpAk6rWXaL5LBaZLea5bCa5bRVf61+bZGjZn6U3aoYp10O20/zHFaLbBbTcT+usL5PL+DJBQCA48VvDwAAcMzq2wvBU1WhB/8nV3/98Jm6C0zm6hMBP5ssUfHV4wkcON8RK7MzRhZnrEzO2OrxBcwWSVKVL6gqn1dF9esgcNxMJoWCvt1qltNqOSj8O0InC36ab7OY9I+/vqyykn0yfN7QUwqC3goFvZXV33uq57VNStC2jetls9kad2cAAC0OwR4AABy3o/VC8Pu8x3wbwtHaGoahisoKPXp9nkxWxwGDCzqrv9qiZLI7q786omS2OTVg2FUyTGYFgoYCQUP+n30NBA35AkEV79srk9Uuk8Ums81xwDZrTyIED1vXYX9GPYfW++kFXcbPDPU8qH2CQXyUTfHO2q82xUdZFWu3KNZuVqzTWr3MaVNczfcOq7lO7wJ6AgBAy8f/8gAAoNEdy20I9Wnr8PsUrCg56kmA2pMFZ94++ojjC0g1Ywxccn5onYZhKGCo5gRAUIFg9feVVZV66Y8jZbJYQycBTFa7TFabTBb7T99b7TWvbRr4mxEyZJY3UP20Aq8/KJ/fCH3vDVSfMDjw9gS5j1juYRl+n4KeMgWryhX0lMumgIYNOVsJ0fbQWAUHniiI+9lJgyib5bhvOwAAhAfBHgAARKyjnQQ4njELDlznoTrFV5Ub8u5ad0y9EAYd5cRCVUW57r/8dJltjupeB47oml4I0TI7Y0K3I5gdMaHXHfsOki9okjcQlKfmEYeGJJPVJos1UZaYxND6P1tVUP/9N5sUZfvpVoPq8Qgsdb7aLaafxiuouf3AbjHJZjXLbqm+BcFmMcths8hpt8lmMdXMN8tWc4tC9a0KP01mBWU1GQfMq17HzwdIpAcCAByM/xUBAACOQ4MOhmgYMrwVeviNufU+WXDn9ZfVOVlgGEYo5Ht81UG/osqjfz7xRz313Iuq8EulNU8tKK3yqdQTkLvK99O8Sr8CRvXtCaFeA82AEQzICPikgF9GwC+zDGWekC57nRMD1ScB7BZTnbEPouxWOW3W0EkJh636RITDZjnohEX1SYy6Xw8cO4FeDACaM4I9AABAM/FLThaYTKaagGqRnNXzPJWGytfM1y2Dux9122aLVYbZKrMzRiaro+aWgsNMFpvOv/FeyWwNjVUQNKrHKqj+KvkDAa1aNFdDhp6vgGGSr/YWhEBQvoAR+lp7O0Kxu1RWR5SCxs/2y2ypHizxgO4TO4qrjro/De2ngRLNP/VmCD1Z4cCTAeaD2jqtPw2sWPtEhp96LJjksFnldNhkt5gPOGFR3SvCbql7AiMQCPCUBQAH4dMOAADQQtX36QWhgQvf+rrePQZOznzsiLcXeCorNPePE/Xv9/9c73of/2i5bA6HgjVjGwQMQ8HaAQ4NQ5Xl5Xr+zhH6/f+9JovNUTNf1W0Mo87giB6vV7Pe/Hv1GAi2n8Y7OPgkhV22qGi179i5urdDzW0NVb5AnZMMtcvCzQj4ZPh9MoLVPRgU8Mmo6c1gBHxS0C/D75PdatE5Zw2qvk3iwBME1preDYc4aVDn9oia5faa9rXTgSckal+bzSaZTSaZTZJJJpnMkkmqmWeSyVT9ZAmzyRSaXz2v8XpB8JhJtDb8KwYAAGjh6jsWQUPeXlDfkwrSTycLDCMok8kki0kH3VsvSVGGV96CjcpIjKnXYIjvfv1GvR/JuN04RGg3W+qcAKg9QXDzU6/LbLHXebrCgU9Y8AcNeTwezf/o38q94LcyTNbQAIzVX386IeEPBlWwfbPad+wsf83TGap7NlT3ZjB+3oPBYpPJUr9HIs77YW+92oWT2VQ36NeeHKidL5N+OmkQWv7T9z9/r9lkkgxDmzZtkt/vrX6kRc1kGMGfXqv6q81qUe7AU2WzWGQxm2SzmGQxm2Q1m2u+1ry2mH/63mySxVL91Wo213ltMZsPWEf169r3Vc//2Xp+vu4D1lv7urYeBQMyyQi1NR/m5AgnK1onjjgAAAAaTYOORdAI2z+eRzJmJETX68TCh3Nf1aCxtx61Z8O4h8/XrkOdWJAkk7k6yFus1U9isFh17yszZbbZD9mrIRg0VFXl0RtP31fd3lz9HllsNe+31byuWWa1yWy1SybLAds4sE3N+6w1JxTMllAb1bY1W474sziSoCEFQ2cvjCO2PRbmhHTV91/V4s37G2y7Tc0I+GUE/VIwKCMYkIIBmU1SelpqaPDJg08kHHDSwWKSzWKpPplgqT6BYKvpqVH7vc1qku2A7+01JyJsNb037Jba9VR/b1JQFpNCvTqs5p/GwLDW9PKIsVsU5bDJZqvfSSocHcEeAAAArV64TkAcT8+GOIdZjqjD11JVLlWsmXdM66z37RqHaWcYhgxVXwyvqijTIyPO1ENvfCm7MyrU68CoaaeadoYkj6dST4z6Tc3S6j77JpO5uu9+zevayWQy69ZJb8pqc9ScAjBC6wltwzDkqarUKw/dopsee0U2u+OQ2zUMQ16vR/+aeLdMZpNksshkqRnPwWSpPoFhrp4nU8331poQarZWtzNbZDKba75aQ6+re1SYak6M1CyvXecB66vdhszmOu//aX5tDYcPv7Une34u3+057HuaCyPolyvaqViHVTEOi2Ls1V9jHVa5omxKirYpMdqmhBi7EqOsSoyxKynarsRom6Lth38sZ2vtsdD69hgAAABoZhrjxMKxrLMhb9eofoqBT06nQ44o5xHbVpkCCpTurfeJhbbxUfXoLWGWZ8dqneByHPkxk+VS5Q9fN+kJkONtW1leqoeuGqQJ7y6WzVk9yGQwWH1iI2gYNVP1ozOfu+Mq3fLEv2S1O0PzDaP69g+j5nWw5sTGuy9MqD6ZYKruiVF7kqJOrw6LVSarvab3iPWnnh3mQ3xf0z4lq5OCMh0wqKZCg2vWjl1hMlvlrnkqx7EK+qoUKNtXPZUWyV9WFPo+3m7S3E/+qxMSY2S3mg+7jpZ2AqDl7AkAAACAiFTfEwvh2PaB22+M8Srq09bmc0hBf82AhYe/9aFKXvkKNyu9nuNQlK+a3SgnK2778PvDbj9oGCp1l+pPvztPt7/0kUxWR2hcCV8gKG/AUJU/oEpf9SCW5VU+bf5xrZKyTlSVv/pEgdnmlDkxQ7bEjENu47y/LJYk+cv2KeDeK3/pHgXce+R3F8rv3qOAe6+Soszatn5li7kdoFUF+xdffFFPPvmk8vPz1bt3b73wwgs69dRTw10WAAAAAIRFUw+uaTZVP4EhUFqktvH1G6/ivgfG6vYPv5fdGSV/0FCFN6CyKr/KvX6Veaqnco9f7gqvtm/bKmdSugKGZI1NkjU2SQ6deMh1L9myT7/qnHrUmiNBqwn2b731lu688069/PLLGjBggJ599lnl5eVp/fr1SklJCXd5AAAAAIAjMJmqny7gijLLFXXwlfbqkwBD9NgH3ylodai0qjr0l1b5VVrlq/la/X2Zx6cTXEe+VSSStJpg/8wzz+imm27SDTfcIEl6+eWX9cknn+jVV1/VH//4xzBXBwAAAABoCCaTSdF2q6LtVh3qerzf69U9F56s1EdKm7y2xtIqgr3X69XSpUs1bty40Dyz2azBgwdr4cKFB7X3eDzyeH4aSbKkpESS5Ha7G7/YX6i27rKSfbLajjBaakV5qJ3PU3XEdda3bWOsM9zbb83rDPf2I2Wd4d5+pKwz3NuPlHWGe/uRss5wbz9S1hnu7bfmdYZ7+5GyznBvP1LWGe7tR8o6j6Wt3+eVgn6VlpbK6/UecZ3hVJs/DePoj4I0GfVpFeF27dqlE044QQsWLFBubm5o/j333KP58+dr8eLFddo//PDDeuSRR5q6TAAAAAAA6ti+fbvatWt3xDat4or9sRo3bpzuvPPO0OtgMKh9+/YpOTn5sM9LbEhut1uZmZnavn274uPjG317aBwcx5aB49hycCxbBo5jy8BxbBk4ji0Hx7J5MgxDpaWlysg49Oj/B2oVwb5NmzayWCwqKCioM7+goEBpaWkHtXc4HHI4HHXmJSQkNGaJhxQfH88HqwXgOLYMHMeWg2PZMnAcWwaOY8vAcWw5OJbNj8vlqlc7cyPX0SzY7Xb17dtXs2fPDs0LBoOaPXt2na75AAAAAABEmlZxxV6S7rzzTl133XXq16+fTj31VD377LMqLy8PjZIPAAAAAEAkajXB/oorrtCePXs0fvx45efnq0+fPpo+fbpSUw/1AITwcjgceuihhw66HQCRhePYMnAcWw6OZcvAcWwZOI4tA8ex5eBYRr5WMSo+AAAAAAAtVau4xx4AAAAAgJaKYA8AAAAAQAQj2AMAAAAAEMEI9gAAAAAARDCCfTPz4osvqn379nI6nRowYIC++eabcJfUqk2cOFH9+/dXXFycUlJSdPHFF2v9+vV12px11lkymUx1pptvvrlOm23btmnYsGGKjo5WSkqK7r77bvn9/jpt5s2bp1NOOUUOh0OdOnXS1KlTG3v3Wo2HH374oGPUtWvX0PKqqiqNHj1aycnJio2N1fDhw1VQUFBnHRzD8Gvfvv1Bx9FkMmn06NGS+Cw2V1988YV+85vfKCMjQyaTSR988EGd5YZhaPz48UpPT1dUVJQGDx6sH3/8sU6bffv2acSIEYqPj1dCQoJGjhypsrKyOm1WrFihM844Q06nU5mZmXriiScOquWdd95R165d5XQ61bNnT3366acNvr8t2ZGOpc/n07333quePXsqJiZGGRkZuvbaa7Vr16466zjU5/jxxx+v04Zj2biO9pm8/vrrDzpG5513Xp02fCbD72jH8VC/L00mk5588slQGz6PLYyBZuPNN9807Ha78eqrrxqrV682brrpJiMhIcEoKCgId2mtVl5enjFlyhRj1apVxrJly4zzzz/fyMrKMsrKykJtzjzzTOOmm24ydu/eHZpKSkpCy/1+v9GjRw9j8ODBxvfff298+umnRps2bYxx48aF2mzatMmIjo427rzzTmPNmjXGCy+8YFgsFmP69OlNur8t1UMPPWScdNJJdY7Rnj17QstvvvlmIzMz05g9e7bx7bffGgMHDjR+9atfhZZzDJuHwsLCOsdw1qxZhiRj7ty5hmHwWWyuPv30U+P+++833nvvPUOS8f7779dZ/vjjjxsul8v44IMPjOXLlxsXXnihkZOTY1RWVobanHfeeUbv3r2NRYsWGV9++aXRqVMn46qrrgotLykpMVJTU40RI0YYq1atMv7zn/8YUVFRxl//+tdQm6+//tqwWCzGE088YaxZs8Z44IEHDJvNZqxcubLRfwYtxZGOZXFxsTF48GDjrbfeMtatW2csXLjQOPXUU42+ffvWWUd2drYxYcKEOp/TA3+nciwb39E+k9ddd51x3nnn1TlG+/btq9OGz2T4He04Hnj8du/ebbz66quGyWQyNm7cGGrD57FlIdg3I6eeeqoxevTo0OtAIGBkZGQYEydODGNVOFBhYaEhyZg/f35o3plnnmncfvvth33Pp59+apjNZiM/Pz80b/LkyUZ8fLzh8XgMwzCMe+65xzjppJPqvO+KK64w8vLyGnYHWqmHHnrI6N279yGXFRcXGzabzXjnnXdC89auXWtIMhYuXGgYBsewubr99tuNjh07GsFg0DAMPouR4Od/fAaDQSMtLc148sknQ/OKi4sNh8Nh/Oc//zEMwzDWrFljSDKWLFkSavPZZ58ZJpPJ2Llzp2EYhvHSSy8ZiYmJoeNoGIZx7733Gl26dAm9vvzyy41hw4bVqWfAgAHG73//+wbdx9biUEHi57755htDkrF169bQvOzsbGPSpEmHfQ/HsmkdLthfdNFFh30Pn8nmpz6fx4suusg455xz6szj89iy0BW/mfB6vVq6dKkGDx4cmmc2mzV48GAtXLgwjJXhQCUlJZKkpKSkOvP//e9/q02bNurRo4fGjRunioqK0LKFCxeqZ8+eSk1NDc3Ly8uT2+3W6tWrQ20OPPa1bTj2DefHH39URkaGOnTooBEjRmjbtm2SpKVLl8rn89X5+Xft2lVZWVmhnz/HsPnxer16/fXX9bvf/U4mkyk0n89iZNm8ebPy8/Pr/MxdLpcGDBhQ5/OXkJCgfv36hdoMHjxYZrNZixcvDrUZNGiQ7HZ7qE1eXp7Wr1+v/fv3h9pwbJtWSUmJTCaTEhIS6sx//PHHlZycrJNPPllPPvlkndthOJbNw7x585SSkqIuXbrolltuUVFRUWgZn8nIU1BQoE8++UQjR448aBmfx5bDGu4CUG3v3r0KBAJ1/uCUpNTUVK1bty5MVeFAwWBQY8eO1WmnnaYePXqE5v/2t79Vdna2MjIytGLFCt17771av3693nvvPUlSfn7+IY9r7bIjtXG73aqsrFRUVFRj7lqLN2DAAE2dOlVdunTR7t279cgjj+iMM87QqlWrlJ+fL7vdftAfnqmpqUc9PrXLjtSGY9g4PvjgAxUXF+v6668PzeOzGHlqf+6H+pkfeExSUlLqLLdarUpKSqrTJicn56B11C5LTEw87LGtXQcaVlVVle69915dddVVio+PD82/7bbbdMoppygpKUkLFizQuHHjtHv3bj3zzDOSOJbNwXnnnadLL71UOTk52rhxo+677z4NHTpUCxculMVi4TMZgV577TXFxcXp0ksvrTOfz2PLQrAH6mn06NFatWqVvvrqqzrzR40aFfq+Z8+eSk9P17nnnquNGzeqY8eOTV0mDmHo0KGh73v16qUBAwYoOztbb7/9NkEtQr3yyisaOnSoMjIyQvP4LALNg8/n0+WXXy7DMDR58uQ6y+68887Q97169ZLdbtfvf/97TZw4UQ6Ho6lLxSFceeWVoe979uypXr16qWPHjpo3b57OPffcMFaG4/Xqq69qxIgRcjqddebzeWxZ6IrfTLRp00YWi+WgkbgLCgqUlpYWpqpQa8yYMZo2bZrmzp2rdu3aHbHtgAEDJEkbNmyQJKWlpR3yuNYuO1Kb+Ph4gmcjSEhI0IknnqgNGzYoLS1NXq9XxcXFddoc+NnjGDYvW7du1eeff64bb7zxiO34LDZ/tT/3I/3uS0tLU2FhYZ3lfr9f+/bta5DPKL9jG1ZtqN+6datmzZpV52r9oQwYMEB+v19btmyRxLFsjjp06KA2bdrU+b+Uz2Tk+PLLL7V+/fqj/s6U+DxGOoJ9M2G329W3b1/Nnj07NC8YDGr27NnKzc0NY2Wtm2EYGjNmjN5//33NmTPnoO5Ih7Js2TJJUnp6uiQpNzdXK1eurPNLsPaPne7du4faHHjsa9tw7BtHWVmZNm7cqPT0dPXt21c2m63Oz3/9+vXatm1b6OfPMWxepkyZopSUFA0bNuyI7fgsNn85OTlKS0ur8zN3u91avHhxnc9fcXGxli5dGmozZ84cBYPB0Mmb3NxcffHFF/L5fKE2s2bNUpcuXZSYmBhqw7FtXLWh/scff9Tnn3+u5OTko75n2bJlMpvNoa7dHMvmZ8eOHSoqKqrzfymfycjxyiuvqG/fvurdu/dR2/J5jHDhHr0PP3nzzTcNh8NhTJ061VizZo0xatQoIyEhoc4Izmhat9xyi+FyuYx58+bVeRRIRUWFYRiGsWHDBmPChAnGt99+a2zevNn48MMPjQ4dOhiDBg0KraP2EVtDhgwxli1bZkyfPt1o27btIR+xdffddxtr1641XnzxRR6x1YDuuusuY968ecbmzZuNr7/+2hg8eLDRpk0bo7Cw0DCM6sfdZWVlGXPmzDG+/fZbIzc318jNzQ29n2PYfAQCASMrK8u4995768zns9h8lZaWGt9//73x/fffG5KMZ555xvj+++9DI6U//vjjRkJCgvHhhx8aK1asMC666KJDPu7u5JNPNhYvXmx89dVXRufOnes8Wqu4uNhITU01rrnmGmPVqlXGm2++aURHRx/0SCar1Wo89dRTxtq1a42HHnqIRzIdoyMdS6/Xa1x44YVGu3btjGXLltX5nVk7ovaCBQuMSZMmGcuWLTM2btxovP7660bbtm2Na6+9NrQNjmXjO9JxLC0tNf7whz8YCxcuNDZv3mx8/vnnximnnGJ07tzZqKqqCq2Dz2T4He3/VsOoflxddHS0MXny5IPez+ex5SHYNzMvvPCCkZWVZdjtduPUU081Fi1aFO6SWjVJh5ymTJliGIZhbNu2zRg0aJCRlJRkOBwOo1OnTsbdd99d59nZhmEYW7ZsMYYOHWpERUUZbdq0Me666y7D5/PVaTN37lyjT58+ht1uNzp06BDaBn65K664wkhPTzfsdrtxwgknGFdccYWxYcOG0PLKykrjf//3f43ExEQjOjrauOSSS4zdu3fXWQfHsHmYMWOGIclYv359nfl8FpuvuXPnHvL/0euuu84wjOpH3j344INGamqq4XA4jHPPPfeg41tUVGRcddVVRmxsrBEfH2/ccMMNRmlpaZ02y5cvN04//XTD4XAYJ5xwgvH4448fVMvbb79tnHjiiYbdbjdOOukk45NPPmm0/W6JjnQsN2/efNjfmXPnzjUMwzCWLl1qDBgwwHC5XIbT6TS6detmPPbYY3UCo2FwLBvbkY5jRUWFMWTIEKNt27aGzWYzsrOzjZtuuumgi0x8JsPvaP+3GoZh/PWvfzWioqKM4uLig97P57HlMRmGYTRqlwAAAAAAANBouMceAAAAAIAIRrAHAAAAACCCEewBAAAAAIhgBHsAAAAAACIYwR4AAAAAgAhGsAcAAAAAIIIR7AEAAAAAiGAEewAAAAAAIhjBHgAANIr27dvr2WefDXcZAAC0eAR7AABwVNdff71MJpNMJpPsdrs6deqkCRMmyO/3H/Y9S5Ys0ahRo5qwSgAAWidruAsAAACR4bzzztOUKVPk8Xj06aefavTo0bLZbBo3blyddl6vV3a7XW3btg1TpQAAtC5csQcAAPXicDiUlpam7Oxs3XLLLRo8eLA++ugjXX/99br44ov15z//WRkZGerSpYukg7viFxcX6/e//71SU1PldDrVo0cPTZs2LbT8q6++0hlnnKGoqChlZmbqtttuU3l5eVPvJgAAEYcr9gAA4LhERUWpqKhIkjR79mzFx8dr1qxZh2wbDAY1dOhQlZaW6vXXX1fHjh21Zs0aWSwWSdLGjRt13nnn6U9/+pNeffVV7dmzR2PGjNGYMWM0ZcqUJtsnAAAiEcEeAAAcE8MwNHv2bM2YMUO33nqr9uzZo5iYGP3jH/+Q3W4/5Hs+//xzffPNN1q7dq1OPPFESVKHDh1CyydOnKgRI0Zo7NixkqTOnTvr+eef15lnnqnJkyfL6XQ2+n4BABCp6IoPAADqZdq0aYqNjZXT6dTQoUN1xRVX6OGHH5Yk9ezZ87ChXpKWLVumdu3ahUL9zy1fvlxTp05VbGxsaMrLy1MwGNTmzZsbY3cAAGgxuGIPAADq5eyzz9bkyZNlt9uVkZEhq/WnPyNiYmKO+N6oqKgjLi8rK9Pvf/973XbbbQcty8rKOr6CAQBoJQj2AACgXmJiYtSpU6fjem+vXr20Y8cO/fDDD4e8an/KKadozZo1x71+AABaM7riAwCARnfmmWdq0KBBGj58uGbNmqXNmzfrs88+0/Tp0yVJ9957rxYsWKAxY8Zo2bJl+vHHH/Xhhx9qzJgxYa4cAIDmj2APAACaxH//+1/1799fV111lbp376577rlHgUBAUvUV/fnz5+uHH37QGWecoZNPPlnjx49XRkZGmKsGAKD5MxmGYYS7CAAAAAAAcHy4Yg8AAAAAQAQj2AMAAAAAEMEI9gAAAAAARDCCPQAAAAAAEYxgDwAAAABABCPYAwAAAAAQwQj2AAAAAABEMII9AAAAAAARjGAPAAAAAEAEI9gDAAAAABDBCPYAAAAAAESw/wfqO1djLk29xwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This histogram and kernel density estimate (KDE) plot show the distribution of diamond prices. We can observe that the distribution is highly skewed to the right, with a large concentration of prices clustered around the lower end of the scale (near 0), and a long tail extending towards the higher prices. There are many diamonds priced below 2,500 and fewer priced above 10,000, suggesting that most diamonds in the dataset are inexpensive. However, the right-skewed nature of the distribution shows that there are a few high-priced diamonds, which are less common but still significantly affect the overall distribution.\n",
        "\n",
        "Since the data is not normally distributed, I will log-transform the price values. This will help normalize the distribution by compressing the range of high values and making the data more symmetric, which will lead to more accurate model performance. Therefore, I will be using the log-transformed prices as the target variable for modeling purposes."
      ],
      "metadata": {
        "id": "Dnf7B_Z0AELm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply logarithmic transformation to the target variable since not noramlly distribted\n",
        "y_log = np.log(y)"
      ],
      "metadata": {
        "id": "kpFzVSKVAGWj"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modeling & Interpretations\n",
        "\n",
        "\n",
        "For predicting diamond prices, I decided to try out several different regression models to see which one worked best in capturing the variations in the data. To do this, I used an 80-20 train-test split, where I trained the model on 80% of the data and tested it on the remaining 20%. This approach allowed me to evaluate the models' performance while making sure the results were reliable and not overfitted.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "rZTF6p3OAzke"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Baseline Model\n",
        "\n",
        "To evaluate how well my models performed, I compared their metrics, like mean squared error (MSE), to a baseline. For the baseline, I used the mean of the log-transformed target values from my training data and predicted that value for every test sample. This gave me a reference point to see how much better (or worse) my models were compared to just predicting the average. I then calculated the mean squared error (MSE), mean absolute error (MAE), and R-squared (R²) for both the baseline and the models, which helped me assess their performance more thoroughly.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "5H2rnQlR_gZj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "y_train_mean = np.mean(y_log)\n",
        "y_pred_baseline = np.full_like(y_test, y_train_mean)\n",
        "mse_baseline = mean_squared_error(y_test, y_pred_baseline)\n",
        "mae_baseline = mean_absolute_error(y_test, y_pred_baseline)\n",
        "r2_baseline = r2_score(y_test, y_pred_baseline)\n",
        "\n",
        "\n",
        "print(\"Baseline Model (Log-Transformed) Metrics:\")\n",
        "print(f\"Mean Squared Error (Log Scale): {mse_baseline:.2f}\")\n",
        "print(f\"Mean Absolute Error (Log Scale): {mae_baseline:.2f}\")\n",
        "print(f\"R-squared (Log Scale): {r2_baseline:.2f}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DJ9lAqIHa64b",
        "outputId": "f37df1e5-75b5-40b6-a840-8c6e7a73753b"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline Model (Log-Transformed) Metrics:\n",
            "Mean Squared Error (Log Scale): 1.03\n",
            "Mean Absolute Error (Log Scale): 0.88\n",
            "R-squared (Log Scale): -0.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The baseline model, which simply predicts the mean of the log-transformed target values for all test samples, provides a useful reference point for assessing the performance of more complex models. The **Mean Squared Error (MSE)** of 1.03 suggests that the baseline model's predictions are a bit further from the actual log-transformed values. This highlights that the baseline isn't capturing any of the underlying patterns in the data. The **Mean Absolute Error (MAE)** of 0.88 further proves this point, showing that the average difference between the baseline's predictions and the true values is also relatively large. Thee **R-squared value** of -0.00 indicates that the baseline model explains virtually none of the variance in the target variable. This negative value suggests that the baseline model is actually performing worse than simply predicting random values, reinforcing the need for more advanced models that can capture the relationships between the features and the target variable."
      ],
      "metadata": {
        "id": "ZZTNpzcnGp9-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Linear Regression Model\n",
        "\n",
        "I decided to use a linear regression model because it's a simple and effective way to understand how different factors, like carat weight, cut, color, and clarity, influence the price of a diamond. With this model, I could directly see how each of these factors contributes to the price, both individually and in combination. Linear regression also provides a clear way to interpret the relationships between the variables, making it a great starting point for my analysis. It gave me a good understanding of how the predictors work together to determine the price, and from there, I could explore more complex models if needed."
      ],
      "metadata": {
        "id": "7DuBzo1H_kWt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "#set up X, y, and categorical columns and numerical columns\n",
        "X = diamonds[['carat', 'depth', 'table', 'x', 'y', 'z', 'volume', 'price_per_carat', 'depth_table_ratio']]\n",
        "y = diamonds['price']\n",
        "\n",
        "categorical_columns = ['cut', 'color', 'clarity']\n",
        "numerical_columns = ['carat', 'depth', 'table', 'x', 'y', 'z', 'volume', 'price_per_carat', 'depth_table_ratio']\n",
        "\n",
        "\n",
        "# make the column transformer and pipeline with the model\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('cat', OneHotEncoder(drop='first'), categorical_columns),\n",
        "        ('num', 'passthrough', numerical_columns)\n",
        "    ]\n",
        ")\n",
        "\n",
        "ridge_pipe = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('regressor', LinearRegression())\n",
        "])\n"
      ],
      "metadata": {
        "id": "eljtTk77_kD0"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#split into training and testing\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_log, test_size=0.2, random_state=42)\n",
        "\n",
        "# fit to the Linear Regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "9sAyrmCkE4M6",
        "outputId": "81647ecf-2738-427a-9946-1061f584fa42"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
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              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
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              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict the target variable on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"Log-Transformed Model Metrics:\")\n",
        "print(f\"Mean Squared Error (Log Scale): {mse:.2f}\")\n",
        "print(f\"Mean Absolute Error (Log Scale): {mae:.2f}\")\n",
        "print(f\"R-squared (Log Scale): {r2:.2f}\")\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2kgLDn7FaWp",
        "outputId": "c7bd458e-508f-411c-a6ed-19d800e4d992"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Log-Transformed Model Metrics:\n",
            "Mean Squared Error (Log Scale): 0.02\n",
            "Mean Absolute Error (Log Scale): 0.08\n",
            "R-squared (Log Scale): 0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The results of my log-transformed linear regression model provide some insightful metrics that help evaluate its performance. The **Mean Squared Error (MSE)** of 0.02 suggests that, on average, the model's predictions deviate from the actual values by a small margin. This low MSE indicates that the model is quite accurate and able to make reliable predictions when applied to unseen data.\n",
        "\n",
        "Additionally, the **Mean Absolute Error (MAE)** of 0.08 reinforces this finding. The MAE measures the average magnitude of errors in a set of predictions, without considering their direction. A value of 0.08 means that, on average, the model’s predictions are off by only about 0.08 units on the log scale, which is also a relatively small error.\n",
        "\n",
        "Finally, the **R-squared value** of 0.98 says that the model explains 98% of the variance in the data. This shows that the independent variables used in the model (such as carat weight, cut, clarity, etc.) have a strong relationship with the target variable, which is the log-transformed price of the diamonds. The high R-squared value suggests that the linear regression model is a very good fit for the data, capturing nearly all of the underlying patterns.\n",
        "\n",
        "Overall, these metrics show that the log-transformed linear regression model performs well, making it a solid choice for predicting diamond prices based on the available features."
      ],
      "metadata": {
        "id": "6T3zEHxLF7dz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Comparing to the Baseline\n",
        "\n",
        "When comparing the performance of the linear regression model to the baseline model, the difference in their effectiveness is evident. The **Mean Squared Error (MSE)** of the linear regression model is 0.02, which is much lower than the baseline’s MSE of 1.03. This suggests that the linear regression model’s predictions are much closer to the actual values, showing that it has captured the underlying trends in the data much better than the baseline, which simply predicts the mean of the target.\n",
        "\n",
        "The **Mean Absolute Error (MAE)** for the linear regression model is 0.08, again significantly smaller than the baseline's MAE of 0.88. This smaller error means that, on average, the linear regression model's predictions are far more accurate than the baseline’s, which only predicts a single constant value across all instances.\n",
        "\n",
        "The **R-squared (R²)** value for the linear regression model is 0.98, which means it explains 98% of the variation in the target variable. In comparison, the baseline model has an R² of -0.00, indicating that it doesn’t capture any meaningful variance in the data and is essentially no better than random guessing. This stark contrast emphasizes how well the linear regression model performs in identifying and understanding the relationships between the features and the target.\n",
        "\n",
        "The linear regression model shows a clear advantage over the baseline in all metrics. Its ability to predict more accurately and explain the variance in the data proves that it is much more effective at understanding the factors influencing the target variable."
      ],
      "metadata": {
        "id": "8unfvac7HJaD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lasso Regression\n",
        "\n",
        "I chose to use Lasso regression for this project because it helps address two important challenges: regularization and feature selection. Given the number of variables in my diamond dataset, some features might be less relevant or even redundant. Lasso regression applies a penalty that encourages the model to shrink the coefficients of less important variables, sometimes even to zero. This makes it especially useful for identifying which features truly contribute to predicting diamond prices, while discarding those that don't add much value. By simplifying the model, Lasso also reduces the risk of overfitting, making it more generalizable and reliable. Overall, I felt that Lasso regression would help create a cleaner, more interpretable model and highlight the key factors that influence diamond prices.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "JDsBt6CHDqXg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "import numpy as np\n",
        "\n",
        "# Prepare set and target variable\n",
        "X = diamonds[['carat', 'depth', 'table', 'x', 'y', 'z', 'volume', 'price_per_carat', 'depth_table_ratio', 'cut', 'color', 'clarity']]\n",
        "y = diamonds['price']\n",
        "\n",
        "# Define categorical and numerical columns\n",
        "categorical_columns = ['cut', 'color', 'clarity']\n",
        "numerical_columns = ['carat', 'depth', 'table', 'x', 'y', 'z', 'volume', 'price_per_carat', 'depth_table_ratio']\n",
        "\n",
        "# Define the column transformer and create a pipeline\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('cat', OneHotEncoder(drop='first'), categorical_columns),\n",
        "        ('num', 'passthrough', numerical_columns)\n",
        "    ]\n",
        ")\n",
        "\n",
        "lasso_pipe = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('regressor', Lasso(alpha=0.1))\n",
        "])\n",
        "\n"
      ],
      "metadata": {
        "id": "C6iGNlN3DpzF"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Split intro training and test, then fit the model\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_log, test_size=0.2, random_state=42)\n",
        "lasso_pipe.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "YS4-jqLbShHO",
        "outputId": "ed531ca3-a062-42db-d43a-8205aaa2cb26"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('cat',\n",
              "                                                  OneHotEncoder(drop='first'),\n",
              "                                                  ['cut', 'color', 'clarity']),\n",
              "                                                 ('num', 'passthrough',\n",
              "                                                  ['carat', 'depth', 'table',\n",
              "                                                   'x', 'y', 'z', 'volume',\n",
              "                                                   'price_per_carat',\n",
              "                                                   'depth_table_ratio'])])),\n",
              "                ('regressor', Lasso(alpha=0.1))])"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                                  [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                                 (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                                  [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;,\n",
              "                                                   &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;,\n",
              "                                                   &#x27;price_per_carat&#x27;,\n",
              "                                                   &#x27;depth_table_ratio&#x27;])])),\n",
              "                (&#x27;regressor&#x27;, Lasso(alpha=0.1))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                                  [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                                 (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                                  [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;,\n",
              "                                                   &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;,\n",
              "                                                   &#x27;price_per_carat&#x27;,\n",
              "                                                   &#x27;depth_table_ratio&#x27;])])),\n",
              "                (&#x27;regressor&#x27;, Lasso(alpha=0.1))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                 [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                 [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;,\n",
              "                                  &#x27;volume&#x27;, &#x27;price_per_carat&#x27;,\n",
              "                                  &#x27;depth_table_ratio&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;, &#x27;price_per_carat&#x27;, &#x27;depth_table_ratio&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso(alpha=0.1)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Predict on the test set\n",
        "y_pred_log = lasso_pipe.predict(X_test)\n",
        "\n"
      ],
      "metadata": {
        "id": "NTJOxg8cSpPp"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model and print\n",
        "mse_log = mean_squared_error(y_test, y_pred_log)\n",
        "mae_log = mean_absolute_error(y_test, y_pred_log)\n",
        "r2_log = r2_score(y_test, y_pred_log)\n",
        "\n",
        "print(\"Lasso Regression Model Metrics (Log-Transformed):\")\n",
        "print(f\"Mean Squared Error (Log Scale): {mse_log}\")\n",
        "print(f\"Mean Absolute Error (Log Scale): {mae_log}\")\n",
        "print(f\"R-squared (Log Scale): {r2_log}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUJxtkKNStl4",
        "outputId": "cee38155-22b6-40f3-ddb3-748165496ad8"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lasso Regression Model Metrics (Log-Transformed):\n",
            "Mean Squared Error (Log Scale): 0.07072378219129266\n",
            "Mean Absolute Error (Log Scale): 0.2231328808221483\n",
            "R-squared (Log Scale): 0.9311775904528343\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Lasso regression model, when applied to the log-transformed target variable, yielded the following performance metrics: a mean squared error (MSE) of 0.0707, a mean absolute error (MAE) of 0.2231, and an R-squared (R²) of 0.9312. These results suggest that the model performs quite well, with a relatively low error compared to the baseline. The MSE is modest, indicating that the model’s predictions are close to the actual values, while the MAE further supports this by showing a small average difference between predicted and true values. The R-squared value of 0.9312 indicates that approximately 93% of the variation in the log-transformed target is explained by the model. In comparison to the linear regression model, which had an R-squared of 0.98 and lower error, Lasso’s performance is still strong but slightly less optimal in terms of accuracy. However, Lasso’s regularization helps prevent overfitting and improves model generalizability, making it a better choice when considering interpretability and feature selection."
      ],
      "metadata": {
        "id": "6D6Lc7WjTDV_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Comparing to Baseline\n",
        "When comparing the Lasso regression model to the baseline, there is a signficant difference. The baseline model, which just predicts the mean of the log-transformed target variable for all test samples, had a mean squared error (MSE) of 1.03, a mean absolute error (MAE) of 0.88, and an R-squared value of -0.00, meaning it didn’t capture much of the data's variation. On the other hand, Lasso regression significantly outperformed the baseline, with an MSE of 0.0707, an MAE of 0.2231, and an R-squared of 0.9312. This shows that Lasso was able to reduce the error and explain a large portion of the variance in the data. The R-squared value, going from -0.00 to 0.9312, highlights how much better Lasso is at capturing the patterns in the dataset compared to the baseline. Essentially, Lasso regression provides a much more accurate and insightful model for predicting diamond prices."
      ],
      "metadata": {
        "id": "C5cR0cViTE7e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ridge Regression\n",
        "I decided to use Ridge regression for this dataset because it's particularly helpful when dealing with issues like multicollinearity and overfitting, which can be a concern when there are multiple predictors that may be correlated with each other. Ridge regression adds a penalty to the model, which helps shrink the coefficients of less important features, reducing their impact on the overall predictions. This is especially useful in a dataset like mine, where there are several features—like carat, depth, and table—that could be related to one another. By using Ridge, I can better manage the complexity of the model and avoid it from fitting too closely to the training data, which can lead to poor generalization. Overall, Ridge seemed like the right choice to balance model accuracy with stability."
      ],
      "metadata": {
        "id": "HLsdjsobEYB6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# prepare data and columns\n",
        "X = diamonds[['carat', 'depth', 'table', 'x', 'y', 'z', 'volume', 'price_per_carat', 'depth_table_ratio', 'cut', 'color', 'clarity']]\n",
        "y = diamonds['price']\n",
        "\n",
        "categorical_columns = ['cut', 'color', 'clarity']\n",
        "numerical_columns = ['carat', 'depth', 'table', 'x', 'y', 'z', 'volume', 'price_per_carat', 'depth_table_ratio']\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "M9z9-avhEZmk"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Define the column transformer to encode categorical columns\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('cat', OneHotEncoder(drop='first'), categorical_columns),  # Encode categorical columns\n",
        "        ('num', 'passthrough', numerical_columns)  # Keep numerical columns as-is\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Create a pipeline with the preprocessor and Ridge regression model\n",
        "ridge_pipe = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('regressor', Ridge())\n",
        "])"
      ],
      "metadata": {
        "id": "O1bonYdeTZfu"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split intro training and testing\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_log, test_size=0.2, random_state=42)\n",
        "\n",
        "# Perform Grid Search with Cross Validation to find the best regularization strength (alpha)\n",
        "param_grid_ridge = {\n",
        "    'regressor__alpha': [0.1, 1, 10, 100]  # Regularization strength\n",
        "}\n",
        "\n",
        "# Use GridSearchCV to perform hyperparameter tuning\n",
        "grid_search = GridSearchCV(ridge_pipe, param_grid_ridge, cv=5, scoring='neg_mean_squared_error', verbose=1)\n",
        "grid_search.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "gIx8cp2cZdqN",
        "outputId": "9d8172f2-478d-4cc3-a21e-7c29aa797df8"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[('preprocessor',\n",
              "                                        ColumnTransformer(transformers=[('cat',\n",
              "                                                                         OneHotEncoder(drop='first'),\n",
              "                                                                         ['cut',\n",
              "                                                                          'color',\n",
              "                                                                          'clarity']),\n",
              "                                                                        ('num',\n",
              "                                                                         'passthrough',\n",
              "                                                                         ['carat',\n",
              "                                                                          'depth',\n",
              "                                                                          'table',\n",
              "                                                                          'x',\n",
              "                                                                          'y',\n",
              "                                                                          'z',\n",
              "                                                                          'volume',\n",
              "                                                                          'price_per_carat',\n",
              "                                                                          'depth_table_ratio'])])),\n",
              "                                       ('regressor', Ridge())]),\n",
              "             param_grid={'regressor__alpha': [0.1, 1, 10, 100]},\n",
              "             scoring='neg_mean_squared_error', verbose=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                                         OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                                                         [&#x27;cut&#x27;,\n",
              "                                                                          &#x27;color&#x27;,\n",
              "                                                                          &#x27;clarity&#x27;]),\n",
              "                                                                        (&#x27;num&#x27;,\n",
              "                                                                         &#x27;passthrough&#x27;,\n",
              "                                                                         [&#x27;carat&#x27;,\n",
              "                                                                          &#x27;depth&#x27;,\n",
              "                                                                          &#x27;table&#x27;,\n",
              "                                                                          &#x27;x&#x27;,\n",
              "                                                                          &#x27;y&#x27;,\n",
              "                                                                          &#x27;z&#x27;,\n",
              "                                                                          &#x27;volume&#x27;,\n",
              "                                                                          &#x27;price_per_carat&#x27;,\n",
              "                                                                          &#x27;depth_table_ratio&#x27;])])),\n",
              "                                       (&#x27;regressor&#x27;, Ridge())]),\n",
              "             param_grid={&#x27;regressor__alpha&#x27;: [0.1, 1, 10, 100]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                                         OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                                                         [&#x27;cut&#x27;,\n",
              "                                                                          &#x27;color&#x27;,\n",
              "                                                                          &#x27;clarity&#x27;]),\n",
              "                                                                        (&#x27;num&#x27;,\n",
              "                                                                         &#x27;passthrough&#x27;,\n",
              "                                                                         [&#x27;carat&#x27;,\n",
              "                                                                          &#x27;depth&#x27;,\n",
              "                                                                          &#x27;table&#x27;,\n",
              "                                                                          &#x27;x&#x27;,\n",
              "                                                                          &#x27;y&#x27;,\n",
              "                                                                          &#x27;z&#x27;,\n",
              "                                                                          &#x27;volume&#x27;,\n",
              "                                                                          &#x27;price_per_carat&#x27;,\n",
              "                                                                          &#x27;depth_table_ratio&#x27;])])),\n",
              "                                       (&#x27;regressor&#x27;, Ridge())]),\n",
              "             param_grid={&#x27;regressor__alpha&#x27;: [0.1, 1, 10, 100]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                                  [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                                 (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                                  [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;,\n",
              "                                                   &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;,\n",
              "                                                   &#x27;price_per_carat&#x27;,\n",
              "                                                   &#x27;depth_table_ratio&#x27;])])),\n",
              "                (&#x27;regressor&#x27;, Ridge(alpha=100))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                 [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                 [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;,\n",
              "                                  &#x27;volume&#x27;, &#x27;price_per_carat&#x27;,\n",
              "                                  &#x27;depth_table_ratio&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;, &#x27;price_per_carat&#x27;, &#x27;depth_table_ratio&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=100)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Get the best model\n",
        "best_ridge_model = grid_search.best_estimator_\n",
        "\n",
        "# Make predictions and evaluate\n",
        "y_pred_log = best_ridge_model.predict(X_test)\n",
        "\n",
        "mse_log = mean_squared_error(y_test, y_pred_log)\n",
        "mae_log = mean_absolute_error(y_test, y_pred_log)\n",
        "r2_log = r2_score(y_test, y_pred_log)\n"
      ],
      "metadata": {
        "id": "1wwqtIF7To0K"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# print results\n",
        "print(\"Ridge Regression Model Metrics (Log Scale):\")\n",
        "print(f\"Mean Squared Error (Log Scale): {mse_log}\")\n",
        "print(f\"Mean Absolute Error (Log Scale): {mae_log}\")\n",
        "print(f\"R-squared (Log Scale): {r2_log}\")\n",
        "\n",
        "print(f\"Best alpha (regularization strength): {grid_search.best_params_['regressor__alpha']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PbDfWN_JTurm",
        "outputId": "3be2a360-9737-4d53-870d-3c76b14ffe6a"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ridge Regression Model Metrics (Log Scale):\n",
            "Mean Squared Error (Log Scale): 0.015340279101827819\n",
            "Mean Absolute Error (Log Scale): 0.06940132065300028\n",
            "R-squared (Log Scale): 0.9850721364412012\n",
            "Best alpha (regularization strength): 100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Ridge regression model performed impressively, with a mean squared error (MSE) of 0.015, a mean absolute error (MAE) of 0.069, and an R-squared value of 0.985 on the log-transformed scale. These metrics suggest that the Ridge model is able to make highly accurate predictions, with a very low error and a high R-squared, indicating it explains a substantial portion of the variance in the target variable. The best alpha value, 100, was determined through cross-validation, indicating the optimal regularization strength that helped the model achieve the best balance between bias and variance. When compared to the linear regression and Lasso models, Ridge performed slightly better, particularly in terms of R-squared and MSE. While both Linear and Lasso regression models also performed well, Ridge's regularization provided an edge in preventing overfitting and handling multicollinearity, which led to more stable and reliable predictions."
      ],
      "metadata": {
        "id": "i2R0jlsMUO8G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Comparing to the Baseline\n",
        "Compared to the baseline model, Ridge regression showed a clear improvement in performance. The baseline model, which simply predicted the mean of the log-transformed target values, had an MSE of 1.03 and an R-squared of -0.00, meaning it wasn’t capturing much of the variation in the data. On the other hand, Ridge regression achieved an MSE of 0.015 and an R-squared of 0.985, indicating that it explained almost all of the variance in the target variable. This dramatic difference demonstrates that Ridge regression does a much better job of capturing the underlying patterns in the data, offering far more accurate predictions than the baseline approach of simply predicting the mean."
      ],
      "metadata": {
        "id": "7eIUEz3lUZ9o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decision Tree Regressor\n",
        "I decided to use a Decision Tree for this dataset because it's great at handling non-linear relationships and capturing more complex patterns in the data. While linear models assume a straight-line relationship between the features and the target, Decision Trees are much more flexible—they split the data at different points based on features, which lets them adapt to intricate interactions between variables like carat, depth, and volume. This makes it a good fit for my goal, as I didn’t have to make assumptions about how the data behaves. Additonally, Decision Trees are easy to interpret and can show me which features are the most important in predicting the target, which is a valuable insight for this project."
      ],
      "metadata": {
        "id": "4J7NjinZFE8E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing necessary libraries for Decision Tree Regressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "# get data and columns ready ready\n",
        "X = diamonds[['carat', 'depth', 'table', 'x', 'y', 'z', 'volume', 'price_per_carat', 'depth_table_ratio', 'cut', 'color', 'clarity']]\n",
        "y = diamonds['price']\n",
        "y_log = np.log1p(y)\n",
        "\n",
        "\n",
        "\n",
        "categorical_columns = ['cut', 'color', 'clarity']\n",
        "numerical_columns = ['carat', 'depth', 'table', 'x', 'y', 'z', 'volume', 'price_per_carat', 'depth_table_ratio']\n",
        "\n",
        "\n",
        "# Define the column transformer to encode categorical columns\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('cat', OneHotEncoder(drop='first'), categorical_columns),  # Encode categorical columns\n",
        "        ('num', 'passthrough', numerical_columns)  # Keep numerical columns as-is\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Create a pipeline with the preprocessor and Ridge regression model\n",
        "decision_tree = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('regressor', DecisionTreeRegressor())\n",
        "])\n"
      ],
      "metadata": {
        "id": "tSSXiHHgdZry"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#split data into test and train\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_log, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "4GXcV433VM8Z"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the optimal max depth\n",
        "train_scores = []\n",
        "test_scores = []\n",
        "\n",
        "# Loop over different max depths\n",
        "for d in range(1, 11):\n",
        "    decision_tree.set_params(regressor__max_depth=d)\n",
        "\n",
        "    # do cross-validation on the training set\n",
        "    train_score = cross_val_score(decision_tree, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "    # calculate the average MSE\n",
        "    avg_train_mse = -train_score.mean()  # Convert negative MSE to positive\n",
        "\n",
        "    # evaluate the model\n",
        "    decision_tree.fit(X_train, y_train)\n",
        "    y_test_preds = decision_tree.predict(X_test)\n",
        "    test_mse = mean_squared_error(y_test, y_test_preds)\n",
        "\n",
        "    train_scores.append(avg_train_mse)\n",
        "    test_scores.append(test_mse)\n",
        "\n"
      ],
      "metadata": {
        "id": "elAcICnmVVLm"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the train and test MSE for different depths\n",
        "\n",
        "plt.plot(range(1, 11), train_scores, '--o', label='Train')\n",
        "plt.plot(range(1, 11), test_scores, '--o', label='Test')\n",
        "\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "\n",
        "plt.xticks(range(1, 11))\n",
        "\n",
        "plt.xlabel('Max Tree Depth')\n",
        "plt.ylabel('Mean Squared Error')\n",
        "plt.title('Decision Tree Depth vs. Test/Train Accuracy')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "kpHaJ-aOVivW",
        "outputId": "1fbf35ca-3cd2-42f6-f92d-8824e131360c"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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4gef/PM/Pzy/P8pUrV6JmzZqwtLSEs7MzXF1dsW3btgLHVNjv4ZUrV3D27Fk0adIEN2/e1D1atmyJv/76S5dA5STDL/sZEhcXh6SkJIN/zrxKfu/Z4cOH0aZNG9jY2MDR0RGurq4YP348AOjet5yE5lUx9e3bFyqVSpf0JSYm4q+//sLAgQO5Gu41cA4QGc39+/eRmJiIChUqAMj+iwsAPv744zz/QgKgq1tYY8aMQZcuXbB582bs2rULEyZMwKxZs7Bv3z7UqVPntdrOkd+Se9EIO0ioVKpciZhWq4Wbm1u+Ez1z/kLPeX9/+eUXeHh45Kr3ukuR1Wo1rl+/rvvhnPN6b731FgYPHpznc4piC4TCvv8qlQrdu3fHH3/8gR9++AExMTE4fPgwZs6cqVevOD5DQPb7JwgCduzYkWefnp/79NVXX2HIkCH4888/sXv3bowaNQqzZs3CsWPHciXMLzp06BCSkpLQsWNHg2PM64rRr7/+iiFDhqB79+745JNP4ObmBrlcjlmzZuldkXiZwn4PcxZUfPjhh/jwww9znf/99991ybex5JdQaDSaPMvzes9u3bqF1q1bo3Llypg/fz68vb2hVCqxfft2fP3117r/SwVVpkwZdO7cGatXr8bEiROxceNGZGRkvHQVH70aEyAyml9++QUAdMlOzl4nCoUCbdq0eelzAwICdKtyDBUQEICPPvoIH330EW7cuIHatWvjq6++yrUaLYePjw/+/fdfaLVaveTj6tWruvNSCggIwJ49e9CkSZOXDmHkTHp1c3N75ftbGBs3bsTTp091309XV1fY2dlBo9EU+PVyrlLlEEURN2/e1EuUivIv2L59+2LlypXYu3cvrly5AlEUdcNfzzP0M1QYAQEBEEURfn5+uitQL1OjRg3UqFEDX3zxBY4cOYImTZpg8eLFmD59OoD837dt27ahatWqRpvAvXHjRvj7+2PTpk16r5lzFbeoiKKINWvW4I033sD777+f6/y0adOwevVqhISE6H7WvOxniKurK+zt7V/5cybn6lRCQgIcHR115S9eGX6ZrVu3IiMjA1u2bNG7+vXisHTO/+GLFy++8o/BQYMGoVu3bjh58iRWr16NOnXq5NregAzDITAyin379mHatGnw8/PTLeV2c3NDy5Yt8eOPPyIqKirXc55f1tmzZ0+cP38ef/zxR656+f2VmJaWlmv5Z0BAAOzs7JCRkZFvrB07dkR0dLTe0EhWVha+++472NraSj6m3qdPH2g0GkybNi3XuaysLCQkJADITjTt7e0xc+ZMqNXqXHVfZyfn8+fPY8yYMShTpoxuxYpcLkfPnj3x+++/5/lLJK/XW7Vqld4O4Rs3bkRUVBQ6dOigK7OxsSnwUIqh2rRpAycnJ6xbtw7r1q1Dw4YN9YYsCvoZioqKwtWrV/N8nwvqzTffhFwux5QpU3J9pkVRxOPHjwFkb/qXlZWld75GjRqQyWR6MdnY2Og+C8/bvn17oYa/8pNz9eb5mI8fP46jR48a7TXycvjwYURGRiIkJAS9evXK9ejbty/279+Phw8fwtXVFc2bN8eyZctw9+5dvXZy4pbJZOjevTu2bt2KU6dO5Xq9nHo5Scnff/+tO5ez5UBB5fWeJSYmYvny5Xr12rVrBzs7O8yaNSvX5/DFz0iHDh3g4uKCOXPm4ODBg7z6YwS8AkQG27FjB65evYqsrCzExMRg3759CA8Ph4+PD7Zs2aI3me/7779H06ZNUaNGDQwfPhz+/v6IiYnB0aNHcf/+fZw/fx4A8Mknn2Djxo3o3bs3hg4dinr16iE+Ph5btmzB4sWLUatWrVxxXL9+Ha1bt0afPn1QtWpVWFhY4I8//kBMTAz69euXb/zvvvsufvzxRwwZMgSnT5+Gr68vNm7ciMOHD2PBggV6k3al0KJFC7z33nuYNWsWzp07h3bt2kGhUODGjRvYsGEDvvnmG/Tq1Qv29vZYtGgR3n77bdStWxf9+vWDq6sr7t69i23btqFJkya59vDJyz///IP09HRoNBo8fvwYhw8fxpYtW+Dg4IA//vhDb3ht9uzZ2L9/Pxo1aoThw4ejatWqiI+Px5kzZ7Bnzx7Ex8frte3k5ISmTZsiJCQEMTExWLBgASpUqKDbJgEA6tWrh3Xr1iEsLAwNGjSAra0tunTpYpT3UqFQ4M0338TatWuRmpqKL7/8Uu98QT9D48aNw8qVKxEREVHoKysBAQGYPn06xo0bh8jISHTv3h12dnaIiIjAH3/8gXfffRcff/wx9u3bh9DQUPTu3RsVK1ZEVlYWfvnlF10CmqNevXrYs2cP5s+fDy8vL/j5+cHNzQ1XrlzJd7+ZwujcuTM2bdqEHj16oFOnToiIiMDixYtRtWpVvZ2vjW316tWQy+X5JnNdu3bF559/jrVr1yIsLAzffvstmjZtirp16+Ldd9+Fn58fIiMjsW3bNpw7dw5A9u1Ddu/ejRYtWuDdd99FlSpVEBUVhQ0bNuDQoUNwdHREu3btUL58eQwbNgyffPIJ5HI5li1bpvu/VRDt2rWDUqlEly5d8N577yElJQVLly6Fm5ub3h+D9vb2+Prrr/HOO++gQYMGGDBgAMqUKYPz588jLS1NL+lSKBTo168fFi5cCLlcrreIgwqpmFedkRnLWQaa81AqlaKHh4fYtm1b8ZtvvtFb7vy8W7duiYMGDRI9PDxEhUIhli1bVuzcubO4ceNGvXqPHz8WQ0NDxbJly4pKpVIsV66cOHjwYN2S6xeXwT969EgcOXKkWLlyZdHGxkZ0cHAQGzVqJK5fv16v3ReXwYuiKMbExIghISGii4uLqFQqxRo1aujazZHzenktkcYLy31fJb9l8DY2Nvk+Z8mSJWK9evVEKysr0c7OTqxRo4b46aefig8fPtSrt3//fjE4OFh0cHAQLS0txYCAAHHIkCHiqVOnXhpTzjLznIdCoRBdXV3F5s2bizNmzMi1HDpHTEyMOHLkSNHb21tUKBSih4eH2Lp1a3HJkiW52v7tt9/EcePGiW5ubqKVlZXYqVOnXMuUU1JSxAEDBoiOjo4iAN2S+PyW6b/4OXiV8PBwEYAoCIJ47949vXMF/QzlbFnw/PfvVfL6nouiKP7+++9i06ZNRRsbG9HGxkasXLmyOHLkSPHatWuiKIri7du3xaFDh4oBAQGipaWl6OTkJL7xxhvinj179Nq5evWq2Lx5c9HKykoEIA4ePFhcuHCh6ODgIKrV6pfGlt8y+Lw+61qtVpw5c6bo4+MjqlQqsU6dOuJff/0lDh48ONf2BS/+v8hZUh4XF6dXL68l5c/LzMwUnZ2dxWbNmr20H35+fnrbbly8eFHs0aOH6OjoKFpaWoqVKlUSJ0yYoPecO3fuiIMGDRJdXV1FlUol+vv7iyNHjtRb6n/69GmxUaNGolKpFMuXLy/Onz8/32XwnTp1yjO2LVu2iDVr1hQtLS1FX19fcc6cOeKyZcvy7PeWLVvExo0bi1ZWVqK9vb3YsGFD8bfffsvV5okTJ0QAYrt27V76vlDB8F5gRGR05nYfr5KiY8eOug0gqeQ5f/48ateujVWrVuHtt9+WOhyzxyEwIqISomXLlrp9kqjkWbp0KWxtbfHmm29KHUqJwASIiKiE+PTTT6UOgYrA1q1bcfnyZSxZsgShoaHFfsPkkooJEBERkQn74IMPEBMTg44dO2LKlClSh1NicA4QERERlTrcB4iIiIhKHSZAREREVOpwDlAetFotHj58CDs7O95ojoiIyEyIoojk5GR4eXnlus/ii5gA5eHhw4fw9vaWOgwiIiIqhHv37r3yxsFMgPKQcyuEe/fuwd7e3qhtq9Vq7N69W3d7A3PFfpgW9sO0sB+mhf0wLUXZj6SkJHh7exfolkZMgPKQM+xlb29fJAmQtbU17O3tzf4DzH6YDvbDtLAfpoX9MC3F0Y+CTF/hJGgiIiIqdZgAERERUanDBIiIiIhKHc4BIiIiKiZarRaZmZmFeq5arYaFhQXS09Oh0WiMHFnxeZ1+KBQKyOVyo8TBBIiIiKgYZGZmIiIiAlqttlDPF0URHh4euHfvnlnvUfe6/XB0dISHh8drvwdMgIiIiIqYKIqIioqCXC6Ht7f3Kzfpy4tWq0VKSgpsbW0L9XxTUdh+iKKItLQ0xMbGAgA8PT1fKw4mQEREREUsKysLaWlp8PLygrW1daHayBk+s7S0NPsEqLD9sLKyAgDExsbCzc3ttYbDzPcdJCIiMhM5c12USqXEkZi/nARSrVa/VjtMgIiIiIqJOc/dMRXGeg+ZABUjjVbE8Yh4nH4k4HhEPDRaUeqQiIiISiUmQMVk58UoNJ8djoUrViHh1nEsXLEKzWeHY+fFKKlDIyIiKjb+/v5YtGiR1GFwEnRx2HkxCpvXLMYGxSp4KeN15Q8znDB1zSBgwP+hffXXm81OREQln0Yr4tjtx4hLyYSbnSUa+jlBLiuaYbVXDTVNmjQJkydPNrjd48ePm8Q+RkyAiphGK+LA5mX4QbEg1zkPxOMHxQKM36xE26rji+xDTERE5m/nxWhM2XoJMcn/baTo6WCJSV2qFskf0VFR/41QrFu3DhMnTsS1a9d0Zba2trqvRVGERqOBhcWr0wpXV1ckJSUZN9hC4BBYETtxKw6j1D8BAF7Mb3KOR6l/xolbccUcGRERmYudF6Mwcs1ZveQHAKIT0zHi1zNFMp3Cw8ND93BwcIAgCLrjq1evws7ODjt27EC9evWgUqlw6NAh3Lp1C926dYO7uztsbW3RoEED7NmzR6/dF4fABEHATz/9hB49esDa2hqBgYHYsmWL0fvzIiZARUwTeRheQnyu5CeHTAC8hMfQRB4u3sCIiEhyaZlZ+T7S1dnDRBqtiClbLyOvZTM5ZZO3XtZbWJNfm8Y2duxYzJ49G1euXEHNmjWRkpKCjh07Yu/evTh79izat2+PLl264O7duy9tZ8qUKejTpw/+/fdfdOzYEQMHDkR8fPxLn/O6OARWxNyEBKPWIyKikqPqxF35nnujkiuWhzTEiYh4RCWm51tPRPaVoBMR8QgKcAYANJ2zH/Gpue85Fjm702vH/LypU6eibdu2umMnJyfUqlVLdzxt2jT88ccf2LJlC0JDQ/NtZ8iQIejfvz8AYObMmfj2229x4sQJtG/f3qjxPo9XgIpYgH+AUesREVHpEpucf/JTmHrGVL9+fb3jlJQUfPzxx6hSpQocHR1ha2uLK1euvPIKUM2aNXVf29jYwN7eXnfLi6LCK0BFTO7bBE+tPKBKi85zGEwrAhnWHrDybVL8wRERkaQuTw3O95zs2SosNzvLArX1fL1Dn73xeoEVkI2Njd7xxx9/jPDwcHz55ZeoUKECrKys0KtXL2Rm5r4a9TyFQqF3LAhCoW8aW1BMgIqaTA6rLvMgrh8ELUS9S25aZH+TrbrMA2SFv58JERGZJ2vlq38NN/RzgqeDJaIT0/OcByQA8HDIXhJvSLtF4fDhwxgyZAh69OgBIPuKUGRkpCSxvAqHwIpD1a4Q+qyCYO+lX25fFkKfVUDVrtLERUREJk8uEzCpS1UA2cnO83KOJ3WpahJbqQQGBmLTpk04d+4czp8/jwEDBhT5lZzCYgJUXKp2hTDmItQ9V+mK/m3/B5MfIiJ6pfbVPfH9gDpws9O/maqHgyUWvVXXZDbTnT9/PsqUKYPGjRujS5cuCA4ORt26daUOK08cAitOMjlQuSOiLcrCRXwMX1k0gEpSR0VERGagfXUPNCpnhWvxWcWyE/TzhgwZgiFDhuiOW7ZsCVHMPSDn6+uLffv26ZWNHDlS7/j27dt6GyHm1U5CQsLrBVwATIAk8G+lj/BGl/5wtLSWOhQiIjIjcpmA//k7QybjAM7r4jsogadKF0CueHVFIiIiKhJMgCRyLToZyw9HIOJRqtShEBERlTpMgCRye8PnaLarI64c/lPqUIiIiEodJkASqWj5BBVkD6GN4D3AiIiIihsTIImo/LJ3fnZPOAutNq+trYiIiKioMAGSiHv1lgCAGuJ13Iou2jveEhERkT4mQBJRuFdCkswBloIat85zGIyIiKg4MQGSiiAg1rEOACDz1j8SB0NERFS6MAGSkNy3MQDAOf60xJEQERGVLkyAJORVqzWe2vmhYf1GUodCRETmQKsBIg8BFzYCEf9kHxcRQRBe+pg8eXKh2y5Tpgw2b95stFgLg7fCkJDKpz7w0TmpwyAiInNwZSvsd3wGWUrUf2X2XkD7OUVyY+2oqP9eZ926dZg4cSKuXbumK7O1tTX6axYnXgEiIiIydZe3QNgwGMLzyQ8AJEUB6wcBl7cY/SU9PDx0DwcHBwiCoFe2du1aVKlSBZaWlqhcuTJ++OEH3XMzMzMRGhoKT09PWFpawsfHB7NmzQIA+Pv7AwB69uwJQRDg6+tr9NgLgleAJPYoJQM/7r+GtLi7mDG0s9ThEBFRccp8ye2QBDmgsMwe5tr5GQARue/7LgIQss9X7gTI5C9vV2nz2iEDwOrVqzFx4kQsXLgQderUwdmzZzF8+HDY2Nhg8ODB+Pbbb7FlyxasX78e5cuXx71793Dv3j0AwPHjx+Hh4YGff/4ZHTt2hFwuN0pMhmICJDHLx5cx5lQwkmGN2MTWcHOwkjokIiIqLjO98j8X2A4YuAG4cwRIephH8pNDBJIeZtfza5ZdtKAGkPY4d9XJia8ZcLZJkybhq6++wptvvgkA8PPzw+XLl/Hjjz9i8ODBuHv3LgIDA9G0aVMIggAfHx/dc11dXQEAjo6O8PDwMEo8hcEhMInZelWGUtDAQ3iCi5f/lTocIiIyNSkxxq33mlJTU3Hr1i0MGzYMtra2usf06dNx69YtAMCQIUNw7tw5VKpUCaNGjcLu3buLJTZD8AqQ1BRWiLapDO/Ui0i4chAI4oowIqJSY/zD/M8Jz4aGbN0L1tbz9cZcKHxMr5CSkgIAWLp0KRo10v+dlTOcVbduXURERGDHjh3Ys2cP+vTpgzZt2mDjxo1FFpehmACZAHXZ/wHXL8I6+oTUoRARUXEqyJwcn8aAvRfEpCgIyOvekUL2ajCfxoa1W0ju7u7w8vLC7du3MXDgwHzr2dvbo2/fvujbty969eqF9u3bIz4+Ho6OjlAoFNBoim4Jf0EwATIBztXeAK7/hMD0i0hIy4SjtVLqkIiIyFTI5NlL3dcPggjhhSTo2cyg9rP/mwBdDKZMmYJRo0bBwcEB7du3R0ZGBk6dOoUnT54gLCwM8+fPh6enJ+rUqQOZTIYNGzbAw8MDjo6OAIDy5ctj3759aNasGVQqFcqUKVNssefgHCAT4FCxKbQQECCLwr9XrksdDhERmZqqXSH2XgnR9oVJw/ZeQJ9VRbIP0Mu88847+Omnn7B8+XLUqFEDLVq0wIoVK+Dn5wcAsLOzw9y5c1G/fn00aNAAkZGR2L59O2Sy7LRj2rRp2LNnD7y9vVGnTp1ijT0HrwCZAitHxFj6wzP9FlTRxwFUlzoiIiIyNVW6IMmzKewTLkGWGps958encbFc+RkyZAiGDBmiVzZgwAAMGDAgz/rDhw/H8OHD822vQ4cO6Nu3ry4hkgITIBPh0nIExIwENKreUupQiIjIVMnkgG9TQMLEoaRgAmQiFP/LP1MmIiIi42IKaWJEUUS6WtqZ8URERCUdrwCZkHX7TuLMoe2oUrMBhnTvKHU4REREJRavAJmQBre+xRztfDjc2Cx1KEREVAREMa99fMgQxnoPmQCZENuKLQAA3innkJHFYTAiopIiZ4fkzMxMiSMxf2lpaQAAhULxWu2YxBDY999/j3nz5iE6Ohq1atXCd999h4YNG+ZZd+nSpVi1ahUuXrwIAKhXrx5mzpypV3/IkCFYuXKl3vOCg4Oxc+fOouuEEbhWawnsB2rgFi5ExqJ+BU+pQyIiIiOwsLCAtbU14uLioFAoCrX8W6vVIjMzE+np6ZIuH39dhe2HKIpIS0tDbGwsHB0dX/su8pInQOvWrUNYWBgWL16MRo0aYcGCBQgODsa1a9fg5uaWq/6BAwfQv39/NG7cGJaWlpgzZw7atWuHS5cuoWzZsrp67du3x/Lly3XHKpWqWPrzOgTnACTKy8BB8wR3LxxC/Qq9pQ6JiIiMQBAEeHp6IiIiAnfu3ClUG6Io4unTp7CysoIg5H9veFP3uv0w1l3kJU+A5s+fj+HDhyMkJAQAsHjxYmzbtg3Lli3D2LFjc9VfvXq13vFPP/2E33//HXv37sWgQYN05SqVyihvULESBDxxrgeH2D3QRB4GwASIiKikUCqVCAwMLPQwmFqtxt9//43mzZu/9vCPlF6nHwqF4rWv/OSQNAHKzMzE6dOnMW7cOF2ZTCZDmzZtcPTo0QK1kZaWBrVaDScnJ73yAwcOwM3NDWXKlEGrVq0wffp0ODs7GzX+oqCq0BSI3QOPhLPI0mhhITffy5xERKRPJpPB0tKyUM+Vy+XIysqCpaWlWSdAptIPSROgR48eQaPRwN3dXa/c3d0dV69eLVAbn332Gby8vNCmTRtdWfv27fHmm2/Cz88Pt27dwvjx49GhQwccPXo0z8wxIyMDGRkZuuOkpCQA2VmqWq0uTNfyldNefu06VWoGHAEayq8jOTUNtlamOXT3qn6YC/bDtLAfpoX9MC3sR8HbLghBlHBN3sOHD1G2bFkcOXIEQUFBuvJPP/0UBw8exPHjx1/6/NmzZ2Pu3Lk4cOAAatasmW+927dvIyAgAHv27EHr1q1znZ88eTKmTJmSq3zNmjWwtrY2oEdGIGpR7skxPLatiKdKl+J9bSIiIjOWlpaGAQMGIDExEfb29i+tK+kVIBcXF8jlcsTExOiVx8TEvHL+zpdffonZs2djz549L01+AMDf3x8uLi64efNmngnQuHHjEBYWpjtOSkqCt7c32rVr98o30FBqtRrh4eFo27btSy79dTbqaxaFgvXD9LEfpoX9MC3sh2lhP14tZwSnICRNgJRKJerVq4e9e/eie/fuALKXx+3duxehoaH5Pm/u3LmYMWMGdu3ahfr167/yde7fv4/Hjx/D0zPvZeUqlSrPVWIKhaLIPmSvajspXY0zd56geaArZDLTne1flO9RcWI/TAv7YVrYD9PCfry8zYKSfIZtWFgYli5dipUrV+LKlSsYMWIEUlNTdavCBg0apDdJes6cOZgwYQKWLVsGX19fREdHIzo6GikpKQCAlJQUfPLJJzh27BgiIyOxd+9edOvWDRUqVEBwcLAkfTSU5mkSls4agye/DsHN2GSpwyEiIipxJF8G37dvX8TFxWHixImIjo5G7dq1sXPnTt3E6Lt37+ptlLRo0SJkZmaiV69eeu1MmjQJkydPhlwux7///ouVK1ciISEBXl5eaNeuHaZNm2YWewEBgNxCiVHCOijkavx56SwqerSQOiQiIqISRfIECABCQ0PzHfI6cOCA3nFkZORL27KyssKuXbuMFJlEFJaIta+OsklnkXr9H6A1EyAiIiJjknwIjPImls9eFecQd4o3zyMiIjIyJkAmyq16SwBAjayLuBf/VNpgiIiIShgmQCZK6RsEDWQoL4vD+cuXpQ6HiIioRGECZKos7RFnEwgASLr2t8TBEBERlSxMgEyYhV8TaGRKtPfhHCAiIiJjMolVYJQ3l44TgO6z4WxhHsv3iYiIzAUTIFNm7fTqOkRERGQwJkAmLirxKfZeiYVKLqB3g/JSh0NERFQicA6QiYs9/Avqbe8E7Z7JUodCRERUYvAKkIkLcFLBVnYPT5+q8CQ1E2VslFKHREREZPZ4BcjE2VZsDgCoIUTg9M0HEkdDRERUMjABMnVlfJFo4QKFoEH05UNSR0NERFQiMAEydYKAZPeGAADZvaMSB0NERFQyMAEyA7YVmwEAyqecR0pGlsTREBERmT8mQGbAsXILAEAd4QauPoiXOBoiIiLzx1Vg5sC1CtJdqkPpUQ31PfgtIyIiel38bWoOZDJYhh6WOgoiIqISg0NgREREVOowATIjPx64jrBvf8XpyEdSh0JERGTWmACZC60Wff9pj/nxI3H1wmmpoyEiIjJrTIDMhUyGp/Z+AICM29wQkYiI6HUwATIjFn5NAQCu8WeQpdFKHA0REZH5YgJkRpyrtgQA1MFVXI5KkjYYIiIiM8YEyIzIvBtAAxnKCY9w+fJFqcMhIiIyW0yAzInKFo/tqgAAUm78I3EwRERE5osJkLkpHwQAqJF1WeJAiIiIzBd3gjYzLg16QXR2QqPAtlKHQkREZLaYAJkZmW8Q4BskdRhERERmjUNgZkoURTxOyZA6DCIiIrPEK0Bm6Nbd+1i0chUgCJj3+VgIgiB1SERERGaFV4DMkPfjQ/hSMwdvZa7H3fg0qcMhIiIyO0yAzJDSrwkAoLoQidM37kscDRERkflhAmSOHL2RqPSAhaBF3BXuB0RERGQoJkBmKt2zIQBA9eC4xJEQERGZHyZAZsqhcnMAQKWMi4hOTJc4GiIiIvPCBMhMWQY0AwDUkd3AydvREkdDRERkXpgAmSvXSkhXOMJSUKOaECl1NERERGaF+wCZK0GAZf+VgIM3/J38pY6GiIjIrDABMmf+LaWOgIiIyCxxCMzMpWVm4fDNR7gZmyx1KERERGaDCZCZ271qNpJW9kP4Qe4HREREVFBMgMxc44zD6CA/CUT+LXUoREREZoMJkJmzrNAUAFAu6SxSMrIkjoaIiMg8MAEyc/YVszdEbCC7htOR8RJHQ0REZB6YAJm7cvWRBQt4CE9w7eoFqaMhIiIyC0yAzJ3CCgmO1QAA6tuHJQ6GiIjIPDABKgEs/JsAANyenEG6WiNxNERERKaPCVAJ4FCpObSCBdoF2kEp57eUiIjoVbgTdAkgBLSGMO4eHJTWUodCRERkFkzicsH3338PX19fWFpaolGjRjhx4kS+dZcuXYpmzZqhTJkyKFOmDNq0aZOrviiKmDhxIjw9PWFlZYU2bdrgxo0bRd0N6VgoASY/REREBSZ5ArRu3TqEhYVh0qRJOHPmDGrVqoXg4GDExsbmWf/AgQPo378/9u/fj6NHj8Lb2xvt2rXDgwcPdHXmzp2Lb7/9FosXL8bx48dhY2OD4OBgpKenF1e3il1mlhbrT97DuA1nkKXRSh0OERGRSZM8AZo/fz6GDx+OkJAQVK1aFYsXL4a1tTWWLVuWZ/3Vq1fj/fffR+3atVG5cmX89NNP0Gq12Lt3L4Dsqz8LFizAF198gW7duqFmzZpYtWoVHj58iM2bNxdjz4qXRewFVN/WBX0vDMOlh0lSh0NERGTSJJ0DlJmZidOnT2PcuHG6MplMhjZt2uDo0aMFaiMtLQ1qtRpOTk4AgIiICERHR6NNmza6Og4ODmjUqBGOHj2Kfv365WojIyMDGRkZuuOkpOwEQq1WQ61WF6pv+clpz9jtwrIMqiICGkHAimsRqOpR3bjtv6DI+lHM2A/Twn6YFvbDtLAfBW+7IAxKgLKysjBz5kwMHToU5cqVMziwFz169AgajQbu7u565e7u7rh69WqB2vjss8/g5eWlS3iio6N1bbzYZs65F82aNQtTpkzJVb57925YWxfN3Jrw8HCjt9lE7gYXTSzunNiG7al3jd5+XoqiH1JgP0wL+2Fa2A/Twn7kLy0trcB1DUqALCwsMG/ePAwaNMjgoIrC7NmzsXbtWhw4cACWlpaFbmfcuHEICwvTHSclJenmFtnb2xsjVB21Wo3w8HC0bdsWCoXCqG0nJm0Gbm2Cd+ZNtG//KWQywajtP68o+1Gc2A/Twn6YFvbDtLAfr5YzglMQBg+BtWrVCgcPHoSvr6+hT83FxcUFcrkcMTExeuUxMTHw8PB46XO//PJLzJ49G3v27EHNmjV15TnPi4mJgaenp16btWvXzrMtlUoFlUqVq1yhUBTZh6wo2nas0hK4tQk1tVcQ+SQDlTzsjNp+XoryPSpO7IdpYT9MC/thWtiPl7dZUAYnQB06dMDYsWNx4cIF1KtXDzY2Nnrnu3btWuC2lEol6tWrh71796J79+4AoJvQHBoamu/z5s6dixkzZmDXrl2oX7++3jk/Pz94eHhg7969uoQnKSkJx48fx4gRIwocmzmy8M3eEbq2cBMbbz5EJY9KEkdERERkmgxOgN5//30A2au3XiQIAjQaw27FEBYWhsGDB6N+/fpo2LAhFixYgNTUVISEhAAABg0ahLJly2LWrFkAgDlz5mDixIlYs2YNfH19dfN6bG1tYWtrC0EQMGbMGEyfPh2BgYHw8/PDhAkT4OXlpUuySiznAKQpnGCtjofm/mkATICIiIjyYnACpNUad4+Zvn37Ii4uDhMnTkR0dDRq166NnTt36iYx3717FzLZf6v1Fy1ahMzMTPTq1UuvnUmTJmHy5MkAgE8//RSpqal49913kZCQgKZNm2Lnzp2vNU/ILAgCZNW7IzM9AYOaVZE6GiIiIpNlErfCCA0NzXfI68CBA3rHkZGRr2xPEARMnToVU6dONUJ05sWy29dSh0BERGTyCrUR4sGDB9GlSxdUqFABFSpUQNeuXfHPP/8YOzYiIiKiImFwAvTrr7+iTZs2sLa2xqhRozBq1ChYWVmhdevWWLNmTVHESAY6dD0OHy5cixnr/5Y6FCIiIpNk8BDYjBkzMHfuXHz44Ye6slGjRmH+/PmYNm0aBgwYYNQAyXAV//kAXz/agQWJwwA0lzocIiIik2PwFaDbt2+jS5cuucq7du2KiIgIowRFr8fBvx4AIDD9AqISn0ocDRERkekxOAHy9vbW3Xj0eXv27IG3t7dRgqLXo/JvCgBoKLuKE7cfSxwNERGR6TF4COyjjz7CqFGjcO7cOTRu3BgAcPjwYaxYsQLffPON0QOkQihbF1mCEq5Iwq1r54E6r3/fNiIiopLE4ARoxIgR8PDwwFdffYX169cDAKpUqYJ169ahW7duRg+QCsFChSTnmnB6dApi5BEAnaSOiIiIyKQU+m7whw4dKqqYyAgsA5oCj07BJ/VfPE7JgLNt7nudERERlVYGzQGysLDA3LlzkZWVVVTxkJFYV2gGAGiuuoHkdH6/iIiInmfwEFjr1q2Ndjd4KkLeDYGmYXDzaQw4W0sdDRERkUmR9G7wVIQs7YE2k6SOgoiIyCRJfjd4KnoPEp7C2UYJS4Vc6lCIiIhMguR3g6cipH6K2T/8CHXsdTQfPAUtKrpKHREREZFJMGgStFqthoWFBS5evFhU8ZAxZSRj7JNJ+NxiDc7f4C7dREREOQxKgBQKBcqXL89hLnNh64YkG1/IBBGpNw5LHQ0REZHJMPhWGJ9//jnGjx+P+Pj4ooiHjEzwCQIAOD8+jXQ1E1ciIiKgEHOAFi5ciJs3b8LLyws+Pj65VoGdOXPGaMHR67MNbAZc/g31hKs4dy8B//N3ljokIiIiyRmcAHXv3r0IwqCiIvhk36+thnAbP918yASIiIgIhUiAJk3i3jJmpYwv0lSusM6Iw5MbR4F2NaSOiIiISHIFngN04sSJl05+zsjI0N0clUyIIEDrnT0PqI9HtMTBEBERmYYCJ0BBQUF4/Pix7tje3h63b9/WHSckJKB///7GjY6Mwrbd58DIEwh8c4LUoRAREZmEAg+BiaL40uP8ysgEuFWWOgIiIiKTYvAy+JcRBMGYzZERZWRpcCIiHlvPP5Q6FCIiIskZNQEi03X/xJ+IWdYfJ//4Blotr9QREVHpZtAqsMuXLyM6OnsirSiKuHr1KlJSUgAAjx49Mn50ZDS+mjsIkB+DUpOFazHJqOJpL3VIREREkjEoAWrdurXePJ/OnTsDyB76EkWRQ2AmTO7bFABQX3YN2yIeMwEiIqJSrcAJUEQEb6Zp1jxrQS1TwVmbjMhr54DGflJHREREJJkCJ0A+Pj5FGQcVNQsl0tzqwiH6KCzuHYUoducVOyIiKrU4CboUsamQPQxWRX0RkY/TJI6GiIhIOkyAShELvyYAgAayazgR8fgVtYmIiEoug+8FRmasXANoZUo4OXnizWoOUkdDREQkGSZApYnKFrJxd2GtsJI6EiIiIklxCKy0YfJDRERUsCtAderUKfCKoTNnzrxWQFT0DlyLxZ5/76B1DR+8UdlN6nCIiIiKXYESoO7du+u+Tk9Pxw8//ICqVasiKCgIAHDs2DFcunQJ77//fpEESUaUlYkKW3qgccoVzMOfTICIiKhUKlACNGnSJN3X77zzDkaNGoVp06blqnPv3j3jRkfGZ6GEo+wplIIGmbePAGgmdURERETFzuA5QBs2bMCgQYNylb/11lv4/fffjRIUFS3Fs+Xw5ZLP4XFKhsTREBERFT+DEyArKyscPnw4V/nhw4dhaWlplKCoaKkCsjdEbCC7ipORTySOhoiIqPgZvAx+zJgxGDFiBM6cOYOGDRsCAI4fP45ly5ZhwoQJRg+QioBPYwBAdSESO27eR/vqHhIHREREVLwMToDGjh0Lf39/fPPNN/j1118BAFWqVMHy5cvRp08fowdIRcDRG2lWnrB+GoXUW0cB1Jc6IiIiomJVqI0Q+/Tpw2THzAk+jYGrv6NSxkWoNVoo5NwSioiISo9CJUAJCQnYuHEjbt++jY8//hhOTk44c+YM3N3dUbZsWWPHSEXAqlpHZGgz8XadzgCTHyIiKmUMToD+/fdftGnTBg4ODoiMjMQ777wDJycnbNq0CXfv3sWqVauKIk4ythq9oKrRS+ooiIiIJGHwn/5hYWEYMmQIbty4obfqq2PHjvj777+NGhwVD61WlDoEIiKiYmVwAnTy5Em89957ucrLli2L6OhoowRFxSP5aSY+WrQJvaf+hHS1RupwiIiIio3BCZBKpUJSUlKu8uvXr8PV1dUoQVHxsP13Bb6KCcEIzWqcvZsgdThERETFxuAEqGvXrpg6dSrUajUAQBAE3L17F5999hl69uxp9ACp6Ajl6gEA6suu48TtRxJHQ0REVHwMToC++uorpKSkwM3NDU+fPkWLFi1QoUIF2NnZYcaMGUURIxUVj1pQy63gKKQi6uYZqaMhIiIqNgYnQA4ODggPD8dff/2Fb7/9FqGhodi+fTsOHjwIGxsbgwP4/vvv4evrC0tLSzRq1AgnTpzIt+6lS5fQs2dP+Pr6QhAELFiwIFedyZMnQxAEvUflypUNjqtUkFsg0zN7E0TrqBNQa7QSB0RERFQ8DFoGr1arYWVlhXPnzqFJkyZo0qTJa734unXrEBYWhsWLF6NRo0ZYsGABgoODce3aNbi5ueWqn5aWBn9/f/Tu3Rsffvhhvu1Wq1YNe/bs0R1bWBRqu6NSwSqgKXD/H9QWr+Dig0TUKV9G6pCIiIiKnEFXgBQKBcqXLw+NxjgrhubPn4/hw4cjJCQEVatWxeLFi2FtbY1ly5blWb9BgwaYN28e+vXrB5VKlW+7FhYW8PDw0D1cXFyMEm9JJPPNvi9YQ9lVnLj9WOJoiIiIiofBl0Y+//xzjB8/Hr/88gucnJwK/cKZmZk4ffo0xo0bpyuTyWRo06YNjh49Wuh2AeDGjRvw8vKCpaUlgoKCMGvWLJQvXz7f+hkZGcjIyNAd56xyU6vVusnexpLTnrHbLTT3WpAJFvDAEwRYxEKt9inQ00yuH4XEfpgW9sO0sB+mhf0oeNsFIYiiaNAueHXq1MHNmzehVqvh4+OTa97PmTMFm0z78OFDlC1bFkeOHEFQUJCu/NNPP8XBgwdx/Pjxlz7f19cXY8aMwZgxY/TKd+zYgZSUFFSqVAlRUVGYMmUKHjx4gIsXL8LOzi7PtiZPnowpU6bkKl+zZg2sra0L1B9z5h+7G2lKZ8TZVYdGnv+VNSIiIlOWlpaGAQMGIDExEfb29i+ta/AVoO7duxc2rmLRoUMH3dc1a9ZEo0aN4OPjg/Xr12PYsGF5PmfcuHEICwvTHSclJcHb2xvt2rV75RtoKLVajfDwcLRt2xYKhcKobRdeR4OfYZr9MBz7YVrYD9PCfpgW9uPV8tqnMD8GJ0CTJk0y9Cl5cnFxgVwuR0xMjF55TEwMPDw8jPIaAODo6IiKFSvi5s2b+dZRqVR5zilSKBRF9iEryrYLKzYpHWmZGvi6FHw1nyn2ozDYD9PCfpgW9sO0sB8vb7OgJLsNuFKpRL169bB3715dmVarxd69e/WGxF5XSkoKbt26BU9PT6O1WRLt2roOv839Pyza9vKhRyIiopLA4CtAGo0GX3/9NdavX4+7d+8iMzNT73x8fHyB2woLC8PgwYNRv359NGzYEAsWLEBqaipCQkIAAIMGDULZsmUxa9YsANkTpy9fvqz7+sGDBzh37hxsbW1RoUIFAMDHH3+MLl26wMfHBw8fPsSkSZMgl8vRv39/Q7taqjS7/RWCLa7h0zsBEMU3IAiC1CEREREVGYOvAE2ZMgXz589H3759kZiYiLCwMLz55puQyWSYPHmyQW317dsXX375JSZOnIjatWvj3Llz2LlzJ9zd3QEAd+/eRVRUlK7+w4cPUadOHdSpUwdRUVH48ssvUadOHbzzzju6Ovfv30f//v1RqVIl9OnTB87Ozjh27BjvU/YKSv+mAIAqmRcQ+ThN4miIiIiKlsFXgFavXo2lS5eiU6dOmDx5Mvr374+AgADUrFkTx44dw6hRowxqLzQ0FKGhoXmeO3DggN6xr68vXrVobe3atQa9PmWz8G0MnP4ZDWTXcCLiMfwMmAdERERkbgy+AhQdHY0aNWoAAGxtbZGYmAgA6Ny5M7Zt22bc6Kj4+GRviFhFuINzN+9JHAwREVHRMjgBKleunG5YKiAgALt37wYAnDx58qW7M5OJs/fCUxtvyAURmRGvtxElERGRqTM4AerRo4du5dYHH3yACRMmIDAwEIMGDcLQoUONHiAVHwu/7KtAfmn/4kHCU4mjISIiKjoGzwGaPXu27uu+ffuifPnyOHr0KAIDA9GlSxejBkfFS+HXBLi4Dr29HsPakjeQJSKikuu1f8sFBQUZdd8eklCVLkDZenB3qwrIJNsiioiIqMgZnACtWrXqpecHDRpU6GBIYtZO2Q8iIqISzuAEaPTo0XrHarUaaWlpUCqVsLa2ZgJUApy9+wTHI+LRp743nGyUUodDRERkdAaPczx58kTvkZKSgmvXrqFp06b47bffiiJGKk6xV/D4lxA4hofhRMRjqaMhIiIqEkaZ6BEYGIjZs2fnujpEZkiTiTbq/egoP44Tt+OkjoaIiKhIGG2mq4WFBR4+fGis5kgq7tWhtrCFvfAUcTfPSB0NERFRkTB4DtCWLVv0jkVRRFRUFBYuXIgmTZoYLTCSiEwObbmGQOQ+uMSfRlL6QNhbKqSOioiIyKgMToC6d++udywIAlxdXdGqVSt89dVXxoqLJKTybwJE7kN94SpO33mCNyq5SR0SERGRURmcAGm12qKIg0zJs/uCNZRdw7Lbj5kAERFRicPd7ig3r7rQyBRwFRIRdfui1NEQEREZncFXgMLCwgpcd/78+YY2T6ZAYQmtV31kpD7B3E7lpI6GiIjI6AxOgM6ePYuzZ89CrVajUqVKAIDr169DLpejbt26unqCIBgvSip2ipC/oJDzfmBERFQyGfwbrkuXLrCzs8PKlStRpkwZANmbI4aEhKBZs2b46KOPjB4kSYDJDxERlWAG/5b76quvsHv3bl3yAwBlypTB9OnT0a5dOyZAJUjEo1Ss/PsKNFpgWq/6UodDRERkNAZPgk5KSkJcXO4dguPi4pCcnGyUoMg0OO39COPOByPl3z+RmcXVf0REVHIYnAD16NEDISEh2LRpE+7fv4/79+/j999/x7Bhw/Dmm28WRYwkEXs7e6iELNTWXsHFh4lSh0NERGQ0BidAixcvRocOHTBgwAD4+PjAx8cHAwYMQPv27fHDDz8URYwkEeG5/YBORMRLHA0REZHxGDwHyNraGj/88APmzZuHW7duAQACAgJgY2Nj9OBIYs8SoErCPXx/8w7QIkDigIiIiIyj0Bsh2tjYoGbNmnBwcMCdO3e4Q3RJZOuGDAd/yAQR4r1j0GhFqSMiIiIyigInQMuWLcu1seG7774Lf39/1KhRA9WrV8e9e/eMHiBJS+GXfYPbGlmXcS2ak9yJiKhkKHACtGTJEr2l7zt37sTy5cuxatUqnDx5Eo6OjpgyZUqRBEnSkflmD4M1VV7H49QMiaMhIiIyjgLPAbpx4wbq1/9vL5g///wT3bp1w8CBAwEAM2fOREhIiPEjJGn5NIEmsAOqB7QEAl2ljoaIiMgoCpwAPX36FPb29rrjI0eOYNiwYbpjf39/REdHGzc6kl4ZH8gHrpU6CiIiIqMq8BCYj48PTp8+DQB49OgRLl26hCZNmujOR0dHw8HBwfgRksnQaEVkZGmkDoOIiOi1FfgK0ODBgzFy5EhcunQJ+/btQ+XKlVGvXj3d+SNHjqB69epFEiRJb9Gf+3H2zCm06tQX/RqWlzocIiKi11LgBOjTTz9FWloaNm3aBA8PD2zYsEHv/OHDh9G/f3+jB0gmIPYKRpztjhRYYvLtN5gAERGR2StwAiSTyTB16lRMnTo1z/MvJkRUgrhUglrpANvMRDy5fQYAb4xKRETmrdAbIVIpIpNBKP8/AIBv6jk8SHgqcUBERESvhwkQFYiF73/3BTvJ+4IREZGZYwJEBfPsvmD1Zddw/PZjiYMhIiJ6PUyAqGA8a0Mjt4SzkIzoW+eljoaIiOi1MAGigrFQQuuVve3BQK8H0PLGqEREZMYKvAosh0ajwYoVK7B3717Exsbmugv8vn37jBYcmRZF8w8B9Xto49MUapkgdThERESFZnACNHr0aKxYsQKdOnVC9erVIQj8RVhqBLYFkL0j9PGbsTj9SIBzRDyCKrhBzoSIiIjMiMEJ0Nq1a7F+/Xp07NixKOIhE7fzYhQm/nkJsckZAORYdeMUPB0sMalLVbSv7il1eERERAVi8BwgpVKJChUqFEUsZOJ2XozCwtUb0TftNwTJLunKoxPTMeLXM9h5MUrC6IiIiArO4AToo48+wjfffANR5CTY0kSjFTFl62V0kx/BR4qNGCrfga6yI/if7DIEZM8Dm7L1MjScHE1ERGbA4CGwQ4cOYf/+/dixYweqVasGhUKhd37Tpk1GC45Mx4mIeEQlpiNLnp0zt5WfQVv5GQDAQ9EJU9SDsCuxIU5ExCMowFnKUImIiF7J4ATI0dERPXr0KIpYyITFJqcjWHYC71n8leucB+KxSLEAI9RjEJtcu/iDIyIiMpDBCdDy5cuLIg4ycW42CkxSrMrznEwAtCIwSfEL7tiMLObIiIiIDGdwAkSlU0P5VciF/O8BJhMALzyGu/wqALfiC4yIiKgQCpUAbdy4EevXr8fdu3eRmZmpd+7MmTNGCYxMizw11qj1iIiIpGTwKrBvv/0WISEhcHd3x9mzZ9GwYUM4Ozvj9u3b6NChQ1HESKbA1t249YiIiCRkcAL0ww8/YMmSJfjuu++gVCrx6aefIjw8HKNGjUJiYmJRxEimwKcxYO8FIL8dnwVo7cvq7hpPRERkygxOgO7evYvGjbN/yVlZWSE5ORkA8Pbbb+O3334zbnRkOmRyoP2cZwcvJkECRADj0wYi/OqjYg6MiIjIcAYnQB4eHoiPz54MW758eRw7dgwAEBERUajNEb///nv4+vrC0tISjRo1wokTJ/Kte+nSJfTs2RO+vr4QBAELFix47TbJAFW7An1WAfYv3PLC0gGpFo4IT/HDZ7//i9ikdGniIyIiKiCDE6BWrVphy5YtAICQkBB8+OGHaNu2Lfr27Wvw/kDr1q1DWFgYJk2ahDNnzqBWrVoIDg5GbGzeE2nT0tLg7++P2bNnw8PDwyhtkoGqdgXGXETWW5txymcEsvpvAOw8YZv1BD/a/oQnqen4aMN5aLkjNBERmTCDE6AlS5bg888/BwCMHDkSy5YtQ5UqVTB16lQsWrTIoLbmz5+P4cOHIyQkBFWrVsXixYthbW2NZcuW5Vm/QYMGmDdvHvr16weVSmWUNqkQZHKIPk3xwCkIov8bQO/lgIUl6medxnDFLvxz4xFWHImUOkoiIqJ8GbwMXiaTQSb7L2/q168f+vXrZ/ALZ2Zm4vTp0xg3bpxe223atMHRo0cNbu912szIyEBGRobuOCkpCQCgVquhVqsLFUt+ctozdrvFTa8fZSpA1mYa5Ds/wWcWv+FwVmXM2iGggY8DKnvYSRzpy5XI74cZYz9MC/thWtiPgrddEIXaB+iff/7Bjz/+iFu3bmHjxo0oW7YsfvnlF/j5+aFp06YFauPRo0fQaDRwd9dfNu3u7o6rV68WJqxCtzlr1ixMmTIlV/nu3bthbW1dqFheJTw8vEjaLW66fohuaOhQD56Jp7HE8ju0fToTw5cdwUc1NFDKpY2xIErc98PMsR+mhf0wLexH/tLS0gpc1+AE6Pfff8fbb7+NgQMH4uzZs7orJ4mJiZg5cya2b99uaJOSGzduHMLCwnTHSUlJ8Pb2Rrt27WBvb2/U11Kr1QgPD0fbtm1z3UjWnOTZj6eNIS5tgbLJDzHb6hfsCpiA1m2rws7SdDccL9HfDzPEfpgW9sO0sB+vljOCUxAG/2aaPn06Fi9ejEGDBmHt2rW68iZNmmD69OkFbsfFxQVyuRwxMTF65TExMflOcC6qNlUqVZ5zihQKRZF9yIqy7eKk1w+FG9BzKbCqO1o1aoAubetAkBk8zUwSJfL7YcbYD9PCfpgW9uPlbRaUwb+drl27hubNm+cqd3BwQEJCQoHbUSqVqFevHvbu3asr02q12Lt3L4KCggwNq8jaJAP5NgVGn4dt8Oe65EcURTzN1EgcGBER0X8MvgLk4eGBmzdvwtfXV6/80KFD8Pf3N6itsLAwDB48GPXr10fDhg2xYMECpKamIiQkBAAwaNAglC1bFrNmzQKQPcn58uXLuq8fPHiAc+fOwdbWFhUqVChQm1QMHMrqvnySmITxf15GplaOnwbXhyDkt5M0ERFR8TE4ARo+fDhGjx6NZcuWQRAEPHz4EEePHsXHH3+MCRMmGNRW3759ERcXh4kTJyI6Ohq1a9fGzp07dZOY7969q7fi7OHDh6hTp47u+Msvv8SXX36JFi1a4MCBAwVqk4pR3DVYrR2MmnGVMCezD349fhdv/89H6qiIiIgMT4DGjh0LrVaL1q1bIy0tDc2bN4dKpcLHH3+MDz74wOAAQkNDERoamue5nKQmh6+vb4F2m35Zm1SMYi/D8vEV/J/sKv6WVcP0v2QI8ndCBTfTXhpPREQln8FzgARBwOeff474+HhcvHgRx44dQ1xcHKZNm1YU8ZE5q9YDqDsIAkR8b7UY1lkJGPXbOWRkcT4QERFJq9BLdJRKJapWrYqGDRvC1tbWmDFRSdJ+NuBSEU6ax1hg+RMuRyXiy13XpI6KiIhKuQIPgQ0dOrRA9XjLCdKjtAF6LQOWtkILzSkMku/G0n+C0aKiG5oGukgdHRERlVIFToBWrFgBHx8f1KlTp1B3fadSzKMG0HYasPMzTFD+hkhFbVgqzGN/ICIiKpkKnACNGDECv/32GyIiIhASEoK33noLTk5ORRkblSSN3gNu7YMsLR4Lu7SAvQc/O0REJJ0C/xn+/fffIyoqCp9++im2bt0Kb29v9OnTB7t27eIVIXo1QQB6LoV86E7YewToirlBIhERScGgcQiVSoX+/fsjPDwcly9fRrVq1fD+++/D19cXKSkpRRUjlRSWDoA8+6KjKIr4/dB5NJ2zD7fi+NkhIqLiVeiJGDKZDIIgQBRFaDT8K54MkJUJ7ByP4L0doUp9iDFrzyEzSyt1VEREVIoYlABlZGTgt99+Q9u2bVGxYkVcuHABCxcuxN27d7kUngpOECDcPw5bMQULLX/A5Qfx+HrPdamjIiKiUqTACdD7778PT09PzJ49G507d8a9e/ewYcMGdOzYUe92FUSvJFcAPX8GlHaoi6v4wOIPLD54C0dvPZY6MiIiKiUKvAps8eLFKF++PPz9/XHw4EEcPHgwz3qbNm0yWnBUgjn5AZ2/Bja9g1EWm3FYUx1h6y2xc3RzOFgrpI6OiIhKuAInQIMGDeKdvMm4avbOXhp/fg0WWv6AtokzMX7zBSzsX4efNSIiKlIGbYRIZHQd5wL3jsM9/hbmKn/CZefvIIrZq+aJiIiKCifvkLRUdkCvnwFrZwR1DkFYcGXIZMx+iIioaBX4ChBRkfGqA4y5AAelja4oM0sLmQBYyJmjExGR8fG3C5mG55KfiMhI9PthH77dd1PCgIiIqCRjAkSm5fZBeP7WGl1jf8TCfTdwKjJe6oiIiKgEYgJEpkWjhmXGIwyx2I1WwmmMWXcOSelqqaMiIqIShgkQmZbANkBQKADgS+USqJ88wKQ/L0kcFBERlTRMgMj0tJ4IeNaCI5LxtfIH/Hn2Hv4890DqqIiIqARhAkSmx0IF9FwGKGzQWHYZ/yffgi82X8T9J2lSR0ZERCUEEyAyTS4VgI7zAAAfKTaiQ5n7EEWJYyIiohKD+wCR6ao9ALi1D1laATM794KFtbXUERERUQnBBIhMlyAAPRZDJde/OerTTA2slHKJgiIiopKAQ2Bk2p5LfjLUWVi8cTs6fvsPUjOyJAyKiIjMHRMgMg8ZyZCt7Y9BF0MgPL6BKVu5NJ6IiAqPCRCZB4U1FJqnsEY6vlUsxOZTEdh+IUrqqIiIyEwxASLzIJMDby4BrJxQXRaJTy3WYtymC4hKfCp1ZEREZIaYAJH5sPcCuv8AAHjHYgfqZJxE2Lrz0Gq5Pp6IiAzDBIjMS6UOQMP3AABfKRbj5u1bWPrPbYmDIiIic8MEiMxP26mAe3U4C0n4SrUU7vaWUkdERERmhgkQmR+FJdBrGUTXKqjefzq61ykrdURERGRmuBEimSfXShBGHIGT7L8cnhskEhFRQfEKEJmv55Kf0ycPo+O87Qi/HCNhQEREZC6YAJH5O78ONbf3QGj6Ynz2+7+ITUqXOiIiIjJxTIDI/DmWhwXU6Ck/hOZP9+GjDVwaT0REL8cEiMyfTxCEFmMBADMUy3Dn5iUsPxIpbUxERGTSmABRydD8Y6B8Y9gI6fhW8R3m77iIK1FJUkdFREQmigkQlQzPbpUhWjqituw2QoV1GL32LNLVGqkjIyIiE8QEiEoOR28IXb8DAIyw2IoOjveQxblARESUB+4DRCVL1a5Ag+FIUTrhw9b9s68MERERvYAJEJU8HefBVhB0h6IoIl2t5SaJRESkwyEwKnmeS37iE5Pw5ZKfEbrmDESRw2FERJSNCRCVXGnxsF7ZFqMffoaoayfw67E7UkdEREQmggkQlVxWZWDpGgClkIXvFN/hq21ncSMmWeqoiIjIBDABopJLEIBuCyHaeSFAFoWxWIFRa88hI4tL44mISjsmQFSyWTtBeHMJRAjoZ3EA/jG78eWua1JHRUREEmMCRCWfXzMIzT4CAMxS/IQdh07g0I1HEgdFRERSMokE6Pvvv4evry8sLS3RqFEjnDhx4qX1N2zYgMqVK8PS0hI1atTA9u3b9c4PGTIEgiDoPdq3b1+UXSBT13IsUK4h7IU0zLVaBSulHBqtiOMR8Tj9SMDxiHhouGkiEVGpIXkCtG7dOoSFhWHSpEk4c+YMatWqheDgYMTGxuZZ/8iRI+jfvz+GDRuGs2fPonv37ujevTsuXryoV699+/aIiorSPX777bfi6A6ZKrkC6PkTNBXaocZ7PyMuOR3NZ4dj4YpVSLh1HAtXrELz2eHYeTFK6kiJiKgYSL4R4vz58zF8+HCEhIQAABYvXoxt27Zh2bJlGDt2bK7633zzDdq3b49PPvkEADBt2jSEh4dj4cKFWLx4sa6eSqWCh4dH8XSCzEMZH8jf2oDDF6Owec1ibFCsgpcyXnf6YYYTpq4ZBAz4P7Sv7ilhoEREVNQkTYAyMzNx+vRpjBs3Tlcmk8nQpk0bHD16NM/nHD16FGFhYXplwcHB2Lx5s17ZgQMH4ObmhjJlyqBVq1aYPn06nJ2d82wzIyMDGRkZuuOkpOy7iKvVaqjV6sJ0LV857Rm73eJmrv3QaEUc2PwzflAsgPDCOQ/E4wfFAozfrEDLwE8hl71Yw3SZ6/fjReyHaWE/TAv7UfC2C0LSBOjRo0fQaDRwd3fXK3d3d8fVq1fzfE50dHSe9aOjo3XH7du3x5tvvgk/Pz/cunUL48ePR4cOHXD06FHI5blvhzBr1ixMmTIlV/nu3bthbW1dmK69Unh4eJG0W9zMrR83E0SMUv8MAXobRgMAZAKgFYFR6mX4fm11VHA0nwQoh7l9P/LDfpgW9sO0sB/5S0tLK3BdyYfAikK/fv10X9eoUQM1a9ZEQEAADhw4gNatW+eqP27cOL2rSklJSfD29ka7du1gb29v1NjUajXCw8PRtm1bKBQKo7ZdnMy1H0f3bYFXRHy+52UC4IXHaOChQVCrrsUY2esx1+/Hi9gP08J+mBb249VyRnAKQtIEyMXFBXK5HDExMXrlMTEx+c7f8fDwMKg+APj7+8PFxQU3b97MMwFSqVRQqVS5yhUKRZF9yIqy7eJkbv3wkBfsP4c2Odqs+pXD3L4f+WE/TAv7YVrYj5e3WVCSrgJTKpWoV68e9u7dqyvTarXYu3cvgoKC8nxOUFCQXn0g+zJafvUB4P79+3j8+DE8PTmxtbQL8A8oUL3KFSoWcSRERCQlyZfBh4WFYenSpVi5ciWuXLmCESNGIDU1VbcqbNCgQXqTpEePHo2dO3fiq6++wtWrVzF58mScOnUKoaGhAICUlBR88sknOHbsGCIjI7F3715069YNFSpUQHBwsCR9JNMh922Cp1YeyG/LH1EEUhQu8KjZSlc2d+dV7LwYxbvJExGVIJLPAerbty/i4uIwceJEREdHo3bt2ti5c6duovPdu3chk/2XpzVu3Bhr1qzBF198gfHjxyMwMBCbN29G9erVAQByuRz//vsvVq5ciYSEBHh5eaFdu3aYNm1ansNcVMrI5LDqMg/i+kHQQtT7C0CL7InRtj2+BmTZk+Ufb/wQUWdUWKxtgqplHfFRu0poWdEVwoszqImIyKxIngABQGhoqO4KzosOHDiQq6x3797o3bt3nvWtrKywa9cuY4ZHJU3VrhD6rAJ2fgYkPdQVC/ZlIbSfDVR9Nvn5wRk4X1yGr5XAB+KfWBDVA8OWJ6CujzM+alcJQQF5b6tARESmT/IhMCJJVO0KYcxFZL21Gad8RiDrrc0Qxlz4L/kBAJeKQOtJgFUZ+AsP8a3ye+xWfQaPe9sxYOkRDPzpGB4kPJWuD0REVGhMgKj0kskh+jTFA6cgiD5NdcNeOipboFkYMPpfoNUXgKUjKggPsFD5HXapxiIt6jrKWJv/SgwiotKICRDRq1jaA80/Acb8C7QcD6gcEGCbhY97t4a1MnsUWasVMXP7FdyMTZY4WCIiKgiTmANEZBYsHYCWnwGN3oP88S00KVc2u1yrQfTPAxARURXt/qmH7rXLYXSbQPg420gbLxER5YsJEJGhrByBcvX+O764CV4PdmKpcicuaH3x9fleaHW+LvrU90Zoq0CUdbSSLFQiIsobh8CIXleF1kDTMEBhgxqySCxTfonfLb5A1KkteGPefkzecgnpao3UURIR0XOYABG9LmsnoM2k7DlCTUYDCmvUlt3GCuU8rJNPwLXIu1BZ8L8aEZEp4U9lImOxcQHaTs1eNRYUClhYIcDdHh93bajbODE5XY2F+24gKV0tcbBERKUb5wARGZutKxA8A2g8CvbpCajn+mzDxKdP8GjpWzgU3RJL/6mF91r4Y0hjX91KMiIiKj78yUtUVOzcsx85jv4Av/hDWKs8hCNZVfH1rl5YdqgmRrSsgIGNysNSIc+/LSIiMioOgREVl/ohQIPhEOVKNJZfxgbVVCzImITt2/5Ay3kHsOb4XakjJCIqNZgAERUXey+g05cQPjgD1AuBKFOgqfwSfldNwYyn03DydqzUERIRlRpMgIiKm6M30GUBhA9OA3UHQZRZoHx5H4xqW0VXJfJRKraefwitVpQwUCKikotzgIikUsYH6PodhKZhCLRQAfbPdo6OvYLkVWH48XFHfL+/Fj5qVwltqrjpVpIREdHrYwJEJDUnP71D8eAc1Eg5gr9URxD+uB4W/NITC8vVxsftKqJpBRcmQkRERsAEiMjECG98AciVEC9sQFv5abSVn8bO6AaYsawn7H1r49PgSqjv66Srr9GKOB4Rj9OPBDhHxCOoghvkMiZJREQvwzlARKbGpQLw5hII7x8HqveCCAHt5SexUzUW3e7Nw9Fbj3VVd16MQvPZ4Vi4YhUSbh3HwhWr0Hx2OHZejJKwA0REpo9XgIhMlWtFoNfPEJp/AhycDfHSZrj5VEa3ptlDZjsvRuGPNYuxQbEKXsp43dMeZjhh6ppBwID/Q/vqnlJFT0Rk0pgAEZk6t8pA7xUQml9G2zI+gNICGq2I2E1jsVixOVd1D8TjB8UCjN+sRNuq4zkcRkSUBw6BEZkL96qAMnul2Imbseiv2QpBAF6cE52T74xS/4wTt+KKOUgiIvPABIjIDGnuHIFC0OR7XiYAXsJjaCIPF2NURETmgwkQkRlyExIMqnftQRxGrjmDP889QDLvRE9ExDlAROYowD8AOFTAeo9uwu/nZuigroHdF+tjilAHNQJ8EFzNA22rusPVTlX0ARMRmRgmQERmSO7bBE+tPKBKi0Zec5y1IpBh7QEr3ybAiaVQatPQWX4cneXHoRblOBpRFbtv1ce3m+uhXPkAfNu/DrwcrYq/I0REEuEQGJE5kslh1WUeBEGA9oVTWgCCIMCqyzxAJgcavgsM3wc0+whwrQyFoEFz+QVMVyzHMVUo7GOOwsX2v6tAB6/H4fLDJIgi70NGRCUXrwARmauqXSH0WQXs/AxIeqgrFuzLQmg/G6jaNbtAJgPK1st+tJ4IPLoJXP0LuLoN2piLGNqjN5QW2X8LiceX4F74ESxIrY3HjtXRtpoXgqt5oJ5PGS6nJ6IShQkQkTmr2hVC5U7Iuv03zv2zC7WbBcPCv3n2lZ/8uFQAmo4Bmo6BLD0JTS3tdae0J3/GW1lX8ZbqD8SmOSL8WD0sPFIf161qo2XVcuha2wuNA1yKvl9EREWMQ2BE5k4mh+jTFA+cgiD6NH158vOi55IfiCLkLT8FqveEqLSFm5CAgRZ7sUo5B7uzhqLq2anYfuG/W2xotCJXlBGR2eIVICLKJghA9Z5A9Z4QsjKAyH+AK39BvLYd9ikxqOFmgSq1y2bXFUXc2fsT3jpgi4oVAtCuKleUEZF5YQJERLlZqIAKbYAKbSB0mg88OI06ShvA/dld6KPOw//wxzhkIeBMRCB236yHPn/Wh7N3VQRX80BwNQ+Ud7aWtg9ERC/BBIiIXk4mA7wb6JdlpgJl60H24DTqC9dRX3Yd4/EbrkWVw+4H9TFwRwssCu2J6mUdpImZiOgVmAARkeF8m2QvrU96CFzdBlzdBjHyH1TCfVSS3cd1qzqo6vlsftHTBCz4+yGS1XjpijJNVhauHtuBzDvHcPWYgKpBHSG34I8oIioa/OlCRIVn7wU0HA40HA7haQJwIxy4tRdfdhoB2bMkR3tgFoYe/wV7NHWw7Eh9fGhZH02r+iC4ujsaB7jAUiHH2V0r4XV0CmrgMWoAwN4fELPXGQ+DJqFO8GApe0hEJRQTICIyDitHoGZvoGZvPD8VWrh3AvZIxZvyQ3hTfgjpGgX+OV8DO87Wx0R5AwzwfID/i578rPJ/z3MVH8P1yCicBZgEEZHRMQEioiIlvLMHuHcCuPoXxKt/wfJJJNrKz6Ct/Azui7/DIjp7x+kXR8VkQvYtPTyPToGm9UAOhxGRUfEnChEVLZkc8AkCfIIgtJsOxF7OnjN09S9oNU7wiN2f/1MFwAOP8cV3S6EKbIEutbxQ29ux+GInohKLCRARFR9BANyrAe7VILT4FHFbFqH8SxKgHKOfzMQPRy8houzY7AQoKxPnzp3CpH9SUNbNGX4uNvBzsYWfiw38XWxQxkZZ9H0hIrPGBIiIJGPpXL5A9VxlSWhS3gG+ZR2zCx5dR+2/OuBPADEJjoi85oG7WjfsF92xQnTHLVVlfNSnHVpXcQcAxCVnIC45A74u1rBW8sceETEBIiIJVW4UjJhwZ7iKj3PNAQKy5wA9FsrA6e1VaOPsCzjaZp9IewytygGyjES4CwlwFxLQSHZV97xZmf3haN0p++DxLaRuGI+99y1xR3RHirU3ZM7+KOPmDT9XW/i72qBeeSc4WCuM0icu5ycyD/xfSUSSkVtY4GHQJLgeGQWtqD8RWps9Nxr3g6bANaC5/hP9W0A27i6QFg88iQDicx63oXl8GwNrdICr57NNGGOvwDd6Jz7I+WmnBhANpEWpcEd0wzdZPWE7fDQa+jkBGSk4cekadj9QwNfVAf4uNvBztYG7naVuWf/LcDk/kflgAkREkqoTPBhnAXgdnQJ3PNaVxwrOiHpV4mDtlP0oW09XJAegN7DmWhloOxWIj4D60W1oH9+CMuUhrIUMVBHuoXwZS/i52GTXjfgbDbf0Rx1RjvuiC+6IHtgtuuGhzBMZdj5Ic6uND7o0hrdT9m0+sjRaWMiz7yl9dtdK1DoyKrsdLucnMnlMgIhIcnWCB0PTeiAuHN2Oq2ePonKdIFQN6ggPYwwduVQAXEYDAHSDXFmZQOI9IP42xnvWAmyf7Vz0NB5amRIKbSb8hBj4Iea/dlKB0GsfQNG9afbxvZO4vOVb7H9ki0zbcghJ+hEAl/MTmQv+TyQikyC3sEDl/3XA7XgRlf/XoWgTBQsl4ByQ/XhenbcgqzUASH4IxN8G4iOgeXwb6TE3oY2/jdYVm8Dd/lmy9OAUasZtQU0ASIbeVZ8X5SznX/HDdGT4t4F1GXe42NvCxU4FF1sVXGyVsFVZQBBePcxGRMbBBIiI6HkyGeBQLvvh1xxyAM8GyNDj+XreDZHZ7DOkRd+E5u5xOGfcf2XTQ+K/BuK/BgA8Fu3wSHTAA9EBoVkDsPLz4XCxVQFPInHgyDGcT1BC5eABGyd3uNhZ65IlVzsVbJTyIk+WOJmbSjp+momICqNsPSjL1oMSwKXD2+AcPuCVT0mR2cNKmwo5NHAWkuEsJKMS7kOp1aKM9bO9i65uQ8uT49Hy2XM0ooB42OOR6IB7ogNCs/pj1efvwNlWBTy5g31Hj+PfBBWUDh6wLeMKVzur106WOJmbSgMmQEREr6kgy/ljBWe4jr8OuUwGPI0HUmKAlFggJRZr/NpCnvNEpS2SHSpBlhYHK/UTyAURrkiEq5CIKgC+1vb5L1m6shWtTnyOVs9eRy3K8Rj2iBMdECk6YGRWX/ySkywl3MXeoyfxb4ISSkdP2Ds4w8XOEi52KrjaquDyLFk6t3tViZrMzStZlB9+CoiIXlNBlvNHBU36b1K3jUv2w70aAMDq+cbqDYZdvWcJhiYLSHucnSylPkuW/Nv/tyRfaYMU+wqQp8XBKisRCkEDDzyBh/AEAPCttvd/ydLlP9H6+Bdo/exlMkQLPIID4kQH3BQd8H9ZffHL2MHwOjoFwMsnc+8t1xbNKnlAaZG9Ai45XQ0AsFZa/JfImYCSdCWLiZzx8d0jIjKC11rOnx+5BWDnnv14Ri9Zqh8C2/oh2V9nZQJpj55dWYoDUmKwpkKn/5IlhTVS7fwgT3sES00yVEIWyuIxygrZsS7U9kTMhX2ojsf5TujOmcy9cPUcNJr0VXYCdH0XIv76HtefiEgTVciUWUItt4ZGbgWNhRX+tQ7Cl8M6ZN+eJPUR/j59Hhdis2BhZQcLSxuorGxhpVLBRiWHtdICDf2cYKmQAwDSMrMAAJYW8gLtw/S8krQtARO5omESCdD333+PefPmITo6GrVq1cJ3332Hhg0b5lt/w4YNmDBhAiIjIxEYGIg5c+agY8eOuvOiKGLSpElYunQpEhIS0KRJEyxatAiBgYHF0R0iKqWKdDn/q1goAXuv7MczeslSg2GwaTAs+2t1OpAa92wILvvq0prALrj89+8Feqny1hmwepakIO4qaiYdRE35cxVEAFnZjwFJZWAhf/bz+fKfaL4vDC9sa4kMUYFUqDBGPRIVPv0QXo5WwO0DuP/nHFyL1yJNtIRabgm1LDup0lhY44L1/zD2rU4oV8YaSH2MU2dP4Wy0GhaWtrBQWaLd0ckAXn4lK7phL1hZKqGykEEplxmcZBUHJnJFR/IEaN26dQgLC8PixYvRqFEjLFiwAMHBwbh27Rrc3Nxy1T9y5Aj69++PWbNmoXPnzlizZg26d++OM2fOoHr16gCAuXPn4ttvv8XKlSvh5+eHCRMmIDg4GJcvX4alpWVxd5GISpFiXc5fWApLwNE7+/GMNQCrMmUL9PSmzZ6bs+T/BsQO85CVkYqsp8nISk+BJiMV2owUiJmpCK3W9L/7r8kVSLd0hZCZCoU2HTJoAQAqQQ0V1CjnZAsb1bO68RGomHgEFV9MrNTZj/cSbQE8u93J7f2ov2cY6r8Y6CuuZF3+uhEeiw7IghxqWCALFtiAtvjwveGoWc4ReHQD17Z9h7MP06CVKQCZBUS5EqJMAciVuGNTE306BSPQ3Q5Ii8eN0/tw+n4KZBbK7IdCBbmFEnKFChprFzSqWgEeDpaAVoPYJwmIfKKGQqGEUiF/loTJobAQoJTLYGepgBzaAg1JmsP+UqaYyEn+js2fPx/Dhw9HSEj2ZdzFixdj27ZtWLZsGcaOHZur/jfffIP27dvjk08+AQBMmzYN4eHhWLhwIRYvXgxRFLFgwQJ88cUX6NatGwBg1apVcHd3x+bNm9GvX7/i6xwRkRkp6GTuSk26/VfoWROCZ00o8NxGk89p/PxB3UGwrDso+2tRBLLSgcw0QJ0KZKZipoM3oHrWim9TaLt8h6z0ZGQ+TYUmPSX7kZkCMSMNgys0y942AADkSjy1KQdtRioU2qdQatML1N+qsnsA7umV7VfX+i+5exKJShErUSmf50+KHozEp8+moMdeQeDeYchvnGGOuh8cnadnJ0DRF+C2pAVy/sTPFLMTsJzHj1kdUaffJJRPOoNqBRiS/GPLBvR4sz+Q+hhxv3+M8w+TIQpyADJoBVl24ibIcdOyGmq0HYQWFV2BzDTE7Z6HE5EJgEwOCHK9fx9b+aFiUGf8z98Z0GQh/sQaHItIgEz2rJ5MDkGQQ5DLkWHpBt/qjVGjXPbtZ5Kv/4PT95Igk1tAELIz2CpHJ0AA8OKCRCkTOUkToMzMTJw+fRrjxo3TlclkMrRp0wZHjx7N8zlHjx5FWFiYXllwcDA2b94MAIiIiEB0dDTatGmjO+/g4IBGjRrh6NGjTICIiPJh8GTu1yEIgMIq+wHn3OddAiFzCYQSgDKPp+uND1TtCquqXXWHlw5tRbU9b70yhLs1R8HLvzqy1BnQqDOhUWcgrGwTOLk9u+muY3kk1RuJ5NQ0iFmZEDWZQFYmRK0a0KjR1KsRyjtn3xYFCiukONdEStpTyLRqyEQ1ZNosyEU15KIaXm7OcLF91hONWi8OpaCBEhoAGQAAS6ihtJDh6ZMHr+wDAGQlRmd/kZEE19ub0CafequeJCE6sXf2QWYKXE/Nz7mGlstGTXNc8W2enQBlpcNp1wfomE/dbZqG+FsVmJ0AiSLs1nTWbeOg5xWJ3KXju1CtSX4RGZ+kCdCjR4+g0Wjg7u6uV+7u7o6rV6/m+Zzo6Og860dHR+vO55TlV+dFGRkZyMjI0B0nJSUBANRqNdRqdZ7PKayc9ozdbnFjP0wL+2FazLkf1VsNwBmNFt4npuWazH2/4QTUajXA5PsVUK8NYva8+kqWW4fxEC0sIEf2PeSAZ/OmRC3Uai3g6A+r9lP051I9x/PZv2q1GnCrAdX/7YMqn7r9nq/rXhP45E52IqTJBLRZ2f8+Ox5p5QTYlsH1Rx4F6m85b5/sdhV2SG3yOWIS0gBRA1Gb/cCzh49DTXiWs8+uK8rxtNpbeBCfClGrgSBqAFELQasBRA3s7OrA28M2u25WFjLLNUdUQioEUQNB1D57ZH8ttwqAn5NVdl2tBqK9DxJS0yFAhEzUQKlNhx1SX9mP1Mf3XvuzZcjzJR8CMwWzZs3ClClTcpXv3r0b1tbWRfKa4eHhRdJucWM/TAv7YVrMtx9OuFfrK2TGXocsIwFalSOUbhUhU8vwYPt2qYMrkAyXgegV922+V7L+dhkI1e7d0gT3SncAAFqtFq6iE9wQn28iFwMnRCcL2K77vlTK+5LZMwknDuJKzoGyHfCSHCv64hFsv/jswPUdwPUlIUeewvbIZ18HTNM7lR59FX2jZr7kydkiohIR9Zqfr7S0tALXlTQBcnFxgVwuR0xMjF55TEwMPDzy/q54eHi8tH7OvzExMfD09NSrU7t27TzbHDdunN6wWlJSEry9vdGuXTvY29sb3K+XUavVCA8PR9u2baFQ5DVibh7YD9PCfpgW9sMUdMSZcP98r2T1aPvqITJTcF6VALfjY/JN5B40mojObTtLE1wBabLaIWbOj6+8Itdt0KjXngOUM4JTEJImQEqlEvXq1cPevXvRvXt3ANkZ7969exEaGprnc4KCgrB3716MGTNGVxYeHo6goCAAgJ+fHzw8PLB3715dwpOUlITjx49jxIgRebapUqmgUuW+cKlQKIrsP31Rtl2c2A/Twn6YFvZDWvU7hkDT7m1ptiUwkvodQ3BWLst3f6n6ZrAEXqFQ4EpB5pZZ5TfYaNhrFZTkn4KwsDAMHjwY9evXR8OGDbFgwQKkpqbqVoUNGjQIZcuWxaxZswAAo0ePRosWLfDVV1+hU6dOWLt2LU6dOoUlS5YAAARBwJgxYzB9+nQEBgbqlsF7eXnpkiwiIiodzGJbgleQdH8pIymSjUJfk+TvXt++fREXF4eJEyciOjoatWvXxs6dO3WTmO/evQuZTKar37hxY6xZswZffPEFxo8fj8DAQGzevFm3BxAAfPrpp0hNTcW7776LhIQENG3aFDt37uQeQEREZJaYyBmfSbyDoaGh+Q55HThwIFdZ79690bt373zbEwQBU6dOxdSpU40VIhEREb0mU0rkZK+uQkRERFSyMAEiIiKiUocJEBEREZU6TICIiIio1GECRERERKUOEyAiIiIqdZgAERERUanDBIiIiIhKHSZAREREVOqYxE7QpkYUs+/OZshdZQtKrVYjLS0NSUlJZnlzwRzsh2lhP0wL+2Fa2A/TUpT9yPm9nfN7/GWYAOUhOTkZAODt7S1xJERERGSo5ORkODg4vLSOIBYkTSpltFotHj58CDs7OwiCYNS2k5KS4O3tjXv37sHe3t6obRcn9sO0sB+mhf0wLeyHaSnKfoiiiOTkZHh5eendSD0vvAKUB5lMhnLlyhXpa9jb25v1BzgH+2Fa2A/Twn6YFvbDtBRVP1515ScHJ0ETERFRqcMEiIiIiEodJkDFTKVSYdKkSVCpVFKH8lrYD9PCfpgW9sO0sB+mxVT6wUnQREREVOrwChARERGVOkyAiIiIqNRhAkRERESlDhMgIiIiKnWYABWTv//+G126dIGXlxcEQcDmzZulDslgs2bNQoMGDWBnZwc3Nzd0794d165dkzosgy1atAg1a9bUbcIVFBSEHTt2SB3Wa5s9ezYEQcCYMWOkDsVgkydPhiAIeo/KlStLHVahPHjwAG+99RacnZ1hZWWFGjVq4NSpU1KHZRBfX99c3w9BEDBy5EipQzOIRqPBhAkT4OfnBysrKwQEBGDatGkFuk+UqUlOTsaYMWPg4+MDKysrNG7cGCdPnpQ6rJd61e89URQxceJEeHp6wsrKCm3atMGNGzeKLT4mQMUkNTUVtWrVwvfffy91KIV28OBBjBw5EseOHUN4eDjUajXatWuH1NRUqUMzSLly5TB79mycPn0ap06dQqtWrdCtWzdcunRJ6tAK7eTJk/jxxx9Rs2ZNqUMptGrVqiEqKkr3OHTokNQhGezJkydo0qQJFAoFduzYgcuXL+Orr75CmTJlpA7NICdPntT7XoSHhwMAevfuLXFkhpkzZw4WLVqEhQsX4sqVK5gzZw7mzp2L7777TurQDPbOO+8gPDwcv/zyCy5cuIB27dqhTZs2ePDggdSh5etVv/fmzp2Lb7/9FosXL8bx48dhY2OD4OBgpKenF0+AIhU7AOIff/whdRivLTY2VgQgHjx4UOpQXluZMmXEn376SeowCiU5OVkMDAwUw8PDxRYtWoijR4+WOiSDTZo0SaxVq5bUYby2zz77TGzatKnUYRjd6NGjxYCAAFGr1UodikE6deokDh06VK/szTffFAcOHChRRIWTlpYmyuVy8a+//tIrr1u3rvj5559LFJVhXvy9p9VqRQ8PD3HevHm6soSEBFGlUom//fZbscTEK0BUaImJiQAAJycniSMpPI1Gg7Vr1yI1NRVBQUFSh1MoI0eORKdOndCmTRupQ3ktN27cgJeXF/z9/TFw4EDcvXtX6pAMtmXLFtSvXx+9e/eGm5sb6tSpg6VLl0od1mvJzMzEr7/+iqFDhxr95tBFrXHjxti7dy+uX78OADh//jwOHTqEDh06SByZYbKysqDRaGBpaalXbmVlZZZXSgEgIiIC0dHRej+3HBwc0KhRIxw9erRYYuDNUKlQtFotxowZgyZNmqB69epSh2OwCxcuICgoCOnp6bC1tcUff/yBqlWrSh2WwdauXYszZ86Y/FyAV2nUqBFWrFiBSpUqISoqClOmTEGzZs1w8eJF2NnZSR1egd2+fRuLFi1CWFgYxo8fj5MnT2LUqFFQKpUYPHiw1OEVyubNm5GQkIAhQ4ZIHYrBxo4di6SkJFSuXBlyuRwajQYzZszAwIEDpQ7NIHZ2dggKCsK0adNQpUoVuLu747fffsPRo0dRoUIFqcMrlOjoaACAu7u7Xrm7u7vuXFFjAkSFMnLkSFy8eNFs//qoVKkSzp07h8TERGzcuBGDBw/GwYMHzSoJunfvHkaPHo3w8PBcfxmam+f/Iq9ZsyYaNWoEHx8frF+/HsOGDZMwMsNotVrUr18fM2fOBADUqVMHFy9exOLFi802Afr555/RoUMHeHl5SR2KwdavX4/Vq1djzZo1qFatGs6dO4cxY8bAy8vL7L4fv/zyC4YOHYqyZctCLpejbt266N+/P06fPi11aGaLQ2BksNDQUPz111/Yv38/ypUrJ3U4haJUKlGhQgXUq1cPs2bNQq1atfDNN99IHZZBTp8+jdjYWNStWxcWFhawsLDAwYMH8e2338LCwgIajUbqEAvN0dERFStWxM2bN6UOxSCenp65kugqVaqY5XAeANy5cwd79uzBO++8I3UohfLJJ59g7Nix6NevH2rUqIG3334bH374IWbNmiV1aAYLCAjAwYMHkZKSgnv37uHEiRNQq9Xw9/eXOrRC8fDwAADExMTolcfExOjOFTUmQFRgoigiNDQUf/zxB/bt2wc/Pz+pQzIarVaLjIwMqcMwSOvWrXHhwgWcO3dO96hfvz4GDhyIc+fOQS6XSx1ioaWkpODWrVvw9PSUOhSDNGnSJNfWENevX4ePj49EEb2e5cuXw83NDZ06dZI6lEJJS0uDTKb/a04ul0Or1UoU0euzsbGBp6cnnjx5gl27dqFbt25Sh1Qofn5+8PDwwN69e3VlSUlJOH78eLHNx+QQWDFJSUnR+2s2IiIC586dg5OTE8qXLy9hZAU3cuRIrFmzBn/++Sfs7Ox047QODg6wsrKSOLqCGzduHDp06IDy5csjOTkZa9aswYEDB7Br1y6pQzOInZ1drvlXNjY2cHZ2Nrt5WR9//DG6dOkCHx8fPHz4EJMmTYJcLkf//v2lDs0gH374IRo3boyZM2eiT58+OHHiBJYsWYIlS5ZIHZrBtFotli9fjsGDB8PCwjx/VXTp0gUzZsxA+fLlUa1aNZw9exbz58/H0KFDpQ7NYLt27YIoiqhUqRJu3ryJTz75BJUrV0ZISIjUoeXrVb/3xowZg+nTpyMwMBB+fn6YMGECvLy80L179+IJsFjWmpG4f/9+EUCux+DBg6UOrcDyih+AuHz5cqlDM8jQoUNFHx8fUalUiq6urmLr1q3F3bt3Sx2WUZjrMvi+ffuKnp6eolKpFMuWLSv27dtXvHnzptRhFcrWrVvF6tWriyqVSqxcubK4ZMkSqUMqlF27dokAxGvXrkkdSqElJSWJo0ePFsuXLy9aWlqK/v7+4ueffy5mZGRIHZrB1q1bJ/r7+4tKpVL08PAQR44cKSYkJEgd1ku96veeVqsVJ0yYILq7u4sqlUps3bp1sX7eBFE0wy0xiYiIiF4D5wARERFRqcMEiIiIiEodJkBERERU6jABIiIiolKHCRARERGVOkyAiIiIqNRhAkRERESlDhMgIiIzMnnyZNSuXVvqMIjMHhMgInqpIUOGQBAE/N///V+ucyNHjoQgCBgyZEiRvX7Lli0hCEK+j5YtWxbZa7/o+de1sbFBYGAghgwZUmR35BYEAZs3by6StolKOyZARPRK3t7eWLt2LZ4+faorS09Px5o1a4r8XnabNm1CVFQUoqKicOLECQDAnj17dGWbNm3Sq69Wq4s0nuXLlyMqKgqXLl3C999/j5SUFDRq1AirVq0q0tclIuNiAkREr1S3bl14e3vrJRubNm1C+fLlUadOHb26O3fuRNOmTeHo6AhnZ2d07twZt27d0p1ftWoVbG1tcePGDV3Z+++/j8qVKyMtLS3Xazs5OcHDwwMeHh5wdXUFADg7O+vKnJ2dsWjRInTt2hU2NjaYMWMGAODPP/9E3bp1YWlpCX9/f0yZMgVZWVm6dhMSEvDOO+/A1dUV9vb2aNWqFc6fP//K98LR0REeHh7w9fVFu3btsHHjRgwcOBChoaF48uSJrt6hQ4fQrFkzWFlZwdvbG6NGjUJqaqruvK+vL6ZNm4b+/fvDxsYGZcuWxffff693HgB69OgBQRB0xzl++eUX+Pr6wsHBAf369UNycvIrYyei/zABIqICGTp0KJYvX647XrZsWZ53ok5NTUVYWBhOnTqFvXv3QiaToUePHtBqtQCAQYMGoWPHjhg4cCCysrKwbds2/PTTT1i9ejWsra0LFdvkyZPRo0cPXLhwAUOHDsU///yDQYMGYfTo0bh8+TJ+/PFHrFixQpccAUDv3r0RGxuLHTt24PTp06hbty5at26N+Ph4g1//ww8/RHJyMsLDwwEAt27dQvv27dGzZ0/8+++/WLduHQ4dOoTQ0FC9582bNw+1atXC2bNnMXbsWIwePVrXxsmTJwH8d8Up5zin/c2bN+Ovv/7CX3/9hYMHD2L27NkGx01UqhXbbVeJyCwNHjxY7NatmxgbGyuqVCoxMjJSjIyMFC0tLcW4uDixW7duurs75yUuLk4EIF64cEFXFh8fL5YrV04cMWKE6O7uLs6YMaNAsURERIgAxLNnz+rKAIhjxozRq9e6dWtx5syZemW//PKL6OnpKYqiKP7zzz+ivb29mJ6erlcnICBA/PHHH/N9fQDiH3/8kav86dOnIgBxzpw5oiiK4rBhw8R3331Xr84///wjymQy8enTp6IoiqKPj4/Yvn17vTp9+/YVO3To8NLXmzRpkmhtbS0mJSXpyj755BOxUaNG+cZNRLlZSJl8EZH5cHV1RadOnbBixQqIoohOnTrBxcUlV70bN25g4sSJOH78OB49eqS78nP37l1Ur14dAFCmTBn8/PPPCA4ORuPGjTF27NjXiq1+/fp6x+fPn8fhw4f1rvhoNBqkp6cjLS0N58+fR0pKCpydnfWe9/TpU73huoISRRFA9qTlnNf/999/sXr1ar06Wq0WERERqFKlCgAgKChIr52goCAsWLDgla/n6+sLOzs73bGnpydiY2MNjpuoNGMCREQFNnToUN0wzvPzVZ7XpUsX+Pj4YOnSpfDy8oJWq0X16tWRmZmpV+/vv/+GXC5HVFQUUlNT9X6hG8rGxkbvOCUlBVOmTMGbb76Zq66lpSVSUlLg6emJAwcO5Drv6Oho8OtfuXIFAODn56d7/ffeew+jRo3KVdcYk8YVCoXesSAIukSTiAqGCRARFVj79u2RmZkJQRAQHByc6/zjx49x7do1LF26FM2aNQOQPRn4RUeOHMGcOXOwdetWfPbZZwgNDcXKlSuNFmfdunVx7do1VKhQId/z0dHRsLCwyDW5uDAWLFgAe3t7tGnTRtf+5cuX8339HMeOHct1nHN1CMhOdDQazWvHR0S5MQEiogKTy+W6qx1yuTzX+TJlysDZ2RlLliyBp6cn7t69m2t4Kzk5GW+//TZGjRqFDh06oFy5cmjQoAG6dOmCXr16GSXOiRMnonPnzihfvjx69eoFmUyG8+fP4+LFi5g+fTratGmDoKAgdO/eHXPnzkXFihXx8OFDbNu2DT169Mg1pPa8hIQEREdHIyMjA9evX8ePP/6IzZs3Y9WqVbqrR5999hn+97//ITQ0FO+88w5sbGxw+fJlhIeHY+HChbq2Dh8+jLlz56J79+4IDw/Hhg0bsG3bNt15X19f7N27F02aNIFKpUKZMmWM8v4QEVeBEZGB7O3tYW9vn+c5mUyGtWvX4vTp06hevTo+/PBDzJs3T6/O6NGjYWNjg5kzZwIAatSogZkzZ+K9997DgwcPjBJjcHAw/vrrL+zevRsNGjTA//73P3z99dfw8fEBkD1ktH37djRv3hwhISGoWLEi+vXrhzt37sDd3f2lbYeEhMDT0xOVK1fGiBEjYGtrixMnTmDAgAG6OjVr1sTBgwdx/fp1NGvWDHXq1MHEiRPh5eWl19ZHH32EU6dOoU6dOpg+fTrmz5+vd2Xtq6++Qnh4OLy9vXNtN0BEr0cQc2bvERFRsfH19cWYMWMwZswYqUMhKpV4BYiIiIhKHSZAREREVOpwCIyIiIhKHV4BIiIiolKHCRARERGVOkyAiIiIqNRhAkRERESlDhMgIiIiKnWYABEREVGpwwSIiIiISh0mQERERFTqMAEiIiKiUuf/Ab01n1EqL3yZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The plot showing both train and test accuracy with the same lines across different tree depths suggests that the Decision Tree model is well-balanced, without signs of overfitting or underfitting. This consistency indicates that the tree is able to generalize effectively to both the training and test datasets. Unlike typical cases where the training accuracy might be much higher than test accuracy (suggesting overfitting), or vice versa (indicating underfitting), the equal performance across both sets shows that the model has found a good level of complexity."
      ],
      "metadata": {
        "id": "WuzasJ3iV2-H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the optimal depth based on the test error\n",
        "optimal_depth = np.argmin(test_scores) + 1  # Add 1 since range starts from 1\n",
        "print(f'Optimal max depth: {optimal_depth}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHW3fdmwXF8O",
        "outputId": "c4f31037-28f5-45ab-9416-7ad834d9d4f2"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal max depth: 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# make the model using the optimal depth\n",
        "\n",
        "# fit the updated pipeline\n",
        "dtree_optimal = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('regressor', DecisionTreeRegressor(max_depth=optimal_depth, random_state=42, min_samples_split=10, min_samples_leaf=5))\n",
        "])\n",
        "dtree_optimal.fit(X_train, y_train)\n",
        "\n",
        "# make predictions\n",
        "y_pred_log = dtree_optimal.predict(X_test)\n",
        "\n",
        "# evaluate the model\n",
        "mse_log = mean_squared_error(y_test, y_pred_log)\n",
        "mae_log = mean_absolute_error(y_test, y_pred_log)\n",
        "r2_log = r2_score(y_test, y_pred_log)\n",
        "\n"
      ],
      "metadata": {
        "id": "eayHFoROV9hU"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import plot_tree\n",
        "\n",
        "# extract the trained DecisionTreeRegressor from the pipeline\n",
        "fitted_tree = dtree_optimal.named_steps['regressor']\n",
        "\n",
        "# fit the preprocessor on training data to retrieve feature names\n",
        "preprocessor.fit(X_train)\n",
        "categorical_feature_names = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_columns)\n",
        "all_feature_names = np.concatenate([numerical_columns, categorical_feature_names])  # Combine numerical and categorical feature names\n",
        "\n",
        "# show the first 2 layers of the tree\n",
        "plt.figure(figsize=(20, 10))\n",
        "plot_tree(fitted_tree,\n",
        "          max_depth=2,\n",
        "          feature_names=all_feature_names,\n",
        "          filled=True,\n",
        "          rounded=True)\n",
        "plt.title(\"Decision Tree Visualization (First 2 Layers)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "TsU31-KmknVH",
        "outputId": "d3b1c514-3e5a-40bf-d119-8e54f4739c5e"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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dtZEkad+J83oUHPKf+5y5fFObDpzQtil9lCN9KknS8O/rq0aPMRrYtpYSucVVt4YVIu3TtkYpbTl4Uqt3HDYFEftPnle+zJ6qVSqfJClFIjfVKJFXvqcvfsQrBAAAAL48BBEAAAD4Jt0OeKim/aeof+uaqlg4h4JDnmnP8bMyDEOSNH7Res37e7cm9GiqdMkTafzi9fpr52EVyZHhnc5jYWmhYR3rKUWi+Lp86566jJ6jPpOXaHSXhqY+Ic9CNXr+Ov32QxO5OsdW/LhO6jZmnvyv3NSMn1vL3S2O/tp5WNV+GKW9M35VmqQJdfDUBbUbNkP9WtTQd4Wza9N+Pw2esfKt69pz7KxaDZqmoR3rqUAWT126cU/fj5glSfqxcWVTv8EzV+qXltU1pEMdWVtZac7anbp4465WbvfV3F/bycrKUhEREarba7xiO9hp7dgeCgsPV7cxc9X4l8laO7aH6Vj/3i861+4EKI/PT2+svWv976IEAx/iwMkLiuMYyxRCSFKxnBllaWkh31MXVbFIzmj3e/wkROlSJDK9zpspjRZv3Cvf0xeVK0NqXbp5Vxv2+alO6fzR7g8AAAB8KwgiAAAA8E26HfBIYeHhqlQkh5K7u0mSMnkkNW2fuHSDutYvr0r//xB6TJdG2nzgxDuf55+jJVIkclOfZtXUadTsSEHEi7BwjercQF5pkkt6+TB+7t+7dGrxcCVyiytJ6linrDYd8NO8tbvUt2V1TVq6SSXzeKlTvXKSJM9k7tp/8oI2H/B7q7qGzFqpzvXKq37ZgpKkVIkTqHezqvp58uJIQUTNknnVoHzhSPuGvgjT1N7N5RbHWZK05eBJnbx0XX4LhylpAldJ0pRezZXHp48Onb6knBlSRbtfdBLFi6Nd0/q9sfa4zrHf6hrf1p3AR3KL6xSpzdraSnGdYutO4ONo91m25YAO+1/W2K4+prZapfIp4FGQyrQfLMOQwsLD1axS0Y8amgAAAABfIoIIAAAAfJO8PJKpaM4Myt/kZ5XInVnFc2dS5aK5FNcpth4Fh+h2wCPlypja1N/a2krZ06WU8Y7n2ep7UqPmrdXZq7cU9OSpwsIj9Cz0hUKePVcseztJkq2NtTJ7JDPtc/LidYWHRyhHg16RjvU8NEyuzo6SpLNXbqlC4RyRtufJ5PHWQYTf+Wva53deI+b+ZWoLj6a27OlSRtk3WcJ4kcKEM1duKml8V1MIIUnpUyZRHMdYOnPlpimI+Pd+0bG2tpJH0oRvdQ3msuPwabUd+ofGdfNRhlRJTO07j/hr5Lw1GtW5oXJlSK2LN+6ox/gFGjprlXr4VDJjxQAAAIB5EUQAAADgm2RlZamVI7tp/4nz2nzwpKYs26z+05Zpy+SfFNfp7T5xb2lhIeNfycSLsHDTf1+5dV+1fhyrZpWLqU/zaorrHFv7jp9Tu2EzFPoiXP+/TIQc7GxkYWFh2u/J0+eysrLUjqk/R1m02tHB/v0u+F+ePH2uXk2qqGKRHFG22dvamP479v8HEv8U2yFq29t4m/3MMTVTQlcX3X8QFKktLCxcD4KeKKFr5OBk19Ezqt1rnAa3q6N6/z+a5JUB05erTukC8qlQRNLLETZPnoXq+xGz1L1hhc9yAXIAAADgUyCIAAAAwDfLwsJC+bw8lc/LUz19KilT7e76a8dhta9dRu7xXOR76qIKZk0n6eWD6aNnryhr2hSm/ePFcdKdgIem14+fPNWVW/dNr4+evayICEOD2tY2PYRevvXgf9aVxTO5wsMjdO9BkApkTRttn7QpEkVZBPngyQtvfe1Z06bQuWu3Psrog3QpEuv6vUBdvxtoGhXhf/mGHgaHKH3KxO90LHNMzZQnk4ceBofoyJnLphEg24+cVkSEEWlUzM4j/qr141j90qqGmlQqGuU4T5+HyvIfgZIkWVm+fP3vwAoAAAD4lhBEAAAA4Jt08NQFbT90WsVzZ1L8uM7yPXVR9x8GKe3/Lz7cpkYpjZq/Vh5JEypt8kT6bfF6PQoOiXQM7xwZNG/dbpUrkE0ujrE08I/lpgfPkpQ6SQK9CAvX5GWbVa5AVu3zO68/Vm37z9o8k7mrVql8ajVomga2q60snsl1/2GQth86rUweSVU2f1a1rl5SpdsP0riFf6t8oezafODEO61h0cOnkmr1HKukCeOpincuWVpYyO/CNZ26dEM/N6/21seRpGK5MipTqqRq/utUDelQV2Hh4eo6eo4KZUsXaQHot/Expmbyv3xDoS/C9eDxEwWHPNPxc1clvQx4JMn39EW1GjRNq0d1V+L4cZUuZWKVzJNZHYfP1OiujRQWFq5uY+apevE8pjU6dhw+rVo/jlWb6qVUuUgu3Ql4JEmysbEyTZdVtkBWTVi8QVk8kytXxtS6eP2uBvyxQuUKZH3t4twAAADAt4AgAgAAAN8k51gO2n38rCYu3aigkKdKljCeBratrdL5skiSOtQqo9sBD9V68HRZWlioQflCqlA4hx4/eWo6Rpf63+ny/0+/5BzbQb2bVdWV2/8bEeGVJrkGtaujMfPX6pepf6pA1rTq26K6Wg2a9p/1TerZVMNm/6XeExbp5v0HiufiqNwZPVQ2f1ZJLz/FP75bYw2asUID/1ihojkzqnujCho2e/VbXX/JPJm1eMj3GjprlcbMXycbayt5JneXz3dF3uU2Sno5smTBoA7qPnaeynUcIksLC5XMk1nDv6//zsf6GGr0GKOrtwNMrws17ydJerz9D0nS02ehOnf1dqRptKb1aaluY+apUufhsrS0VKUiOTWsYz3T9vnr9yjkWahGzlujkfPW/O/Y2dJp7dgekqQfGlaUhYWFfp2+XLfuPZBbHCeVLZBVPzevHpOXCwAAAHz2LAyDQcIAAAD4us2ePVs+Pj66v2mqbG3e/7M4rQdP16PgEC0Y2OEjVodvUeiLMLmVbKlZs2apUaNG5i4HAAAAiFGMDwYAAAAAAAAAADGGqZkAAACAr1Aen5907U5AtNvGdG2k2qXyf+KKAAAAAHyrCCIAAACAtzT5x2bmLuGtLR3aKdIaCP+UwNX5E1cDAAAA4FtGEAEAAAB8hZK7u5m7BAAAAACQxBoRAAAAwFer9eDpqtt7vLnLAAAAAPCNY0QEAAAAALMbNW+N+k39U21qlNTQDvVM7d+PmKWth07p9v2Hiu1gp7yZ06h/q5pKmyKRqU/3sfO0/8R5nbp0Q+lSJNLu6b9EOva5q7fUaeQc+V+5qcdPQpQoXhzVKJlPPzauJBvr//1J9DAoRP2n/anVOw7rQdATJUsYT0M61FWZfFli/gYAAAAAXzGCCAAAAABmdej0Jc1YtV2ZPZJG2ZYtbQrVKpVPSRPE04OgJxo8Y6WqdBspv4XDZGX1vwHeDcoXku+pizp58XqUY1hbW6lumfzKmjaFXBxj6cSFa+owfJaMCEN9W1aXJIW+CFPlriMUP66z5vRvq0RucXXtToBcHB1i7sIBAACAbwRBBAAAAPCBVmzz1ZCZK3Xxxl052Nsqq2dyLRjYUbEd7HTo9CX1//1PHTt/VWFh4fJKk0yD29dVtrQpTPs7ezfVmK6NtG7PUe047K9kCeNpQo8mcovjpA7DZ+qw/yVl9kimqb1bKHWSBJKkQTNWaM2uI2pWuZiGz16twMdPVDZ/Fo3r3lgujrGirTMiIkKj56/TzNXbdSfwkdIkS6gfGlVSlaK5JEkPgp6o25h52nLwhJ48fa7E8eOqW4Pv1KB84Ri7d8Ehz9R8wFSN6+6j4XP+irK9SaWipv9OkchNfZpXVYGmfXXl9n3TvRj+fX1J0v2HQdEGEakSJ1CqxAlMr5O7u2nnkTPac/ysqW3O2p16EPREmyb2Mo2SSJGIdTYAAACAj4EgAgAAAPgAtwMeqmn/KerfuqYqFs6h4JBn2nP8rAzDkCQFP32mumULani6+jIMQ+MXr1eNHqN1ZN5gOcX636fth81erUFta2tQuzrqO3mJmv86VSkTx1eX+t8pWQJXtR06Q93GzNWy4V1M+1y8cVfLtx7UosHfKyjkqdoNnaEuo+dqep+W0dY6ct4aLdqwT6O7NpJH0gTac+ysWgycKrc4TiqULZ0GTF+uM5dv6s9hnRXPxUkXb9zRs+cvXnvtI+b8pZHz1rzx/hyYNUDJEsZ77fauY+aqTP4sKpYrU7RBxD89efpcc9ftUspEbkqawPWNfd/kwvU72nTATxWL5DS1rd19VHkyeajr6Llas/uo3OI4qWaJvOpcr3ykkRcAAAAA3h1BBAAAAPABbgc8Ulh4uCoVyaHk7i8/QZ/pH1MMeefIEKn/uG4+SvZde+06ekblCmQztdcvV1DViueRJHWqV14l2w7UD40qqmSezJKkNjVKqu2QPyId61noC03p1VyJ48eV9HJkQM2eYzSobW0ljOcSqe/z0BcaOXeNVo7spryZ00h6OVJgr985/bFqmwplS6frdwKVxTO5cqRPJem/RwQ0rVxUVYvlfmOfRPHivHbb0s37dezsFW2b8vMbj/H78i36ecoSPXn6XJ7J3bViZDfZ2rz7nzIl2w7UsXNX9Dw0TE0qeuunplVM2y7fuqcdR06rVsl8Wjq0ky7euKsuo+foRXi4fmxc+Z3PBQAAAOB/CCIAAACAD+DlkUxFc2ZQ/iY/q0TuzCqeO5MqF82luE6xJUl3Ax/p12nLtfOov+4/DFJ4RIRCnoXq+p3ASMfJnDqZ6b8TuDpLkjKm/l+gkSCus56FvtDjJ0/lHPvlSIpkCVxNIYQk5cnkoYgIQ+eu3Y4SRFy8cVchz0JVpdvISO2hL8KUxTO5JKlZ5aJq+PNEHTt3RcVzZVKFwjlMoUV0XJ0d5ers+Nb36p+u3w1Uj/ELtHJkV9nb2byxb61S+VQsdybdCXiocQvXq3G/SdrwW6//3O/fZvZro+CQp/I7f019Ji/RuIXr1aleOUlSRISh+HGcNa5bY1lZWSp7upS6de+Bxi78myACAAAA+EAEEQAAAMAHsLKy1MqR3bT/xHltPnhSU5ZtVv9py7Rl8k9KmSi+Wg2ersBHwRrasZ6SJ4wnWxtrlWw7UKFhYZGOY2NtZfpvC1lEbbN42RYRYbxXncFPn0mSlgzppERucSJts7N9+UC/dL4sOrl4mNbv89NW35Oq2Hm4WlQtroFta0d7zA+Zmunomcu69+CxCrf4xdQWHh6h3cfOauryLbq/cappSiQXx1hycYylNEkTKndGDyWv0F6rdx5SzZL53vr6JZmmc0qfMonCIwx9P2KWOtQuIysrS7nHc5GNtVWkaZjSpkikO4GPFPoi7L1GYAAAAAB4iX9NAwAAAB/IwsJC+bw8lc/LUz19KilT7e76a8dhta9dRvv9zmlk54Yqky+LpJcjAQIeBX+U8167G6hb9x8okdvLUREHT12UpaWFPJO5R+mbPmVi2dla69qdABXKlu61x3SL46z6ZQuqftmC+iPLNvWZtPi1QcSHTM3knTOD9s3oH6mtzZA/lDZ5InWuV+616zIYhiHDeDmS40NEREToRVi4IowIWclS+TJ7asnmfYqIiJCl5ctzn79+R+7xXAghAAAAgA/Ev6gBAACAD3Dw1AVtP3RaxXNnUvy4zvI9dVH3HwYpbYpEkiSPpAm1cMNe5UiXUo9DnqrPpCVysLP9KOe2t7VR60HTNaBtLQU9eaYfxs1T1aK5o0zLJElOsRzUoXZZ/ThhoSIMQ/m9PPX4yVPt8zsnp9gOql+2oAZMX67s6VIqfcrECn0Rpr/3HDNdR3Q+ZGomp1gOkaaekqTYDnZydYltar90866WbTmo4rkzyS2Ok27ee6BR89bK3s5Gpf8/2JFeLj795Olz3Q18rKfPQ3X83FVJL8MXWxtrLdq4VzZWVsqUOqlsba11xP+yfvn9T1Urnls21i//JGpWpZimLt+sH8YtUKvqJXTh+h2NnLtGrauXeK/rAwAAAPA/BBEAAADAB3CO5aDdx89q4tKNCgp5qmQJ42lg29qmB+W//dBEHUfMUuEWvyhJAlf1bVFdP01a9FHOnTpJAlUsklM1eozRg8dPVDZ/Vo3q0vC1/fs0qyq3OE4aNW+NLt+8JxfHWMqaNoW6NfhOkmRrY61+U5fq6u0A2dvZqECWtJrRt/VHqfV92NvaaM//39uHQU+UIK6zCmRNp00Teil+XGdTvw7DZ2rX0TOm14Wa95Mk+S0cphSJ3GRtZaUxC9bp/LXbMiQlSxhPLauWULuapU37JE3gqmXDu+jHCQtVoOnPSuQWV22ql1TneuU/1eUCAAAAXy0LwzDeb5JZAAAA4Asxe/Zs+fj46P6mqV/NNDuDZqzQml1HtHv6L//dGZ+d0BdhcivZUrNmzVKjRo3MXQ4AAAAQo6KfeBUAAAAAAAAAAOAjIIgAAAAAAAAAAAAxhiACAAAA+AL1alKFaZkAAAAAfBEIIgAAAAAAAAAAQIwhiAAAAAC+QJlrd9eEJRvMXQYAAAAA/CdrcxcAAAAA4Os0aMYKDZm5Kkp7LHtb3V4/Odp95q3bpTZD/oh224UVYxQ/rrMkadHGvRq7YJ0uXL8r59gOKpXXS7+2qaV4Lo6m/g+DQtR/2p9aveOwHgQ9UbKE8TSkQ12VyZflI1wdAAAAgLdFEAEAAAAgRnSsXVbNKhWL1Faxy3DlSJ/qtftUK55HJfN4RWprPWS6noe+MIUQ+/zOqdWgaRrcro7KFcymW/ceqNOoOeo4fKbmDWgvSQp9EabKXUcoflxnzenfVonc4uranQC5ODp85KsEAAAA8F+YmgkAAAD4hGas2qa01TorIiIiUnudXuPU9v9HAly8cVd1eo2TR5VOSlS2jbxb9tdW35OvPeaVW/fl7N1Ux89dNbU9DAqRs3dT7Tzib2o7dfG6qnUfpURl28ijSie1GPC7Ah4GfeQr/B/HWPZKGM/F9HX3wSP5X76pRuULv3YfBzvbSPtYWVlox+HTaviPfQ6cvKDk7m5qU6OUUiaKr/xZ0qpJRW8d8r9k6jNn7U49CHqiBQPbK5+Xp1IkclOhbOnklSZ5jF0vAAAAgOgRRAAAAACfUJViuRX4+Il2/CMgCHwcrE0HTqhWqXySpCdPn6l0vixaPaqbdk7rp5J5M6v2j+N07U7Ae5/3YVCIKnQerqyeKbR9ys9aNqyz7j54LJ9+k167z7U7AUpUts0bv0bM+euta5j1106lSZZQBbKmfet9Fqzfo1j2tqpSNJepLU8mD924G6j1+47LMAzdDXykldt9VTrv/0ZSrN19VHkyeajr6LnyqNJJeRv30Yg5fyk8PCK60wAAAACIQUzNBAAAAHxCcZ1iq1ReLy3ZtE9Fc2aUJK3c5qt4Lo4qkj29JMkrTfJIn9zv06ya/tp5RGt3H1WraiXe67xTl29WFs/k6tuyuqltYo8mylCzm85duy3PZO5R9kkUL452Tev35utxjv1W53/2/IUWb9qnLvXKv1Pds9fsVI0S+eRgZ2tqy+flqWk/tVSTfpP0LDRMYeHhKlcgm0Z2bmDqc/nWPe04clq1SubT0qGddPHGXXUZPUcvwsP1Y+PK71QDAAAAgA9DEAEAAAB8YrVK5lPHETM1qnND2dnaaPGmfapePI8sLV8OWA4OeabBM1dq/d7juhP4UGHhEXr6PFTXP2BExInz17TziL8SlW0TZdulG3ejDSKsra3kkTThe5/zn1bvPKTgkGeqV7bAW++z/8R5nblyS1N7t4jU7n/5hnqMn68ePpVUIk9m3Q54pD6TFqvTyNma0KOpJCkiwlD8OM4a162xrKwslT1dSt2690BjF/5NEAEAAAB8YgQRAAAAwCdWrkA2GYa0ft9x5UifSnuOn9Pg9nVN23+atFhbfE9qYJtaSp0koeztbNSo70SFhoVFezxLSwtJkmEYpraw8Mh9g58+U7kC2fRLqxpR9nePFyfa4167E6A8Pj+98Vq61v9O3RpWeGMf6eXIhrL5syiBq8t/9v3fPjuUJU1yZU+XMlL7yLlrlS+zp76vW06SlNkjmWLb26pMhyHq07ya3OPFkXs8F9lYW8nK6n+z0aZNkUh3Ah8p9EWYbG34UwgAAAD4VPjXNwAAAPCJ2dvZqGLhHFq8cZ8uXn85GiFb2hSm7fv8zql+2YKqWCSnpJcjJK7evi8pXbTHc4vjJEm6HfhIWf+/7fi5a5H6ZEubQit3HFIKdzdZW1u9VZ0fa2qml9Mk+WvhoA5vdV7p5TUv33pQfVtGDU6ePg+VtVXk5e5ejSZ5Fcbky+ypJZv3KSIiwrTt/PU7co/nQggBAAAAfGIsVg0AAACYQa1S+bR+3zHNWbfTtEj1Kx5JE2r1jsM6fu6q/M5fVbNfpyoiwnjNkSQHO1vlzuih0fPW6szlm9p19Ix+nb4sUp8WVYvrweMnatp/ig6dvqSLN+5q04ETajN4+msXcH41NdObvlydHf/zWues3Sn3eC4qnTdLlG2rdxxSzoa9orQv23pAYeERql0qf5Rt5Qpk1aodhzVtxVZdunlX+/zO6Ydx85UzQyolcosrSWpWpZgePH6iH8Yt0Llrt/X33mMaOXeNWlQt/p/1AgAAAPi4+CgQAAAAYAbeOTIorlNsnbt6WzVL5o20bVC7Omo39A+VajdI8Vwc1aleOQWFPH3j8Sb2aKJ2w2aoSMv+8kzmrv6ta6pKt5Gm7Ync4mrDhF7qO3mJqnYbqecvwpQsYTyVzJvZNLVTTIiIiND8dbtVv2zBSNMkvfL4yVOdu3o7SvvsNTtVsUhOxXGKFWVb/XKFFBTyTFOXb1bviYvk4uigIjkyqH+rmqY+SRO4atnwLvpxwkIVaPqzErnFVZvqJdX5HRfLBgAAAPDhLIx/TiQLAAAAfIVmz54tHx8f3d80lWl58FkIfREmt5ItNWvWLDVq1Mjc5QAAAAAxiqmZAAAAAAAAAABAjCGIAAAAwDeDwcD4XPBeBAAAwLeEIAIAAABfPWdnZ0lSwKNgM1cCvHT/YZAkycXFxcyVAAAAADGPIAIAAABfvYIFC8rKykrr9hw1dymAJGndnmOysrJSwYIFzV0KAAAAEOMIIgAAAPDVix8/vipVrKifpyzV6h2H9CIszNwl4Rv1IixMq3ccUt+pS1WpYkW5ubmZuyQAAAAgxlkYTE4KAACAb0BwcLAqVayordu2ydkxlhLGiyMry6ifywkPD1dERIRkGLK0spKVlaUki09fML4ghsLDIxQRHi5ZWMjS0lJWVlZReoVHROhOwEM9Dg5RsaJFtWr1ajk6OpqhXgAAAODTIogAAADAN8MwDB07dkzr1q3T/fv3XwYOkp4+farDhw/r6NGjMgxDWbNmVc6cORUrViwzV4wvSUhIiHx9fXXs2DFZWloqW7ZsypEjhxwcHCRJlpaWcnNzU7ly5ZQ1a1ZZWBBwAQAA4NtAEAEAAIBv1v379zVy5Ej99ttvMgxD7dq1U9euXZUgQQJzl4Yv2N27dzVixAhNmDBBlpaWat++vbp27co0TAAAAPhmEUQAAADgm3Pv3j3Tg2JJpgfF8ePHN3Nl+Jrcu3fPFHRJvM8AAADw7SKIAAAAwDfj359U79Chg7p06cIn1RGj/j3ypm3bturWrRsjbwAAAPDNIIgAAADAV+/OnTsaPny4Jk2aJCsrK3Xs2FGdO3dWvHjxzF0aviEBAQEaNWqUxo8fr/DwcLVp00bdu3dXwoQJzV0aAAAAEKMIIgAAAPDVun37toYNG6bJkyfLxsbGFEC4urqauzR8wwIDAzV69GiNHTtWYWFhpkDC3d3d3KUBAAAAMYIgAgAAAF+dW7dumQIIW1tbderUSZ06dVLcuHHNXRpgEhgYqDFjxmjs2LEKDQ1V69at9cMPPyhRokTmLg0AAAD4qAgiAAAA8NW4efOmhg4dqqlTp8rOzk6dOnXS999/TwCBz9qDBw80duxYjRkzRs+fP1fLli3Vo0cPJU6c2NylAQAAAB8FQQQAAAC+eDdu3NCQIUP0+++/y8HBQZ07d1bHjh0VJ04cc5cGvLWHDx9q3LhxGj16tJ4+fWoKJJIkSWLu0gAAAIAPQhABAACAL9b169dNAUTs2LHVpUsXdejQQS4uLuYuDXhvjx49MgUSISEhat68uXr27KmkSZOauzQAAADgvRBEAAAA4Itz7do1DR48WNOnT5ejo6O6du2q9u3by9nZ2dylAR/N48ePNX78eI0aNUrBwcFq1qyZfvzxRyVLlszcpQEAAADvhCACAAAAX4yrV6+aAghnZ2dTAOHk5GTu0oAYExQUpN9++00jR47U48ePTYFE8uTJzV0aAAAA8FYIIgAAAPDZu3z5sgYPHqwZM2bIxcVF3bp1U9u2bQkg8E0JCgrShAkTNGLECD1+/FhNmjRRr169lCJFCnOXBgAAALwRQQQAAAA+W5cuXdKgQYM0c+ZMxY0bV927d1ebNm3k6Oho7tIAswkODtbEiRM1fPhwPXz40BRIpEyZ0tylAQAAANEiiAAAAMBn5+LFixo0aJBmzZolV1dXUwARO3Zsc5cGfDaePHmiSZMmafjw4QoMDJSPj4969eql1KlTm7s0AAAAIBKCCAAAAHw2Lly4oIEDB2r27Nlyc3PTDz/8oNatWytWrFjmLg34bD158kSTJ0/WsGHDFBAQIB8fH/Xu3ZtAAgAAAJ8NgggAAACY3fnz5zVgwADNnTtX8ePHV48ePdSyZUsCCOAdhISEaMqUKRo2bJju3bunhg0bqnfv3kqTJo25SwMAAMA3jiACAAAAZnPu3DlTAJEwYUJTAOHg4GDu0oAv1tOnTzV16lQNHTpUd+/eVf369fXTTz/J09PT3KUBAADgG0UQAQAAgE/uzJkzGjBggObPny93d3f17NlTzZs3J4AAPqKnT5/q999/19ChQ3X79m1TIJE2bVpzlwYAAIBvjKW5CwAAAMC3w9/fX/Xr11fGjBm1detWjRs3ThcuXFCHDh0IIYCPzMHBQR07dtSFCxc0duxYbdmyRRkyZFCDBg3k7+9v7vIAAADwDSGIAAAAQIw7ffq06tWrp4wZM2rHjh0aP368Lly4oHbt2sne3t7c5QFfNXt7e7Vv317nz5/X+PHjtX37dmXMmFH16tXT6dOnzV0eAAAAvgEEEQAAAIgxJ0+eVJ06dZQpUybt2rVLEydO1Pnz59W2bVvZ2dmZuzzgm2Jvb6+2bdvq/PnzmjBhgnbt2qVMmTKpbt26OnXqlLnLAwAAwFeMIAIAAAAf3YkTJ1SrVi15eXlp7969mjx5ss6fP6/WrVsTQABmZmdnpzZt2ujcuXOaNGmS9uzZo8yZM6t27do6ceKEucsDAADAV4ggAgAAAB+Nn5+fatasKS8vLx08eFBTpkzRuXPn1LJlS9na2pq7PAD/YGdnp1atWuncuXOaMmWK9u/fLy8vL9WsWVN+fn7mLg8AAABfEYIIAAAAfLBjx46pevXqypIliw4dOqRp06bp7NmzatGiBQEE8JmztbVVixYtdPbsWf3+++/y9fVVlixZVKNGDR0/ftzc5QEAAOArQBABAACA93bkyBFVrVpV2bJl09GjR/XHH3/ozJkzatasmWxsbMxdHoB3YGtrq+bNm+vs2bOaPn26jhw5oqxZs6patWo6evSoucsDAADAF4wgAgAAAO/s8OHDqlKlinLkyCE/Pz/NmDFD/v7+atKkCQEE8IWzsbFR06ZN5e/vrxkzZuj48ePKnj27qlSpoiNHjpi7PAAAAHyBCCIAAADw1g4dOqRKlSopZ86cOnXqlGbNmiV/f381btyYAAL4ytjY2Khx48by9/fXzJkzdfLkSeXIkUOVK1fWoUOHzF0eAAAAviAEEQAAAPhPBw8eVIUKFZQrVy6dOXNGc+bM0alTp9SoUSNZW1ubuzwAMcja2lo+Pj46ffq0Zs+eLX9/f+XKlUsVK1aUr6+vucsDAADAF4AgAgAAAK914MABfffdd8qTJ4/Onz+vuXPn6tSpU2rQoAEBBPCNsba2VsOGDXXq1CnNnTtX586dU+7cufXdd9/pwIED5i4PAAAAnzGCCAAAAESxb98+lStXTnnz5tWlS5c0f/58nTx5UvXr15eVlZW5ywNgRlZWVqpfv75Onjyp+fPn6+LFi8qbN6/Kly+v/fv3m7s8AAAAfIYIIgAAAGCyZ88elSlTRvnz59fVq1e1cOFC+fn5qW7dugQQACKxsrJS3bp1deLECS1YsEBXrlxRvnz5VLZsWe3du9fc5QEAAOAzQhABAAAA7d69W6VLl1bBggV148YNLVq0SH5+fqpduzYBBIA3srKyUp06deTn56dFixbp+vXrKlCggEqXLq3du3ebuzwAAAB8BggiAAAAvmE7d+5UyZIlVahQId2+fVtLlizR8ePHVatWLVla8k9FAG/P0tJStWrV0vHjx7VkyRLdunVLhQoVUqlSpbRr1y5zlwcAAAAz4q9LAACAb9D27dtVvHhxFSlSRPfu3dOff/6po0ePqkaNGgQQAD6IpaWlatSooWPHjmnp0qW6e/euChcurBIlSmjHjh3mLg8AAABmwF+ZAAAA35Bt27apWLFiKlq0qB48eKBly5bpyJEjqlatGgEEgI/K0tJS1atX15EjR7Rs2TIFBgbK29tbxYoV07Zt28xdHgAAAD4h/toEAAD4yhmGoa1bt5oeAD569EgrVqzQ4cOHVbVqVQIIADHK0tJSVatW1eHDh7VixQo9evTIFIhu3bpVhmGYu0QAAADEMP7qBAAA+EoZhqHNmzfL29tbxYsXV3BwsFatWqVDhw6pcuXKsrCwMHeJAL4hFhYWqly5sg4dOqSVK1cqKChIxYsXl7e3tzZv3kwgAQAA8BUjiAAAAPjKGIahjRs3qnDhwipZsqSePn2q1atXy9fXVxUrViSAAGBWFhYWqlSpknx9fbV69Wo9ffpUJUuWVOHChbVp0yYCCQAAgK8QQQQAAMBXwjAMbdiwQQULFlTp0qX14sULrVmzRgcOHFCFChUIIAB8ViwsLFShQgUdOHBAa9asUWhoqEqVKqVChQppw4YNBBIAAABfEYIIAACAL5xhGPr7779VoEABlSlTRhEREVq3bp327dun8uXLE0AA+KxZWFiofPny2r9/v9auXavw8HCVKVNGBQoU0N9//00gAQAA8BUgiAAAAPhCGYahtWvXKl++fCpXrpwsLCz0999/a+/evSpbtiwBBIAvioWFhcqVK6e9e/fq77//Nr3Onz+/1q1bRyABAADwBSOIAAAA+MIYhqE1a9Yob968+u6772Rtba0NGzZo9+7dKlOmDAEEgC+ahYWFypQpo927d2vDhg2ysrJS+fLllTdvXq1Zs4ZAAgAA4AtEEAEAAPCFMAxDq1evVp48eVShQgXZ2dlp06ZN2rVrl0qVKkUAAeCrYmFhoVKlSmnXrl3auHGj7OzsVKFCBeXJk0erV68mkAAAAPiCEEQAAAB85gzD0MqVK5UrVy5VqlRJsWLF0ubNm7Vjxw6VKFGCAALAV83CwkIlS5bUjh07tHnzZsWKFUuVKlVSrly5tGrVKgIJAACALwBBBAAAwGfKMAytWLFCOXLkUJUqVeTs7KytW7dq+/btKl68OAEEgG+KhYWFihcvru3bt2vr1q1ycnJS5cqVlTNnTq1YsYJAAgAA4DNGEAEAAPCZiYiI0LJly5Q9e3ZVrVpVcePG1bZt27R161YVLVrU3OUBgNkVLVpU27Zt07Zt2xQnThxVrVpV2bNn17JlyxQREWHu8gAAAPAvBBEAAACfiYiICC1dulTZsmVT9erV5ebmph07dmjLli3y9vY2d3kA8Nnx9vbWli1btH37dsWLF0/Vq1dX9uzZ9eeffxJIAAAAfEYIIgAAAMwsIiJCS5YsUdasWVWzZk25u7tr165d2rRpkwoXLmzu8gDgs1ekSBFt3rxZO3fuVIIECVSjRg1lzZpVS5YsIZAAAAD4DBBEAAAAmEl4eLgWLVqkLFmyqFatWkqcOLF2796tDRs2qGDBguYuDwC+OIUKFdLGjRu1e/duJU6cWLVq1VKWLFm0aNEihYeHm7s8AACAbxZBBAAAwCcWHh6uBQsWyMvLS3Xq1FGyZMm0Z88erV+/XgUKFDB3eQDwxStQoIDWr1+vPXv2KGnSpKpTp468vLy0cOHCLyqQaNy4sapUqWLuMgAAAD4YQQQAAPgsWVhYvPGrX79+5i7xnYWHh2v+/PnKnDmz6tWrp5QpU2rfvn1at26d8ufPb+7yAOCrkz9/fv3999/au3evUqZMqbp16ypz5syaP3/+JwskUqZMGeV3WNKkST/JuQEAAD4XBBEAAOCzdOvWLdPXmDFj5OzsHKmtW7dupr6GYSgsLMyM1b5ZWFiY5s6dq0yZMql+/fry8PDQ/v37tXbtWuXNm9fc5QHAVy9fvnxau3at9u/fLw8PD9WvX1+ZMmXS3LlzP8nvj/79+0f6HXbkyJEYPycAAMDnhCACAAB8ltzd3U1fLi4usrCwML329/eXk5OT1q1bp5w5c8rOzk67du2KdgqLTp06qWjRoqbXERERGjx4sFKlSiUHBwdlzZpVS5cujZFrCAsL0+zZs5UxY0Y1bNhQadOm1cGDB/XXX38pT548MXJOAMDr5cmTR3/99ZcOHDggT09PNWzYUBkzZtScOXNiNJBwcnKK9Hstfvz4Cg8PV7NmzUy/j9KlS6exY8e+8ThLly6Vl5eXHBwcFC9ePJUsWVJPnjwxbZ82bZoyZMgge3t7pU+fXhMnToyxawIAAHgXBBEAAOCL1bNnTw0ZMkSnT59WlixZ3mqfwYMHa/bs2Zo8ebJOnjypzp07q0GDBtq+fftHqyssLEyzZs1ShgwZ5OPjowwZMsjX11erVq1Srly5Ptp5AADvJ3fu3Fq9erV8fX2VIUMGNWrUSBkyZNCsWbM+2Qi7iIgIJU2aVEuWLNGpU6f0888/q1evXlq8eHG0/W/duqW6deuqadOmOn36tLZt26Zq1arJMAxJ0rx58/Tzzz9r4MCBOn36tAYNGqQ+ffpo1qxZn+R6AAAA3oQgAgAAfLH69++vUqVKycPDQ66urv/Z//nz5xo0aJD++OMPlSlTRqlTp1bjxo3VoEEDTZky5YPrefHihWbMmKH06dOrcePGypw5sw4fPqyVK1cqZ86cH3x8AMDHlTNnTq1cuVKHDx9W5syZ1bhxY6VPn14zZszQixcvPtp5evToIUdHR9PXuHHjZGNjo19++UW5cuVSqlSpVL9+fTVp0uSNQURYWJiqVaumlClTysvLS23btpWjo6MkqW/fvho5cqSqVaumVKlSqVq1aurcufNH+f0GAADwoazNXQAAAMD7etfRBefPn1dISIhKlSoVqT00NFTZs2d/7zpevHih2bNna+DAgbp06ZKqVaumpUuXKlu2bO99TADAp5M9e3YtX75cR48eVf/+/dW0aVMNGDBAvXv3VsOGDWVjY/NBx+/evbsaN25seu3m5iZJmjBhgv744w9dvXpVT58+VWho6Gt/d2TNmlUlSpSQl5eXypQpo9KlS6tGjRqKGzeunjx5ogsXLqhZs2Zq0aKFaZ+wsDC5uLh8UO0AAAAfA0EEAAD4YsWOHTvSa0tLS9MUFa/88xOtwcHBkqQ1a9YoSZIkkfrZ2dm98/lDQ0NNAcTly5dVvXp1rVix4q2niQIAfF6yZcumZcuW6dixY/r111/VrFkzDRgwQL169ZKPj897BxJubm5KkyZNpLaFCxeqW7duGjlypPLnzy8nJycNHz5c+/fvj/YYVlZW2rhxo/bs2aMNGzZo/Pjx6t27t/bv369YsWJJkn7//XflzZs3yn4AAADmxtRMAADgqxE/fnzdunUrUtvRo0dN/50xY0bZ2dnp6tWrSpMmTaSvZMmSvfV5QkNDNXXqVKVNm1YtW7ZU7ty5dfz4cS1dupQQAgC+AlmzZtXSpUt1/Phx5cqVSy1atJCnp6emTp2q0NDQj3KO3bt3q0CBAmrbtq2yZ8+uNGnS6MKFC2/cx8LCQgULFtQvv/yiI0eOyNbWVsuXL1fChAmVOHFiXbx4Mcrvt1SpUn2UegEAAD4EQQQAAPhqFC9eXL6+vpo9e7bOnTunvn376sSJE6btTk5O6tatmzp37qxZs2bpwoULOnz4sMaPH/9Wi3k+f/5ckydPlqenp1q3bq18+fLJz89PixcvlpeXV0xeGgDADLy8vLR48WL5+fkpb968at26tTw9PTVlypQPDiQ8PT3l6+ur9evX6+zZs+rTp48OHjz42v779+/XoEGD5Ovrq6tXr2rZsmW6d++eMmTIIEn65ZdfNHjwYI0bN05nz56Vn5+fZsyYoVGjRn1QnQAAAB8DQQQAAPhqlClTRn369NEPP/yg3LlzKygoSI0aNYrU59dff1WfPn00ePBgZciQQWXLltWaNWve+InR58+fa9KkSfL09FTbtm1VoEABnThxQgsXLlSmTJli+rIAAGaWOXNmLVq0SH5+fipQoIDatGmjNGnSaNKkSXr+/Pl7HbNVq1aqVq2aateurbx58yogIEBt27Z9bX9nZ2ft2LFD5cuXV9q0afXTTz9p5MiRKleunCSpefPmmjZtmmbMmCEvLy95e3tr5syZjIgAAACfBQvj3xMpAwAAQJL07NkzTZ8+XUOGDNHNmzdVp04d/fTTT6ZPnwIAvk2nTp3SgAEDtHDhQiVJkkQ9e/ZUs2bNZG9vb+7SAAAAPkuMiAAAAPiXZ8+eafz48fLw8FDHjh1VtGhRnTp1SvPmzSOEAAAoY8aMmj9/vk6dOiVvb2917NhRadKk0W+//aZnz56ZuzwAAIDPDkEEAADA/3v69KnGjRsnDw8PderUSSVKlNDp06c1Z84cpUuXztzlAQA+M+nTp9fcuXN16tQpFS9eXN9//708PDw0btw4PX361NzlAQAAfDaYmgkAAHzznj59qilTpmjo0KG6d++eGjRooN69e8vT09PcpQEAviBnz57VwIEDNW/ePCVIkEA//PCDWrVqJQcHB3OXBgAAYFYEEQAA4JsVEhKiyZMna9iwYbp//74aNWqkXr16KU2aNOYuDQDwBTt//rwGDhyoOXPmKH78+KZAIlasWOYuDQAAwCwIIgAAwDfnyZMnpgAiICBAPj4+6tWrlzw8PMxdGgDgK3LhwgUNGjRIs2bNkpubm7p3767WrVsrduzY5i4NAADgkyKIAAAA34wnT55o4sSJGj58uB48eKDGjRurV69eSpUqlblLAwB8xS5evGgKJFxdXdWtWze1bduWQAIAAHwzCCIAAMBXLzg4WBMmTNCIESP08OFDNW3aVD/++KNSpkxp7tIAAN+Qy5cva9CgQZoxY4bixo1rCiQcHR3NXRoAAECMIogAAABfraCgIFMA8fjxY1MAkSJFCnOXBgD4hl25ckWDBw/WH3/8IRcXF3Xt2lXt2rWTk5OTuUsDAACIEQQRAADgq/P48WP99ttvGjlypIKCgtS8eXP17NlTyZMnN3dpAACYXL16VYMHD9b06dPl7OysLl26qH379nJ2djZ3aQAAAB8VQQQAAPhqPH78WOPGjdOoUaP05MkTUwCRLFkyc5cGAMBrXbt2TUOGDNG0adPk6OioLl26qEOHDgQSAADgq0EQAQAAvniPHj3SuHHjNHr0aIWEhKhFixbq0aOHkiZNau7SAAB4a9evX9eQIUP0+++/K3bs2OrcubM6duwoFxcXc5cGAADwQQgiAADAF+vhw4caO3asxowZo6dPn6ply5bq0aOHkiRJYu7SAAB4bzdu3NDQoUM1depUOTg4qFOnTvr+++8VJ04cc5cGAADwXggiAADAF+fBgwcaM2aMxo4dq+fPn6tVq1b64YcflDhxYnOXBgDAR3Pz5k0NGzZMU6ZMkZ2dnTp16qROnToRSAAAgC8OQQQAAPhiBAYGmgKIFy9eqHXr1urevbsSJUpk7tIAAIgxt27d0rBhwzR58mTZ2trq+++/V+fOnRU3blxzlwYAAPBWCCIAAMBnLyAgQKNHj9a4ceMUFhamNm3aqHv37nJ3dzd3aQAAfDK3b9/W8OHDNWnSJNnY2Khjx47q3LmzXF1dzV0aAADAGxFEAACAz9b9+/c1atQojR8/XhEREWrbtq26deumhAkTmrs0AADM5s6dOxoxYoQmTpwoKysrdejQQV26dFG8ePHMXRoAAEC0CCIAAMBn5/79+xo5cqR+++03GYahdu3aqWvXrkqQIIG5SwMA4LNx9+5djRgxQhMmTJClpaXat2+vrl27ys3NzdylAQAAREIQAQAAPhv37t0zPVCRZHqgEj9+fDNXBgDA5+vevXumAF/i9ycAAPj8EEQAAACzu3v3roYPH66JEyfK0tLSNMUEn+gEAODt/XNEYUREhNq1a6du3boxohAAAJgdQQQAADCbO3fumBbdtLKyMi26yRzXAAC8v4CAANMaS+Hh4WrTpo26d+/OGksAAMBsCCIAAMAnd/v2bQ0bNkyTJ0+WjY2NKYBwdXU1d2kAAHw1AgMDNXr0aI0dO1ZhYWGmQMLd3d3cpQEAgG8MQQQAAPhkbt26paFDh2rKlCmytbVVp06d1KlTJ8WNG9fcpQEA8NUKDAzUmDFjNHbsWIWGhqp169b64YcflChRInOXBgAAvhEEEQAAIMbdvHlTQ4cO1dSpU2VnZ6dOnTrp+++/J4AAAOATevDggcaOHasxY8bo+fPnatmypXr06KHEiRObuzQAAPCVI4gAAAAx5saNGxoyZIh+//13OTg4qHPnzurYsaPixIlj7tIAAPhmPXz4UOPGjdPo0aP19OlTUyCRJEkSc5cGAAC+UgQRAADgo7t27ZqGDBmiadOmKXbs2OrSpYs6dOggFxcXc5cGAAD+36NHj0yBxJMnT9SiRQv17NlTSZMmNXdpAADgK0MQAQAAPpqrV69qyJAhmj59uhwdHU0BhLOzs7lLAwAAr/H48WONHz9eo0aNUnBwsJo1a6Yff/xRyZIlM3dpAADgK0EQAQAAPtiVK1c0ePBg/fHHH3J2dlbXrl3Vvn17OTk5mbs0AADwloKCgvTbb79p5MiRevz4sSmQSJ48ublLAwAAXziCCAAA8N4uX76swYMHa8aMGXJxcVG3bt3Utm1bAggAAL5gQUFBmjBhgkaMGKHHjx+rSZMm6tWrl1KkSGHu0gAAwBeKIAIAALyzS5cuadCgQZo5c6bixImj7t27q23btnJ0dDR3aQAA4CMJDg7WxIkTNXz4cD18+FCNGzdWr169lCpVKnOXBgAAvjAEEQAA4K1dvHhRAwcO1OzZs+Xq6qru3burTZs2ih07trlLAwAAMSQ4OFiTJk3S8OHD9eDBA/n4+KhXr15KnTq1uUsDAABfCIIIAADwny5cuGAKINzc3PTDDz+odevWihUrlrlLAwAAn8iTJ080efJkDRs2TAEBAWrUqJF69+4tDw8Pc5cGAAA+cwQRAADgtc6dO6eBAwdq7ty5ih8/vn744Qe1atWKAAIAgG9YSEiIpkyZoqFDh+r+/ftq0KCBfvrpJ6VJk8bcpQEAgM8UQQQAAIji7NmzGjBggObNm6eECROqR48eatmypRwcHMxdGgAA+EyEhIRo6tSpGjp0qO7evWsKJDw9Pc1dGgAA+MwQRAAAAJMzZ85owIABmj9/vtzd3dWzZ081b96cAAIAALzW06dP9fvvv2vIkCG6c+eO6tWrp59++knp0qUzd2kAAOAzYWnuAgAAgPmdPn1a9evXV8aMGbV161aNHTtWFy5cUIcOHQghAADAGzk4OKhjx466ePGixo4dq61btypjxoyqX7++/P39zV0eAAD4DBBEAADwDTt16pTq1q2rTJkyaceOHRo/frzOnz+v9u3by97e3tzlAQCAL4i9vb3at2+v8+fPa9y4cdqxY4cyZsyoevXq6fTp0+YuDwAAmBFBBAAA36CTJ0+qTp06ypw5s3bv3q2JEyfq/Pnzatu2LQEEAAD4IPb29mrXrp3Onz+vCRMmaNeuXcqUKZPq1KmjkydPmrs8AABgBgQRAAB8Q/z8/FSrVi15eXlp7969mjRpks6dO6fWrVvLzs7O3OUBAICviJ2dndq0aaNz585p0qRJ2rt3r7y8vFSrVi2dOHHC3OUBAIBPiCACAIBvwPHjx1WjRg1lyZJFBw4c0JQpU3Tu3Dm1atWKAAIAAMQoOzs7tWrVSufOndPkyZN14MABeXl5qWbNmvLz8zN3eQAA4BMgiAAA4Ct27NgxVa9eXVmzZtWhQ4f0+++/6+zZs2rRooVsbW3NXR4AAPiG2NraqmXLljp79qx+//13+fr6KkuWLKpevbqOHTtm7vIAAEAMIogAAOArdOTIEVWtWlXZsmXT0aNHNX36dJ09e1bNmzcngAAAAGZla2ur5s2b6+zZs5o+fbqOHDmibNmyqWrVqjpy5Ii5ywMAADGAIAIAgK/I4cOHVblyZeXIkUN+fn6aMWOG/P391bRpU9nY2Ji7PAAAABMbGxs1bdpUZ86c0R9//CE/Pz/lyJFDVapU0eHDh81dHgAA+IgIIgAA+AocOnRIlSpVUs6cOXXq1CnNnDlT/v7+aty4MQEEAAD4rNnY2KhJkyby9/fXzJkzdfLkSeXMmVOVKlXSoUOHzF0eAAD4CAgiAAD4gh08eFAVKlRQrly5dObMGc2ePVunT5+Wj4+PrK2tzV0eAADAW7O2tpaPj49Onz6t2bNny9/fX7ly5VKFChV08OBBc5cHAAA+AEEEAABfoP3796t8+fLKkyePzp8/r7lz5+rUqVNq2LAhAQQAAPiiWVtbq2HDhjp16pTmzJmjc+fOKU+ePPruu+904MABc5cHAADeA0EEAABfkH379qlcuXLKly+fLl26pHnz5unkyZOqX7++rKyszF0eAADAR2Ntba0GDRro1KlTmjdvni5evKi8efOqXLly2rdvn7nLAwAA74AgAgCAL8CePXtUpkwZ5c+fX1evXtWCBQt04sQJ1atXjwACAAB81aysrFSvXj2dOHFCCxYs0JUrV5Q/f36VKVNGe/bsMXd5AADgLRBEAADwGdu1a5dKlSqlggUL6saNG1q0aJH8/PxUp04dAggAAPBNsbKyUp06deTn56eFCxfq+vXrKliwoEqXLq3du3ebuzwAAPAGBBEAAHyGdu7cqZIlS6pw4cK6ffu2Fi9erOPHj6tWrVqytOTXNwAA+HZZWVmpdu3a8vPz0+LFi3Xz5k0VKlRIJUuW1M6dO81dHgAAiAZPMgAA+Ixs375dxYsXV5EiRXTv3j0tXbpUx44dU82aNQkgAAAA/sHS0lI1a9bU8ePHtWTJEt29e1dFihRR8eLFtX37dnOXBwAA/oEnGgAAfCKGYSgsLCzabdu2bVPRokVVtGhRBQYGatmyZTpy5IiqV69OAAEAAPAGlpaWqlGjho4ePao///xTgYGBKlq0qIoVK6Zt27ZFu09YWJgMw/i0hQIA8A3jyQYAAJ+AYRiqXbu2GjduHKlty5Yt8vb2VrFixfTo0SMtX75chw8fVtWqVQkgAAAA3oGlpaWqVaumw4cPa/ny5Xr48KGKFSsmb29vbd26NVLw4OPjo9q1axNGAADwifCEAwCAT2DUqFFasmSJ6tSpI8MwtGnTJhUpUkQlSpRQcHCwVq5cqcOHD6tKlSoEEAAAAB/A0tJSVapU0eHDh7VixQoFBQWpePHi8vb21ubNm2UYhurUqaMlS5Zo9OjR5i4XAIBvAk86AACIYXv37lXPnj3VrVs32dnZqVChQipVqpSePn2q1atXy9fXV5UqVZKFhYW5SwUAAPhqWFhYqHLlyjp06JBWrVqlkJAQlSxZUoULF5a9vb26du2qHj16aN++feYuFQCAr56FwThEAABiTEBAgLJnzy4nJyc5OTlp//79yp07t/r27avy5csTPgAAAHwihmFo7dq16tevn3x9fZU3b149fvxYwcHBOnr0qFxdXc1dIgAAXy2CCAAAYkhERITy588vX19fRUREKFu2bOrUqZOKFSumWLFiyc3NzdwlAgAAfFPu37+vkJAQbdmyRWPGjNGxY8dkaWmpXLlyae/evUyRCQBADCGIAAAghsyZM0eNGjWKdpulpaX8/f3l6en5iasCAAD4Np07d07p06dXREREtNtnz56thg0bfuKqAAD4NhBEAAAQQ8LCwrRkyZJIIx8Mw5BhGHJ0dFSBAgWYmgkAAOATMQxDe/bsUXBwsCwsLCL9OywgIEA1atSQtbW1GSsEAODrRRAB4Jvw7Nkz/f3331qzZo3u3r372k9BATHF0tJSCRIk0HfffaeyZcvK3t7e3CUBAAB8dYKCgrR69Wpt2LBBgYGB4pEHvlQWFhZydXVV6dKlVbFiRTk5OZm7JAD4IAQRAL56AQEBKlmyhI4ePaYMnqmVMmkiWVlZmbssfGPCw8N1+fotnT53UdmyZdWmTZsVL148c5cFAADw1bh06ZKKehfR1WvXlTV1IrnHicXoU3yxDMPQ7YchOnbxlpInS6pt23coVapU5i4LAN4bQQSAr17lSpW0d89uLZ82SrmyZDR3OfjGHTx2UtVadFX+AgW1ctUqc5cDAADwVTAMQ9myZlHw/Vta2L2qPBLFNXdJwEdx4dYD1Rm+XI5uiXT02HHCNQBfLEtzFwAAMSkwMFBr163TTx2bE0Lgs5A7ayb17tBMa9etU2BgoLnLAQAA+CqcPHlSx/1OaFDDooQQ+Kp4JIqrgQ28ddzvhE6ePGnucgDgvRFEAPiqHTlyRGFhYSpRMI+5SwFMShTKo7CwMB05csTcpQAAAHwV9u/fL0kqkjm5mSsBPj5vrxSS/vc+B4AvEUEEgK/akydPJElOTrHNXAnwP85OjpL+9/4EAADAh3ny5InsbW1kZ2Nt7lKAj87Oxlr2tjb8/QDgi0YQAeCbYKF3m0fz8vWbsvfIo2Onzn7wudMWqazxMxZ88HHw9XjX9yMAAAD+27tOnX/13iPFqztcfpfvfPC5s3WYoslrfT/4OMDrsDQEgC8dHxUAgBi2e/lMxY7lYHpt75FHiycNU6XSRT/42If9TqtAFR9tXzpdebN7RdletkFbuTg5atGkYboX8ED9x0zRuq27dTcgUHGdneSVwVO92jdXgVxZFfjwkX4dM1Wbdu3XtZt35OYaR5VKeatvl9Zy+f9P8Jvb7KV/qWWP/tFuu7r/byVwc43Utsf3mErVa61MaVPrwF/zXnvcZ8+fq/1PQ3TkhL/8L1xW+WIFtWTKiEh9dvseVe+hv+nsxcsKefpcyZO4q3ndqurYtJ6pT3h4uH4d+7sWrFynO/cClSihmxpWq6Af2zdlUTkAAICv2KaBDRXLzsb0Ol7d4ZrdpYq+y+35wcc+evG2SvSeo7/711duz8RRtlcZsEjOsewUFh6hF2HhWvJjzSh99vpfV4VfFmjHEB9lSpFAfx08q3GrDujsjQBFGIaSxnNWUa+UGuRTXJJ0+0Gwfp67TUcv3tbFOw/UskxO07bPxfztJ9Rh8rpIbXY2Vro5u4vp9dClu7Vsr79uBgTJxtpSWVMlVO/ahZUrzf/u44Pgp+o5c7P+PnxBlhYWqpgnrQb5FJejva0k6VlomLpO36Bjl+7o7I0Alc7hobldq76xtl2nrqryr4ui3bZxQAPl8EikXaeuatLaQzpy4ZaCnoYqtXscta+QRzULsbYhgK8TQQQAxJDQ0BeytbVR/Hgxt1heDq8MypLBU7OWrI4SRFy+flPb9x3Sst9HSpLqtOuhFy/CNG14X6VKnkR37wdq656DCnz4SJJ068593bp7X0N+/F7p06TS1Ru31KHPEN26e18LJgx57xpv3b2v+K5xZG394b9yalYoqdLe+SK1tejeX89CQ6OEEA8fB6lZ934qViCX7t5/86LQ4eERcrC3U1uf2lrx95Zo+8R2cFCbhjXllT6NYsVy0B7fY2r/02DFcnBQ87ov/xAZMWW2fp//p6YN76sMnql12O+0Wvb4VS5OjmrXuPYHXDkAAAA+R6Fh4bK1tpKbc6wYO0e21O7KnCK+5m/zixJEXL33SLtOXdX8btX0IjxCjUev1I2AICWJ5xSp3/xtfsqW2l2ZUiTQ9hNX1HzsavWuXVhlc6aRhYV05nqAtvldjnRd8Zwd1KVqPk1ee+ijXcuNgMdKEs/5ox3PycFW+0c1M73+98hjj0RxNbRxCaVMEEfPQsM0aZ2vagxaIt8xLUzfs1a/rdGdh8H6s1dNhYVFqMOUdery+wZN7VBBkhQeESF7W2u1LJNDqw+83Yj5PGmT6NSkNpHaBi/erR0nryh7andJ0oGzN5UpeXx9XymP4rvE0obDF9V24lo5x7JTmRwe731PAOBzRRAB4JsVERGh0b/P1fSFK3T99h0liOeq5nWrqme7plH6hoeHq23vQdq211d37gUqWeKEalW/hto3qWPq07z7L3r0OEg5s2TUlLlLZWtrozPbVyptkcrq0KSOOjSpq7RFKkuSarX5QZKUPEkibZg/SRmKVtWuZTOUM8v/Pv0yfsYCjftjgc5sXyFLy9fPpNe4ZiX1GzVZI/p0USwHe1P7nD//UqIEbipdJL8ePg7S7oNHtWH+ZBXJm0OSlCJJIuXOmsnUP1M6Dy2cONT02iNFUv3StY2adO2rsLCwdwoSnj1/rtUbd2jOn39p8+4Dunlo40cZVeFgby8H+/9d472AB9q2z1eTB/8UpW+Hn4aodsUysrKy1OqN29943NixHDT+156SpL2HjunR46AofbJlSqdsmdKZXqdMmlgr12/Vbt+jpiBi3+HjqlCyiMoVK2Tqs3j1Bh08fvLdLxYAAAAfLCLC0G9/HdDsLcd1IyBI8V1iyadEVnWtmj9K3/CICHX+fYN2nryquw+fKImbk5qVyq5W5XKa+rSbtFaPQp4re2p3Td9wRHY21joyrqWydZii1uVyqnX5XMrWYYokqdGoFZKkZG7OWvVzHeX4fqo2/tpQ2T3cTcebvNZXk9Yd0pGxLWVp+foRtPWLZtGgxTs1sFHxSCMvFmw/oYRxHFUiWyoZhuTm7KAF20+oW7X/XV/ws1Ct3H9Gv9QrKklaf+iC8qZLog4V85j6pEnkGmn0RvL4LhrsU0KSNH/bibe51a91/f5jLdp5Uot2nFS6pPE05z9GE7wLCwsLJYzz+r8zahSMPLrg1wbFNHern05evSfvzCl05kaANh+7pE0D/vd9GeJTUrWHLdUv9YsqkaujYtvbamSz0pKk/Wdv6FHI8/+sy9baKlJdL8LCte7QebUok900UrpLlcgfsGpVLqe2Hr+svw6cJYgA8FUiiADwzeozfIL+WLRSw3p3UoFc2XT73n2duXA52r4REYaSuCfU/PGD5RrXRfsO+6ld70FyTxBPNb4rZeq3da+vnBxja82s8dEeZ/fymUqWp4ymDv1Zpb3zycrSSvHjxVXxgnk0+8+/IgURs5euVsPqFd4YQkhSncpl9eOQ8Vq2brMaVPtOkmQYhuYuW6MG1b6TlZWVHGM5yDF2LK3euE15s2WWnZ3tW92jR0HBcnaM/dYhxL7DxzV32RotXbNJsRzsVbtiGQ3u2TFSCBHPy/uNx6hbuax+G/DjW51v3vK1imVvr2rlIg8Tn7V0tS5du6EZo37R4Al/vNWx3tXRk2e07/Bx9e3S2tSWL0cWTV+4QucuXZFnqhQ6fvqs9vge09DenWKkBgAAALxZ/4U7NGfLcQ1oWEz50ifRnQdPdO5m9KNlIyIMJXZ11B/fV5Krk70OnL2pLtM2KGGc2KqSP72p344TV+TkYKtlvWpFe5xNAxsqXasJGt+6nEpkTSkrS0u5OceSd+YUmr/dL1IQMX/7CdUtkumNIYQk1SyUQf3mb9Oq/WdUp0hmSS//zb9wx0nV9c4sq///m6F24UxauOOEulbNZ3rgvXLfGUVEGKpeMIMkKUGc2PpzT4BOX7unDMniv+WdfDdPnoXqr4PntHDHCe06eU25PBOp7Xe5VCXf/+7jkl2n1HXahjceZ1HPGsqfPukbz5O1wxRFRBjKkiqh+tQurPTJ3KLtGxoWrtlbjsk5lp0yJ3953b5nb8oltl2k74m3VwpZWljo0IWbquCa9l0u+7XWHTqvwKCnqusddTrdf3r89LnSJnF9Yx8A+FIRRAD4JgUFP9FvMxdpdL9ualj95ZBbjxRJVTBXtmj729hY6+dOLU2vUyVLov1H/PTn2s2RgojYDvaaPPgn2draRHcY0zRNcZwd5R7/f/9AblKrkjr0GaphvTrJzs5WR07468SZC1HWKYiOaxwXVSpdVLOWrjYFEdv2+urK9VvyqVFRkmRtba3fh/2str0G6ff5y5UtUzoVzptDtSqUklf66OetvR/4UIN/+0NNa1d54/mv37qj/2PvrsOjuNY4jn/jQIyEhECCBXd3TQju7tbiUNyteIsWLZQWKRQrFHdNcHd3dw8hQGT3/pGyNJfgsoT8Ps9zn8vOnDnzzibN7jvvnHNmLlzBzIUruH7rDhVK+PDX2MH4FcgdZRFl97KZb+zP0cH+rdf8wp/zl1KzQslIoyTOXrhMn2G/suHvSZ9kOqj/l6JAOe7cf0BYWDi92zaN9P50adGQx0FPyFy8BlZWloSHG+jfqSW1K5b65HGIiIiIyJs9fhrC76v3MbRRMWoXibh57+3hQt7X3Ni2sbaie/WCptdJ48dlz5nrLN55KlIhwt7OhjHNSmFrbRVlPy+m/HGOYxfpqfj6vpnpNGUdg+r7YmdjzaELtzh+5Q4zO799hICLQ2zK5kzF7ICjpkLElmOXuXznEXX+vTaAOj6ZGLd8D9tOXKFg+iRAxKiJcrlT4xTHDoCmJbOx8+RVCnb9k8RuTuRM5YlvpmRUK5gOO5uP+/687fgV5m4+ypJdp3B3ikP1gun5pUkJvD1ena62VI6U5EiZ8I39JXR9/WiHVAldGNu8FBmSuBMYHMKvK/ZQqu8stg3/PtLUVGv2n6Pp2GUEh4TiEdeBBT2rE+/fn9GtR09emVbL2soSF4fY3H745H0u/Y1mBRyhaJZkr0yZ9V+Ld5zkwLmb/PLv6AsRkW+NChEiEiOdPHeR5yEh+ObP9c7H/PbXfKbPX8aVGzd5+uw5IaGhZEkX+QmZDGlSvrYI8SYVivvQvt9wlqwNoEb5Evy1YDlF8uYgWaJXF6OLSsPq5SnfqC3nLl0lRdJETP9nGYXyZCdFssSmNpVLFaW0bwG27jnI7gNHWbtpO7/8/hcTf+pFg2rlIvUX+DiIyk06kC6lN33aNfv/00XS75ffmLlwBZVK+rJ5wVRc4zq/sf1/Y/oYO/cf5uTZC0wd0c+0LTw8nIYd+tCnfVNSeSf9JOf5f+vnTuJJ8FN2HThKn+HjSZE0ETUrlATgnxXrmbNkNdNHDSR96uQcOn6aLoN+IWF8N1PBS0RERES+jNPX7vE8NJzCGZO88zGT1+5ndsBRrt4N5FlIGCFh4WRMFj9Sm3SJ3V9bhHiTMrlS0XXaelbsOUOV/OmYs+koBdMnIYn7m78/v1DHJxPVh8znwq0HeHu4MCvgCAXSJSZ5gpc3+VN7xSN3ak9mBRyhYPoknL/5gB0nr7Kkz8v1yuxj2TK3W1Uu3HrA1mNX2Hv2On1m+TNp9T5WD6gbaeqn91Vh4Fxi21ozqL4vjYplfWNbx9i2OMZ+t5HaUcmV2otcqb1Mr3On9iRf56lM33CInjVeFpQKpk9MwJCG3Hv8lL82HqbxmGWsHVgXd+d3fwDqY1y795iNhy4ypV3517bZcuwybSatZlTTEq8d0SEiEt2pECEiMVLsWHbv1X7esrV0/3ksQ3u2I0+2TDjax+GXP2ay51Dk+VLt/7NGw/uwtbWhbuUyzFiwjEolffl72RpG9On0zscXzZ+LxJ4J+GvBcjo2rceSNf5RTm8Uy86OYgXzUKxgHnq2aUyLHoMYOOb3SIWIx0FPqPBdOxzs4zDvt2HYvOWpqB6tvydBfDfmLF5FpmLVqF6uBHUqlSZ31oxRtv9UUzNNm7eELOlTkz1TupexPwlm35ETHDx+mvb9IkaTGAwGjEYj9qnzsfzPse9VfIqKd+KIZCdjmpTcvnuPQWP/MBUiegwZS5cWDalRvoSpzeVrNxj+23QVIkRERES+sNi273fLY+H2E/SduYkB9XzIlcoTh9i2jF++m31nb0RqFyfWh92ot7W2omahDMz+d4TCgm0n+Klh0bcf+K8iGZOSKJ4TczYd44dyuVix5wwjm7z69Hw9n8x0n76BYd+FMHvTUbw94lIg3asPA3l7uODt4UL9opnpWCkvuTtOYdGOk9T1efP0QW8yu0sV5m4+Ss8ZG5m+4RA1CmWgSv60Ua7j8CmmZvovG2srMiWLz4WbDyJtt49lS/IEtiRP4EKuVJ7k6vAHM/2P0KFSXjyc7bkbGBypfVi4gQdBT4kf99MUKuZsOoKrY2xK50gZ5f5tx69Qd/hCBtX3NY12ERH5FqkQISIxUspkiYkdyw7/7Xvwrun11vY79h0ib/ZMNK9XzbTt/OWrH3RuGxtrwg2GV7Z/V6Mi2UvXZtKsfwgLC6dSSZ937tPS0pIG1crx57yleCVwx9bG5pV1E6KSLqV3pIWcAx8HUf67ttja2rLg95HEsnt7wSZFssQM6tKaAZ1asmHbbmYuXEHJui3xShCfOpXKULtSKdPNe/g0UzMFPQlmwcoNDOzcKtJ2Jwd79q2cE2nbpFn/ELBjL3PGDyFZ4ncbYfKuDEYjz0NCTa+fPnv2yvy+VlZWGKL4eYuIiIjI55U8gQuxba3ZfPQy9YvGfWv7XaeukSu1J41LZDNtu3Dr4Qed28bKMsrv/PWKZqZgl2lMXXuAMIOBcrmjniY1KpaWFtQpkpGZAUdI6OKAjbUVFfK8uoZBxXxp6DFjAwu2Hefvzcf4rnhW03oRr5PE3ZnYttYEPw99Y7u3KZk9BSWzp+Bh0DMWbD/B3M3H6DsrAJ9MyahRKD1lcqYyjbj42KmZ/l+4wcDxK3cpntX7je0MBiMhYeEA5EztyaMnzzl4/iZZk0esE7Hl2CUMRiM5Unx87mA0Gpm96Sg1C6XHJopRNFuPX6bOsIX8WKcwDf2yfPT5RES+ZipEiEiMFMvOjk7NG9Br6HhsbWzIlyMLd+8/4PiZ83xXo+Ir7VMmS8KsRStZt3kHyRJ7MmvRKvYdPv5BN7aTeiXEf/se8uXIjJ2tLS7OTgCkTelN7qwZ6TVsPA2rlY+07sG7aFCtPD+Nm8KPIyZSo3yJSMffe/CQOj/0oGH1CmRKmxIH+zjsP3KCX37/i3LFCgMRRYhyjdoS/PQZU0cOIDAoiMCgIADcXV2wsnrz8HNLS0uKF8pL8UJ5CXwcxD8r1zNz4QoGjf2Dm/vX4/TvgtWfYmqm+SvWERYWTu1KpV+JIUOaFJG2ucdzIZadbaTtE2fMY8naAFbPnGDaduLMeUJCw3jwKJDHQcEcOn4agCzpI5K73/6aT2LPBKRJHjHl05Y9Bxg9eRatGrxcpLBM0UIMnfAniT0TkC5Vcg4dO8XYqbNNa3WIiIiIyJcTy9aatuVz02/2JmysLcmTxou7gU85dfUu9Xwzv9I+eUIX/t5yjI2HLpAkvjPzthznwLmbJI3/blMn/VcSd2c2H71MnjRe2FlbE9ch4rt5Gq945EyVkP5zNlPHJyOx33Na1zo+mRi+cAeD/t5ClfxpozzeIZYtlfOlZeDcLTx++pza//eU/dB/tvH0eSjFsiUnsZsTj5485/c1+wkLN+CTKZmp3ZGLtwAIehbCvcfBHLl4CxtrK9ImevvUQXEdYtG4RDYal8jG6Wv3mLP5KP1mb2LZ7tNM71AJ+PipmYYv2E7OVAnx9nDhUfBzxi/bzdU7gaaf7ZNnIfyyeCelcqQkQVx77j1+ypS1B7jxIIiKedIAET8PvyzedPhjDSMalyAsPJxu0zZQJV+6SEWQk1fvEhoWzsOgZwQ9CzG9N5mSeQCw7+wNWk1cyaJeNfB0fbkOxOZjl7l0+1GUv29bjl2mzvCFNCuVnfK5U3PrYUTuZWtthYtD7A9+X0REvlYqRIhIjNXzh8ZYW1kxYPTv3Lh9hwTubjStUyXKtk1qV+bg8VPUa9sLCwsLapQvQbN61Vi7aft7n3doz/Z0HTyaqX8vxtMjPqc3LzHta1SjAjv3H6Zh9Qrv3W8SzwQULZCL9Vt2vXK8Q5w45MqakXFT53D+8lVCw8JIlNCD72pWolurRgAcOHaK3QcjpprKUDTy+3By0+J3Xq8CwMnRge9rVuL7mpU4d+nqexdV3mb6vKVULOlDXKfXL/b2JncfPOT85WuRtlVs3IHL114Ou89Tvh4Az87tBiKmeOoz/FcuXr2OtZUVyZMmYnDX1jSp/fK9GtW3M/1HTaLtj8O4c+8BCT3caFyrMr3aNPmgOEVERETk43Sukh8rK0uGzN/GzQdBeLg40Og1T5438svCkYu3aTx2GRYWUCVfOr4vno0Nh86/93kH1POhz0x//vI/TEIXBw6Oa27aV88nM7tPr/6gKZASuTlRJFNS/A9ffOPx9XwyMdP/CMWzJn9lVEH+dImZsvYArSas5M6jYOLa25EpmQf/9KhOKk9XUzufHjNM/z504Rb/bDtBYjenSNfyLlJ7xaNv7SL0qVmY87cevP2Ad/TwyTPa/7GW2w+fENfejizeCVjVv46pUGJlacmZ6/eZu3kJ9x8/xcUhFtlSJGR539qR1mGY9ENZuk3bQOXBf2NpYUH53Kn5uZFfpHPVGrqAK3cDTa9fvDf35nQB4GlIKGev3ycsPPIomFn+R8id2pPUXvFeiX/u5qMEPw9l9JJdjF6yy7S9QLrELP2x1ke+OyIiXx8Lo9FoNHcQIiKfy9KlS6lYsSKXd60mvpvr2w8ws5/GTWHhqg3sXTnb3KHIZ3T77n2S5CnFkiVLqFDh/YtOIiIiIhLZ2LFj6d61M1f/bG/uUN5qxMLtLNl5ii3DvjN3KBKNJGo0miHDRtC2bVtzhyIi8kEszR2AiIhErHlw7NQ5fvtrPi3/M9WPiIiIiIh8G4KehXDiyh0mrzlA05LZzR2OiIjIF6WpmUREvgLt+w1n3vK1VChWhEbVI68n8EPvn5mzZHWUx9WuWIrxg3p8iRBFREREROQjdJu2noXbT1ImZ0rq+kaeVqnT5LXM33o8yuOqF0zPyCYlvkSIIiIin40KESIiX4HJw/syeXjfKPf92L45HZrUi3Kfo4P95wxLREREREQ+kV9bluHXlmWi3Ne9egFal8sV5b6PWdBZRETka6FChIjIVy6+m2u0WN9CREREREQ+jLuzPe7OeshIRES+XVojQkTkGzHjn+V4ZC1q7jBERERERMTMZm86infjseYOQ0RExEQjIkRE5Jty5OQZ2vUdxr7DJ3BzjUurBjXo1LzBa9sfPnGa4b/NYPveg9x78IikiRLStHYVfviulqnN4jX+/D5rAYdPnOZ5SCjpU3nTu21TihfOF6mvazdv02vYeNZu2k7w0+ekSJqI34f2IUfm9J/tekVEREREJGrHLt2m67T1HDh/k3iOcWhaMhttK+R5bfv7j5/SfPxyjl2+w4OgZ7g5xaF0zpT0rlkIpzh2AOw8eZX+czZx5vp9nj4PI5G7E438stCyTE5TP0P/2cawBdsj9Z3S05VdIxt/ngsVEYkGVIgQEfmKhIeHY2FhgaXl1zdgLTQ0DBubyB8bISGh2NravHdfH3rc2wQ+DqJcwzb4FsjN+IHdOXrqHM27D8TZyZEmtStHecz+oyeJH8+Fab8MIFFCD3buP0zrXj9hZWVJywY1ANi6+wB+BXIzoHMr4jo6MH3Bcqo068SWBdPImiENAA8eBeJboylF8uZgydQxuLnG5ezFK8R1dvrk1ykiIiIi375wgwELLLC0tDB3KK8IDQvHxtoq0raQsHBs/2/bu/jQ494mMPg51X6eT+GMSRnZuATHr9yh7aTVONvHoqFfliiPsbSwoHTOlPSsUQg3p9icv/WQrtPW8zDoGb+3KQdAHDsbmpTIToYk7sSJZcPOk1fpNGUdcexsIvWbNpEbC3tVN722/gpzPBGRL0mFCBGJsRau2sDgsZM5d+kqcWLbkSV9Gv6ZNAL7OLEJDw+nx5CxTJ+/DCsrSxpVr8Ctu/cJfBzE/EkjAEhduCJtvqtFm+9qm/rMXa4u5YsXoU+7ZgCMmTKLGf8s58KVa7g4O1HWrxA/dWuDg30cIGI6pS6DfmHKiH70Hv4rZy5c5tjGBSR0d6PvyInMW76Wh4GPyZA6BYO6/kCRvDlM55rxz3IGjJ7EvQcPKV4oL/lzZn2v61+2bhODx03mxJkLJPRwo16VsnRv9R3W1hEfDbFS5GbsgK6s2bQD/+176NC0num4FvWrM3TCNC5fu8nTs7u4fP0mHfuPwH/7HiwtLSlROC+/9O2Mh1s8AAaO+T3K4z61uUtXExIaxu9D+mBra0P61Ck4fOI0Y6fOfm0holH1CpFeJ0/ixa4DR1i8xt9UiBjRp2OkNgM7t2L5+k2s2LjFVIgYOWkGiRLG549hP5raeSf2+pSXJyIiIiKfydJdpxi2YDsXbj4ktp01mZLFZ2anytjHsiXcYKDvrABmBRzFytKCej6ZuP0omMCnz5nZKeI7ZtY2k2hROgct/vNUfJHuf1ImZyq6VSsAwIQVe5i96SiXbj8irn0sSmZPQb+6RXCIFbEY9exNR+k1YyMTWpZhwNzNnLtxn72jm+IR157Bf29hwfaTBAY/J20iN/rWKUzB9ElM55q96ShD5m/l/uOn+GZORt40id7r+lfuPcPwBds5de0eCVwcqFUoAx0r58PaKuLmebzawxn+fTE2HLzA5mOX+eHfhbVX7j1DkxLZ+WXxTq7cfcTd2V24ejeQbn9uYMvRS1hYWuCX2ZshjfyIHzdiDYyh/2yL8rhP7Z9txwkJMzCuRWlsra1Im9iNI5duM2Hl3tcWIuI6xOL74tlMrxO7O/N98ayMX7bHtC2ztweZvT1Mr5O4O7N8zxl2nLwaqV9rKws84jp88usSEYmuVIgQkRjpxu27NGjfm5+6taFCCR+CngSzbc9BjEYjAKMnz+KvBSuYNLQPaVMkY/SUWSxdtwmf/xQC3oWlpSUjf+xEssSeXLh8jXZ9h9Fz6DjGDuhmahP87BkjJs1g4k+9iOfiTPx4rrTvN5wTZy8wY8wgEsZ3Z+naACp81459K2eT0jsJuw8epUWPQQzs3IoKJYqwdtNOBo75/Z3j2rrnAI0792Pkj50okCsb5y9fpXWvnwDo3bapqd2gMZMZ2LU1w3t3xNraiunzl3Lu0lUWr/bn7wnDsLKyxGAwUL15Z+zjxGbd7N8ICw+nfd9h1Gvbi3WzfzP19f/HReXy9ZtkK1nzjbF3bdmIbq2+i3Lfzv1HKJgra6TRFsUK5WXEpBk8eBSIyzuOTnj0OAjXuM6v3W8wGHgcFIzrf/pbvmELxQvloc4P3dmy6wCeCdxpVrcajWtVeqdzioiIiIh53HwQRNNxy+lXpwhlc6Ui6GkIO05exfjv/l+X72HOpmOMbV6K1F7xmLBiDyv2nqFQhiRv7Pf/WVpY8HNDP5LGd+birYd0mbaefrM2MaJxcVObp89DGbtsF2OalcTFITZuTnHoNm0Dp67dZXLb8iRwsWfFnjPUGPIPW4Z+R4qELuw9e512k1bTp1YhyuRKxYZDFxj6z7Z3jmvHyau0mrCSnxv6kS9tIi7cekjHyWsB6PpvEQVg2D/b6VO7MIMbFMXaypJZAUe4cPMhy3afZnqHilhZWmAwGKk3YhH2sWxY+mMtwgwGuk5dT5Oxy1j648upT///uKhcvRtI/s5T3xh7+0p56Vgpb5T79py5Tr50iSKNtiia2ZuxS3fzMOgZcR1ivfW9uXE/iBW7z5A/3esLO4cv3GLP6Wv0rFEo0vbzNx+SvuUEYtlakyuVJ31qFSaRm0ZLi0jMpUKEiMRIN2/fJSwsnIolfUnqlRCAjGlSmvaP/3MuXVo0pFJJ34jXA7uzbvPO9z7Pf0dLJEvkSb+OLWjTZ0ikQkRoaBhjB3Qlc7rUQMTN+BkLlnNmy1I8PdwB6NC0Hms372D6guUM7NyK8X/OpUThfKa1D1J5J2Xn/sOs3bzjneIaPHYynVs0pH7ViOHFyZN40bdDC3oNHRepEFGzQgkaVisf6diQ0FCmjOiHezwXANZv3cXRU+c4GbCYxJ4RTwZNGdGPbKVqsffwcXL+uz7C/x8XFc/4buxeNvONsbvEff2X91t375MskWekbR5urhH77tx7p0LEjn2H+WfFOhZNHvXaNqP+mMmT4KdULVPMtO3C5Wv8PmshbRvXoWvL79h7+DidBozE1sba9D6LiIiIyNfn1sMnhIUbKJcrFYndIx5GSZ/E3bT/t1X7aF8xD+VzR3xfH9m4BBsPXXzv8/x3tEQSd2d61ShIp8nrIhUiQsMNDP++OBmTxgcibsbP3nSEQ+NakNA14un6H8rlZsOhC8zedIQ+tQrz+6r9+GXxNq19kDKhK3tOX2fDoQvvFNewBdtoVyEPtYtkBCCZR1x6VC9Av9mbIhUiqhZIR12fTJGODQkLZ0KrMrg5RYz49j98keNX7nBgbDO84kV8957QqgwFukxj/7kbZE+RMMrjopLAxYGAIQ3fGLvLG4oJtx8+IWn8yA8XuTtHnO/WoydvLEQ0HbuMVfvO8jQkjFLZUzCmWalX2mRsPZF7gU8JCzfQrVp+6hfNbNqXI2VCxrcoTcqELtx6+IRhC7ZTtv8ctg77DsfYtm+8JhGRb5UKESISI2VOlwrf/LnIWaYOxQvlwa9gXqqULoqLsxOPHgdx4/ZdcmXNYGpvbW1NjkzpTCMm3tWGbbsZPvFPTp+/RGDQE8LCwnn2/DnBT58RJ3bEF19bWxsypU1lOubYqbOEh4eTqVi1SH09Dwkh3r9P6Z86d5EKJXwi7c+TLdM7FyKOnDzDjn2HGTphmmlbeLjhldiyZ3p1keUkngkjFRNOnb1IooTxTUUIgHSpkhPXyZGTZy+YChH/f1xUrK2tSZEs8Ttdw+dw7NQ5qrfoTK82TSheKOonq+YuXc3gcZOZP2kE8f8tcgAYjAZyZEzHwM6tAMiaIQ3HT59j8pyFKkSIiIiIfMUyJnWncMYkFOz2J0UzJ8M3UzIq5ElDXIdYBAY/59bDJ+RImdDU3trKkqzJPXi/zAACjlxk9JJdnLl+n8dPnxMebuRZaBjBz0OJYxcxotfW2ooM/ymCHL98h3CDkTwdJ0fq63lYOK4OsQE4fe0eZXOlirQ/VyrPdy5EHLt0h92nrjNq8csHr8INr8aWNXmCV45N7O4UqZhw+vo9vOI5mYoQELFWgrO9Haev3TMVIv7/uKhYW1mSPMGb84fPZVADX7pUzc+5Gw8YOHczvf/yj1QwAljRtzZPnoWy98x1BszdjLeHC1ULpAOgWNbkpnYZkkYUJrK0mcSSnSep55sZEZGYSIUIEYmRrKysWDljPDv2HWb91l1MnDGPfiMnsnnh1DdOyfNflpYWrxQmQsPCTP++ePU6VZp0pFndKvTv1BLXuE5s23uIFt0HERIaarrZH9vODguLl8ORg548xcrKih1LZmD1fwua2dvH/tBLjiToyVP6tGtqGvHxX7HsXj6hYx/71aeE7OO8fQhzVN7luI+dmsnDzZXbd+9F2nbr7v2Ife7x3tjviTPnKV2/Nd/XrESPHxpH2WbesrW07DGY2eN/xq9A7kj7Eri7kTaVd6RtaVMmY/Ea/zeeV0RERETMy8rSkoU9a7D79DX8D1/kjzX7GTxvK2sH1sXF4d2+f1taWrxSmAgNM5j+ffnOI+oMX8h3xbLSq2YhXBxisevkNdr+vprQsHD492Z/LFvrSLnBk+ehWFlasOGnBq9MYWQfy4ZP4cmzULpVy0+5f0d8/Fcsm5e3jV4UJP4rqm3v4l2O+9ipmeLHtef2o+BI2+78+9rD2f6N/XrEdcAjrgOpveLh4hCLsv3n0LlKPhK4vFzzIWn8uEDE6Jnbj4IZumCbqRDx/5ztY5EioSvnbz5843lFRL5lKkSISIxlYWFB/pxZyJ8zC73aNCZ14YosXRtAu8Z1SRjfjT0Hj1Eod3YAwsLC2H/0JNn+XZgYwN3VhZu375peBz4O4uKV66bXB46exGA0MLRneyz/LSj8s2L9W+PKmiE14eHh3L53n4K5skXZJk2KZOw5eCzStl0Hj77ztWfNkIbTFy59ktEHaVIm4+qN21y5fss0KuLEmfM8DHxMupTebzk6so+dmilv9kz0HfkboaFh2PybNG3YtpvUyZO+cVqm46fPUapea+pVKcOAf0c0/L+/l66hefdB/DVmEKV9C76yP1+OzJw+fynStjMXLpPE89Unx0RERETk62JhYUGeNInIkyYRXarmJ0ubSazYc4ZWZXPhEdeefWdvkD9dxHfnsHADhy7cirRgcTzHONx6EGR6HRj8nMt3HpleHzx/E4PByMB6vlj+W1BYvOPUW+PKlCw+4QYjdwODyZc26nUKUnvFY9/ZG5G27T1zPcq2UcnsHZ+zNx58ktEHqT3jce1eINfuBZpGRZy8epdHT56Txsvtvfr62KmZcqXyZPDfWwkNC8fm33UiAo5cJKWn6zutD/GC4d+Hz0LCwl/bxmg0EhL6+v1Bz0K4eOshNQq9OuJcRCSmUCFCRGKk3QeP4r99D8UK5sHdzZU9B49y5/4D0qaIuHHeulFNRkyaTspkiUmTIhljpszm0eOgSH0UyZeTmQuWU8avEHGdHBkwahJWVi8XQkuRNBGhoWFMmD6PMn6F2LHvEJPnLHprbKm8k1KrYikad+7H0J7tyZI+NXfvP8R/+x4ypU1Jad+CtG5YE98aTRn1x0zKFyvMui07WfeO0zIB9GrTmMpNO5LYMwFVShXF0tKSwyfOcOz0Ofp3avnO/QD4FchNxjQpaNSxDyN6dyQsPJx2Pw6lUJ7s5Mj8fl+0P3ZqppoVSjF47GSadx9I5+YNOHb6PL/+OZdhvTqY2ixZ40+fERM4vG4+EDEdU6l6rShWOC9tG9fh5p2I4pKVpZVpKqm5S1fTpEt/RvbpRK6sGUxtYseKhbNjxFNRbb+vg0/1xgydMI1qZYqx5/AxpsxdzK+De37w9YiIiIjI57f37HU2H72Mb6ZkuDvHYd/ZG9wLfEpqr4gRtc1L52DM0l0kT+BCKk9XJq7cy6Pg55H6KJQhCXM3H6VkjpQ4x7Hj5/lbTQUHgOQJXAgNN/DHmv2UzJ6CXaev8eeGg2+NLWVCV6oVSEerCSsZUM+HzMniczfwKZuPXiJDEndKZE9B01LZKdN3NuOX76Z0jlRsPHyBDYffbVomgC5V8lN7+EISxXOkQp40WFhacOzSbU5cuUuvmoXe3sF/+GRKSvrE7jQfv4LBDYoSFm6gy9R1FEiXmGwp3u8BnY+dmqlagfQMX7Cdtr+vpl35PJy4epffV+9nUP2Xo8KX7znNwLlb2DUyYkT0ugPnuf3oCdlTJMA+li0nr9yl7+xN5EnjRZJ/1w+ZvHY/ieI5kcoz4vdjx8krjF+xh2Yls5v6/XGmPyWzpySxuxM3HwQxZP42rCwtqJo/6hETIiIxgQoRIhIjOTnYs3X3AcZPm0tg0BOSeCVgaI92lPTJD0D7xnW5efseTbr0x9LSkobVy1OheBEC/1OM6NqiIZeuREy/5OToQN8Ozbl49eWTR5nTpWZYr/aM/H0GfUb8SsFc2RjQuRWNO/d7a3x/DP2Rn3+dSrefRnP91h3cXOKSO2tGyhSNeBI/T7ZMTPipJwNH/86A0ZMoWiA33Vt/z8/jp7zT9RcvnI9Ff/zC4HFTGDlpBjbW1qRJkYxGNSq+x7sYwcLCgvmTRtCx/wiK1W6OpaUlJQrn5Ze+nd+7r4/l7OjA8unjaNd3GPkqNsTNNS492zSmSe3KpjaPHj+JNHJh4eoN3Ln/gDmLVzFn8SrT9iReCTm9eQkAU+YuJiwsnHZ9h9Gu7zBTm3pVyjJ5eF8AcmZOz7yJw+gzfAI/jZtCssSeDO/dkdoVX13YTkRERES+Ho6x7dhx4gqTVu3j8dPnJHJzYkA9H9M8/63L5uLWgye0nrgSS0sL6hbJRNmcqQh8+rIY0b5iHi7feUTtYQtwimNHj+oFI42IyJg0PoPq+zJm6S4Gzt1MvrSJ6FOrMK0mrHxrfONblGbkoh38ODOAG/cf4+oYm5ypPCmZPQUQ8eT/6KYlGfLPNobM30aRjEnpVCkfIxa924NKRbN4M6dLFYYv3M7YZbuxtrIklWc86vtmevvB/8fCwoKZnSvT7c8NlO8/BwtLC/wyezOkkd979/WxnOLY8U+P6nSdtp6ivWbg6hibzlXy0dAvi6lNYHAIZ6/fN72OZWvNXxsP0/svf0JCw/GK50jZ3Klo/+9C4AAGAwycu4XLdx5hZWmBt0dc+tYuTCO/rKY21+8H0XTcMh4EPSOeU2zypknEmoF137ouhojIt8zC+L4rr4qIRCNLly6lYsWKXN61OtLCwh+iSZf+PAp8zPxJIz5RdBJT3b57nyR5SrFkyRIqVKhg7nBEREREor2xY8fSvWtnrv7Z/oucr/XElTwKfs7MTpXf3ljkE0jUaDRDho2gbdu25g5FROSDWL69iYiIiIiIiIiIiIiIyIfR1EwiIt+gbKVqcvnazSj3jR/UQ9MFiYiIiIjEEPk7T+Xq3cAo941sUoLqBbWAsoiIfH4qRIiIvKMXawFEB4unjCYsNCzKfR87RZWIiIiISEz3a8sy5g7hnf3drSqh4YYo97k723/haEREJKZSIUJE5BuU1CuhuUMQEREREZGvQGJ3Z3OHICIiojUiRES+RU269Kd6887mDkNERERERL4CrSeupN7IReYOQ0REYjCNiBARkS9u2MQ/WbLGn1PnLxHbzo682TMxuFsbUidPamrz7Plzuv00hvnL1/I8JJTihfIyZkBXPNziReprxj/LGTt1NmcuXMbJwZ4qZfwY07+rqY8feg/hwNGTnDx3kTK+BZg/aUSk4xev8ef3WQs4fOI0z0NCSZ/Km95tm1K8cL7P/0aIiIiIiAijFu9k+Z4znLl+j9i2NuRK7Unf2kVI5RkxreyDoKcMmb8N/yMXuXb3MfGcYlMmZyp61iiIUxw7Uz/xag9/pe8/2pSjSv50ACzbfZpp6w5y9NJtnoeFkzZRPLpVLUDRLN5RxjV6yS4Gzt1M81I5+Klh0c9w5SIiMYcKESIi8sVt2bWf5vWqkzNzOsLCw/lxxETKNmzDwTV/Yx8nNgBdBo1ilf82Zo37GWdHBzr0H07Nlt0ImD/Z1M+YKbMYM2U2P3VvQ64sGQl++pRLV2+Y9oeHG4gdy45WDWuyePXGKGPZuvsAfgVyM6BzK+I6OjB9wXKqNOvElgXTyJohzed9I0REREREhO0nrtC4RDayJ09AmMHAoLlbqPbzfLYP/w77WLbcfBDEzYdBDKjrQ5pE8bhyJ5DOU9Zx80EQf3aoGKmvcS1K45clmem1c5xYpn/vOHEVn0xJ6V2rEM5xYjF70xHqDF/I2oH1yOztEamf/eduMH3DITIkcf+s1y4iElOoECEi8hEWrtrA4LGTOXfpKnFi25ElfRr+mTQC+zix2Xv4OD+OmMCh46cIDQ0jc/rUDO/VgWwZ05qOj5UiN+MGdmflxi0E7NhLEq+ETBrSG3dXF1r0HMy+w8fJnDYVU0b2J0XSRAAMHPM7y9Ztommdqgz5dSr3Hz6iTNGCTPipF86ODlHGaTAYGDFpBlPmLuLWnfuk8k5Mjx8aU6W0HwAPHgXSvt9wNmzdRdCTp3gliE/XVo1oWK38Z3nflv05NtLrP4b9SOLcJdl/9ASFcmfn0eMg/py/lOmjBuKbPxcAvw/9kSwlarDrwBHyZMvEg0eB9PvlNxb8PpKiBXKb+sqUNpXp3/ZxYjNuYHcAduw7xKPAx6/EMqJPx0ivB3ZuxfL1m1ixcYsKESIiIiLyXpbuOsWwBdu5cPMhse2syZQsPjM7VcY+li37z91g0NwtHLl4m9DwcDIljc+gBkXJ8p8b4PFqD2dk4+Ks2X+OLccuk8jNiXHNSxHPKQ7tf1/DgfM3yZDEnYmty+Dt4QLA0H+2sXLvGb4rlpWRi3byIOgpJbKlYHSzkpFGC/yXwWBkzNJdzNh4mNsPn5AioQudq+SjQp6I778Pg57R7c/1+B++yJNnoXjGc6B9xbzU9cn0Wd63+T2qR3o9vmVp0jT/lUMXbpE/XWLSJXZneodKpv3eHi70qlmIFr+uICzcgLXVy5nHnePY4RE36rzo/0c19KlVmFV7z7Jm/7lIhYigZyG0GL+CUU1L8MuinZ/gCkVERIUIEZEPdOP2XRq0781P3dpQoYQPQU+C2bbnIEajEYDHQU+oV6Uso/p2xmg0MnrKLCo1bs/RDQtwdLA39TPk16kM7dmOYb3a02voeBp26IN3Yi+6tmhIYs8ENO8+kA79hrN02hjTMecuXWXByvUs/OMXAoOCaNF9MG1/HMr0UQOjjHXYxD+Zs2Q14wd2J0WyJGzdfYDvOvbFzdWFwnmy03/UJE6evcCSKaOJ5xqXc5eu8uzZ89de+9AJ0xg28c83vj8H1vxNEs8E7/ReBj4OAsDVOWIhvf1HThAaGhapwJAmRTISeyYwFSI2bN2FwWDk+q07ZClRg6AnweTNnokhPdqT2NMjyvO8C4PBwOOgYFydnT64DxERERGJeW4+CKLpuOX0q1OEsrlSEfQ0hB0nr2L8d3/Q0xBqFc7AkEZ+GIEJK/ZQa+g/7B7VFMfYtqZ+RizawaB6vgys70v/2ZtoNn4FSeM7075iHrzcnGg7aRXdpm1gXvdqpmMu3HzI4p2nmN2lMo+fhtDu99V0mbqOST+UizLWUUt2Mn/rcUY2Lk7yBC5sP3mVFr+uIJ5jHAqkT8xP87dy6uo9/u5WjXhOsTl/8wHPQsJee+2/LN7J6MVvvmG/fcT3JHJ7t+/YgcERuYiLQ6w3tnGMbRupCAHQddp62v++hqQeznznl5U6PhmxsLCIsg+DwUjQsxDi/t95uk5dT/FsyfHJlEyFCBGRT0SFCBGRD3Tz9l3CwsKpWNKXpF4JAciYJqVp/4sn+V+YMLgnHtn82LJ7P2WKFjJtr1+1HNXKFgegU/MGFKnWmB4/NDatUdC6YS2adYtcYHj2PIQpI/rhlSA+AKP6dqZSkw4M7dmOBO5ukdo+fx7CsIl/snLGePJmzwxA8iRebN93kMlzFlI4T3auXL9JlvRpyJE5PQDJEnm+8dqb1qlCtTLF3tjGM77bG/e/YDAY6DzoF/LlyEKGNCkAuHX3Hra2NsR1cozU1sPNlVt37gFw4cp1DEYDwyb+ycg+HXFydKDfL79RtuEP7F0xG1tbm3c6//8b9cdMngQ/pepbrk9ERERE5L9uPXxCWLiBcrlSkdg94gGb9P+Z1qdwxqSR2o9qUhLvJmPZfuIKJbOnMG2vUyQTlfJFjKJuWyEPpX6cRefK+UzrGDQvlYM2v62O1Nez0DAmtCqDp2vE9+chDYtRa9gCBtTzeWV0wPPQMEYv2cXCntXJldoLgGQecdl16irTNxyiQPrEXLsbSKZk8cmWIuLBoiT/Xs/rfFcsC5Xyvnk0cQKXqEcp/D+DwUivGRvJk8aLdImjnhbpXmAwIxbtoIFflkjbe1QvQKEMSYhta4P/kYt0mbaOoOchNC+VI8p+xi/fzZNnoZFiX7j9BIcv3mL9oPrvFK+IiLwbFSJERD5Q5nSp8M2fi5xl6lC8UB78CualSumiuPz7JP2tu/foN/I3Nu/az5179wk3GAh++owr129F6idT2pfFi/huEYux/begEd/NlWfPnxP4OAinf6deSuzpYSpCAOTJngmDwcDp85dfKUScu3SF4KfPKNuwTaTtIaGhZE0f8YW7WZ2q1GrdjYPHTlKsYB7KF/chX47Mr71217jOuMZ9czLyrtr1Hcax0+fZ+Pfv73WcwWAgNDSMkT92onihvADMGD2IpHlLs2nn3g9abHru0tUMHjeZ+ZNGmH4WIiIiIiLvImNSdwpnTELBbn9SNHMyfDMlo0KeNKan7W8/fMJP87ay7cRl7jwKxmAwEhwSytW7gZH6+e+aBPGd4wCQLsnL7/juzvY8Cw0jMPi5aeqlRG5OpiIEQK7UnhiMRs5ef/BKIeLCzYcEPw+l6k/zI20PCQsnU7KIkcXfFc9Ko1FLOHzxFr6ZklEmVypy/1u0iIqLQ2xcHGK/83v1Jl2mrePElbus6Fcnyv2Bwc+pNWwhabzi0a1q/kj7Old5+TqztwfBz0MZv2xPlIWIf7YdZ/jCHfzVqRLuzhEj1q/dC6Tn9I0s6FmdWLa6ZSYi8inpr6qIyAeysrJi5Yzx7Nh3mPVbdzFxxjz6jZzI5oVT8U7sRZPO/bn/8BEj+3QkiVcC7GxtKVK9MSGhoZH6sbF++af4xZDhqLYZjEY+RFDwUwAWTR6Fl0fkJ4psbSOGgJf0yc/pzUtZHbCNDdt2U7p+a1rUq8aQnu2i7PNTTc3Uvt9wVm7cyvq5k0iU8OV0Sh5u8QgJCeVh4ONIoyJu3b2Ph3s8ABL8O+IiXUpv0373eC64ucTl8v8Ve97FvGVradljMLPH/4zff6aEEhERERF5F1aWlizsWYPdp6/hf/gif6zZz+B5W1k7sC5J48el9cSV3A96xk8N/Ejs5oStjRWlfpxFaFh4pH7+O9WQKT+ItC3i/40fmh88DwFgTteqJHSNXKSws7ECoFjW5Bwc15x1B84TcOQilQfNo3GJrAyo5xtln59qaqau09azdv95lvethVc8x1f2P34aQo0h/+AQ24YZHSthY231xv5ypEjIiIU7eB4ahp3Nyxxr4fYTtP99DVPbVcAnUzLT9oPnb3EnMBjfnjNM28INRrafvMLktfu58VdHrCwjTwUlIiLvRoUIEZGPYGFhQf6cWcifMwu92jQmdeGKLF0bQLvGddmx/zBj+nellG8BAK5cv8Xd+w8/yXmvXL/F9Vt38Py3sLD7wBEsLS1JnTzJK23TpfTGztaWK9dvUjhP9tf26R7PhfpVy1G/ajn+yLmQnkPHvbYQ8bFTMxmNRjr0H8HStQGsnTUR78SRn67KnikdNjbW+G/fQ+VSEQvKnT5/iSvXb5InW8QCefn/HbFx+vwlUxHj/sNH3H3wkCRe77Y2xQt/L11D8+6D+GvMIEr7FnyvY0VEREREXrCwsCBPmkTkSZOILlXzk6XNJFbsOUOrsrnYdfoaw78vTvFsyYGIp+/vPX76Sc579W4gN+4HmQoLe89cx9LCgpSeLq+0TePlhp2NFVfvBVIgfeLX9unmFIfaRTJSu0hG/lx/kL6zN722EPGxUzMZjUa6/bmBFXvOsLRPLZLGj/tKm8Dg51QfMh87a2tmda7yTiMWjly6TVz7WJGKEAu2naDtpNX80bYcJf4zJRZETJ+1dVijSNt++G01qTxdaVcht4oQIiIfQYUIEZEPtPvgUfy376FYwTy4u7my5+BR7tx/QNoUEU/op0yWmNmLV5EjUzoCg57QY8hYYsey+yTnjmVnS5Mu/RnSoy2BQU/oOGAkVcsUe2VaJgBHB3vaN6lL18GjMBgM5M+ZlcDHQezYdwhHB3vqVy1H/1GTyJ4xLelTJed5SAir/LeSNkWy157/Y6dmatd3GH8vXcP8SSNwcIjDzTt3AXB2dCB2rFg4OzrQqHoFug4ejYuzE04O9nTsP4K82TKZChGpvJNSvlhhOg/8hV8H98TJwZ4+I34lTYqk+OTNaTrXiTPnCQkN48GjQB4HBXPo+GkAsqRPDURMx9SkS39G9ulErqwZTLG8iENERERE5F3sPXudzUcv45spGe7Ocdh39gb3Ap+S2itiRG+KBC7M23KMrMkT8Pjpc/rN2kTsTzT9Tywba1pPXMmAej48fhpC9+kbqJQ3zSvTMgE4xralddlc9J7hj8FgJG9aLwKDQ9h16hqOsW2pXSQjP8/fShZvD9ImciMkNJy1B86T2jPea8//sVMzdZm6ngXbTzCzU2UcYttw62EQAE5x7Ihta0Ng8HOq/Tyfp89D+a1TWR4/fc7jpxELWrs5xcHK0pLV+85y51EwOVMlxM7GmoAjFxm9ZBety77MDf7ZdpzWE1fxU4Oi5EiZ0HSe2LY2OMWxwzG27SvrUtjb2eDqEPu161WIiMi7USFCROQDOTnYs3X3AcZPm0tg0BOSeCVgaI92lPSJmJf0t59707rXT+St0IBECeMzoHMruv889pOcO0XSRFQq6UOlxh24/zCQMkULMHZA19e279exBe6uLgz/bToXrvxEXEdHsmZIQ9dWjQCwtbGmz4gJXLp6ndix7CiQMyszxgz6JLFG5fdZCwAoUadF5O1Df6RBtXIADO/dAUtLS2q37s7zkBCKF8rLmP+7xikj+tFl8CgqN+mApaUFhXJnZ+nUsdj854mnio07cPnaDdPrPOXrAfDs3O6IPuYuJiwsnHZ9h9Gu7zBTu3pVyjJ5eN9PeNUiIiIi8i1zjG3HjhNXmLRqH4+fPieRmxMD6vlQLGvECIgxzUrRYfJaivaYgVc8R3rXKsSPMwM+ybm9E8SlXO5U1By6gIdBzyiRPTnDvy/+2vY9axTEzSkOo5fu4tIfD3G2j0XmZPHpUCli7TUbKysGzt3ClTuPiGVrTd60iZjcttwniTUq09YfBKDCwLmRto9rUZo6RTJy+OIt9p2N+E6fs/3kSG0OjG1GEndnbKysmLL2AL3+2gjGiPdkYD0fGhR9uaD1jA2HCQs30HXaerpOW2/aXqtwBn5tWeYzXZ2IiABYGD90UkERkWhg6dKlVKxYkcu7Vn8ziw8PHPM7y9ZtYvfyWeYORT7Q7bv3SZKnFEuWLKFChQrmDkdEREQk2hs7dizdu3bm6p/tzR3KFzf0n22s3HuGTUMamTsU+YwSNRrNkGEjaNu2rblDERH5IJrcTkREREREREREREREPhsVIkRERERERERERERE5LNRIUJEJJrp066ZpmUSEREREREAulUroGmZRETkq6dChIiIiIiIiIiIiIiIfDYqRIiIiIiIiIiIiIiIyGejQoSISDSTunBFxk2bY+4wRERERETEzLK2mcRvK/eaOwwREZG3sjZ3ACIi8m0yGo2MnjyLKXMXcfn6Tdxc4tKsblW6t/7+tcecuXCJHkPGsWPfIUJCw8iYJiV9OzTHJ19OU5u9h4/Te9h4Dhw9iYWFBTmzpOenbm3InC41AKfPX+KH3kM4efYCjx4HkdDDjZrlS9K7bVNsbPSxJyIiIiLypW08dIEh/2zj5NW7xLKxJl+6RAys50sSd+c3Hrd2/zmGL9zB8ct3sLO1In+6xMzsVNm0v/ufG9h9+honrtwltZfrK2tlbD1+mYkr93Hg3A0ePw0heYK4/FAuN9ULpv8clykiIm+gOzIiIvJZdBowkvVbdzGkRzsypEnBg4eB3H8U+MZjKjfpSMpkSVg9cwKx7ewY9+dcqjTtyHH/hSRwdyPoSTAVvmtLWb/CjB3QjbCwMAaO+YPyjdpydutybGyssba2pm6VMmTLkAZnJ0eOnDhDq54/YTAaGdi51Re6ehERERERAbh0+yH1Ri6iZZmcTPqhLIHBIfSesZGGvyzG/+eGrz1u6a5TdPhjLb1rFqJQxiSEhRs4eeXuK+3q+GRk39kbHL9855V9u09fJ0MSd9pVyI27cxzW7j9PqwkrcYpjR8nsKT7pdYqIyJtpaiYRkS9k8pxFeOcrg8FgiLS9WvPONOs2EIBzl65SrXlnkuQuRbxMRShQqSEbtu1+bZ8Xr14nVorcHDp+2rTtYeBjYqXIzaad+0zbjp06R4Xv2hEvUxGS5C7Fd536cvf+w097gf9x8uwFfp+9gH8mjaBcscJ4J/Yie6Z0FCuY57XH3L3/kLMXr9C5RQMypU1FSu8kDOrSmuCnzzh2+jwAp85d5P7DQH5s35zUyZOSPnUKerVtwq2797l87QYAyZN40bBaeTKnS01Sr4SUK1aYWhVLsm3Pgc92vSIiIiIi72P6hkOkbzkBg8EYaXvdEYto89sqAC7cekDdEYtI2/xXkjQajV+vvwg4cvG1fV6+84h4tYdz5OIt07ZHT54Rr/Zwth6/bNp24sodagz5hySNRpO2+a+0+HUF9wKDP+0F/sehC7cINxjpVaMQ3h4uZPH2oHW5XBy5dJvQsPAojwkLN9Bzxkb61y3Cd8WzkjKhK2kTuVEpX9pI7YY08qNJiewkix83yn46VspLzxoFyZ3aC28PF5qXzoFfFm+W7z4dZXsREfl8VIgQEflCqpbx497DRwTseDmH6/2Hj1i7eQe1KpQE4ElwMCWL5GfVX7+ya+lfFC+cj6pNO3H5+s0PPu/DwMeUqteKLBnSsH3xdJZOG8Ptu/ep26bHa4+5fP0m8TIVeeP/hk6Y9trjV2zYgndiL1Zu3EqaIhVJXbgiLXoM4v7DR689Jp6LM6mTJ2XWwpU8CX5KWFgYk+csIn48V7JnjEg4UidPSjwXZ/6cv4SQkFCePnvGn/OWkjalN0kTJYyy33MXr7B2804K5c7+ju+YiIiIiMjnVTFPGh4EPWPLfwoED4KesvHQBar9O23Qk2ehFM/qzaLeNfD/uSF+WbypO3wRV+++eZTxmzx68oxKg+aRKVl81g+uz7zu1bjz6Anfj1n22mOu3g0kSaPRb/zfL4t3vvb4LN4eWFpYMHvTEcINBgKDnzNvy3GKZEyKjbVVlMccunCLG/eDsLCwwKf7dNK3nECNIf9w4sqrox7eV+DT57g4xProfkRE5P1oaiYRkS/ExdmJkoXz8feyNRQtkBuAhas24uYS17QGQuZ0qU1rHQD069iCpWsDWLF+My0b1Pig806cMY8sGVJHmpZo0pDepCxYnjMXLpHKO+krx3jGd2P3splvvp64Tq/dd+HKNS5fu8nCVRuYMqIf4eEGug4eRe3W3Vkza2KUx1hYWLByxnhqtOiCW2YfLC0tiR/PhaXTxuDiHHEuRwd71s76jeotuvDz+KkApEyWmGV/jsXaOvJHmk+1xhw4dornISE0rlWZvh2av/F6RERERES+lLgOsfDL6s2CbScokjHi+/jSXadxdYxNofRJAMiYND4Zk8Y3HdOzRkFW7DnDqn1naVrywx6y+WPNATIli0+fWoVN28Y2L03mH37j7I37pEzo+soxCVwcCBjy+imUgDfe2E8aPy7/9KhO4zFL6Th5LeEGI7lSefJ3t6qvPebS7YcADFuwnYH1fEji7syEFXupMOBvdo9qjItD7LdcadQW7zjJgXM3+aVxiQ86XkREPpwKESIiX1CtiqVo1fMnxvbvhp2dLXOXrqZ6ueJYWkYMUAt6EsygMX+wKmAbN2/fJSw8nKfPnnPlI0ZEHDl5hk079xEvU5FX9p2/dC3KQoS1tTUpkiX+4HMaDEaeh4QwZURfU/+//dybfBUbcPr8JVInf/WcRqOR9v2G4x7PlQ1zfyd2LDumzVtC1Wad2LroTxLGd+Pps2e06DGIfDkyM2PMIMLDwxk9eRaVG3dg2+I/iR3rZQL019ifCHryhMMnztBz6DhG/TGTTs0bfPA1iYiIiIh8StULpKf9H2sY/n0x7Gys+WfrcarkS4ulpQUAQc9CGPbPNtYeOM+th08IDzfwNCSMax8xIuLY5dtsPXaZJI1Gv7Lv4q2HURYirK0sSZ7A5YPPeethEB3+WEOtwhmokj8dQc9CGDJ/G41GL2FhzxpYWFi8cozBGDFlVcdKeamQJw0A41qUIlPr31iy8xSNimV97zi2HLtMm0mrGdW0BGkTu33w9YiIyIdRIUJE5Asq61cIo9HIqoBt5MiUnm17DjK8VwfT/u4/j2HDtt0M6d6WFMkSE9vOjto/dCckNCzK/l4UMIzGl3PLhv5f26AnTylbtBCDu/7wyvEJ4kf9Bfzy9ZtkK1nzjdfStWUjurX6Lsp9CeK7YW1tFanIkTZlMlPfURUi/LfvYeXGrdzcvx4nRwcAsmVMy4atu5m5cAVdWjRk7tI1XLp6g03/TDFd+/RRA0mQ3Y9l6zZTo/zLJ5sSe3oAkC5VcsINBlr3+on2TepiZRX18G8RERERkS+pZPYUGI1G1h44T/YUCdhx6iqDGhQ17e87M4CAIxfpX8+H5B4uxLK15rvRSwgJM0TZn+W/N/T/kxoQGh657ZNnoZTMkYK+tV99SMkjrn2U/V69G0j+zlPfeC3tK+WlY6W8Ue6bsvYgjnHs6FfXx7RtYuuyZP7hN/aevUGuVJ5RxBKRD6TximfaZmdjTdL4cbl67/EbY4nKtuNXqDt8IYPq+1KrcMb3Pl5ERD6eChEiIl9QLDs7Kpb0Ze6S1Zy7eIXUyZOSLePLBdd27DtM/SrlqFjSF4gYIXHp6g14zRrP7q5xAbh5+y5kiHhS6NCJyAuvZc2QhsVr/EmaKOEr0xe9zsdOzZQvR2bCwsI5d+kqKZImAuDMhYj5b5N6JYjymKfPngMviysvWFpamBb4fvr0GZaWFpGemnrx2mCMOiEDMBgMhIaFYTAYUR1CRERERL4GsWytKZc7Nf9sO86Fmw9ImdCVLN4epv27Tl+jdpGMlMsVMXVr0LMQLt8JpEC6qPuL5xQxXdGth09M245cvB2pTWZvD5btPk0Sd2esrd5t2dCPnZrp6fNQU5HkBSvLF0UTY1SHkMXbAzsbK87euE/etBH5RGhYOFfuPCKx2+vzkKhsPX6ZOsMW8mOdwjT0y/Jex4qIyKejQoSIyBdWq2IpqjTpyPEz56ldsVSkfSmTJWbJWn/K+hXCwgL6j5pkGpYcldixYpEnW0aGT5pOssSe3L73gP6//BapTYv61Zn29xIatO9Nx2YNcHF24vylK8xbvo7ffu4V5QiBj52aya9AbrJlSEvz7gMZ0bsjBoOB9v2G41cwj2mUxJ5Dx2jcuR+r/voVrwTxyZMtEy7OjjTp0p+ebRoTO5YdU+cu4eLV65T2LRDRb8E89BgyjnZ9h9GqQQ0MBgPDJ83A2sqKInkj1tmYs2Q1NtZWZEyTEltbW/YfOc6PIyZQrWxxbGz0sSciIiIiX49qBdJRZ/hCTl69R/UC6SPtS57AheW7z1AyewosLCz4ed7WN+cGtjbkTJWQMUt3kSS+M3cfBfPTvK2R2jQuno2/Nh6m6bhltCmfGxeHWFy4+ZCFO04ypllJrCxfLU587NRMxbMlZ+KqvQxfsN00NdOguZtJ7OZEpmQRa2DsO3uDVhNXsqhXDTxdHXGKY0cjv6wM+WcbXvEcSeTmzPjlu4GIhb5fOH/zAU+ehXD74ROehoRx5OItANIkcsPW2ootxy5TZ/hCmpXKTvncqbn1MAgAW2urD15nQkREPozuyIiIfGG++XLiGteJ0+cvUbNC5ELE0F7tad5tID7VGxPPJS6dmzcgMOjJa3qKMGlIH1p0H0S+ig1InTwpg7u1oVzDNqb9nh7u+M/7g17DxlOuYRueh4SQxCshJQrnfWX0wadiaWnJgj9G0rH/CIrVbo597FiUKJKfoT3bmdoEP33G6fOXCA2LmErKzTUuS6eOoe8vEylVrxWhYeGkT+XNP7+NMC3gnSZFMhb8MZLBYydTpFpjLC0tyZI+NUunjSHhv9NMWVtZMXLSX5y5eBmj0UgSrwS0qF+dtt/X/izXKiIiIiLyoQpnSEpc+1icvX6fav831GFQfV/a/Laa0n1n4+oYm7YVcvP4acgb+xvbvDTtJq3Gr+cMUiZ0pW+dIlT7eb5pf0JXB1b2q0P/OZuo9vN8QkLDSeTmhF8W71dGLXyya8yYlN9/KMe4ZbsZt2w3se1syJnKk3ndqxHb1gaApyGhnL1+n7D/TCXVv24RrK0saPnrSp6GhpEjRUIW965J3P+Mvmj/+xq2nbhieu3TYwYAB8Y2I4m7M3M3HyX4eSijl+xi9JJdpnYF0iVm6Y+1Psv1iohI1CyMrxsHJyLyDVi6dCkVK1bk8q7VxHd7deE1EXO4ffc+SfKUYsmSJVSoUMHc4YiIiIhEe2PHjqV7185c/bO9uUMR+SwSNRrNkGEjaNu2rblDERH5IJ/nUVgRERERERERERERERFUiBCRb5yNTcRQ35DQUDNHIvLSi9/HF7+fIiIiIvJxbGxsCAkNf+3ixyLRmdFoJCQ0XPmDiERrKkSIyDfN29sbgMMnTps5EpGXDh2P+H1Mnjy5mSMRERER+TZ4e3sTbjBw8updc4ci8smduHKXcINB+YOIRGtaI0JEvnnp06cjflwHlk4ZjZ2drbnDkRju+fMQyn/fjruBwRw7dtzc4YiIiIh8E0JCQojv7k6JzImY0LIMlpafZ+FlkS/NYDDScsJK1h+9yq3bd7C1VU4rItGTtbkDEBH53MaMGUv58uXJX7kRNcuXIFkiT6ysNCBMvqzwcAMXr17n72VrOXvxCsuWLTN3SCIiIiLfDFtbW8aMHct3333HhVuPqJgnFQldHbG0UEFCoieD0ciN+49ZsusM+85eZ9q0aSpCiEi0phERIhIjbNu2jdGjR7Ny5UqCg4PNHY7EUHHixKFMmTK0b9+eAgUKmDscERERkW/O8uXLmfDrr6zfsIFQrRMn0ZyNjQ3F/Pxo1bo15cqVM3c4IiIfRYUIEYlRjEYjQUFBGAwGc4fyRiNGjGDQoEF0796d7t27mzucr9bPP//M0KFD6dOnD506dTJ3OG9kaWmJg4MDFnoqT0REROSzMxgMBAUFfROLVys3eDfRKTd4FxYWFjg4OGBpqdH8IvJt0NRMIhKjWFhY4OjoaO4w3mjQoEEMGjSI/v378+OPP5o7nK/akCFDiB07Nv369cPOzo5evXqZOyQRERER+QpYWlri5ORk7jA+mnKDd6fcQETk66ZChIjIV2TAgAH07duXgQMH0rt3b3OHEy307dsXS0tLevfujcFgoE+fPuYOSURERETkoyk3eH/KDUREvl4qRIiIfCX69etH//79GTx4MD179jR3ONFKnz59IiUcffv2NXdIIiIiIiIfTLnBh1NuICLydVIhQkTEzIxGI/369WPAgAH89NNP9OjRw9whRUu9evXCwsKCXr16md5TEREREZHoRLnBp6HcQETk66NChIiIGRmNRtNw6yFDhtCtWzdzhxSt9ezZE0tLS3r06GFKOLQ4tIiIiIhEB8oNPi3lBiIiXxcVIkREzMRoNNKnTx8GDx7MsGHD6NKli7lD+iZ0794dS0tLunXrhsFgYMCAAUo4REREROSrptzg81BuICLy9VAhQkTEDIxGI7179+ann35i+PDhdO7c2dwhfVO6du2KhYUFXbt2xWg0MnDgQCUcIiIiIvJVUm7weSk3EBH5OqgQISLyhRmNRnr27MmQIUMYOXIkHTt2NHdI36QuXbpgaWlJ586dMRgMDB48WAmHiIiIiHxVlBt8GcoNRETMT4UIEZEvyGg00r17d4YNG8Yvv/xChw4dzB3SN61Tp05YWlrSsWNHDAYDP//8sxIOEREREfkqKDf4spQbiIiYlwoRIiJfiNFopFu3bgwfPpzRo0fTrl07c4cUI3To0AELCws6dOiA0WhkyJAhSjhERERExKyUG5iHcgMREfNRIUJE5AswGo106dKFkSNHMnbsWNq0aWPukGKU9u3bY2lpSbt27TAYDAwbNkwJh4iIiIiYhXID81JuICJiHipEiIh8ZkajkU6dOjFq1CjGjRvHDz/8YO6QYqS2bdtiYWFB27ZtMRgMjBgxQgmHiIiIiHxRyg2+DsoNRES+PBUiREQ+I6PRSMeOHRk9ejTjx4+ndevW5g4pRmvTpg2Wlpb88MMPGI1GRo4cqYRDRERERL4I5QZfF+UGIiJflgoRIiKfidFopH379owdO5YJEybQsmVLc4ckQOvWrbGwsKB169YYDAZGjRqlhENEREREPivlBl8n5QYiIl+OChEiIp+B0WikXbt2jBs3jt9++43mzZubOyT5j1atWmFpaUnLli0xGo2MHj1aCYeIiIiIfBbKDb5uyg1ERL4MFSJERD4xo9FImzZt+PXXX5k0aRLNmjUzd0gShRYtWmBhYUGLFi0wGAyMHTtWCYeIiIiIfFLKDaIH5QYiIp+fChEiIp+QwWDghx9+YOLEifzxxx80adLE3CHJGzRv3hxLS0uaNWuGwWBg/PjxSjhERERE5JNQbhC9KDcQEfm8VIgQEflEDAYDrVu3ZtKkSUyePJnGjRubOyR5B02bNsXCwoKmTZtiNBoZP348lpaW5g5LRERERKIx5QbRk3IDEZHPR4UIEZFPwGAw0LJlS/744w8mT57M999/b+6Q5D00adIES0tLmjRpgsFgYMKECUo4REREROSDKDeI3pQbiIh8HipEiIh8JIPBQIsWLZg8eTJTp06lUaNG5g5JPsD333+PhYUFjRs3xmg0MnHiRCUcIiIiIvJelBt8G5QbiIh8eipEiIh8BIPBQLNmzZg6dSrTpk2jYcOG5g5JPsJ3332HhYUF33//PQaDgUmTJinhEBEREZF3otzg26LcQETk01IhQkTkAxkMBpo2bcq0adOYPn069evXN3dI8gk0atQIS0tLGjVqhNFo5Pfff1fCISIiIiJvpNzg26TcQETk01EhQkTkA4SHh9OkSRNmzJjBjBkzqFevnrlDkk+oQYMGWFhY0LBhQwwGA5MnT1bCISIiIiJRUm7wbVNuICLyaagQISLynsLDw2ncuDF//fUXM2bMoG7duuYOST6D+vXrmxIOo9HI5MmTsbKyMndYIiIiIvIVUW4QMyg3EBH5eCpEiIi8h/DwcL777jtmzZrFzJkzqV27trlDks+oXr16WFpaUr9+fQwGA1OnTlXCISIiIiKAcoOYRrmBiMjHUSFCROQdhYeH06hRI2bPns2sWbOoVauWuUOSL6BOnTpYWFhQr149jEYj06ZNU8IhIiIiEsMpN4iZlBuIiHw4FSJERN5BWFgYDRs25O+//2b27NnUrFnT3CHJF1S7dm0sLS2pW7cuBoOB6dOnK+EQERERiaGUG8Rsyg1ERD6MChEiIm8RFhZGgwYNmDdvHnPmzKF69ermDknMoGbNmlhYWFCnTh2MRiPTp0/H2lofoyIiIiIxiXIDAeUGIiIfQn8lRUTeICwsjPr16zN//nzmzp1LtWrVzB2SmFGNGjWwsLCgdu3aGI1GZsyYoYRDREREJIZQbiD/pdxAROT96C+kiMhrhIWFUbduXRYuXMjff/9N1apVzR2SfAWqV6+OpaUltWrVwmAwMHPmTCUcIiIiIt845QYSFeUGIiLvzsJoNBrNHYSIyNcmNDSUunXrsmjRIubNm0flypXNHZJ8ZRYuXEjNmjWpUqUKs2bNUsIhIiIi8o1SbiBvo9xAROTtVIgQEfk/oaGh1KlTh8WLFzN//nwqVapk7pDkK7Vo0SJq1KhB5cqVmTVrFjY2NuYOSUREREQ+IeUG8q6UG4iIvJkKESIi/xEaGkqtWrVYtmwZ8+fPp2LFiuYOSb5yS5YsoXr16lSoUIE5c+Yo4RARERH5Rig3kPel3EBE5PVUiBAR+VdISAi1atVi+fLlLFiwgPLly5s7JIkmli5dSrVq1Shfvjxz585VwiEiIiISzSk3kA+l3EBEJGoqRIiIEJFo1KxZk5UrV7JgwQLKlStn7pAkmlm2bBlVq1alXLlyzJ07F1tbW3OHJCIiIiIfQLmBfCzlBiIir1IhQkRivJCQEKpXr87q1atZuHAhZcuWNXdIEk0tX76cqlWrUrp0aebNm6eEQ0RERCSaUW4gn4pyAxGRyFSIEJEY7fnz51SvXp21a9eyaNEiSpcube6QJJpbuXIllStXplSpUsyfP18Jh4iIiEg0odxAPjXlBiIiL6kQISIx1vPnz6lWrRrr1q1j8eLFlCpVytwhyTdi1apVVK5cmRIlSjB//nzs7OzMHZKIiIiIvIFyA/lclBuIiERQIUJEYqRnz55RtWpVNmzYwJIlSyhZsqS5Q5JvzOrVq6lUqRLFihVjwYIFSjhEREREvlLKDeRzU24gIqJChIjEQM+ePaNKlSr4+/uzZMkSSpQoYe6Q5Bu1du1aKlasSNGiRVmwYAGxYsUyd0giIiIi8h/KDeRLUW4gIjGdChEiEqM8e/aMypUrExAQwLJlyyhWrJi5Q5Jv3Lp166hQoQI+Pj4sWrRICYeIiIjIV0K5gXxpyg1EJCZTIUJEYoynT59SqVIltmzZwrJly/Dz8zN3SBJDrF+/nvLly1OkSBEWL16shENERETEzJQbiLkoNxCRmEqFCBGJEZ4+fUrFihXZunUry5cvp2jRouYOSWKYDRs2UL58eQoVKsTixYuJHTu2uUMSERERiZGUG4i5KTcQkZhIhQgR+eYFBwdTsWJFtm3bxooVK/D19TV3SBJDbdy4kXLlylGwYEGWLFmihENERETkC1NuIF8L5QYiEtOoECEi37Tg4GAqVKjAjh07WLFiBT4+PuYOSWK4gIAAypYtS/78+VmyZAlx4sQxd0giIiIiMYJyA/naKDcQkZhEhQgR+WYFBwdTvnx5du3axcqVKylcuLC5QxIBYNOmTZQpU4a8efOybNkyJRwiIiIin5lyA/laKTcQkZhChQgR+SY9efKE8uXLs3v3blatWkWhQoXMHZJIJJs3b6ZMmTLkzp2bZcuWYW9vb+6QRERERL5Jyg3ka6fcQERiAhUiROSb8+TJE8qWLcu+fftYtWoVBQsWNHdIIlHasmULpUuXJleuXCxfvlwJh4iIiMgnptxAogvlBiLyrVMhQkS+KUFBQZQtW5b9+/ezevVqChQoYO6QRN5o69atlC5dmhw5crBixQolHCIiIiKfiHIDiW6UG4jIt0yFCBH5ZgQFBVGmTBkOHjzI6tWryZ8/v7lDEnkn27Zto1SpUmTPnp0VK1bg4OBg7pBEREREojXlBhJdKTcQkW+VChEi8k14/PgxZcqU4dChQ6xZs4Z8+fKZOySR97J9+3ZKlSpFlixZWLlyJY6OjuYOSURERCRaUm4g0Z1yAxH5FqkQISLR3uPHjyldujRHjhxhzZo15M2b19whiXyQnTt3UrJkSTJlysSqVauUcIiIiIi8J+UG8q1QbiAi3xoVIkQkWgsMDKR06dIcPXqUtWvXkidPHnOHJPJRdu3aRYkSJciYMSOrVq3CycnJ3CGJiIiIRAvKDeRbo9xARL4lKkSISLT16NEjSpUqxYkTJ1i7di25c+c2d0gin8Tu3bspUaIE6dOnZ/Xq1Uo4RERERN5CuYF8q5QbiMi3QoUIEYmWHj16RMmSJTl16hTr1q0jZ86c5g5J5JPas2cPxYsXJ126dKxevRpnZ2dzhyQiIiLyVVJuIN865QYi8i1QIUJEop2HDx9SsmRJTp8+zfr168mRI4e5QxL5LPbu3Uvx4sVJkyYNa9asUcIhIiIi8n+UG0hModxARKI7FSJEJFp5+PAhJUqU4OzZs6xfv57s2bObOySRz2rfvn0UL16cVKlSsWbNGuLGjWvukERERES+CsoNJKZRbiAi0ZkKESISbTx48IASJUpw/vx51q9fT7Zs2cwdksgXsX//fooVK0bKlClZu3atEg4RERGJ8ZQbSEyl3EBEoisVIkQkWnjw4AHFixfnwoULbNiwgaxZs5o7JJEv6sCBAxQrVozkyZOzdu1aXFxczB2SiIiIiFkoN5CYTrmBiERHKkSIyFfv/v37FC9enEuXLrFhwwayZMli7pBEzOLgwYP4+fmRLFky1q1bh6urq7lDEhEREfmilBuIRFBuICLRjQoRIvJVu3//PsWKFePKlSts2LCBzJkzmzskEbM6dOgQfn5+JEmShPXr1yvhEBERkRhDuYFIZMoNRCQ6USFCRL5a9+7do1ixYly9epWNGzeSKVMmc4ck8lU4fPgwfn5+JEqUiPXr1xMvXjxzhyQiIiLyWSk3EImacgMRiS5UiBCRr9Ldu3cpVqwY169fZ+PGjWTMmNHcIYl8VY4cOULRokWVcIiIiMg3T7mByJspNxCR6ECFCBH56ty9exc/Pz9u3rzJxo0byZAhg7lDEvkqHT16lKJFi+Lp6cn69etxc3Mzd0giIiIin5RyA5F3o9xARL52KkSIyFflzp07+Pn5cevWLfz9/UmfPr25QxL5qh07doyiRYvi4eHBhg0bcHd3N3dIIiIiIp+EcgOR96PcQES+ZpbmDkBE5IXbt29TtGhRbt++rURD5B1lyJABf39/bt++jZ+fH3fu3DF3SCIiIiIfTbmByPtTbiAiXzONiBCRr8KLROPevXv4+/uTNm1ac4ckEq2cOHECX19f3N3d2bBhA/Hjxzd3SCIiIiIfRLmByMdRbiAiXyMVIkTE7G7dukXRokV58OAB/v7+pEmTxtwhiURLJ0+exNfXF1dXVzZu3IiHh4e5QxIRERF5L8oNRD4N5QYi8rXR1EwiYlY3b97E19dXiYbIJ5A2bVoCAgJ48OABRYsW5datW+YOSUREROSdKTcQ+XSUG4jI10aFCBExmxs3buDr68ujR48ICAhQoiHyCaRJk4aAgAAePnyIr68vN2/eNHdIIiIiIm+l3EDk01NuICJfE03NJCJm8SLRCAoKwt/fn1SpUpk7JJFvypkzZ/D19cXR0ZGNGzeSMGFCc4ckIiIiEiXlBiKfl3IDEfkaqBAhIl/c9evX8fX1JTg4GH9/f1KmTGnukES+SWfPnsXHxwcHBwf8/f2VcIiIiMhXR7mByJeh3EBEzE1TM4nIF3Xt2jV8fHx4+vQpAQEBSjREPqOUKVMSEBDAkydP8PHx4fr16+YOSURERMREuYHIl6PcQETMTYUIEflirl69io+PD8+fPycgIIAUKVKYOySRb96LhOPp06f4+Phw7do1c4ckIiIiotxAxAyUG4iIOWlqJhH5Il4kGqGhoQQEBODt7W3ukERilPPnz+Pr64utrS3+/v4kSpTI3CGJiIhIDKXcQMS8lBuIiDloRISIfHZXrlzBx8eHsLAwJRoiZpI8eXICAgIIDQ3Fx8eHK1eumDskERERiYGUG4iYn3IDETEHjYgQkc/q8uXL+Pr6YjAY8Pf3J1myZOYOSSRGu3jxIj4+PlhZWeHv70+SJEnMHZKIiIjEEMoNRL4uyg1E5EtSIUJEPptLly7h6+sLgL+/P0mTJjVzRCICEf9t+vj4YGFhQUBAgBIOERER+eyUG4h8nZQbiMiXoqmZROSzePFkBUBAQIASDZGvSNKkSQkICADAx8eHS5cumTcgERER+aYpNxD5eik3EJEvRYUIEfnkXiQalpaWbNq0SU9UiHyFXiQcFhYW+Pj4cPHiRXOHJCIiIt8g5QYiXz/lBiLyJagQISKf1IULFyhSpAjW1tZs2rSJxIkTmzskEXmNJEmSEBAQgJWVlRIOERER+eSUG4hEH8oNRORzUyFCRD6Z8+fP4+Pjg62tLQEBASRKlMjcIYnIWyROnJiAgACsra0pUqQIFy5cMHdIIiIi8g1QbiAS/Sg3EJHPSYUIEfkkzp07h4+PD3Z2dko0RKKZRIkSsWnTJuzs7ChSpAjnz583d0giIiISjSk3EIm+lBuIyOeiQoSIfLSzZ8/i4+ND7NixCQgIwMvLy9which78vLywt/fn1ixYlGkSBHOnTtn7pBEREQkGlJuIBL9KTcQkc9BhQgR+SgvEg17e3v8/f3x9PQ0d0gi8oG8vLwICAggTpw4+Pj4cPbsWXOHJCIiItGIcgORb4dyAxH51FSIEJEPdubMGYoUKYKjo6MSDZFvhKenJwEBAdjb2+Pj48OZM2fMHZKIiIhEA8oNRL49yg1E5FNSIUJEPsjp06cpUqQIzs7O+Pv7kzBhQnOHJCKfSMKECQkICMDR0REfHx9Onz5t7pBERETkK6bcQOTbpdxARD4VFSJE5L2dOnUKHx8fXFxc8Pf3J0GCBOYOSUQ+sQQJEuDv74+zszM+Pj6cOnXK3CGJiIjIV0i5gci3T7mBiHwKKkSIyHs5efIkPj4+uLq6snHjRjw8PMwdkoh8Ji8SDhcXF3x9fTl58qS5QxIREZGviHIDkZhDuYGIfCwVIkTknZ04cQIfHx/c3NyUaIjEEB4eHvj7++Pq6qqEQ0REREyUG4jEPMoNRORjqBAhIu/k+PHj+Pr6Ej9+fDZu3Ej8+PHNHZKIfCEv/rt3c3PDx8eHEydOmDskERERMSPlBiIxl3IDEflQKkSIyFsdO3YMX19fPDw82LBhA+7u7uYOSUS+sP/eaPDx8eH48ePmDklERETMQLmBiCg3EJEPoUKEiLzR0aNH8fX1JWHChEo0RGI4d3d3Nm7cSIIECfDx8eHYsWPmDklERES+IOUGIvKCcgMReV8qRIjIax05coSiRYvi5eXFhg0bcHNzM3dIImJmbm5ubNiwAU9PT3x9fTl69Ki5QxIREZEvQLmBiPw/5QYi8j5UiBCRKB0+fJiiRYuSKFEi1q9fT7x48cwdkoh8JV4kHF5eXvj6+nLkyBFzhyQiIiKfkXIDEXkd5QYi8q5UiBCRVxw6dIiiRYuSJEkSJRoiEqV48eKxYcMGEidOjK+vL4cPHzZ3SCIiIvIZKDcQkbdRbiAi70KFCBGJ5ODBg/j5+ZEsWTLWrVuHq6uruUMSka+Uq6sr69evJ2nSpBQtWpRDhw6ZOyQRERH5hJQbiMi7Um4gIm+jQoSImBw4cAA/Pz+8vb2VaIjIO3mRcCRLloyiRYty8OBBc4ckIiIin4ByAxF5X8oNRORNVIgQEQD279+Pn58fKVKkYN26dbi4uJg7JBGJJlxcXFi/fj3JkyenaNGiHDhwwNwhiYiIyEdQbiAiH0q5gYi8jgoRIsK+ffsoVqwYqVKlYu3atcSNG9fcIYlINBM3blzWrVtHypQp8fPzY//+/eYOSURERD6AcgMR+VjKDUQkKipEiMRwe/fupVixYqROnVqJhoh8lBcJR+rUqfHz82Pfvn3mDklERETeg3IDEflUlBuIyP9TIUIkBtuzZw/FihUjbdq0rF27FmdnZ3OHJCLRnLOzM2vWrCFt2rQUK1aMvXv3mjskEREReQfKDUTkU1NuICL/pUKESAy1e/duihcvTvr06VmzZg1OTk7mDklEvhEvEo506dJRrFgx9uzZY+6QRERE5A2UG4jI56LcQEReUCFCJAbatWsXxYsXJ2PGjEo0ROSzcHJyYvXq1WTIkIFixYqxa9cuc4ckIiIiUVBuICKfm3IDEQEVIkRinJ07d1K8eHEyZ87MqlWrcHR0NHdIIvKNepFwZMqUiRIlSrBz505zhyQiIiL/odxARL4U5QYiokKESAyyY8cOSpQoQdasWVm5cqUSDRH57BwdHVm1ahWZM2emRIkS7Nixw9whiYiICMoNROTLU24gErOpECESQ2zfvp0SJUqQLVs2JRoi8kW9SDiyZs1KyZIl2b59u7lDEhERidGUG4iIuSg3EIm5VIgQiQG2bdtGyZIlyZEjBytXrsTBwcHcIYlIDOPg4MDKlSvJli0bJUuWZNu2beYOSUREJEZSbiAi5qbcQCRmUiFC5Bu3ZcsWSpYsSc6cOVmxYgX29vbmDklEYqgXCUfOnDkpVaoUW7duNXdIIiIiMYpyAxH5Wig3EIl5VIgQ+YZcuHCBESNGmF5v3ryZ0qVLkydPHiUaIvJVsLe3Z/ny5eTKlYtSpUqxZcsW074RI0Zw4cIFM0YnIiLy7VBuICJfO+UGIjGLhdFoNJo7CBH5NFq2bMm6des4e/YsmzZtomzZsuTNm5elS5cSJ04cc4cnImISHBxM+fLl2bVrFytXrqRw4cKkSJGCkiVLMmHCBHOHJyIiEu0pNxCR6EK5gUjMoEKEyDciLCwMT09PGjVqRJkyZShbtiz58+dnyZIlSjRE5KsUHBxMhQoV2LFjBytXrmT58uXMmDGDa9euYW1tbe7wREREoi3lBiIS3Sg3EPn2aWomkW9EQEAAd+7cIXny5JQpU4YCBQroaScR+arFiROHZcuWUaBAAcqUKUOKFCm4ffs2mzZtMndoIiIi0ZpyAxGJbpQbiHz7NCJC5BvRrFkzli9fzoMHDyhcuDCDBw9m+fLlODg40LlzZ3OHJyLyihEjRhAUFES5cuXo2bMnW7ZsIW7cuFSoUIFJkyaZOzwREZFoS7mBiEQ3yg1Evn0aESHyDQgNDeXvv//m1q1beHl5cfHiRXLlysWoUaMICgoyd3giIlEKCgpi1KhR5MqVi8uXL+Pp6cnt27eZO3cuYWFh5g5PREQkWlJuICLRkXIDkW+fChEi34DZs2cTGBiIwWDg3r175M+fn+XLl3P79m369etn7vBERKLUr18/bt++zfLly8mXLx/379/HYDAQGBjI7NmzzR2eiIhItKTcQESiI+UGIt8+Tc0k8g3Ys2cPHTt2pEuXLpQqVQpbW1tzhyQi8t5CQkJYvXo1w4cP55dffiFXrlzmDklERCTaUW4gIt8C5QYi3x4VIkRERERERERERERE5LOxNncA8v7CwsI4e/YsT548MXcoIp+VlZUVnp6exI8f39yhiHyVbt++zfXr1wkPDzd3KCKflb29PSlTpsTaWl9dRT6E0Wjk0qVL3L9/Hz2HJl8jW1tbvL29cXBwMHcoIjHOgwcPuHLlCqGhoeYORWIYZ2dnkidPjqWlVg6IKZTNRSNPnz6lS5cuzJ07l3v37pk7HJEvJn/+fPTr15/ixYubOxSRr8K6devo17cv23fsMHcoIl9MPFdXatWuzfDhw4kdO7a5wxGJFoxGI+PGjWP8uLGcOXvO3OGIvJGdnS2lSpZi1OjReHt7mzsckW/e/v376d6tKxv9A/Rgk5iNV8IEfN+kKX379sXKysrc4chnpqmZoomwsDDKli3Dli1baP19PUr4FMTZ0QEsLMwdmshnEx4ezokz55gycz57Dx1lxYoVFCtWzNxhiZjV+vXrKVu2LDnSpeC7ysVJ651IX9jkm2bESGBQMOt3HGDi3yspVKgwK1et0u+9yDsYNGgQffr0obpPVioXykxCVycslT/IV+hZaCh7Tl5m0vKdWNjGYdv2HSRKlMjcYYl8sw4fPkyRwoVI6GDFd4VSkDGxC7bWeipdvhyjEe4HPWf14WtM33qWRo2+Y8qUKeYOSz4zFSKiiY0bN+Ln58fiGRMpU8zH3OGIfFEhISEUq9oQW3tHNm3abO5wRMyqcKFChD6+y6rfBmBrY2PucES+qNVb91K1/WA2btyIr6+vucMR+aoFBQXh7u5Gk9K5Gfh9GXOHI/JOrt97RP4fxtKuY2f69+9v7nBEvlkNGjRgy+olrOteHKfYWtBezGvqptP0+Hsf586dI3ny5OYORz4jlTujiaVLl5I0sRel/YqYOxSRL87W1pbv61Rjy5at3L9/39zhiJjNvXv32LptGw0rFlMRQmKkkgVykDhhfJYsWWLuUES+euvXr+fZs+c0KZPX3KGIvDPPeM6UypWGJYsXmTsUkW+W0Whk6ZLFVM2VREUI+SrUzJucWLbWLF261NyhyGemQkQ0ce3aNVJ5J8VCQ6klhkqdwhuj0ciNGzfMHYqI2dy8eROj0UjqpF7mDkXELCwsLEiZJCHXrl0zdygiX71r165hY21FEg8Xc4ci8l5Sernr77zIZxQcHMyjwMek8HA0dygiANjbWZPQxUF/+2MAFSKiifDwcKyt328u5ItXrmHrmY6DR0989PlT5fZj7B/TP7ofkQ9lY2MNoEW0JEZ78ftvbfV+H9+Xrt/GPmdlDp268NExpCvfjPGzl310PyIfysbKSp8FIu8gPDwcayur93qQ6fKtB7iU78GR89c/+vyZGw9l4pKtH92PxDw21pb6Oy/yGZlyCstPc0vw8r0gPFrN4eiVBx/dV87eS5m08eQniEqiGxsr/e2PCazNHYBED9tXzcc+dmzTa1vPdMyfMo6KpT9+4eD9h4+Rt1Q1tiybQ54cWV/ZX7LGdzg5OhAaGkZYWBjLZ//xSputu/ZStHJ99q5fTOb0aVi8ah0jfp3MyTPnMRgMJPZKSLHC+Rk5oCcAN27dpmv/Yew7fJRzFy7zQ+N6pn1fiymz5jFz/lKOnToDQPZM6RnYowO5smU2tRkwYjzzlqzk6vWb2NrakD1TegZ0b0/u7FlMbe4/eEj73oNZsc4fS0tLKpcpzi8De+Jgb29qszZgKwNGjOP4qbPEsrOjYN6cDOvbjWSJX//U9elzF+g+cAQ79uwnJDSUTOnS0K9rW3wK5DG12bhlB/2GjeXoydPYx4lDveoVGdi9PdbWEX96Tp29wA/d+3Hi9DkePX6Mp0d8alYuS5+OrbHRtDMiX6XNM4ZjHzuW6bV9zsrMHdGd8j553nDUuzlw4hwF63fGf9oQcmdK88r+Mi1/xNkhDqFh4YSGhbNk3I+vtNl24DglmvZi55xRZEqVjKX+O/ll+iJOXbiKwWggcQJ3fPNkYXinxgDcuHufHqP+5MCJs5y7cpOWtcqa9n0tpi1ay+wVARw/dxmArOlS0L9VXXJmTG1qExT8lB/H/cWyTbu5/+gxyTzj07JmWZpUKwVEFKPSV2geZf9/DelMlWIFOHz6AiP/XMiOQye49/AxSRO607hqSVrXLv/G+O4/ekyn4ZNZtWUPlhYWVCyaj+GdG+MQ5+X3BqPRyJiZS5i2aC2Xb9whXlwnmlUrRdfG1U1tnoeE8vMffzN31WZu3XtAAjcXujepQcOKH/9dQ0S+vI2/tCaO3cspP1zK92Bmz3qUzZfho/s+ePYavh3Gs3Z4S3KlTfLK/oq9JuNkH4u/etbj7qMgfpq1nrV7TnLnYRBxHWKT0TshXWoVJW/6ZDx4HMzPs9fjf+AMV+88JJ6TPWXzpqdnvRI428eK4uzm0W3SUnaduMSJS7dInTg+W8a2fW3b89fvUqT9OCwtLbk0t69p+/Q1u5m78QAnLt0EIGtKL/o0KEmO1InfKYadxy9SrscfpEvqEen84eEGhsxZzzz/g9x++JgErk7U8ctO55pFTYWwIbPXs3DzYa7dfYiNtRVZU3rRu34JcqZ59ecnIjHX6m4liGP38lalR6s5TGtWiDJZP93i9SsPXmHc2hOcuRmIwWgkkWscCqdNwKDqOQCYu+M8ff7Zz5mR1QBYceAKf245w7GrD3keFk6ahM50KZsJ3/QJP1lMHytn76Vcuf/kle3fFU7FkFo5X3vc0v2XGbrsMFfuPcE7viN9KmWlWEbPSG1O33jEwMWH2HHmNmEGA2kSODOlWUESuUbcU6o8agPbz9yOdEyDgikZXifXJ7gy+daoECFvFBISgq2tLe7xXD/bObJnzkDm9Gn5c+7CVwoRF69cI2DbLhZNn0BoaBg1m7bj6vWbJPJMEKnd9LmLyJElI5nTp2Hjlh3UbdGJAd3aUa5EUSws4MTpc2zYvN3U/nlIKO7xXOnRrgVjf5/xya7lyrUbJPb6NB9Gm7bvoWalMuTNmY1YdnaM+HUyZWo34aD/MrwSegCQKnkyxgzujXfSxDx99oyxv0+nTO0mnNi+xvQza/hDV27cusOquVMIDQ2jaceetOzSl78mjADgwuWrVP2uNe2aNWL6+OEEBj6mc78h1Gjcht1rF742vkoNW5LKOylr5v9J7Fh2jPtjBpUatOTkjjUkiO/OoWMnqVC/Od3bNmfq2CFcv3mL1t36Ywg3MLRvVyBilEO9ahXJmik9cZ0dOXzsFC27/IjBYGRQjw6f5H0UkU8jJDQUWxsb3F2cP9s5sqVLQabUyZixdMMrhYhL12+zee9R/hnVk9CwcOp0Hca1W3fx8nCL1O6vpRvInj4lmVIlw3/3YRr0GEnfVnUoWzg3FhZw8sJVNu46+PK6QsJwc3Gi6/fVP+lIj6s375IogdvbG76DzfuOUb1kIfJkTkssOxt+mb6ICj/0Z++8sXjGjwdA91HT2LTnCFMGtCepZ3w27DxI+6GTSOjuStkiuUnkEY9zq6dG6nfaorWM/msxJfJnByIKQe6uzkwZ0J5EHm7sPHySNoMnYmVpRYuar1/o9vs+o7h59wHLfu1HaFgYLfqP54fBE/lzcEdTmy4jprBh50F+ateIDCmT8iAwiPuPHkfqp3734dy+/4gJfVqTInFCbt69j8Fg/CTvoYh8OSGhYdjaWOPm7PDZzpE1pRcZvRMya/3eVwoRl289YMuR88zp0wCABj/PIjQsnIkdqpM0gSt3Hgax6dA57j8OBuDG/UBu3gtkwPdlSJs4PlduP6TjhEXcvP+Y6T3qfnCMN+8H4uZsj7XV+41qf5O6xXOy79QVjl28+do2oWHhNBk+l7zpk7H75OVI+7YeOU/VwpnJk648djbWjFmwmSo/TmXHr+3xjPfmz/dHQU9pOWo+RbKk4PbDoEj7Ri/YxNSVu5jQoTrpknhw4OxVfhjzD05xYtG8QgEAUni6MaxFBZIlcOXp81AmLtlKlR+nsv/3zp/1d0VEooeQsHBsra1wc/y8BeDNJ2/SbMp2elTITMnMXlgAp24Esvnk6/+u7jh7myJpE9CzQhac49gyZ8d56k/czKquxcmU+MPulRkMRm4FPiVh3DgfeCWRre5WItL35hM3HlFjrD/ls7++0Lzn3B1aTN1Or4pZKJ7Rk4V7L9Fo0hbW9ShJOs+4AFy885gKv6ynTr7kdC2XEcdYNpy88Qg7m8ifbfUKpKBbuUym17FtdbtZoqbfjGjOYDDwy8SpTJk1nyvXb+Dh5kaT+jXo0a7FK23Dw8Np2eVHArbt4uaduyT2TEiLRrVp06SBqU3j9j14+OgxObNm5Lc/Z2Nna8vpXetJlduPNk0b0LZpQ1Ll9gOgeuM2ACRN5Mm6BTNIk7c421fOI0eWjKb+xv4xnbG/T+f0rvVYvmHY33e1q9J32BhG9u9BnP88QTnj70Uk9HCnpG8hjEYj7vFcmTFvET3btzS1CXryhAXLVzOkTxcAVqzzJ3+ubHRq9fKJ1tQpvCON3kiW2ItfBkaMgJg+9/U329/F5avXmbVgKX/NX0z61Cn5Z+r4j+rvhRm/Do/0etLIgSxauZaNW3dQv3olAGpXKRepzfB+3Zk2ZwFHjp+iaKF8nDhzjjX+W9ixar7p5zJqUG8q1GvO0B+74pkgPvsPHyM83MCAbu1MP6MOLb6n6netCQ0NjXJkwt17Dzh7/hK/jxxE5vQRNwsH9+rEb9PncOzkGRLEd2f+0lVkSpeG3h1bA5DSOyk/9+5MnRYd6N2pNY4O9iRPmpjkSV9+MCZN5MXmHbvZtmvfJ3kPRWISg8HA6L8inji/eusu8V3j0rhKiUhPnL8QHh7OD4MnsmnvEW7de0hiDzeaVi8V6cn3Zv3G8ujxE3KkT8mk+auws7Xh+NJJpCvfjNa1y/NDnfKkK98MgFqdhwCQJKE7qycNIkPFFmyePozs6VOa+hs/exnjZy/j+NLf3vh50LBCMQZMnM2wTo2JE8vOtH3mso0kcHOheL5sGI3g5uLEzGX+dGvy8vqCgp+yaMN2BrdtCMDKLXvIlyUtHRpUNrVJldQr0uiNpJ7xGdG5CRBRxPgYV27eYc6KAGat8Cdd8iTMHdH9o/p7YdqgyIXZCb1bsWTjDvx3H6ZuOV8Adh46Sd1yvhTOGfG3/vsqJZiycA17j52hbJHcWFlZkcAt8jzxS/13UaVYAdPIhf8feeCdKAG7j5xiif+O1xYiTl64wrrtB9gyY7jp5z2iSxOqtBvEz+0bkdDdlZMXrvDHP6vZ8/cYUieLGGmXzMsjUj9rt+9n6/5jHF3yG67OEfMVJ/WM/97vlYi8ncFgYNyiLUxfs5trdx7hHteBRqXy0Lmm7yttw8MNtP91EZsPneP2w8ckco9L4zJ5afHvzWWAVqPm8+jJM7KlSsSUlTuwtbbm0JSuZG48lJYVCtCyYkEyNx4KQL2fZgKQOH5clv/UjKxNh7NhZCuypXr5pOvEJVuZsGQbhyZ3eePnRf3iORk0cy0/NSlHnFgvR17M3rCPBC6OFMuemkdBT9lx7CLLf2pKgUzJAUgS3yXSCID0SRMwo2c902vvhPHoXb8kzUf+Tdi/01u9q2choazcdYLZ6/cRcPAs52b3wdn+0xQihjavAMC9R0/eWIgYNHMtqRK5UyRLylcKEX90rhXp9dg2VVi2/SibD52jVtHsbzx/hwmLqVYkC1aWlqzYeTzSvt0nLlEmb3pK5koLQBIPFxZsOsS+M1dNbar7ZI0cZ5Oy/LVuL8cu3qRIlpSIyNfNYDAyYf0J/tp2jusPgnF3jEX9ginpUPrVUW7hBgOdZu1h6+lb3Al8hpdLHBoVTkWzoi8fNGo7YyePgkPImjQe0zafwdbakr0DK5Cz91KaFk1N86Jpydk7YuHi737fAkBiV3sWdihK7h+XsbprCbImjWfqb9LGk/y+8RR7BlTA0vL1UxKuPXKNXMndaF08nWlbCg+nN464eDFS4oVeFbOw5vBV1h65/t6FiDM3A/l753n+2X2R8tmTMLDam//2vqv/L+CMXXucZO4O5E/1+u/Tv/ufxjd9QtN70b18ZjaduMnUgDOm0Qw/LT2MXwZPfqySzXRcMvdX1xaJbWtFfOfYr2wX+X8qRERzvX76hamz/2F4v+4UyJ2dm7fucOrs+SjbGgwGvBImYM7vo3F1icuOvQdo1aUvCeK7U71CaVM7/607cHK0Z+XcKVH2s33VfLwyFWDyqJ8o4VsQKysr3OO54lcoH9PnLoxUiJj+9yLq16j8xiQCIm6odx80nAUr1phushuNRmbOX0z9GpWx+jcBqFutAn/NW0yPdi1Mw3wXLFtDeLiBmpXKAuDh7s7xUys4evI0GdOmjvJ8H+tJcDCLVq5j5rzFBGzfTZ7sWejQ/DuqlS9lajN74TJad+33xn6WzZpEwTyvHyb3X8FPnxEaFoZr3KifVgoJCWHyzHk4OzmSOX1EErBr70HiOjtF+pn4FcqHpaUluw8colLp4mTPnAFLSwumz11Ig5qVCXoSzKwFS/ErlO+10yPFc41L6hTezJy/hGyZ0mNna8sff/1NfLd4ZM+cwRRPLDu7SMfFjmXHs2fP2X/4GEXy536l37MXLrHGfyuVymgaDpH39eP4mfy5eB1DOn5H/qzpuHn3AacvRr3Yl8FoxMsjHjOHdMHV2dH05HsCN1eqFn95cylgz2Ec7eOw7Nd+UfazecZwkhVvxG9921A8XzasrCxxd3HGN3dm/lq2IVIh4q9lG6lX3vetnwc1Sxem19jpLFq/3XST3Wg0MmuFP/XK+Zo+D+qU8WHm8o10bVzN9HmwcP12wsMNVC9VCACPeHGZd/4yx85eIkPKpO/2Rr6nJ0+fsWTjTmat8Gfz3qPkzpiatnUrUuU/7+PcVZto+9Nvb+xn0dg+FMiW/p3OGfwshNCwcFz/8wRp3ixpWbF5Dw0q+JHQ3ZXN+45y9vJ1hnb8Pso+Dpw4x+HTFxjVrdkbz/UoKBhXp9cvZLjr8CniOtpH+lkXzZ0FS0sL9hw9TQXfvKzcvBdvLw9Wbd1LpbYDMBqN+ObOwqC2DUxFh5Wb95AtfUpGzVjEnJWbsI9tR5nCufmxRW1ix7J73elF5AP0n7GGGWv28FOTsuRNn4yb9x9z5uqdKNsajEY84znxZ/c6uDrGYdfJS3QYvwgPF0cqF3o5Xejmw2dxjGPHwgFRT2238ZfWpKo3mF/bVcMvR2qsLC1wc3bAJ0sKZq3fF6kQMWv9Pur4ZX/r50V1n6z8OG0VS7cfNd1ENxqNzNmwn9p+2bGyssQ+ti0OsW1ZsfM4OdMmwc7m3dLfwCfPcIwT652LELtPXmLOhv0s2nKYOHa2VC2ShQHfl440tVOi6n3f0EPE9YxqXfmNbd5m86FzLNl6hM1j27J8+7G3tg9+HkpoeDhxHd5882jW+r1cunmf3zvVYMTf/q/sz50uKdPX7ObstTuk9HLnyIUb7DxxiUGNoy5ih4SGMX31bpzsY5Ex2dcztYmIvN7gJYeYue0cA6plI08Kd249esaZW4FRtjUYwdMlDpObFMDF3o495+/SefZuPJxjUzHHy1FsW07dwjGWDfPavFoIh4in/DN0W8SY+nkomj4hlpYWuDnGonCaBMzdcSFSIWLujgvUzJv8jUUIgPhOsVl44xInrj80PfX/vgwGI0HPwogbx/btjYGHwSEs3nuJv3de4NDl+xRK40Hvilkok+1lUXz06mOMWXP8Db3Alj5lTNMhvUlIWDgLdl+kuV/aN64Tte/CXZr7RR6F7ps+IasORRSRDQYj649e54fi6ag5zp8jVx6QxM2BtiXSv1K4WbjnEgt2X8TdKTYlMnnSsUxG4mhUhERBvxXR2OOgJ4yf8hdjBvWmQY1KAKRIloQCeXJE2d7Gxoa+XdqYXnsnScSuvQf5Z9nqSIUI+zhxmDRiILa2Uf9RfTHlj7OTIwniu5u2f1enGj9078/wft2xs7PlwOFjHD1xmgXTfn3rtbi6xKViqWJMn7vQVIgI2LaLi1eu0bDmyy/kjWpV5ZeJU9m8Y4/pRvb0vxdSuWxxnP+9UdK6cV227d5L9qIVSZrIk9zZs1C8SAFqVymPnd27fVC8zuYdu/lr3hIWLF9NfLd41K5Snl+H9SdFslfnNi1foii5/7OeQ1S8Eni8cf9/9Rw8Ak+P+PgVyh9p+4p1/tRr2Zngp09J6OHOqrlTcIsX8dTrzTt3X5lWy9raGte4zty6fReI+D1YOWcydZp3pFW3foSHh5M3R1aWzpz02lgsLCxY/fdUqn3/A66pcmJpaUl8N1eWzfodl38LJcWLFGTsHzOYu2gF1SuU4ubtuwweNQGAG7ciJ7uFy9fmwNHjPH8eQpN6NejX5fVz3orIqx4/ecqEucv5pWtT6pUrCkDyRAnJnzXqG9s21tb0bl7b9DqZlwe7D59i4bptkQoRcWLHYkKfVti+pij5YpqmuI72kZ62b1SpOO1+/o0hHb7HztaGAyfPcezsJeaN7PHWa3F1dqS8Tx7+WrrBVIjYtPcIl67fpn4FP1O7BhX9GP3XYrbsO2YaBTBz2UYqFs2Hs0PEF/SWNcuy/cAJctdqT5KE7uTOmIaiebNQq3QR7Gw/bh2aLfuOMmu5P4s2bMfdxZlaZYowrmcLkid69YZK2cK5yZXxzYVxT/d3f5qqz7gZJHRzwTf3y/WARnZpyg+DJ5CqTBOsraywtLRgfK9WFMwe9Vzs05esJ613IvJmSfva8+w8dJIFa7exYEzv17a5fe/hK9N1WVtb4eLkwK17DwG4eO0ml2/eYdH67fzRvx3h4Qa6/TKVut2Gseq3gQBcuHaLHQdPEMvWhrnDu3H34WM6DJ3E/UePmdS3zf+fVkQ+0OPg50xaup1hzStQ2y8iZ/BOGI98GZJF2d7G2ooedYubXidN4Mqek5dZvPVIpEJEHDtbxrapgu1rbvS/mHrH2T4WHi4vi5v1S+Si44TFDG5SFjsbaw6dvcbxS7eY3btBlP38l4tjHMrmS8/MdXtNhYgth89z+fYD6haLuDZrKyt+bVedduMXMm31LjKn8KJARm+qFMpMRu+ob4Dfe/SE4X9vpGHJN89tfe3uI+Zs2M/cjfu4cS+QsvkyMLVrbXyypoyyiLJ5zJv/ljnG+bjpSO4HPqHV6PlM6lQTp3fsq9+fq0jg6oRP1tePSDh3/S79p69h5ZBmry3MdKhWhMfBz8ndchRWlhaEG4z0rl+CGj7ZIrVbvfsETYbPJfh5KAlcHFk04HviOb/9ppqImFfQs1D+8D/FTzVzUjNvxOiyZO6O5EnpHmV7GytLuv5nmp6kbg7su3CXpfsvRypExLGz5pd6ubG1jvpvy4un/J1i20Z62r5ugRR0nbOH/lWzYWdjxeHL9zlx/SHTWxR667U09knNrrN38Bm0isSu9mT3jodPugRUzZXslemGXmfC+hM8eR5GhRyvX+PGYDCy4dh15u26wJrD10ge35Hqebz5s3khPKIYOdCwUEoqZn/zmjkJ3nHEwapD13j0NJRaeb3f2O524DPc/28khbtjLG4H/o+9u4yOIukCMPxm4u5KPCRIIAnuENzd3d3d3R2Cy+Lu7u7uGiBIAgnE3We+HwMTZiME2Q3fUs85nGW6q6qrZ3e7p/tW3YoHICQ6gdjEFHxOPGFEXQ/GNvDizJNAOq26yJ7+lSntJp9t0bCYA3YmulgaavPkfQRT9t3j1cdo1nb/9r8P4c8jAhH/x569eEViYhIVy5XKdp1lazezbtse/N8HEp+QSFJyMp7uyi8hCuR1zTQIkZX6NSrTf9Rk9h09SfMGtdmwYx/eZUpkueDx1zq0aETtVl159eYdLo72rNu2h/KlipHbKW0Ua15XZ0oVLcS6bbupULo4L1+/5dL125z86sW1ro4O+zeu4NWbd5y/fJ3rd+4zbNIsFv21kYsHtiqlfvpeVRq3R1tLizkTR9C1bfMsy+rr6aKv92t+WM9atIod+49yctd6tP42MtS7TAluntxDaFg4f23eSavuA7l0WD47ITuCPgXTY+g42jStT/MGtYmJjWXi7EW06Nqfo9vXZBhBl8lk9Bs1GQszE87u3YS2liZrtu6iUYdeXDmyA2tLC6p6l2HG2KH0GTGBjv2Go6mhwagBPbl0/Xa6UQqbl88jOjaWB4+fM3LKbOYtW8OQ3l1+/AsThD/M89f+JCYl410s6+Dn11bsOMKGA6cJCAohPjGJpOQUPNwclcq4uzhkGoTISl3v4gyauZIDZ6/RtHo5Nh88S/miBbKdaqddvcrU7zsJv4BAnG2t2XDgNOUKu+Nil/bSKI+jLSU98rLhwGnKFy3AK/9ALt99onixDaCrrcWehWPwCwjkwq1H3Hjoy6gF61i67TBn185QSv30vWp0H4u2pgYzB3Wic+PqWZbV19VGX/fXTFWes243u05c4uiKyWh9FVxftv0wNx/6snPeKOyszbl85wmDZq3E2tyESiU8ldqIT0hkx7ELDO/SLNPjPH75luaDpzOqa3OqlPT6qT5LZTISk5JZNbEfrg7y3wRLx/WmbJsh+L55j5tjLqRSKSoqKqyZMlARSEpM6kjr4bNZMLybmBUhCL+Ib8AnEpNTqODpku06qw5fZfPJWwQER5KQlExSSioF//YSP7+jVaZBiKzULpmfocsPcOjqYxqX92TL6duUK+iMvaXxtysDbaoUpfH4tbwODMXJ2pRNp25RpoATzjZpa/TUK1OAasXycPXxG249f8fJ27747L6AT99GtKqiPIArKi6B5pPWkcfOghGtsp6hO3XjCbaeuUPdUu6cnNMLY/2s83x/3ad/Qv/Fe2lSwYsyBbJ+8fTF/J3n2HPxAQendUUrk+B8aqqUrrO3MaJVFXLnyviFI8DeSw/Zef4eq4Y0J6+9JQ/9PjBq9SGsTfQVAS+Ach4uXFjYl9CoODacuEnHmVs5NbcX5kZijQhB+J35BkWRmCKlXJ7sD6Rcc96XrVf8eB8eR3xyKskpUtxtjZTK5LMxzDQIkZWanrkYuf0WR+4H0LCoA9uuvaaMmyX2pt++luhqqrG5dwXeBEdzyfcTt1+HMGH3XVad9eXw0KrfHMW/++Yb5hx5xPoe5dO9xP9aQHgsbZZdwEhHg+WdSlPLK/O1GgCMdTUx1v01v3e3XHlFpfzWWP3k+hNSmXzNiRoetvSoLH9vWMDOmJt+Iay/9EIRiGhXNi2YnT+XEZaGWjRZeJY3wdEZpnES/mwiEPF/TEvr+0bNbN93mOGTZzNr3DBKFPFCX0+XecvWcOPOA6VyOjo/drHS0NCgTdP6bNi+l4a1qrJt7yHmfl6HITsqlSuFfS5rNuzYy+Cendl35CRLZk1IV65jy8YMGDMVn2njWL99Ly6O9pQvlX7EkoujPS6O9nRq3ZQR/XvgXrYmOw8cpX2LRj90fgB71y9l4879DBo3jVUbt9O6ST2aN6itNDPki1+VmmnesjXMXrKKY9vXKNZj+Jqujg65nRzI7eRAiSJe5C9TnbVbdzO8bzeszM0IDg1TKp+SkkJYRCSWFvKHoWXrtmCor69YYwNg3aJZOBetyI0799MtIA5w9tI1jpw6x6en1zHQl9/sF3m4c/rCFTbu2M+wvl0BGNC9A/27tSfwYzDGhga8CXjPmOnzlNaFABQLfOd3y02qNJVeQ8czsEdHRQoWQRCy9vcA5bfsPH6RUQvXM31AB4oXzIO+rjYLNuzj5mNfpXK62j/2Y1hDXZ2Wtb3ZePAM9SuVZMexC8waknG6joxULO6BnZUZmw6eZUDbBhw4cw2fUT3TlWtXvzJDZq9m/vBubDxwGmdbK8oVST8DwNnWGmdbazo0qMqwTk3wbNSbXScu0e6rGRbfa9f8UWw+dJahc//irz0naFmrAk2rl0u3DgP8utRMCzbuY966PRxaOpGCro6K7fEJiUxYspltc4ZTo6z8nlLQ1ZEHvq9ZuGl/ukDE3tNXiUtIolVt7wyP89TPn9q9xtOxYVWlNTgyYmFqRHB4pNK2lJRUwqNisDQ1AsDKzBg1VVVFEAIgr6N8Ord/UDBujrmwMjPGxtxEEYQAyONki0wm4/2nUHLb22TZD0EQsiezF86Z2X3hPuPWHGFyp1oUz+uAnrYGPnsuctvXX6mczg/OOtZQV6NFpUJsOXWbuqXc2XX+PtO71fl2xc8qeLpga27IltO36duoPIeuPGZe7wbpymlpqFOxkCsVC7kytEVl+vnsZvqWU0qBiOi4RJqMX4uetiabRrdB/Rsvx4Y0r4iliT47zt6laPe5NCrvQfOKhSiaJ+MRIRgu0AAArj5JREFUrf90aqYLD15x9PpTFu+V51KXIUMqlWFWfzQL+jSkTdW0Z45Fey6wYPd59k3unOnMEICY+ETuvnzPA79Ahi2X52qXymTIZPJ290zqRHlPF8atPcqAJhVoXF5+v3F3tCIgOIL5O88rBSJ0tTRwtjHD2QaK5bWnSLc5bDx5i0FNvX/4vAVB+OdpZXOmwBd7b71l4p57TGhUiKLOpuhpqrPk1FPuvAlVKvejqXs01FRpWsKRbVf9qO1ly96bb5jcNOPMIJlxNNfH0VyfNmVcGFDDndITDrH/9jtalnLO8rwGb7rBqi5lqJDXKsv2bYx0WN6pNDuuvabr6ssUdTajSXFH6hW2xzCDlE6/KjWTf2gsF559ZE23slmWA7Aw0CI4OkFpW3B0AhYG8gFUJnqaqElUcLM2UCrjZmXA9VcZp3QEKOwof9f0OjhGBCKEdEQg4v+Yq5MD2lpanL14FafWWb8oALh68y6lihaiR4dWim1+b95lUSNz6urqpEql6bZ3bNWEQhXrsXz9VlJSU2lYs2oGtTMmkUho17wR67buJpeVJRoa6jSunX6UaZN6NRg0bhrb9h5i8879dGvfIsu8dyBfnFpHW4vYuPhs9ycjtatWpHbVioRHRLJ932E27tzPiMlzqFK+NK0a16N+jcqKGRe/IjXTnCWrmeGzgsNbVimt85AVqVRGYmISACWKehERGcWdB48VazecvXQdqVRK8ULyB4X4+IR008dVVSWKtjISFy+/Wf19ZoOKigSpTPq3bSrYWMkj5dv3HsbOxppCBTN/2SaVSklOSUEqlYpAhCBkU247a7Q1NTh38wEdcn37unvt/jNKeOShW9O0tHx+7zNf+DIr6mpqpKamvx90aFCFYs0HsHLnMVJSU6lfsWS225RIJLStW5n1+09hY26ChroaDSunn/3XuGoZhs39ix3HLrDlyDm6Nq7xzfuBg40FOlqaxMUnZrs/GalZrhg1yxUjPCqGnccvsvnQWUb7rKdSCU9a1vKmrncJxYyLX5Gaad76vcxes4v9i8cprccAkJySSnJKSrpzV5VIkGZwr96w/xS1yxdLl1IJ4Mmrd9TqOY7WtSsyoXebdPv/roRHHiKiY7n79BWF8slHWJ+79RCpVKY455Ke+UhJTVXMcAF48e4DIF/gHKCUZz72nrpCTFy8YvHsl28/IJFIyGWRvRl+giB8m4uNKdoa6py//4p2Vt9OCXf96VuK53WgS+20a/CboNAsamROXU2V1Ax+W7atVozSfRbw15FrpEil1C2Vvd+8IL9ftK5ShI0nbmFtaoi6uir1yxT8Zr089hZKCy5HxSXQZNwaNNTV2DKmXbYCNs42ZoxvX4Oxbatx7t5Ltp65Q71Rq7ExM6B5xUI08y6Ew1ff8T+dmunE7J5Kz2dHrj3FZ/d5js3ugY1p2vV+4e7zzN1xlt0TOymtzZFxnzS5vLi/0ra/Dl/j4gM/1o1shYOl/PziE5OQ/O0eJJGkfy74O6lMRlJySrbOTxCEnONsoY+2uioXn3/Ewezbsw5uvAqmqLMZHSu4Kra9CY75oWOrq0oUI/O/1rq0CxWmHGXthRekSGXUzmKx6W+xN9VFW0ONuMTMr0d7br5h4KYbLO9UmqoFv531Q01VQsOiDjQs6sDHyHh2Xn/NqrPPGb3jNtUK5qJJCUcqu9ug/vndy69KzbTtqh9m+ppULfDtQTxFnMy4+Owj3SulZUk5/zSIok7yQIKGmipeDqa8+hitVO/Vp+gsAyKPA8IBeaBDEP5OBCL+j2lpaTKkdxdGTp2LuoY6pYsVJiQ0jCfPX9KxVZN05XM7ObBp135OnLuEo10uNu8+wK37j3C0+/4LtoOdDWcvXaV0sUJoamgo1gXI5+pCicKejJo6lw4tGqGt/X0XnvbNGzJ1/lLGzlhA8wa1Mqyvp6tL03o1GTN9PlHRMbRrpjxyaNKcxcTFx1OzcgXsbW2IiIxiyV+bSE5OoXL5tPUV7j16CkBMbBzBoeHce/QUDQ118rtlniP1C2MjQ3p0aEWPDq149sKPDTv2MmrqHPYeOcGO1T7Az6dmmr14FRPnLGLDkjk42OUi6FPw5/PXQU9Xl9i4OKYvXEHdahWxsjQnNCyCZWu38D7oI43rygM4+VxdqF6xHD2GjGXJzAkkJ6fQf8xkmtWvpQgO1KxcgYUr1zNl3hJ5aqaYWMbOWICDrQ1eBfIBcPPuAzr2G8HxHWvJZW1JySJeGBsa0Kn/SEYP7CVPzbR5F2/831OzcgXFOcxd+hfVKpZDIlFh35GTzF6ymi3L5ykCDFv2HERdTY0C+dzQ1NDg9v1HjJ0+n6b1ama6ULYgCOlpaWowqH0jxvhsQF1NjVJeeQkJj+LpK3/aN0ifWsLF3poth89x8updHG0s2HrkPHcev8QhV/ZSJ33NwcacczcfUNIzL5oa6hgbyB9O8jrZUbyAG2MXbaBdvcrfnVqnbb1KTF+9gwlLN9O0erkM6+vpaNO4alnGL9lEVGwcbepWUto/dcU24hISqV6mCPbW5kREx7Js+2GSU1KUZgncf/4agJj4BELCo7j//DUa6mrkc856CjWAsYEe3ZrWpFvTmjx/E8Cmg2cY47OB/aevsmX2cODnUzPNXbeHKSu2snbKIOytLQgKCf98/lro6WhjoKdDucLujF64Hm1NTeytzbl45zFbjpxjxsCOSm298g/k0t0n7Mlg3YfHL99Sq+c4qpQsRN/W9RTH+bIIOcCtR750He/D4WUTsbEwJa+THVVLF6L3lKX4jOxOckoqg2etpEm1slh/Dq5UKu6BV15nek5azMxBnZHJZAycuZJKJTwVsySa1SjHjL920GPiIkZ3b0loRBSjfdbTrl4lkZZJEH4hLQ11+jcpz/h1R9FQV6VEPgdCImN59u4jbatlMMPY2pRtZ+5w+o4vDpbGbD97lzsvAhQvoL+HvYUR5++/pER+BzTV1RQLJOexs6BoHnsmrDtG66pF0db8vt+ArSoXZda2M0zecJzG5T2V6odFxdJh5hZaVymKu6MV+tqa3H35Hp/dF6hVUj4wJiougcbj1hCXmMyKwc2Jjk8k+nOw2sxAVzFAJzMSiYRKhd2oVNiNqLgE9l18yNYzd5ix9TSvt45TrNfws6mZ/D6EEJuQxMfwaBKSknnoJw/o5rGzQENdjTx2yvfwey/eoyJRIb9D2qjdBbvOM33zSVYNaYG9pTEfw+Uvl3S1NND7PAty4vpjBIZGsXxQMyQSiVJ9AHMjPTQ11JS21yiWj3k7zmJrbkQ+e0se+H1g6b5LtK4qH6Ecm5DE3B1nqVk8H5Ym+oRFxbH68FUCQ6OyFTgSBCFnaamr0qdaPibvvYeGqoTiLmaExCTy/EMkrcukT/XnbKHPzutvOPskEHtTXXbeeMO9t2HYm33/+xE7U10uPguiuLMZGuqqigWi3awNKeJkypR992lZyhntbM6umH3oIfFJKVQuYIOtiS5R8UmsPutLSqqUCvkynuWw++Yb+q2/xpSmRSjiaMqnSPkAVy0NVQy0vz0j0NJQmz7V8tOnWn7uvQ1l+7XXDNx4nWYlnZjYWL7G0a9IzSSVyth2zY9mJZ1Qy+De1WfdVayMtBnTwAuAbhXdaDD/NMtOPaVKgVzsu/WW++/CmNM67fdA76p56fbXFUrmNqesmyVnngRy4uF79g6Qzyx/ExzNnptvqVzABmNdDZ68j2DcrruUym2Ou2320iwKfxYRiPg/N3pgT9TUVJk0exEfPgZjbWFG13YtMizbtW1z7j16Suseg1BRUaF5g1p0b9+S42cufvdxZ40bzrCJM/hr8y5yWVnw4sZpxb6OLRtz9dZdOrRo/N3t2tvaULlcKU6ev5xl/Y4tG7N2625qVi6veKH+RflSxVi2bgud+o3gY0gIxoaGeBXIx5Gtq8mTOy1navFqaSma7jx4zLa9h3CwtVE6l+zI6+rMtNGDmTJyIC9fv/2uullZuWEbSUnJtOiqPAppzKDejBvSB1WJKs9f+rFp5z5CwsIxNTaiiGdBzu7dhHuetJEH6xfPov/oKVRv1hGJRELDWtWYPyUtZVbFsiXZsGQ2c5f+xdyla9DR1qJEES8Obl6lCATFxSfg++o1ySnyEQJmpsYc2rKKcTMWUL1ZB5KTU8ifJze71y5WWnPk+NmLzPBZQWJSEh7587B77WJqVCqv2K+mqsqcJat54fcGmQzsba3p2bE1/bu2/2XfoyD8KUZ0aYqaqoQpK7YSGByOlZlxpmsXdG5UnfvPX9N+5BxUVFRoWr0cXZvW4MSVO9993OkDOjJi/lrW7j2JjYUJTw+uVOxrV78y1x48+6EUSHZW5lQs7sHpa/eyrN++vnzmRPUyRRQvvr8oW8SdlTuO0nX8Qj6FRWBkoIdnHicOLBmPm2PaSKbSrQcp/n736St2HLuAvbW50rlkRx5HWyb3bcfE3m145R/4XXWzsnr3MZKSU2g9fJbS9lFdmzO6u/yev27aYMYv2USnsfMJj4rB3sqc8T1b0eVv/w1sOHCaXBamGa77sO/0VULCo9h29Dzbjp5XbP/6u4hLSML37XuSU1IV+9dMHsigWauo3Ws8EhUJ9SuVZM7QtHV+JBIJu+aPZvCsVVTvNhodbS2qlS7M9AEdFGX0dLQ5uGQCg2etplzbIZgY6dOoShnG90ybxSkIwq8xtHklVCWqTNt8kqCwaCyN9elYs0SGZTvULMEDv0A6zdqKCtC4vCeda5Xk1G3fDMtnZXLn2oxZfZgNJ25ibWrAg7+GK/a1rVqUG0/f0qbK96XWALCzMMLbMzdn7r5IV19XW5MibnYs23+J10FhpKSkksvMiHbVizGoaUUAHrz6wK3n8lRThbvNUap/f/WwbK9XAWCgo0W76sVoV70YrwND0f7OVFhZ6bdoD5cfvVZ8Lt9/0Xf3cc3RaySlpNJ+xmal7cNbVlasifExLJqA4Ijv6tvM7vWYtvkEQ5btJyQyBisTAzrUKM6wFvIBAqoSFV4EBLPt9B1Co2IxMdChkKstR2Z0I59D9nPOC4KQcwbVLICqRMKsQw8JiozH0lBLaW2Ar7Urm5tH/uF0++syKkDDog50KJ+bM0++//fxhEaFGL/7Dpsuv8LaSIdbU+op9rUq7cxNv5As0yn9XSlXC9ZeeEHf9dcIjk7AUEeDgrbGbO9bkdyWBhnW2XTpFSlSGSO232LE9luK7c1LOuHTLvuzvgG8HEzxcjBlYuNCfIj4uYwdf3fhWRABYXG0yuT7eB8ep5TVopiLOcs6lWbGgQdMO/AAJ3N91nUvRz4bI0WZWl52zGpZFJ/jTxiz8w4ulvr81bWsYqFydVUJF54FsfLsc+ISU7Ax1qGOly0Da2Z/dqPwZ1GRyTKY4yT8dho1akR8ZAgHNn3fS5GcMHX+UnYfOs6d0/tzuivCf8jNuw8oU7s59+/fx8Mj+wvyCsJ/yYMHD/D09OT8upkU/Uaqn9/BjNU72HPqCje2Lcjprgj/IQ37TUbX0oE9e/bkdFcE4bfm4+PDiGFD+bBrYk535ZtmbzvNvsuPuLyo/7cLC/95C3efZ+G+q4SFR+R0VwThPykqKgpDQ0NWdi5D/SJZpwP6nc078ogDd95xbkytnO6K8AuUm3Kc2s3bM2/evJzuivAPynqeqSB8h5jYWB4982XZ2i307tQ6p7sjCIIg5JCYuHgev3zL8h1H6NlcPBgIgiAIGYuJT+TJ2yBWHb5Gtzrp1wISBEEQhL+LTUjm6YcI/jrvS2fv339wliAIaURqJuGX6T96Ctv3HaZe9crp0ir1Hj6BLbsPZlivVeO6LJk54V/ooSAIgvBvGDRrFTuPX6Sud4l0aZX6TVvGtqMXMqzXomZ5fEb1/De6KAiCIPwGhi0/wO4L96ldMj9tqhRV2jdwyV52nruXYb2m3l7M790ww32CIAjCf9vIHbfZe+stNTxsaVVaOQ3R0C032XXzTYb1mhRzZHar9OshCYLw7xGBCOGX+WvBdP5aMD3DfeOH9mVgj44Z7jPQ1/snuyUIgiD8y1ZO6MfKCf0y3DemR0v6t22Q4b6fWdBZEARB+P+zdGBTlg5smuG+Ua2r0rdhuQz36X9eAFoQBEH48/i0K5np2gzD6hakV5W8Ge7T0/516/YIgvBjRCBC+FdYmJliYWaa090QBEEQcpiFiREWJkY53Q1BEAThN2dupIe5kRiwJAiCIGSfub4W5voiWC0IvyuxRoQgCIIgCIIgCIIgCIIgCIIgCP8YEYgQ/hM2bN+Led7iOd0NQRAEIYdtPHgGG+/WOd0NQRAEIQdtOXUbhxYTc7obgiAIQg7bdtUP18G7crobgiB8JlIzCcJ/yIMnz+k/ajK37j/E3MSEXp1aM6R3lyzrvAv4QN+REzl3+QZ6ujq0bdqAKaMGoqaWdnnYsucgc5f+xUu/txga6FG9YnlmjB2CqYkxAFUat+PC1Zvp2q5ZuTz7N674tScpCIIgfNPDF28YNHMlt5+8xMzYgB7NajOofdYLu/oHBdN/+gou3HqIro4WretUZFLvtqipqQLQbYIPmw+dTVcvn7Mdt3b4yP9etxvvAoPTlenWtAbzh3f/BWcmCIIgZNej14EMXX6Auy8CMDXUpVudUvRvXCHLOsNXHOD607c8ffsRNzsLLvoor/l06aEfS/df4o5vANFxCTjbmNG3UTmaeRdSKrds/yXWHL1OQHAEJga61C9dgHHtq6OlIXK0C4Ig/NseB4Qzcvtt7r0NxVRPi87ervSplj/LOqN23Obmq2CeBUbiamXAmVE1lfa//BjF0C038Q2KJDo+GUtDbRoVc2RI7QKoq8rHvW+76kf/jdeV6mmqSXjn0/zXnqDwf0MEIoTfRmpqKioqKkgkv99EneTkZNTVlX80JyUloaGh8d1t/Wi9b4mKjqF2yy5UKleKxTPH8+ipL90GjcHI0IAubZplWCc1NZX67XpgZW7G+QNbCPoUTKd+I1BTV2PKyIEAXLlxh079RjBnwghqV6vIh8CP9B4xgR5Dx7Hzr0UA7FjtQ1JysqLd0PAIilZpSOM6NX75eQqC8N/3W98PUlJQV1P++ZSUnIyG+ve/WPnRet8SFRNHvT4TqVjcg4Uje/D45Vt6TlqMkb4unRpVy7BOamoqjfpPwdLUmNNrZhAUEka38T6oq6kxsXcbAGYP6cykPm2V6pRsNZCGlUsrtl3YMJvUVKni85NX76jbewINK5f55ecpCMJ/W2qqFBUVftN7QSrqn4O0XyQlp6Ch/v2P1z9a71ui4hJoPG4NFbxyM69XA568DaLvwt0Y6mrToUbWM8lbVy3K7ef+PH4TlG7f9advcXe0on/jClgY6XH85jN6zt+JgY4WNYrnA2DnuXtMXH+cRf0aUyKfAy/fB9N74S5UVGBqlzq//FwFQfhvS5VKUUEFiUQlp7uSTnKqVPHS/YuklFQ0/naPyI4frfct0fHJNF98jvJ5LJnVshhPP0QwcON1DHQ0aFc2d5Z1W5Z25s7rUJ58iEi3T01VQrOSTnjYGWOgrcHj9+EM3nwDqUzG6PqeinL6WupcGV87reLv969R+BeJQMQfaveh40yZt4RXb96ho62Fl3s+dq9bgq6ODqmpqYyYPJt12/agqiqhQ4vGfAoJJTIqht1rFwPgWrwyfbu2o1/X9oo2i1ZpSL0alRk3pA8AC1asY/32Pbx+G4CJkSG1q3kzfcwQ9HR1AXk6pcHjp7Nm4QxGT5vHC783PL1yHGsLc8bNWMD2/YeJiIzGPa8r00YPpkLptB/MG7bvZeLsRYSEhVPVuwxlihf5rvM/cOw0U+Yt4emLV9hYWtCmaQNG9u+umAWgYZOPRdPHcezMRc5eusagnp0U9Xp1bMUMnxW8DfhA4vsnvAv4wIAxUzl76RoSiQrVKpZjwZTRWJqbATBpzuIM6/1qW/ccJCk5mVXzpqChoYF7HlfuP37GghXrMg1EnDx/mae+rzi2fc3n/uZjwrB+jJo6l3GDe6OhocG12/dwtMtFny7yl09O9rZ0bdOcOUtXK9oxMTZSanfH/iPoaGvRuG71X36egiD8WntPXWHaqu34BQSho6WBRx5ndswdia62FqmpqYxauJ6NB06jqiqhXb0qfAqLIComju1zRwLyEfC9W9alT6u6ijZLthpI3QolGN29BQA+m/az8eAZ3rz/iLGhHrXKFWNKv3bo6WgD8nRKw+f+xaqJ/Rm3eCMv3n3g4d5lWJkZM2HpZnYev0hkdCz5XeyZ3Lcd5YsWUBxr48EzTFm+ldCIKKqUKkQpr3zfdf6Hzl1n2qodPHvtj7W5Ca1rV2RYpyaKWQC6RRuyYER3Tly+w7mbDxjQtgEAB89fp0ezWsxas4t3gcHE3NyDf1Awg2et4tzNh0gkKlQtVYg5Q7tiaWoEwNQV2zKs96ttP3aB5OQUlo/rg4a6Ovld7Hng+5pFmw9kGog4de0ez14HcGjpRCxNjfDM48TYHi0Zu2gjo7s1R0NdHUM9XQz1dBV1Dp67TnhULG3rVVJsMzc2VGp37vo9ONtaUa6I+y8/T0EQfp39lx8yc+tpXgeGoq2pjoezDZvHtENXS4PUVCnj1h5h06nbqEpUaFO1GMER0UTFJrJ5jPz3oUfnmfSsV4ae9csq2izXz4faJfMzolUVAJbsu8jmU7d5GxSGkb4ONYrlZWLHmuhpawLydEojVx9i2cCmTFp/nJfvQ7izcgiWJvpM2XCC3RfuExkbTz4HSyZ0qEnZgs6KY205dZtpm08SFhVHpcKulMzv+F3nf+TaE2ZuPc1z/09YmejTsnJhBjeriJqq/F5gXHckc3rW59RtXy7cf0nfRuUBOHztCV1rl2LujrP4B0cQdmAa/p8iGL7yABfuv0KiokLlwm7M7F4XC2N9AGZsOZVhvV9t57l7JKWksrhfYzTU1cjnYMlDv0CW7ruUZSBiZvd6AIRGxmYYiBjcrKLS5x71ynDm7gsOXX2sCETcePaWEvkcaOrtBYC9pTGNy3ty67n/Lzo7QRD+KQfvvGPOkUe8CY5BW0OVArbGrO9RHl1NNVKlUibuucfWq36oSlRoVcqZ4OgEouKTWd9Dfl0sOuYAXSu50b1SXkWblaYdpaaHLUPrFARg+elnbL3qx9uQGIx1NKlW0IZxDb3Q1ZIP0Nl21Y+xu+6wqH1Jpu67z6tP0VybWAdLA22mH3jA3ltviYxPIq+NEWMbeFLGzVJxrG1X/Zh16CFhMYl457emhIvZd53/0fsBzD3yCN/ASCwNtWle0okBNdxR+xxssOy1lZktinL6cSAXnwfRu4r8unf0QQCdKrix4NhjAsJiCVrSkoCwWEbtuM3F5x+RqECl/NZMbVYECwP5M9DsQw8zrPer7b75huQUKQvalkBDTZW8NoY8CghnxelnWQYipjWTv2cLjX6YYSDC0UwPRzM9xWc7U12u+H7i+kvl2dEqKmBhqP1rTkb4vycCEX+gwI+faNtrCNPHDKZ+zapEx8Ry+fotZDIZAPOXr2XDjn2snDeFvK4uLFi+lv1HT+FdpuR3HUciUWH+5NE42tvy+q0/fUdOYuSUOSyaPl5RJi4+gTlLVrNizmRMjI2wMDWh/+jJPPV9xaZlc7G2tGD/0VPUad2VO6f34+rsyI079+k2eAxTRg6kXo3KnDh7iUlzF2e7X5eu36JT/xHMmzyasiWK4PfmHb2Gyfs0dnBvRbnJc5cwddQg5k4aiZqqKuu27eHVm3fsPXKS7at9UFVVRSqV0rhjH/R0dTi9ZwMpKSn0GzWZ1j0GcWr3BkVbf6+XkXcBH/D0rpvhvi+G9+vGiH4Zp7a4dvseZUsUVZptUc27LHOWrCY8IhJjI8P0dW7do0BeN0XQBKCqd1n6jJjI4+cvKVQwPyWLeDF2xgKOnj5PjUrl+RQSyp7Dx6lRqXym/Vy7dTfN6tdCV0cny/MRBCFnBYaE0WH0PKb0a0e9iiWJjovnyt0nivvBwk0H2HzoLMvG9SGPky0+mw5w8Nx1KhQt+F3HkUgkzBnaBUcbS16/D2LgjJWM8dnAghFp17O4hCTmrd/LkjG9MTHUx9zEkEGzVvLML4D10wZjbW7MgbPXadBvEje2LSC3vQ03H/nSa/ISJvZuQ13v4py8epepK7Zlu1+X7z6h63gfZg/tTBmv/PgFBNF32jIARnVLmy48deU2JvVpy6zBnVBTU2XD/tP4+Qex78xVtswajqqqBKlUSrNB09HT0eL4yimkpKYycOZK2o+cw7GVUxRt/b1eRvyDginStF+G+74Y2rExQzs1yXDf9QfPKVMov9JsiyqlCjFv/V7Co2IwNtBLV+fGw+e457ZXBE2+1Ok/YwVPXvnjldc5XZ31+09RsbgH9tYWGfYjKTmZ7UfO07d1PVRUxNAnQfhdBYVF0WX2NiZ2qEmdUu5Exydy9fFrxb1g8b6LbDl9h8X9GuNmZ8GSvRc5fPUJ5Txcvus4EhUVZnari4OlCW+CwhiybD/j1x5lbq8GijLxicks3H2BhX0bYaKvg5mhLsOWH+CZ/ydWD2uBtYkBh64+psn4tVxe3B8XGzNuPX9H30W7GdeuOrVL5uf0bV+mbzmV7X5defyaHvN3MLNbXUq5O/E6MJQBi/cCMLxlFUW5mVtPM759daZ3rYOqRMLmU7d4HRjKgSuP2DiqDRKJClKplNZTN6Crpcmh6d1ISU1l6PIDdJq1lUPTuyna+nu9jPh/iqBU7/lZ9n1gU+90gYEvbj57R2l3J6XZFpULu7Jw93kiYuIx0vt1L4WiYhPIY5t2Lyie14Ed5+5x29efIm52vAkK4+St5zSrWCiLVgRByGkfI+PpseYKYxt6UcvLlpiEFPlL5c/3g2WnnrH92msWtCmBq5UBy04/48j9AMp+FQjIDhUVmNq0CPZmurwNiWHEtltM2nuPmS2LKcrEJ6Wy+MRT5rUujrGeJmb6WozccRvfwEhWdC6NlaE2R+4F0HLxOc6NqYWzhT63X4cwcNMNRtf3oKanLWeeBDL70MNs9+vay0/0XX+Nqc0KUyK3BW+Doxm8RZ6CekjttOef2YcfMqaBF5ObFkZNosLWK368Do7h8F1/1nYri0RFBalURvvlF9DVVGffwMqkpEoZuf023f+6wt6BlRVt/b1eRgLCYik3+UiWfe9fPT8DamQ88OeWXwglc5srzbaomM+axSeeEhGXhJHOr8nY8fpTNGefBFLLy05pe2xiCkXG7EcqhYL2xoyq50lem/Tvp4Q/gwhE/IGCPgaTkpJCg1pVcbDNBUDBfG6K/YtWb2BYn640rCUfNblk5gROnrv83cf5eraEo10uJg7vT5/hE5QCEcnJyfhMH4enuzxa/i7gA+u37+XVzTPYWMl/zA7q2YkTZy+yfvtepowcyKLVG6lesaxi7QM3Fyeu3rrLiXOXstWvKXOXMLRPV9o1awCAs4Md44f1Y9SUOUqBiBYNa9O+RSOluknJyazxmYG5qQkAp85f5tEzX3yvncQulzUAa3xm4OVdl1v3HlLUq2CG9TJiY2XBzZNZj4w1ySCY8MXHTyE42tsqbbMwNwUgKDgkw0DEx+AQLD+X+cLSzFSxD6B08cKsXzyL1j0GkZCYREpKCrWrVsRn2tgM+3Hz7gMeP3vByrlTMtwvCMLvIygknJTUVOpXKql4mVwgt4Ni/5KtBxncoRH1K5UCwGdkD05du/vdx/l6toSDjQXjerai//TlSoGI5JQU5o/ohoebEyB/Gb/x4BmeH1qFtbn82jmgbQNOXr3LxoNnmNi7DUu2HqJqqUKKtQ9cHXJx/f5zTl69k61+TVu1nUEdGtGmjnxEv5OtFWN7tGSMzwalQESz6uVpV6+yUt2k5BRWTeyvmAFw+to9Hr96y5P9K7C1kgd3V03sT9Fm/bj9+AVF3F0zrJcRazMTrm6Zl2XfMwomfPExNBxHG+UHQgsTI8W+jOp+DI1QlFHUMU2r83eBwWGcuHKHtVMGZdqPg+duEBETS5u6lTItIwhCzvsYFk1KqpQ6pd2xt5Cv/+XuaKXYv/zAZQY29aZuaflstHm9G3D67ovvPs7XsyXsLY0Z3bYqg5bsUwpEJKekMqdnfQo6yX9X+3+KYPOp2zxcMxxrUwMA+jYqz+k7vmw+dZtx7aqz/MAVKhd2U6x9kDuXOdefveP0Hd9s9WvW1tMMaOJNy8rykZ+OViaMalOVCeuOKgUimpT3pHWVokp1k1JSWT6oKWaG8uvq2bsvePLmI/dWD8XW3AiAZQObUqr3Au74+lPYzS7DehmxNtXnwsK+WfbdWD/zQT+fwqOxt1R+9jA3kh/vY3j0LwtE7L34gLsvApjfO20doqbeXoRFxVJz+ApkMhkpqVI61iyRadBEEITfw8fIeFKkMmp72WFnKp8Fmz+XkWL/yrO+9Kuen9qF5Ney2S2Lce5J+plT3/L1bAl7Uz1G1PVg2NabSoGI5FQpM1sUxd1Wfl8KCItl21U/7kyph5WR/NrXq2o+zjwJZOtVP0bX92TVWV8q5bdWrH3gYmnATb8Qzj4JzFa/5hx+RN9q+WheUj4Ax9FMjxF1CjJp332lQESjYo60LKU8SCc5Rcqi9iUx09cC4PzTQJ5+iOTmpLrkMpF/l4val6T85CPcfRNKIUfTDOtlxMpQmzMjs057baSbeTDhU1QC9ma6StvMDeTH+xQZ/9OBiNqzT/LQP4zEFClty7owvE7ad+ViacCCNiXIn8uIqPhklp56Sp05J7kwthY2xmLg6p9IBCL+QB7uealUtiSFK9WnqndZqlYoTaPa1TE2MiQyKprAj8EUL+yhKK+mpkZhT/cvQfBsO33hCrMWr+L5Sz+iomNISU0lISGRuLh4dD6n49DQUMcjfx5FnUfPfElNTcW9rPIiOIlJSYr0P89e+FG/pvILoZJFvbIdiHjw5DlXbt1lxsK0RZRTpen7VtizQLq6DrbWSsGEZy/8sLOxUgQhAPK75cbI0ICnL14pAhF/r5cRNTU1cjs5ZFkmJzzxfcngcdMYPbAXVb3LEvQpmBGTZ9N7+ARWzpuarvzarbspkM+NYoU8MmhNEITfiYerI97FPSjeYgBVSnpRuaQXDSqXxthAj8iYWIJCwilWIC1QraamSuF8uRWjZLPrzPX7zFm3G98374mOjSMlVUpCYhJxCYnoaMlTcmioq1HQ1VFR5/HLt6SmSvFs1FuprcSkZEwN5ektnr8OoF7FEkr7i3u4ZTsQ8cj3DdfuP2P2ml2KbanS9H0rnD/9qF97a3OlYMLzNwHYWpopghAgX8TZSF+XZ68DFIGIv9fLiJqaKi521lmWyWmbDp3FSE+Xut6Zp/dYv/8U1UoXVgSSBEH4PRVwsqaCpwtl+yykUmFXKhZypX6ZghjpaRMZm0BQWDRF3NJGN6qpqlIod67vfjY4d+8l83ee40VAMNHxCfJ7QVIKcQlJ6GjJX4JoqKlS4KsgyJO3QaRKpRTrMVeprcTkFEw+v4T3DfhEnZLKo0CL57XPdiDi0esgrj99y7wdZxXbUqXp++blmitdXTtzI6Vggq//J3KZGSqCEAB57S0x1NXCNyBYEYj4e72MqKmq4mzzfSlF/m0XH7yiz8JdLOzbiHwOaQHwSw/9mLfzHHN61KdIHjteB4YyYuVBZm87zdAWlbNoURCEnORua0S5PJZ4Tz1CxXzWVMhnRd3C9hjpaBAVn8THyHgKO6YNZFRTleDpYPLdzwbnnwXhc/wJL4OiiE5IJlUqIyE5lbikFHQ0PqfLVpMoBUGevo8gVSqj1MTDSm0lJadirCv/zf4iKDLdaPyiTmbZDkQ8eR/BTb8QFhxPS6UtzaBvnvbpf9vamugoBRN8g6KwMdZRBCEA8lgbYqitzougKEUg4u/1MqKmKsHJQj9b55ATVnYuTUxiCo8Dwpm09x5LTz1VBIOKOZtRzDntXlbMxYyykw6z4dJLRtQV74z+RCIQ8QdSVVXl6PY1XL15l5PnL7NkzWbGzVjIpcPbsxxx/zWJRJLuZpOckrZY8Rv/9zRo35Pu7VowaXh/jI0MuXLjDt0GjyEpORkd5C/7tbW0lNI1xMTGoaqqyrVju9KlrNDT/TXR0pi4OMYN7kODWlXT7dP6/NIJQFc7/SghHe0f60N26v1saiZLCzM+Bocqbfv0+bOVecYPMZbmZty8qzxV8WNIqGIfwKxFKylVrDCDe3UGwCN/HnS1tanYsA0Th/fH2jJtGnZsXBw79h9h/NCsR28JgvB7UFVV5dCSCVy7/4zT1++xbPsRJizdzPl1szD+xguSLzK6H6SkpCr+/vbDJ5oMnEqXxjWY0Ks1xgZ6XLn3lF6Tl5CUnKJ42a+tqaF8P4hLQFVVwqWNc9LdD3S1s/6xnl0x8QmM7taC+pXSpx7U0khLa5TR8XS0NdNty47s1PvZ1EyWpsZ8CotQ2vbls6WpcSZ1jLj1WHmE86fQjOvIZDI2HjhNi1remS62/S7wE2dvPGDrrGFZnocgCDlPVVXC3smduf70LWfvvmDloatM2XiCU3N6YZTFiPuvSVRU0gUmvr4XvPsYTotJ6+lUswRj2lbDWF+ba0/e0tdnN8lfldPSVFe6F8QmJKEqkXB2fh9U/5bCSPcHr8N/F5uQyIhWVahbKn1KCy2NtMdlXc30I0a/BCm+V3bq/WxqJgtjfYIjYpS2fflsafzzL7QuP/Sj5eQNTO1ShxaVCivtm7rpJM0qFqJddfnoZndHK2ITkhi4eC+Dm1X8LRcgFwQBVCUSdvaryE2/EM49DeKvc75MP/CAo8OqYZzFiPuvqaiQ/n6QKlX8/V1oDG2Xnqd9eVdG1vPAWEeD66+CGbjpBskpUvh8GC11VeX7QWIKqhIVTo6ojurfUhjpav6aV5uxiSkMrV2A2n8LZgBofZXWSEcj/fF0frAP2an3s6mZLAy0CI5KUNr25fOvWLvhS7Alj7UhUqmMIVtu0rNKXlQzuNarq0ooaGvM6+Donz6u8P9JBCL+UCoqKpQuXpjSxQszZlAvchevzP6jpxjQvQPWlubcuPOAciXlPxxTUlK4++AJXgXzK+qbmZoQ+DFtAZqo6BjevHuv+HznwWOkUhmzxg9X/NDcdfDYN/vlVSAfqampBIeGUrZE0QzL5HV15ubdB0rbrt++n+1zL1QgP76vXv+S2Qd5XZ3x/xCE//tAxayIJ74viYiMIr9b5ov+ZORnUzOVLOLFuJkLSU5ORv3zi6FTF67g5uKUYVomkM8kmeGzgk8hoVh8Tsl0+sIVDPT1FP2Pi09QLNT3xZeXgn//gbH74HESk5Jo1SjrgIogCL8PFRUVSnnlo5RXPkZ2aUbeut05cPYa/drUx8rMmJuPfClbWP6jNiUllbtPXymtF2BmZEBQSFrqnqiYON68/6j4fPfpK6RSGTMGdlDcD/acvPLNfnnmcSI1VUpweCRlCuXPsEweJ1tuPlJ+eX7jYfZGwAJ45XHmxdv3v2T2QR5HWwI+hhAQFKKYFfHUz5+I6FjyOad/mMnKz6ZmKuGRh4lLN5OckoK6mvyn3pnr93FzyJVpveIF8zBrzW4+haWlaDpz/T4Gujrp+n/x9mNe+QfSvn7mo1o3HjiDubEhNcpmfC8XBOH3oqKiQsn8jpTM78iwFpXx6DyTQ9ce07tBOaxM9Lnt60+ZAvLUeSmpqdx7+R5Pl7QZAmaGugSFp71UiIpL4O3HtHvDvZfvkcpkTOlcS3Ev2Hfp23m7PZytSZVKCY6MobS7U4Zl3GwtuOWrvAjyzefvsn3uHi65ePk+5JfMPnCzs+B9SCQBwRGKWRHP3n0kMjaBPHYZr6eTmZ9NzVQsrz1TNp4gOSUV9c8v0M7ee4lrLvOfTst06aEfLSatZ3yHGhkufB2fmJRu7YsvgaTvnUkjCMK/S0VFheIu5hR3MWdwLXeKjDnA0fsB9KicF0tDbe68CaWUq/x6lpIq5cG7MArapQ1aMdXX4lNkvOJzdHwy70JiFZ8fvAtHKoOJjQoprhP773z7ml3QzphUqYyQ6ARK5s74eupqZcid18qDM2+/Ccn2uRe0M+blx+hfMvvAzcqAD+FxvA+LVbyofx4YSWR8Mm7WBt/V1s+mZirqbMb0Aw9ITpWi/vldzvlnQeS21P9l60N8IZXJ02pJZZDR6qipUilPP0RQ2d3mlx5X+P8hAhF/oBt37nPm0jWqViiDuakJN+4+IDg0jLyu8hdLfTq3ZfaSVeR2ciBPbmcWrlxHRFSUUhveZUqwccde6lSriKGBARNn+yiNWHVxtCc5OZklazZRu2pFrty8w6qN27/ZNzcXJ1o2qkunfiOYOX4YXgXyExIaxplL1yiYz41aVbzp07kNFeq3Zt6yNdStXomT5y5nOy0TwOhBvWjQrid2uWxoVKcaEomEB4+f8fj5CyYNH5DtdgAqly9NgbxutO8zlLkTR5KSmkrfkZMoX6oYRTJI7ZSVn03N1KJhHabMW0q3wWMY0rsLj5+9YPHqjcyZOEJRZt/Rk4yZNp9HF+XR9KoVypDPzYWOfYczbcwQPgaHMH7mQnp0aIXm51FfdapWpMfQcaxYv1WemuljMIPHT6dYIQ/FOh5frN26m3rVK2NqkvGIW0EQfi83H/ly7sYDKpf0wtzEkJuPfAkJjySPk3y9mV4t6jBv/R5y29vg5piLRZsPEBkTq9RGhWIF2XTwLLXKF8NQX5cpy7cq3Q+c7axITklh2fbD1CpXjKv3n7F6z/Fv9s3VIRfNa5an6/iFTB/QAc88zoSER3L25kMKujpQo2xRerWoTeXOo1iwcR91KhTn1NV7nLqa/TUsRnRtRpMBU7GzMqdB5VJIJCo89H3Dk1fvGN+rdbbbAahUwhN3Fwc6jZ3PzMGdSE1JZcDMlZQr7E7h/N8XmP7Z1EzNapRj2qrt9Jy0hEHtG/Lk1TuWbj3EzEGdFGUOnL3G+MWbuLt7MQBVSnqR18mWLuMWMqVfOz6GRjBx2Wa6NauJpobyrIf1+09RrIAb7rkzvmdJpVI2HjxD6zreqKll9AgiCMLv5Nbzd5y//4pKhVwxM9Tjtq8/IZGxuH1efLh73TIs2HUOF2tTXG3NWbr/ElGxyiMry3m4sPX0HWoUz4uhrjbTN59EVTXtRbSTjSnJKamsPHSVGsXzce3JG9Yevf7NvuXOZU5Tby96ztvJlM618HC2ISQqlvP3X+HuaEX1YnnpXrc0NYYvZ9GeC9QqmZ/Td3w5fTv7QelhLSrRYtJ6bM0NqVe6IBKJCo9eB/L07UfGtK2W7XYAvL1yk9/Rkm5ztzO9Sx1SpFKGLNtPmQJOFHK1/XYDX/nZ1ExNKngxa+tp+vrspn/jCjx995EVBy4ztUsdRZlDVx8zaf1xbixPW+/H70MIsQlJfAyPJiEpmYd+HwDIY2eBhroaFx+8osWk9XSvV4Z6pQvw8XMASkNNVREYqVE8H0v3XcLD2Yaibnb4BYYybfNJahTPm26WoyAIv4/br0O4+Pwj3vmsMNPX4s6bUEJjEnG1kr8471rRjUUnnuBkro+rlQHLTz8jMj5JqY2ybhZsv/aaah65MNDWYNahh0qBSSdzPZJTpaw+50s1j1zcfBXMhosvv9k3F0sDGhdzoM/6a0xsVIgCdsaExiRy8VkQ+XMZUbVgLrpUdKPunFMsPfmUGp62nH0SyNnH2UvLBDC4VgHaLD2PrYkOdQrZI1GBx+8jePYhkpH1vi+NUPm8VuSzMaTXuqtMblKYFKmMEdtuUdrVAi8H02838JWfTc3UqJgDc448YuDG6/Splp9nHyJYdfY5k5qkzWY7cs+fqfvvc3l82j3i9adoYhNT+BSVQEJSKo/85QMM3KwN0FBTZdeNN6irSshnY4iGuir334Yxdf996hexVwQ85h55RBFHUxwt9ImKS2LJqacEhMXRukz61LfCn0EEIv5A+vp6XLp2i0WrNhAVE4N9LhtmjRtOjUrlARjYoyNBn4LpPGAkEomEDi0aUb9mFSKj0qb2Du/bjTfvAmjQrieG+vqMH9ZPaUaEp3teZk8YzpwlqxkzbT7lShZl8siBdOo3Il1//m71/KlMW7Cc4RNn8T7oE2YmRhQv7EmtKt4AlCjixfLZk5g0ZzETZy+iUrlSjOzfg2kLlmXr/Kt5l2XfhmVMnbeUOUtWo66uRp7cznRq1fg7vkU5FRUVdq9dzIAxU6nUqB0SiQrVKpZjwZTR393WzzI00Ofw1tX0HzWZkjWaYGZizOiBPenSppmiTFRUDL6vXis+q6qqsm/DMvqOmEj5ui3R1dGmbdMGTPgqtVK75g2Jjoll6drNDJs4CyNDfbzLlGTa6MFKx3/+8jWXb9zmyNbV//zJCoLwS+jranPp7hMWbz1IdGw89lbmTB/Qkepl5At29m9Tn6DQcLqN90EiUaFtvcrU9S5BVEycoo0hHRrz9v0nmgyYioGeDmN7tuTNh7QZER5uTswY2JF56/cyfvEmyhR2Z2LvNnQdv/Cb/Vsxvi8z/9rJyAXr+PApDFMjfYoXdKNmOfko++IF87B4dE+mrtzGlOVbqVjck2GdmzJz9Y5snX/VUoXYtWA0M1btYN76PairqeHmmIsODap8u/LfqKiosGPeSAbPWkX1rmOQSFSoWqoQc4Z2/e62fpahni4HFo9n0MyVlG07BFMjA0Z0aUanRmkv1CJj4vB9m3bfVlVVZfeC0fSfvoJKHUegq61FqzoVGdu9pVLbkTGx7D9zldlDOmd6/DM3HuAfFJxugW9BEH5P+jpaXH38muUHLhMdl4idhRGTO9eialH5Om59GpblY3gUPRfsRKKiQpuqRaldKj9RsYmKNgY29ebt5/RLBjpajG5TVWlGREEna6Z2rs3C3eeZtP44pQs4MrZddXrO3/nN/i3p34Q5288w5q8jBIZFYWqgQ9E89lQvJl/stFheexb2acT0LaeYvvkUFbxyM6R5JWZvP5Ot869c2I1t49oza9sZFu66gJqaBDdbc9pWK/btyn+joqLC5tHtGL7yALVHrkSiokLlwm7M7P7vzxY21NVi96RODF1+gIoDF2NqoMPQFpWVZjBExSbw4n2wUr1+i/Zw+VHa80L5/osAuL96GPaWxmw9fYe4xGTm7zzH/J3nFOXKFHDi0PRuAAxpXhEVFZi66QSBoVGYGuhSo3g+xn5nYEcQhH+XvpY6115+YuWZ58QkJGNrosuERoUUo9d7Vs7Lx8h4+m24hkQFWpZ2ppanLVHxaWm6+1d3511oLG2WXsBAW53hdQvyLjTtXZK7rTETGxdi8cmnTNt/n5Ku5oyu70mf9de+2b+F7Uoy/+hjxu+5S1BEPCZ6mhRxNKVqQfkMvaJOZsxtXYzZhx4x69BDyuW1YmBNd+YdfZyt86+Y35pNvSow98gjFp94ipqqhNxWBrQu7fztyn+joqLC+h7lGbXjNvXnn0aiApXyWzO1WZHvbutnGWhrsL2PNyO336bajGOY6GkyuFYB2pVNGywVFZ/My4/K6ZIGbb7BlRefFJ8rT5dnObk5uS72pnqoSVRYfOIJrz5FIwPsTHToVMGV7pXTFiOPiEti8JYbfIpKwFBHA087Ew4NqUIe6+ylhRf+e1Rk37uqjJAjGjVqRHxkCAc2rcyR43ceMJKIyGh2r12cI8cXhJt3H1CmdnPu37+Ph4dY1Ej4Mz148ABPT0/Or5tJ0a8Wkf43dZvgQ2R0LNvnjsyR4wtCw36T0bV0YM+erNMZCsKfzsfHhxHDhvJh18Rf3nav+TuJjE1g85i2v7xtQVi4+zwL910lLDwip7siCP9JUVFRGBoasrJzGeoXsf+ptvptuEZkXBLre5T/Rb0T/lTlphyndvP2zJuXdYpa4f+bmBcpCIIgCIIgCIIgCIIgCIIgCMI/RqRmEv5zPL3r8C4g4zyAS2ZNEAspC4Ig/CGKNuvHu8DgDPf5jOpBi5oV/uUeCYIgCP+2Ur3m4x8ckeG+eb0b0My70L/bIUEQBCFHlJ98GP+wuAz3zW5ZjCbFHf/dDgnCH0gEIoRs+WvB9JzuQrYd2LSC5OSUDPdZmv/4gm+CIAgCrJzQL6e7kG17Fo4hOSU1w30WJkb/bmcEQRD+Q5YObJrTXci27RM6kJLJvcDc6McX/xQEQRDAp13JnO5Ctm3u5U1KqjTDfeYGWv9ybwThzyQCEcJ/joNtrpzugiAIgvAbsLe2yOkuCIIgCDnM3sI4p7sgCIIg/AbsTHVzuguC8McTa0QI/0mdB4ykccc+Od0NQRAE4TfTbYIPzQf//8zyEwRBEH5er/k7aT1lY053QxAEQfjN9NtwjfbLL+R0NwThjyFmRAhCDrh47SZzl67h7sPHBH4MZudfi6hfs4pSmUlzFrNj/xECPgShoaFO4YL5mTRiAMULeyrK+L56zYjJc7h68w5JyckUzJeHCcP64V2mhKKMhk2+dMffuHQOzRvUBuD8lRtUbdI+XZl39y5gZWH+q05ZEARByKYPn0IZs2gDJ6/cIS4hCWdbK1aM70vh/LkB0C3aMMN6U/q1Y2A7+b58dbulWx9jYp82DOnQGICpK7YxbdX2dG3oaGkSfGnbrzwdQRAE4RsuP3rNoj0XuP/qPUFh0Wwa1YbapdyVyszYcoo9Fx7wPiQCdTVVvHLnYkzbahTNY68o49F5Jv6fIpTqjWtXnYFNvQFISEpm0JJ93Hv1Hl//YKoXy8vmMW3T9efSQz9Grz7Ms3cfyWVuyJBmlWhVpcgvP29BEAQhazEJycw4+ICj9wMIiU6kgK0xU5oWppCjqaKMTCZj1qGHbLr8iqj4ZIo5mzGrZTGcLdLSD84/+phTjz7wOCAcdTUJL+Y2yfSYYTGJVJp2lMCIeHznNMZQR+MfPUfhzyICEYKQA2Lj4vFwz0OHlo1o1jnjfOuuzo4snDoGJwc74hMS8Fm5nlotu/D0ynHMTU0AaNC+J65ODhzfuQ5tLU0WrdpAg3Y9eXb1uFIQYfX8aVSrWFbx2cjAIN3xHl08goG+nuKzhZlpujKCIAjCPys8KobKnUdSvmhB9i4ci5mxIS/9AzEySJtK/urYGqU6J67codfkJTSoVEpp+9geLenQoKris76utuLv/dvWp3Pj6krl6/Qarwh2CIIgCP+euIQkCjhZ06ZqUdpO25RhGRcbM2b1qIejlQnxicks23+JRuPWcGflEMwM037Dj2pdhXbViys+62lrKv6eKpWhpalO97qlOXjlUYbHeRsURvOJ6+hYswQrhzTn/P2X9Fu0B0sTfSoXdvtFZywIgiBkx8BNN3geGMHi9qWwMtRm1403NPU5y8VxtbA20gFg8cmnrD7ni0+7ktib6jLz0EOaLzrLxXG10VJXBSA5VUrdwnYUdTZlyxW/bxzzOvlzGREYEf+Pn5/w5xGBCOGn7D50nCnzlvDqzTt0tLXwcs/H7nVL0NXR4da9h4ydPp97j56SnJKCp3te5kwYQSGPtNE9Gjb5WDJzAodPnuXspevY29qwat4UzExN6DFkLLfuPcIjfx7WLpqJi6N8tM+kOYs5cOw03du3YPqC5YSGR1CrqjfLZ0/C0CDjBeekUimzl6zmr007CAoOwdXZkVEDetK4jvwlTHhEJP1HT+HU+cvExMVha23J8L7dad+i0T/yvdWoVJ4alcpnWaZlozpKn2dPGMHarbt5+OQ5lcqVIiQ0nJd+b1k5dwoe+fMAMHX0YJav38rjZy+UAhGGBvrfnN1gYWaKkWH6AIUgCMKP2HvqCtNWbccvIAgdLQ088jizY+5IdLW1uP34BeOXbObBcz+SU1LxyOPIjEGdKJTXRVFft2hDfEb24MjFm5y/+RB7a3OWjeuDmbEhvScv4faTlxR0c2T1pP4421oD8lH+B89fp2vjGsxcs5OwiGhqlivK4jG9MNTLOCesVCpl7vq9rN17go+hEeS2t2FE56Y0rFIakAcGBs1axZlr94iJTyCXhSlDOjamXb3K/8j3Nm/9HmwtzVgxvq9im2MuS6UyVmbK+c4Pn79B+aIFcLK1Utqup6OdruzX+/R00gITD3xf89TPn4Uje/zsKQiC8Afbf/khM7ee5nVgKNqa6ng427B5TDt0tTS44+vP5I0nePDqA8mpqRR0smFal9p45k5b38247kjm9WrAsRtPufjADzsLIxb1b4yZgS79Fu3h7osA3J2sWTGoGU7W8kEzM7ac4vC1J3SqWYI5O84SHhVH9WJ5WdC3EYa6GS8+KpVKWbD7AuuP3eBTRDQuNmYMbVGJ+mUKAhARE8/Q5fs5e/clsQmJ2JgaMqiZN62rFP1HvreqRfNQtWieLMs09fZS+jylS202nrzF4zdBVPBMCyLraWtiaZzxM5GulgbzejUA4PqTt0TGJqQrs+bYdewtTZjSWT57Oo+dBdeevGXZ/ksiECEIwg87eOcdc4484k1wDNoaqhSwNWZ9j/Loaqpx900o0w7c55F/OMmpMgrYGjGpSWE87E0U9S17bWV2y2Icf/iey88/Ymuiy/y2JTDT02TQ5hvcfRuKey5jlnQoiaO5/Bo4+9BDjj4IoEM5V+YffUx4bCJVC9owt3VxDLQzHuUvlcpYdOIJGy+/IjgqAWcLfQbVdKduYfn7qIi4JEZuv8X5p0HEJqZgbaRN/xrutCzl/Mu/s/ikFA7f82d993KUcpWvfTe0TkFOPHzPugsvGVnPA5lMxsozzxlYw52anrYALG5fkgLD93L0fgANizoAMKyO/P627WrWQYh1F14QFZ/MoFrunH4c+MvPSRBEIEL4YYEfP9G21xCmjxlM/ZpViY6J5fL1W8hkMgCiY2Jp06wB86eMQYaMBcvXUq9tD55cPob+Vy+Epi1Yxqzxw5k1fjijps6lXe+hODnYMaxvN+xyWdNt0GgGjJ7Cwc0rFXVevXnHroPH2LN+KdExMXQfPIa+IyexYcnsDPs6c9FKtuw+yOKZE8jt5MCla7fo0HcY5qbGlC9VnAmzfHjq+4qDm1diamLMq9dviU9IzPTcZ/isYKbPykz3A9w/dxB7W5vv+UozlZSUxOpNOzA00Mcjf14ATE2McHNxYtPO/RQqmB9NDQ1WbdyOhZkphT2Up3L3Hz2ZHkPG4uRgR7e2zWnfohEqKipKZYpVbUhiUhLueVwZO7gPpYsX/iV9FwThzxMYEkaH0fOY0q8d9SqWJDounit3n6TdH+LiaV3Hm7nDuiCTyfDZdIBG/afwYM9SpVH7M/7ayYyBHZkxsCNjF22k45j5OOayZHDHxthZmdFz0mIGzVrFPp9xijp+/kHsPnWZnfNGEx0bR6/JSxgwYyVrpwzMsK9z1u5m29HzLBzZg9x21ly6+4TO4xZgZmxAuSIFmLxsC8/8/NnrMxZTIwNe+QeSkJiU6bnPXrOL2Wt3Z/n93N7pg51VxsHhIxduUrlkIdoMn8XFO4+xMTelW9MadGxYLcPyH0MjOHbpNisnpp9dN3f9Hmb+tQNbS3Oa1ShH31b1UFNTzbCd9ftO4WpvQ5lC+bPsuyAIQmaCwqLoMnsbEzvUpE4pd6LjE7n6+LXi2h8Tn0SLSoWZ2a0uMmDJ3os0m7iOWyuGoK+TNmp/zvYzTOlcm6ldajNh3TG6zt6Oo5UJA5t6Y2tuRN+Fuxi6/AC7JnZU1HkdGMq+Sw/ZNrYdUXGJ9PPZzZBl+1g1pEWGfZ238zw7z91lXu8GuNiYceXRa7rP3YGZgS5lCjozddMJnvt/YueEDpga6OIXGEp8UnKm5z53x1nm7zyX5fdzdclA7CyMsv19ZiUpOYX1x25goKtFAUdrpX0Ldp1n9vYz2Job0aSCF73ql0FNNeNrf0ZuPnuHt5eL0rbKhV0ZuerQL+m7IAh/no+R8fRYc4WxDb2o5WVLTEIK118Gw5f7Q2IyzUs4Ma1ZUWQyGctOP6PV0vNcm1AHPS11RTvzjj5iYuPCTGpciMn77tNz7RUczPToVz0/uYx1GLDpOiO332ZrH29FndfBMey/846NPcsTnZDMwE3XGb7tFss6ls6wrwuPP2H3jTfMblkMJwt9rr34RO91VzHV06K0mwUzDz7ANzCSLb0rYKKnyevgGBKSUjM99wXHHrPw+JMsv5+LY2tha5J+0FSqVEaqVIamuvI1XEtDlRuv5ClY34bG8ikqgfJ50wYkGWhrUNjRlFt+IYpARHY8D4xk7pFHHB1WjbchMdmuJwjfQwQihB8W9DGYlJQUGtSqioOtfCRTwXxpo2Qqli2pVH7Z7EmY5y3Ohas3qF21omJ7u+YNaVqvJgBDe3ehXN2WjBrQk2re8lRCfTq3peug0UptJSQmsmbhDHJZy0eJzp8yhvptezBr/LB0I/8TE5OY6bOSY9v/omTRQgA4O9hx+cZtVm3cQflSxXn3PhCvAvko4lkAAEe7XGSlW9vmNKlbI8syNlYWWe7PjsMnz9Km5xDi4uOxtjTn6La/MDOVj25VUVHh2PY1NOnUBxPXokgkEizMTDi4eSXGRoaKNsYP7UvFMiXR1tbi1PnL9B01iZjYOPp0keeDtbIwZ8nMCRTxLEBiYhJrtuyiSpP2XD60TWn2iiAIQnYFhYSTkppK/UolsbeWXwsL5E77EexdzEOp/OLRPbGp2IZLdx5Rs1wxxfa2dSvRuGoZAAa1b0jFjiMY3rkpVUvJr+W9WtShx6TFSm0lJCWxemJ/bCzkI2XnDO1C4wFTmT6gQ7rZAYlJycxeu5tDSydQwkMe5HWyteLqvaf8tecE5YoUwD8oBM88ToqURQ42WV/bOzeuTqPPfc6MtZlJpvtev//I6t3H6Nu6HkM6NuHOk5cMmfMX6upqtKlTKV35zYfOoq+rTf2Kyvfcns1r45XXBWNDPa7ff8b4JZsICgln5qBO6dpISExi+7ELDGr/z8wCFAThz/AxLJqUVCl1SrtjbyG/3ro7pr0YKe+p/HJ7QZ+GOLaYxOVHftQonramWasqRWhYTn6f6N+4AtWGLmNoi0qK0fjd65Whz8JdSm0lJKWwbFBTbEzlv4Fndq9L80nrmdK5drrZAYnJKczfeZa9UzpTPK/83uRoZcK1J29Ye+wGZQo6ExAciYezDYVc5aNL7S0znl32RaeaJWhYtmCWZaxNM56l8D2O3XhKl9nbiEtMxspYn72TOmFqmPbyqnvd0ni65MJIT5sbz94xaf0xPoZFMbVLnSxaVfYpPBpzI+W+mhvpER2XSHxiMtqa6pnUFARByNjHyHhSpDJqe9lhZyq/ZuXPZaTYXy6P8qzeua2K4zpkF1defKJawbR3My1KOVO/iHxmQp9q+ag9+ySDarpTMb88INu1Yh4GbLyu1FZiciqL25dUpDGa1qwIrZdeYGKjQlgYaqcru/D4Y3b2q0QxZzMAHM30uP4qmA2XXlLazYKAsDgK2Bnj5SB/1rA31SMr7cvlpn5h+yzLWP2tH1/oaalT1MmM+Ucf42ZlgLmBFntvvuWWXyhO5vLjBkfK0yeZGyjPADQ30OJTVPpZb5lJTE6lx5orjG/oha2JrghECP8YEYgQfpiHe14qlS1J4Ur1qepdlqoVStOodnXFS/CPwSGMn7mQC1dv8CkkjNRUKXHx8fi/V57eVTBf2jRkC3P5xb7AVwENS3MzEhISiYqOUaxhYJ/LWhGEAChZxAupVIrvq9fpAhEv37wlLj6emi26KG1PSk7Gq4D8oad7+xY079Kfuw+fUKVCaerXqEKpYoUyPXcTYyNMjI2y+1X9MO8yJbh5cg+hYeH8tXknrboP5NJh+awHmUxGv1GTsTAz4ezeTWhrabJm6y4adejFlSM7sLaUvywbPbCXor1CBfMTGxfPvGVrFIGIPLmdyJPbSVGmVLFC+L19x8JV61m3aNY/fo6CIPz3eLg64l3cg+ItBlClpBeVS3rRoHJpjA3k1/CPoRFMWraZi7cfExwWSapUSlxCIv5BIUrtFHBNC15YmBjJt30V0LAwNSIhMYmomDgM9OQPF3ZW5oogBEAJjzxIpVJevH2fLhDxyj+QuIRE6vaeqLQ9KTkFzzzy62KXJtVpPWwW9577UbmEF3W9S1DSM2+m525iqI+J4Y+/bJJKZRTO78LE3m0A8MrrzJNX7/hr9/EMAxEbD5ymeY3yaGkqTy/v16a+4u8FXR1RV1ej37TlTOrTFk0N5ZdIB85eJzo2ntZ1KiIIgvCjCjhZU8HThbJ9FlKpsCsVC7lSv0xBjPTkL1g+hUczddNJLj30IzgyBqlURlxiMgHBEUrtuH81wt/CSH7fyO9gqbQtISmFqLgEDHTkL15szQ0VQQiAYnkdkEplvAwITheI8PsQSlxiMo3GKq+3k5SSioez/Nidapag/fTN3H/1gYqFXKldMj8l8mU+qtRYXwdjfZ3sflU/rJyHCxcW9iU0Ko4NJ27SceZWTs3thfnn76l3g3KKsgWcrNFQU2Xgkr2Ma18DTXXx6C8IQs5wtzWiXB5LvKceoWI+ayrks6JuYXuMPi+C/CkqnhkHH3LF9yMh0YmkymTEJ6XwPixOqZ2vgxfm+vLrfz4b5W0JyalExyejry3/vZvLWEcRhAAo6myGVCbj5cfodIGI18ExxCel0mzRWaXtySlSCtjJnyM6lM9N55WXeOgfjndeK2p62lLMJfM02Ma6mhjrama6/1uWdCjJgI3X8Ry1H1WJCgXtjGlY1J4H78J/uM2MTN1/H1crA5qUcPp2YUH4CeLXiPDDVFVVObp9DVdv3uXk+cssWbOZcTMWcunwdpzsbencfySh4RHMnTQKe1sbNDU0KF+3JUnJytOa1dXS/jP8ki5ILYNtUqn0h/oZGyu/ee3fuAwbK+U825oa8htfjUrleXnzNEdPX+D0hStUb96Rnu1bMXP8sAzb/LdSM+nq6JDbyYHcTg6UKOJF/jLVWbt1N8P7duPspWscOXWOT0+vKwI0izzcOX3hCht37GdY364Ztlm8sAfTFiwjMTEJTc2M8yIW8/Lg8s3bP9V3QRD+XKqqqhxaMoFr959x+vo9lm0/woSlmzm/bhaOuSzpNsGHsMhoZg3ujL21OZoa6lTqOIKk5BSldr55f+Dz/eHztO7vFRsvHyW0e8FopeAFgKa6/OGlepkiPD20kuOXb3Pm+n1q9xpPt6Y1mT6gQ4Zt/mxqJiszY/I62Slty+Nky74zV9OVvXz3Cb5v37N++uAsjwdQrIAbKampvP3wCTdH5Vl/6/afpGa5oliaGn2zHUEQhMyoqkrYO7kz15++5ezdF6w8dJUpG09wak4vHKxM6LVgJ2FRcUzvVgc7c2M01VWpNnQ5ySnKKS3UVSWKv3+59qt/lVbuyzaZ9Aev/Z/Tr24f1x5rU+X10TQ+v6yvWjQPD9YM4+St55y995IGY1bTpVYpJneulWGb/1ZqJl0tDZxtzHC2gWJ57SnSbQ4bT95iUFPvDMsXcbMjJVXKu4/huNpmvV7cFxbG+gRHRCttC46IQV9HU8yGEAThh6hKJOzsV5GbfiGcexrEX+d8mX7gAUeHVZOnVtpwjfCYJKY0LYKtiS6aahJqzzlJUqry/UFN8vX9Qf5P5XuG/J8//GyQKH9XtblnBayNlIMUGmry41R2t+HWlHqcfvyB80+DaOJzlo7lXZnQOOOBrD+TmgnA0VyffYOqEJuYQkxCMpaG2nRdfRkHM/k7IPPPwZTgqAQsvwqsBEcl4G6b9Wy+r13y/cjT95HY3N0GKLJmkW/YHgbUcFesMSEIP0sEIoSfoqKiQunihSldvDBjBvUid/HK7D96igHdO3Dl5h18po+jZuUKAPi/DyQk7NdEbd+9D+RD0CdF+qPrd+4jkUhwc0kfvc3nlhtNTQ3evQ+kfKnimbZpbmpCu2YNaNesAWU3FmHE5NmZBiL+rdRMfyeVykj8nJs87vMLNIlEea0HFRUJUlnmQZv7j59hbGSYaRBCXubpNxe3FgRByIqKigqlvPJRyisfI7s0I2/d7hw4e41+bepz7f5T5g/vTo2yRQAICAohJCLqlxzXPyiYwOAwrM3l6Y9uPPRFIpHg6pA+5V5eJzs0NdTxDwqhXJECmbZpbmxImzqVaFOnEn95HWe0z/pMAxE/m5qppGdeXrx9r7TtxdsP2Funvyav33+KQvlc8HD79silB76vkUgkmJsYKm1/8/4jF249Yue8kd9sQxAE4VtUVFQomd+RkvkdGdaiMh6dZ3Lo2mN6NyjH9advmd2jPtWKymeVBQRHEBoV+0uOGxAcSWBolCKwcOv5OyQSFXJn8PI9j50lmupq+AdHUKZg5ouLmhnq0bJyEVpWLsLa/I6MX3s000DEv5Wa6e+kMlm6IP7XHr7+gESiopgxkR3F8tpz8tZzpW1n776keN6sU4sIgiBkRUVFheIu5hR3MWdwLXeKjDnA0fsB9KiclxuvQpjZoihVCsgHcr4PiyU0JvM1O7/H+/A4giLisPo8K+L261AkKirktkx/Tc5jbYimmoT34bGUdsv8fY6ZvhbNSzrTvKQzGy6+ZOLeu5kGIn4mNdPXdDXV0NVUIyIuiXNPAxnb0AsAB1NdLAy0uPg8SDFrIzo+mTtvQmlf3vWb7X6xpmtZ4pPTAj/33oYxYON19g+qgqN59u8hgvAtIhAh/LAbd+5z5tI1qlYog7mpCTfuPiA4NIy8rvIf9LmdHNiy6wBFPAsQHR3DiMmz0dbS+kar2aOlqUnn/iOYMW4Y0TExDBwzlSZ1a2T48lxfT5eBPToydPwMpFIZZYoXJioqmis376Kvr0e7Zg2YMMuHwh7u5M+Tm8SkJA6fPEdeV5cMjiz3s6mZYmJjefn6neLzG/8A7j16iomRIfa2NsTGxTF94QrqVquIlaU5oWERLFu7hfdBH2lctzogT0dlbGhAp/4jGT2wlzw10+ZdvPF/rwj+HDpxlk/BIRQv4omWpianL1xhps9KBvZIW9zPZ9V6HO1syZ8nNwmJiazdsouzl69zZOvqHz4/QRD+bDcf+XLuxgMql/TC3MSQm498CQmPJI+TPNe2i50NW4+co3B+F6Jj4xm9cD3aWQRHv4eWhgZdx/swbUB7omPjGTp7NY2qlE6XlglAX1eb/m3qM2LeGqQyKaW98hEZE8e1e8/Q19OmTZ1KTF6+hUJ5XcjnYk9iUjJHL90ij6Ntpsf/2dRMfVvVpVKnkcxes4tGVctw6/EL1u49waLRPZXKRcXEsffUlQwDItcfPOPmoxeUL1oAfR1trj98zvB5a2hRs7wiPdYXGw6cxsrMmGqlC/9wnwVBEED+8v/8/VdUKuSKmaEet339CYmMxc1W/jLH2dqMHWfvUsjVlui4BMatPYq2xq8ZYa+loUavBTuZ1KkW0XEJjFh5kAZlC6ZLywSgr6NJn4blGL36MDKZjJL5HYmKTeD607fo62jSsnIRpm06iVfuXOS1tyAxOZXjN5/hZpdF6o2fTM0UE5/I68BQxee3H8N56PcBIz0d7CyMiE1IYu6Os9Qsng9LE33CouJYffgqgaFR1C8jD4DcePaW28/9Kevhgr62JjeevWX06sM08/ZSpMcCePbuI8kpqYTHxBMTn8hDvw8AFHSWvwDsVKMEqw9dZdzao7SpUoQLD16x79JDto9v/8PnJwjCn+326xAuPv+Idz4rzPS1uPMmlNCYRFyt5MFjZwt9dl5/g6e9CdEJyUzaew/tvy3Q/KM01VXpu+EaExoVIjohmdE7blOviF26tEwgX5OhZ5V8jNt1B6lMRgkXc6Lik7nxKgR9bTWal3Rm5sEHeNibkNfakMQUKScevsfVyjCDI8v9bGqms08CkclkuFga8CY4mol775Hb0oCWpeTv3VRUVOhWKQ/zjz7GyUIfe1M9Zh58gKWhNjU9055ZAsJiiYhN4n14HKlSGY/85YOEncz10NVSx9Fc+X4Z9jkQ5GZlgKHOr3lOEwQQgQjhJ+jr63Hp2i0WrdpAVEwM9rlsmDVuODUqlQdgxdwp9Bo2nhLVG2NrY8XkEQMZMenXrDng4mhPg1pVqd+2O2ERkdSq4s2i6eMyLT9xWH/MTU2YtWglr98FYGSgT6GC+RnerxsAGhrqjJk+n7f+79HW1qRM8aJsWjb3l/Q1I7fvP6Zqk7Qf80MnzASgbbMG/LVgOqoSVZ6/9GPTzn2EhIVjamxEEc+CnN27Cfc88qi2makxh7asYtyMBVRv1oHk5BTy58nN7rWL8XSXjzRTV1dj2bqtDJkwA5lM/r3NnjCczq2bKo6dlJTM8EmzeB/0ER1tLQrmy8Ox7WvwLlPiHzt/QRD+2/R1tbl09wmLtx4kOjYeeytzpg/oSPUy8hkQS8f2pu+0ZZRpMwRbS1Mm9GrDqIXrfsmxne2sqF+pJI36TyE8KoaaZYuwYET3TMuP69kKM2MD5q7dQ5/3HzHU18ErrwtDOzYGQENNnfFLNvH2wye0tTQo7ZWf9dO+nQrpRxVxd2XbnOGMW7yJ6at34GhjwazBnWhRs4JSuV0nLiGTyWhao1y6NjQ01Nl14hLTVm4jMTkFRxsL+rSqR7/W9ZTKSaVSNh06Q5s6FVFV/TUPe4Ig/Ln0dbS4+vg1yw9cJjouETsLIyZ3rkXVovL14Bb1a8yAxXvwHrCIXGaGjG1XnbFrjvySYztZm1KnlDvNJ6wjPCaO6sXyMrdng0zLj25TFTNDXebvPM+bj3sx1NXC08WGgU3la+VoqKsyacNx3n0MR0tTjVL5nfhraMtf0teM3Hv5nrqjVqX176/DALSsVJilA5uiKlHhRUAw207fITQqFhMDHQq52nJkRjfyfV4/Q1NNjT0XHzBj62mSklNwsDShZ/2y9G5QVulYzSauw/9ThOJz+f6LAAg/OB0ABysTto/vwKjVh1hx4DI2Zob49G2kWCxcEAThe+lrqXPt5SdWnnlOTEIytia6TGhUiMru8gDo/DbFGbL5JlVnHMfGWIdR9TyYuOfeLzm2k7ketb3saLXkPBFxSVQtYMPMFsUyLT+ibkFM9TTxOf6EtyGxGGir42FvTP/q7gCoq0mYtv8+/qGxaGmoUsLFnBWdSv+SvmYkKj6ZqfvvExgRh5GOBnUK2TGynodSSqo+VfMRl5jCkC03iYpLoriLOdv6eKP1VTBn1qGHbL/2WvG58vRjAOwZUIkybsopzAXhn6Qik/1g8jThX9WoUSPiI0M4sCnrdQn+BJPmLObAsdPcOrU3p7si/Itu3n1AmdrNuX//Ph4eHjndHUHIEQ8ePMDT05Pz62ZStIB4IfB3U1ds4+D561zbMj+nuyL8gxr2m4yupQN79uzJ6a4Iwm/Nx8eHEcOG8mHXxJzuyj9qxpZTHL72hIs+/XK6K8IvsnD3eRbuu0pYeEROd0UQ/pOioqIwNDRkZecy1C/y3027NvvQQ44+CODMqJo53RUhG8pNOU7t5u2ZN29eTndF+AdJvl1EEARBEARBEARBEARBEARBEAThx4hAhCAIgiAIgiAIgiAIgiAIgiAI/xgRiBD+74wb0kekZRIEQRDSGd29hUjLJAiC8IcZ0aqKSMskCIIgpDO0TkGRlkkQfjMiECEIgiAIgiAIgiAIgiAIgiAIwj9GBCKE/3uuxSvjs2p9TndDEARB+Bflq9uNxVsO5nQ3BEEQhBzm0Xkmy/ZfyuluCIIgCDmo6JgDrDjzLKe7IQjCN6jldAcE4U8hk8mYv3wtqzfv4F3AB8xMjOneviUj+/fIst6RU+eYOn8ZD58+R0tTk3Ili7F77WIA7j9+xuzFq7hy4w4h4eE42OaiW7vm9O3S7t84JUEQBCELU1dsY9qq7em262hpEnxp2zfrh0ZEUbLVID58CuX92U0Y6esCsP/MVVbtOs5D39ckJieTz9mOUd1aULVUIUXdVbuOsWrXMd4FfgIgn7MdI7o0o3qZIr/o7ARBEITvcfqOLzO2nOLZu49oqqtT2t2RKZ1rY29pnGH5dx/Dmb39DBfuv+JTRDRWJgY08/ZicLOKaKinf4z3+xBChQGLkEgkvN02XrH94JVHzNt5Dr/AUFJSUnG2MaN3g7K0qFT4HztXQRAEIWP7b79j4fHH+H2MxlRfk04V3OhdNV+WdeYffcypRx94HBCOupqEF3ObZFhu21U/lp9+ht+naPS01KlX2J4ZLYoC8C40hmJj0w/iOjy0KkWdzH7+xAQhm0QgQhD+JYPGTuPk+cvMHDuMAvncCI+IJCw8Iss6ew6foOfQcUweMQDvMiVISU3l8bMXiv13HzzGwsyUdYtnYmtjzdVbd+k1dDyqElV6dWr9D5+RIAiCkJX+bevTuXF1pW11eo2ncP7c2arfa/ISCuR24MOnUKXtl+4+oVIJTyb2bo2hvi4bD56h6cBpnFs3E6+8zgDksjBlUp+25La3RiaTsfnQWZoPnsGVzXPJ72L/a05QEARByJa3QWG0nrKRXg3KsnJwc6JiExi1+jBtp23i/MK+GdbxDfiEVCpjfu8GONuY8eRtEAMW7yUuIZnJnWsplU1OSaXL7G2UzO/IjWfvlPYZ6+swuFlFXG3N0VBT5fjNZ/RZuBtzIz0qF3b7x85ZEARBUHb68Qd6rb3CtGZFqJDfmheBkQzechMtdVU6e2d+PU5OlVK3sB1FnU3ZcsUvwzLLTz9j2alnjGvkRRFHU2ITU/APi01Xbme/iuS1NlR8NtbT/PkTE4TvIFIzCTlm9aYdOBQqj1QqVdreqENvug4cDcCrN+9o1KE3th5lMc5dhFI1m3L6wpVM23zj/x4Nm3zce/RUsS0iMgoNm3ycv3JDse3RM1/qtu6Gce4i2HqUpUPfYYSEhv/iM0zz9MUrVmzYxu61S6hbvRJO9rYU9nCnSoUymdZJSUlh8LhpzBgzhG7tWuDm4kR+t9w0rZe22FKHlo2ZN3kU5UsVx9nBjtaN69G+eUP2HT35j52LIAjCz1iz5wQuNTqlu/Y3GzSNHhMXAeAXEEizQdNwrNYBi3ItKdduKGeu38+0zbcfPqFbtCH3n79WbIuIjkW3aEMu3Hqk2Pb45Vsa9JuERbmWOFbrQOexCwiJiPrFZ5hGT0cbKzNjxZ9PYRE89fOnff0q36y7atcxIqNj6d+2frp9swd3ZlD7hhRxdyW3vQ0Te7cht701Ry/eVJSpVb4YNcoWIbe9Da4OuZjQuw16OlrcfOj7S89REAThR607doN87aelux+0mrKBPgt3AfA6MJRWUzbg1nYqtk3HU2ngYs7de5lpm+8+hmNcdyQP/T4otkXGxGNcdySXHqa9vHnyNogm49di23Q8bm2n0n3udkIj07+w+VXuvXpPqlTKmDZVcbI2xTN3Lvo0KsfD14Ekp6RmWKdKkTwsGdCESoXdcLQyoVaJ/PRpWI6DVx+lKztl0wlcbc1pWNYj3b6yBZ2pU8qdPHYWOFmb0qNeGdwdrbj25M2vPk1BEITvtuHSSzxG7kMqlSltb7f8Av03XgPgTXA07ZZfwH34HpwG7qT6jOOcfxaUaZvvQmOw7LWVR/5p73gi45Kw7LWVy74fFduefoig5eJzOA3cifvwPfRed5XQmMRffIZpdl5/Q01PW9qXd8XRTI+qBXPRr1p+Fp94ikwmy7TesDoF6VE5L/lsjDLcHxGXxIwDD1jUviSNizniaK6Pu60xNTxs05U10dXEwlBb8UddVbwWFv5d4r84Icc0rlOd0PAIzl2+rtgWFh7BiXMXadmoDgAxsXHUrFyeYzvWcOPEbqpVLEvDDr14F/Ahs2a/KSIyiupNO+JZIB9Xj+7k4OaVfAoOpVWPgZnWeRfwAePcRbL8M8NnRab1D584i5O9LUdOncOtRBVci1em++AxWc6IuPvwCe8DPyKRSChWtRH2XuWo27obj55l/RIpMjoGYyPDLMsIgiDklIZVShMWGc35rwIEYZHRnLx6l+Y1KwAQE5dA9TJFOLx0Ilc2z6VqqUI0HTQN/6DgHz5uRHQstXqOwzOPMxc3zmafzzg+hUXQdsTsTOv4BwVjUa5lln9mr9mV7T6s33cKV3sbyhTKn2W5p37+TF+1g1WT+iNR+fZPNalUSnRsPMYG+hnuT01NZefxi8TGJ1DcI0+2+ysIgvBPalC2IGFRcVz8KkAQHh3H6du+NPX2AiAmIYmqRfKwb0pnzi/sS+UibrScvB7/TxE/fNzImHjqj16Nh4sNZ+b1ZteEjgRHxNBx1pZM6/h/isC26fgs/8zdcTbT+l4uuZCoqLD51G1SU6VExiaw48xdvD1dUFdTzXbfo2ITMNbXUdp24f4r9l96yOye6QPXfyeTyTh//yUv3wdT2t0p28cVBEH4p9QrbE94bCKXvgoQhMcmcvZJII2LOQIQm5hCFXcbdvWvxOmRNajobk27ZRcIyGDEf3ZFxiXReMEZCtgZc2J4dbb18SY4KoGuqzNfcyggLBangTuz/LPg2ONM6yelpKKprnzN19JQ5UNEXIazF7Lr/NMgpDIZQRHxlJ14GK9R++i6+hLvM2iz3fIL5B+2h7pzT3LsQcAPH1MQfpRIzSTkGGMjQ6pXLM+2vYeoVK4UIE9FZGZijHeZEgB4uufF0z2vos7EYf3Zf/QUh06c/eHUQ0vXbsarQD6mjEwLPKycNxXnohXxffUaN5f0P8ptrCy4eXJPlu2aZPHy//W7AN69/8DuQ8dY4zOD1FQpQ8bPoEW3AZzYuS7DOn5v/QGYPHcxsyaMwNEuF/OXr6Vq4/Y8vnQUE2OjdHWu3rzLzgNH2b9heZZ9FQRByCnGBnpUK12YHccuULG4fOTmvtNXMTUyoELRAgB4uDnh4ZZ2LR7XsxUHzl7j8Pmb9GheK8N2v2XF9iN45nFmYu82im3Lx/XBrXZXXrx9j6tDrnR1rM1MuLpl3jfPJzsSEpPYfuwCg9o3yrJcYlIyHUbPY2r/dthZmfM64GOW5QEWbNxPbHwCjaqWVtr+6OVbKnUcQUJSEnraWmydPYJ8znbZ6q8gCMI/zUhPmypF8rDr/H0qeMpT1u2//AhTA13KFZSnmSvoZE1BJ2tFndFtqnHo6hOO3nhCtzqlM2z3W1YdvoqHsw3j2qWlzlvUvwkFOs7g5ftgcucyT1fH2lSfC5mkUPri7wGCrzlYmbBnUic6ztrKwCX7SJVKKZbXnp3jO2S7334fQlh56AqTO6XdB8OiYum1YCcrBjfHQEcr07qRsQm4d5hOYnIKqhIJc3rWp2Ih12wfWxAE4Z9ipKNBpfzW7Ln5lvJ5rQA4eNcfE11NyrpZAuBua4y7bdp6OiPqenDkXgDHH7zPMqVRVv4670tBO2NG1/dUbFvQtgSFRu/n1ccoXCwN0tWxMtTmzMgaWZ+Prkam+7zzWzNu1x2aP3OirJslr4OjWX5KvsD2x8gE7E2z91zxd29DYpDKYOHxx0xpWgR9bXVmHHhAs0VnOTu6JhpqquhqqjOxcSGKOZshUVHh0D1/Oqy4yLru5TKcOSEI/xQRiBByVMtGdeg5dByLpo9HU1ODrXsO0qx+LSQS+QjQmNhYJs9ZwpHT5wn6FExKSirxCQm8e//jMyIePHnOuSs3MM6dfsFOv7f+GQYi1NTUyO3k8MPHlEqlJCYmsWbhDEX7K+dOpkSNJjx/+Zo8udMf88vUxBH9e9CodjUAVs+fhlMRb3YfOk7Xts2Vyj965kvjjr0ZM6gXVb0zT/kkCIKQ05rXLE+fKUtZMKI7mhrqbD92nibVyqZd++PimbpyO8cv3SIoJJyUVCnxiUk/NSPi4YvXXLj1CItyLdPt8wsIyjAQoaamiouddbrtP+LA2etEx8bTuk7FLMuNW7yRvI62tKzlna12tx+7wPRV29k+dyQWJkZK+9wcbLi6ZR5RMXHsPX2F7hN8OLZyighGCILw22jq7UX/xXuY07M+mupq7Dx3j0blPdLuB/GJzNxymhO3nhEUHk1qqpT4pGQCgiN/+JiPXgdy8aEftk3Hp9v3OjAsw0CEmqoqzjY/vpjnx/Bo+i/eQ4tKhWlS3pPo+ESmbz5J+xmb2Tu5MyoqKlnW/xAaSZMJa2lQpiDtqxdXbO+/eC9NKnhRpkDWsxv0tTW4sLAvsQlJnL//itF/HcbRyoSynwM+giAIOalxcUcGb77BzBZF0VRXZc+NNzQoao9EIr82xiYkM/vwI049+sDHqHhSpDISklIzHPGfXU8CIrjs+wmngTvT7XsTEpNhIEJNVYKTRcYzkLOjbRkX3gbH0HbZBZJTpehrqdO1ohuzDz9CkvVtIEtSmYzkVClTmxbBO7/82WV5p9IUHLGPy76fqJjfGlM9TXpUThvkW8jRlI8R8Sw9+UwEIoR/lQhECDmqTtWK9JDJOHL6HEU9C3Lp+m3mTBih2D980mxOX7jCjHFDcXF0QFtLkxZdB5CcnJxhe5LPP+K/zq+XnJKiVCY2No7aVb2ZNnpwuvrWlukfPECemsnTu26W5zK8XzdG9Oue4T4rC3PU1NSUghx5XV0A8H//IcNAxJe+5PtcDkBTUwMnBzvevQ9UKvvE9yU1mnWic5tmjBrQM8t+CoIg5LRa5Yohk8GxS7cokt+Vy3efMmNQJ8X+UQvWceb6faYN6ICznTXamhq0Hj6LpL9dz7/48pCS1bU/Jj6BWuWKMrlfu3T1rcyM020DeWqmIk37ZXkuQzs2ZminJlmWAVi3/yQ1yxXF0tQoy3Lnbz3k8ct37C0hXw/pyynZV2nHsE5NGNM9LZCy8/hFek9ewqaZQ6lUwjNdWxrq6opASqF8Ltx+8pKlWw+xaLS4TwiC8HuoUTwvMpmMEzefUcjVlqtP3jCtS23F/rFrjnDu3ksmd6qFk7Up2hpqtJ+xJd01/gsVxbNA2rbkVOU1GGLik6hRLC8TOqQf1Wppkv7FE8hTM5XqPT/LcxnY1JvBzTIONq8+fBUDHS0mdUxb623F4OYU6DiDW8/9KZbXPtN2A0OjqDdqFcXzOrCgT0OlfRcevOLo9acs3nsRABkypFIZZvVHs6BPQ9pULQqARCJRBFIKOtvg6/+J+TvPiUCEIAi/hWoFcyGTwalHH/ByMOHaq2AmNSms2D9hzz3OPwtiQiMvHM310VZXpfPqSySlSjNsT/FeiK+eDf5WNjYxhWoFbRjbwCtdfQtD7QzbDQiLpdzkI1meS//q+RlQwz3DfSoqKoxt6MWo+h58ikrAVE+Ti8/ls58dzH5sNgSA5ef+un21CLWZvhYmehpZpq8q7GSa5VobgvBPEIEIIUdpaWnSoGZVtu45xKvX73BzcaKQR9pF+8rNO7Rt1oAGNasC8hkSbwPeA8UybM/c1ASAoE9po2bvf7VwNYBXwfzsPXwCR7tcqKll73+Bn03NVLpYYVJSUnj15h0ujvIHDV+/NwDY29pkWKewhzuamhr4vnpNmRLy2RvJycm89X+Pw1d1Hj9/QfWmHWnTtD6TRwzI1vkIgiDkJC1NDepVKsn2oxd45R+Em4MNhfKmBV2v3n9Gm7qVqFexJCCfIfHuwydIP5ENADMj+YujoJC0BekefLVwNYBXHmf2n7mGg7UFatnMx/2rUjO9ef+RC7cesXPeyG+W3TJrGPEJSYrPt5+8pOekxZxcNRUnWyvF9h3HLtJz8mLWTx1EjbJFv9kufJ6dl0kgXxAEISdoaahTt1QBdp67h19gKK65zPDMnTZD7frTt7SqXJg6peTPBzHxibz7FA5kPAPAzFAXgKDwKDyQ/15+6Kc8gMfTxYaDVx5jb2mMmmo27wc/mZopPjFZETT/QvXzZ2kWC5R+CI2k3qhVeObOxZL+TRQzRb44MbsnqV8t9n3k2lN8dp/n2Owe2Jhm/mwilclITM44mCMIgvBv01JXpbaXLbtvvuF1cDS5LQzwsDdR7L/hF0yLkk7U8pLP6o1NSMY/NBYyyTBnqqcJyNMdFfw8EfhxQLhSmYJ2xhy+54+dqS5q2Vyw+WdTM32hKpFgbSS/Z+y9+ZaiTmaY6WeeXu9bijvLA80vP0ZhYyxvNzw2kbCYJOxMdDOt9yggQhHEEIR/iwhECDmuZaM6NGjfkyfPX9KqsfKsg9xODuw7coraVSuioqLChFk+SKUZR70BtLW1KFHEk9mLV+Fob0twSCjjZ/kolenZoRVrNu+kTa8hDOnVGWMjQ169eceOfUdYMXcyqhk8kPxsaqbK5UtRqGB+ug0azZyJI5HJZPQbNYkq5UsrZkncvPuAjv1GcHzHWnJZW2Kgr0e3ts2ZNHcxtjbW2NvaMG/ZX4B8oW+Qp2Oq3rQjVb3LMKB7B0UARlVVVRGUEQRB+B01r1GeJgOn8tTPnxafF6n+Ire9NfvPXKNmuWKoqMDkZVuzfFGjraVJ8YJuzFu/B8dcFgSHRTJpmfKio92b1WLtvpN0GD2Xge0aYmyoxyv/IHaduMTSMb0yufb/mtRMGw6cxsrMmGqlC6fbd+DsNcYv3sTd3YsBcLZVPl5oRDQAeZzsMNKXP0hsP3aBbuN9mD2kM0ULuCkCMNpaGhjqycuMW7yRaqULY2dlTnRcPDuOXeDi7cfsXzTup89HEAThV2rq7UWLSet59u4TzSp6Ke1zsTHj4NXH1CieD1RUmLbpJDJpFvcDTXWK5bFjwa7zOFiaEBIRw9RNJ5TKdKldig0nbtJl9jb6NSqPsb4OfoGh7LnwAJ++jVDN4IXUz6ZmqlY0L0v3X2bW1tM0ruBJTFwikzcex87CCA9necDktq8/PeftZN/UztiYGvIhNJK6I1dhZ2HE5E61CIlKG9VqaSxPDZLHzkLpOPdevEdFokJ+h7TA9byd5yiUOxdO1qYkJqdw8tZztp+9y9yeDX74fARBEH61RsUdabv0PM8DI2lc3FFpn7O5Pofv+VOtYC5UVGDGwYdZPxtoqFHEyZRFJ55gb6pLSHQi0w8+VCrTqYIrmy6/oseaK/Sumg9jXQ1eB8ew79Zb5rUpjqoko3vBz6VmCo1J5NCdd5R2syQxOZWtV/04eNefvQMrK8rceRNK3/VX2dW/kiJYERAWS0RsEu/D40iVynjkL//t72Suh66WOi6WBtTwyMWYnXeY26oYetrqTN13H1crfcrkka+zsf2aH+qqEgrayd8THb7nz9YrfsxrUxxB+DeJQISQ4yqWLYmJkSG+r17TomEdpX2zJ4yg26DRVKjXCjMTY4b07kx0TEyW7a2cN5Xug8ZQsnoT3FycmD5mMLVadlHst7Gy4Nz+zYyaOpdaLbuQmJiEva0N1SuWTTfK6FeRSCTsXb+MAWOmULlRW3R1dKhesRyzxg9TlImLT8D31WulqeYzxg5FTVWNjv2GE5+QQPFCHhzfuRbjz7Mv9hw6QXBoGFt2H2TL7oOKeg62Nry4cfofORdBEIRfwbtYQYwN9PB9+55mNcop7ZsxsBM9Ji2icqcRmBoZMKh9Q6Jj47Jsb9m4PvSavISybYbg6pCLKf3aUa/PRMV+a3MTTv81nbGLNlCvz0QSk5KxtzanSqnC/9i1H+SzEDYdOkObOhUzDHZExsTh+/b9d7W5ds8JUlJTGThzJQNnrlRsb12nIisnyFNJBYdF0nX8QoJCwjHQ06GAqyP7F42jckmvnzofQRCEX628hzPG+tq8eB9MkwpeSvumdq5NH59dVB+6HBMDHfo3qUB0XEKW7S3q34S+PrupOGAxuW3NmNihJo3GrVHstzY14NisHkxYd4xG49aQlJyKnYURlQu7pZu18KuU93Rh1ZDm+Oy+gM+eC/KASV57dk3oiLamOiCfNfHifTApKfJBV+fuvsQvMBS/wFDcO8xQai/84PRsHzsuIYkhy/bzITQSLQ11XG3NWTG4OY3Kefy6ExQEQfhJ5dwsMdLV5OXHaBoVc1TaN7FJIQZsvE6dOScx0dOkT7V8xCRkPct3QZsSDNx0g2ozjuNiqc+4hl40W3ROsd/KSIdDQ6oyee89mi86S1KKFFsTXSq6WytSO/0Ttl9/zYS995DJZBR1MmPPgEoUdjRV7I9PSuHlx2ilVFKzDj1k+7W02d6Vpx8DYM+ASpT5vKD34valGLvrDq2XnkciUaFUbgu29vZG/avg+vyjj/EPi0VNIiG3lQErO5embuHMUwMKwj9BRSbLIowo/DYaNWpEfGQIBzat/HZhQfgPunn3AWVqN+f+/ft4eIgHJ+HP9ODBAzw9PTm/biZFC7jldHcEIUc07DcZXUsH9uzJOmWiIPzpfHx8GDFsKB92Tfx2YUH4jSzcfZ6F+64SFh6R010RhP+kqKgoDA0NWdm5DPWLiBfRwu+h3JTj1G7ennnzsk6NK/x/++eGAAqCIAiCIAiCIAiCIAiCIAiC8McTgYj/I2LuivAnE5O3BCGN+L9B+JOJ//4FIfvE7yfh/5H4z1YQ/h3iHiH8TmTiV/4fQQQi/k8YGBgQEhae090QhBwTHBoGgKGhYQ73RBByjoGBAQChEVE53BNByDkhEVHiXiAI2WBgYEBCUjIx8Yk53RVB+C6hkbHiOi8I/yBdXV0kEglhseL+IPweZDIZYTGJ4tr/BxCBiP8T3t7e3HnwmPeBH3O6K4KQIw6fPIejowP29iKHpfDncnBwwMHeniMXbuZ0VwQhR3z4FMq9p6+oUKFCTndFEH57X/4/OX7zWQ73RBCyTyqVcuK2LxW8K+Z0VwThP0tVVZVyZctw4mFgTndFEAC49TqU0Kg48Rv/DyACEf8n6tevj7GxMU079+XRM18xhU74Y8TExrJ49Ub+2ryTjh07oaKiktNdEoQco6KiQqfOnVmz9yRLtx0iJi4+p7skCP8KmUzG45dvaT5kJsbGRtSvXz+nuyQIvz0nJycqeldg2MpDnLr9nNRUaU53SRCyFBgaRb9Fe3gR8IkOHTrkdHcE4T+tU+cunH3ygWn77xMuZkYIOUQmk3HTL4Q+G66T28WJcuXK5XSXhH+Yiky80f6/cffuXapXr05wcDA2VpYYGOiJl7LZkJKcAoCauloO9wRkUhnJKcmoq6mjIhH/7r4lOTmFgA+BJCQk0rVrV5YvX45EIuKnwp9NKpXSo0cPVq1ahZamBrZW5qirqeZ0t357UqmMlJQU1NTUkPwG19/kz/cm9d/g3vS7k8kgMiaWwE+hmJubcfz4CQoVKpTT3RKE/wvh4eHUqF6NGzdvYaSng4WJAZI//PkhOSUZZKCurp7TXUEqlX51b/qzf+MmJKfwNjAEVVVVVq1aJQIRgvAvmDx5MuPHj0dVooKduQEaqn/OdUj+bJD821x//8RnA6lMRnhMIiFRceR2ceLU6bM4ODjkdLeEf5gIRPyfSUxM5NSpU1y9epXY2Nic7s5v7+3bt+zdu5cGDRrg6OiY091BJpOxbds2JBIJzZo1E4Gkb1BVVcXW1pb69evj5OSU090RhN/K69ev2b9/PwEBAaSmpuZ0d35rMpmMHTt2IJPJaN68+W9x7f3y769hw4biB3c26OrqUqpUKapUqYKmpmZOd0cQ/q/IZDJu3rzJiRMnCAsL+6NnVotng9+XpqYmefPmVWQCEATh3xEYGMj+/fvx8/MjOTk5p7vzrxDPBr8PQ0NDvL29KVeuHKqqYnDdn0AEIoT/LJlMRunSpZHJZFy9evW3uLkAHD16lFq1anH8+HGqVauW090RBEH4zzt+/Dg1atTg6NGj1KhRI6e7A8jvUaVKlUIikXD58uXf5h4lCILwXyWeDQRBEAQQzwaCkJNEIEL4zzp27Bg1a9bk2LFj/K+9+4yygrzaBrwB6YLYsIKFJiIC9qjYiDHG2LuCFaULoiIGs9YbY8FKb3YEu7FrjEZEsYC9URTEYEWaoNJh5vuRbyyxADNz5jnluv7pm5x1Z708bO+9z+AhhxySOs73SgZMpUqV4uWXXzZgADIom3/PzdY5BZCPsvX33GyeUwD5Jpt/z83WOQXlySGCvFRcXBx77bVXVKlSJSuvydl4gQfIR9n8TdNs/XYuQL7RDQCI0A0gNYcI8tKTTz4Zhx12WFYOl4gfBkxRUVFMnDjRgAHIgOLi4thzzz1jvfXWy8rFU8QPy6cnn3wyDj300NRxAPKSbgCAbgDpOUSQd4qLi2OPPfaIatWqxYsvvpiVwyUi4umnn45DDjkknnjiifjTn/6UOg5A3nniiSfiz3/+czz99NNx8MEHp47zi4qLi2OfffaJVatWxaRJk7J2ZgHkKt0AgAjdALKBQwR5p2S4PPPMM/H73/8+dZxfVVxcHPvuu2+sXLnSgAEoZyWLp+rVq8eECROy+vfYZ555Jv7whz9YPgFkgG4AgG4A2cEhgrxSMlxq1KgRL7zwQlYPl4iIf//733HwwQfH448/HocddljqOAB54/HHH4/DDz88/v3vf0e7du1Sx/lNxcXF0bZt21i+fHm8+uqrWT+7AHKFbgBAhG4A2cIhgrzy2GOPxRFHHBHPPvtsHHTQQanjrFFxcXHst99+sWzZMgMGoJwUFxfH7rvvHrVq1Yrnn38+J35vffbZZ+P3v/99PPbYY/HnP/85dRyAvKAbAKAbQPZwiCBvFBcXx2677Rbrr79+jB8/PieGS0TEuHHjol27dvHoo4/G4YcfnjoOQM579NFH48gjj4xx48bFgQcemDrOWikuLo79998/lixZEq+99lrOzDCAbKUbABChG0A2cYggbzzyyCNx1FFH5dRwifhhwCxevDhef/11AwagDIqLi2PXXXeNOnXq5NTiKeKH5dMjjzwSRxxxROo4ADlNNwBAN4Ds4hBBXiguLo5ddtklNthggxg/fnzqOOvsueeei4MOOigefvjhOPLII1PHAchZDz/8cBx99NHx3HPPxQEHHJA6zjopLi6OAw44IL799tt44403cqooAWQT3QCACN0Aso1DBHmhZLiMHz8+9t9//9RxSuWAAw6IRYsWxZtvvmnAAJRCyeKpXr168dxzz6WOUyrjx4+PAw880PIJoAx0AwB0A8g+DhHkvKKiothll11io402inHjxqWOU2rPP/98HHDAAfHQQw/FUUcdlToOQM556KGH4phjjonnn38+9ttvv9RxSu3AAw+MhQsXWj4BlIJuAECEbgDZyCGCnPfggw/GscceGy+88EK0bds2dZwyOeigg2LBggXx5ptvRuXKlVPHAcgZRUVF0aZNm9hkk03i2WefTR2nTF544YXYf//948EHH4yjjz46dRyAnKIbAKAbQHZyiCCnFRUVRevWraN+/frx73//O3WcMisZMP/4xz/imGOOSR0HIGf84x//iOOOOy4vFk8REe3atYt58+bFW2+9ZfkEsJZ0AwAidAPIVg4R5LQHHnggjj/++JgwYULsu+++qeOUi3bt2sXcuXPj7bffNmAA1kJRUVG0atUqNttss7xYPEVETJgwIfbbb7944IEH4thjj00dByAn6AYA6AaQvRwiyFklw2XzzTePZ555JnWccvPiiy9G27Zt4/7774/jjjsudRyArHf//ffHCSecEC+++GLss88+qeOUm9///vfx1VdfxTvvvGP5BLAGugEAEboBZDOHCHJWyXB56aWXYu+9904dp1wdfPDBMXv2bAMGYA2Kiopi5513ji233DKefvrp1HHK1UsvvRT77ruv5RPAWtANANANILs5RJCTSobLVlttFf/6179Sxyl3L7/8cuyzzz5x3333xfHHH586DkDWuu++++LEE0+Ml19+OX73u9+ljlPu/vCHP8SXX35p+QTwG3QDACJ0A8h2DhHkpHvvvTdOOumkvB0uERGHHHJIfP755/Huu+8aMAC/YPXq1bHzzjtHgwYN4qmnnkodJyNKlk/33ntvnHDCCanjAGQl3QAA3QCyn0MEOWf16tXRsmXLaNiwYd4Ol4iIV155Jfbee++455574sQTT0wdByDr3HPPPXHyySfHK6+8EnvttVfqOBlzyCGHxGeffRbvvvtuVKlSJXUcgKyiGwAQoRtALnCIIOfcfffdccopp8TEiRNjzz33TB0no/74xz/GJ598Eu+9954BA/Ajq1evjp122im23Xbb+Oc//5k6TkZNnDgxfve738Xdd98dJ510Uuo4AFlFNwBAN4Dc4BBBTikZLtttt108+eSTqeNk3KRJk2KvvfYyYAD+R8niadKkSbHHHnukjpNxhx56aMyaNcvyCeBHdAMAInQDyBUOEeSUu+66K0499dSCGS4REX/605/i448/jvfff9+AAYgfFk/bb799PPHEE6njVIhXX3019txzz7jrrrvi5JNPTh0HICvoBroBgG6gG5A7HCLIGatXr44WLVpE48aN4/HHH08dp8KUDJg777wzTjnllNRxAJK78847o3379vHqq6/G7rvvnjpOhTnssMNi5syZlk8AoRvoBgD/pRvoBuQOhwhyxtixY6NDhw7x2muvxW677ZY6ToX685//HDNmzIjJkycbMEBBW7VqVbRo0SKaNm0ajz32WOo4Feq1116LPfbYI8aOHRunnnpq6jgASekGugGAbqAbkFscIsgJq1atih133DGaNWtWcMMl4ocBM2bMmGjfvn3qOADJjBkzJk477bSCXDxF/Hf5NH369Jg8eXKst956qeMAJKEb6AYAEbqBbkCucYggJ9xxxx1x+umnx+uvvx677rpr6jhJHH744fHBBx/ElClTDBigIK1atSqaN28ezZs3j0cffTR1nCRef/312H333eOOO+6IDh06pI4DkIRuoBsA6Aa6AbnHIYKsVzJcdtxxx3jkkUdSx0nmjTfeiN12282AAQpWyeLpjTfeiF122SV1nGSOOOKImDZtmuUTUJB0g//SDYBCpxv8l25ALnGIIOuNHj06zjjjjHjzzTejTZs2qeMkdeSRR8aUKVNi6tSpBgxQUEoWTy1atIiHH344dZyk3nzzzdh1111j9OjRcdppp6WOA1ChdIMf6AZAodINfqAbkEscIshqq1atih122CFatmwZDz30UOo4yb311luxyy67xO233x6nn3566jgAFeb222+PM888M956661o3bp16jjJHXXUUTF58mTLJ6Cg6AY/pRsAhUo3+CndgFzhEEFWu+222+Kss84yXH7k6KOPjvfeey+mTZtmwAAFYeXKlbHDDjtEq1at4sEHH0wdJyuULJ9uu+22OOOMM1LHAagQusHP6QZAodENfk43IFc4RJC1Vq5cGc2aNYvWrVsbLj/y9ttvR5s2beLWW2+NM888M3UcgIy79dZb4+yzz4633347WrVqlTpO1jj66KPj3XffjWnTpkXVqlVTxwHIKN3gl+kGQKHRDX6ZbkAucIgga91yyy3RsWPHeOedd2LnnXdOHSerHHPMMfH222/HBx98YMAAea1k8dSmTZv4xz/+kTpOVnnnnXeidevWccstt8RZZ52VOg5ARukGv043AAqFbvDrdANygUMEWWnlypXRtGnT2HXXXeOBBx5IHSfrvPvuu9GqVSsDBsh7Fk+/7dhjj4233nrL8gnIa7rBb9MNgEKhG/w23YBs5xBBVrr55pvjnHPOiXfffTdatmyZOk5WOu644+LNN980YIC8tWLFimjWrFnstttucf/996eOk5VKlk8333xznH322anjAGSEbrBmugGQ73SDNdMNyHYOEWSdFStWRNOmTWOPPfaI++67L3WcrPXee+/FzjvvHDfddFN07NgxdRyAcnfTTTfFueeeG++9917stNNOqeNkreOPPz5ef/31+OCDD6JatWqp4wCUK91g7egGQL7TDdaObkA2c4gg69x4443RuXPnePfddw2XNTj++OPjtddeiw8//NCAAfLKihUrokmTJrHnnntaPK1ByfLpxhtvjHPOOSd1HIBypRusPd0AyFe6wdrTDchmDhFklZLhstdee8W9996bOk7We//992PnnXeOkSNHxrnnnps6DkC5GTVqVHTp0iXee++9aNGiReo4We+EE06ISZMmxfTp0y2fgLyhG6wb3QDIV7rButENyFYOEWSVkuHy/vvvx4477pg6Tk448cQTY+LEiQYMkDdWrFgRjRs3jr333jvuueee1HFywuTJk6Nly5aWT0Be0Q3WnW4A5BvdYN3pBmQrhwiyxvLly6NJkyaxzz77xN133506Ts6YMmVK7LTTTjFixIjo1KlT6jgAZTZy5Mjo2rWrxdM6Oumkk+KVV16xfALygm5QOroBkG90g9LRDchGDhFkjREjRkS3bt1i8uTJ0bx589RxcsrJJ58cL730UkyfPj2qV6+eOg5AqS1fvjwaN24cbdu2jbvuuit1nJxSsnwaPnx4dO7cOXUcgDLRDUpPNwDyhW5QeroB2cghgqxQMlz222+/uPPOO1PHyTlTp06NFi1axLBhw6JLly6p4wCU2vDhw6N79+4xZcqU2GGHHVLHyTmnnHJKTJgwIWbMmGH5BOQs3aBsdAMgX+gGZaMbkG0cIsgKw4YNi/POOy8mT55suJSSAQPkumXLlkXjxo1j//33t3gqpZLl09ChQ6Nr166p4wCUim5QdroBkOt0g7LTDcg2DhEkVzJcDjjggBg7dmzqODlr2rRp0aJFixg8eHB069YtdRyAdTZ06NDo2bNnTJkyJZo1a5Y6Ts469dRT4/nnn48ZM2ZEjRo1UscBWCe6QfnQDYBcpxuUD92AbOIQQXKGS/lp3759jB8/3oABcs6yZcuiUaNGcdBBB8WYMWNSx8lpH3zwQey4446WT0BO0g3Kj24A5CrdoPzoBmQThwiSKhku7dq1izvuuCN1nJz34YcfRvPmzWPQoEHRvXv31HEA1tqQIUOiV69eMXXq1GjatGnqODmvQ4cOMW7cuPjoo48sn4CcoRuUL90AyFW6QfnSDcgWDhEkNXjw4Ojdu3dMnTo1mjRpkjpOXjjttNPi2WefNWCAnLF06dJo1KhRHHzwwTF69OjUcfJCyfJp4MCB0aNHj9RxANaKblD+dAMg1+gG5U83IFtUTh2AwrV06dK46qqron379opGOfrrX/8as2fPjhtvvDF1FIC1cuONN8acOXPir3/9a+ooeaNp06bRvn37uOqqq2Lp0qWp4wCskW6QGboBkGt0g/KnG5AtHCJIZtSoUTF37ty49NJLU0fJK02aNDFggJyxdOnS6N+/f7Rv3z4aN26cOk5eufTSS2POnDmWT0BO0A0yQzcAcolukDm6AdnAIYIklixZEv37948OHToYLhnw17/+NebOnRujRo1KHQXgN40cOTLmzp3rG08ZULJ86t+/v+UTkNV0g8zSDYBcoRtkjm5ANnCIIIlRo0bFvHnzfOMpQxo3bhwdOnSIq6++2oABstaSJUvi6quvjtNOOy0aNWqUOk5esnwCcoFukFm6AZALdIPM0w1IzSGCClcyXE4//XTDJYMuvfTSmDt3bowcOTJ1FIBfNHLkyJg/f77FUwY1atQoTjvttOjfv38sWbIkdRyAn9ENKoZuAGQ73SDzdANSc4igwo0YMcJwqQCNGjWK008/Pa6++moDBsg6ixcv/n7xtP3226eOk9cuvfTSmD9/vuUTkJV0g4qhGwDZTDeoOLoBKTlEUKFKhssZZ5wR2223Xeo4ea9fv34xf/78GDFiROooAD8xYsSIWLBgQfTr1y91lLy3/fbbf798Wrx4ceo4AN/TDSqWbgBkK92g4ugGpOQQQYUaPnx4fP3114ZLBTFggGy0ePHiuOaaayyeKlC/fv1iwYIFlk9AVtENKpZuAGQj3aDi6Qak4hBBhfnuu+/immuuiTPPPDO23Xbb1HEKxqWXXhpff/11DB8+PHUUgIiIGDZsmMVTBdtuu+3ijDPOiGuuucbyCcgKukEaugGQbXSDiqcbkIpDBBVm+PDhsWjRIsOlgm277bZx5plnGjBAVvjuu+/i2muvjbPOOsviqYL169fP8gnIGrpBGroBkE10g3R0A1JwiKBC/Hi4bLPNNqnjFJx+/frFokWLYtiwYamjAAVu2LBhFk+JbLvttnHWWWfFNddcE999913qOEAB0w3S0g2AbKEbpKMbkIJDBBVi6NChsWjRovjLX/6SOkpB2mabbeKss86Ka6+91oABkvn222/j2muvjbPPPjsaNmyYOk5BsnwCsoFukJZuAGQD3SA93YCK5hBBxpUMl44dOxouCf3lL3+JRYsWxdChQ1NHAQrU0KFD45tvvrF4Sqhhw4Zx9tlnx7XXXhvffvtt6jhAAdINsoNuAKSmG6SnG1DRHCLIuCFDhsR3330Xl1xySeooBc2AAVL65ptv4rrrrouOHTtGgwYNUscpaJdcckl88803lk9AErpBdtANgJR0g+yhG1CRHCLIKMMlu/zlL3+J7777LoYMGZI6ClBgShZPvvGUXsOGDaNjx45x3XXXxTfffJM6DlBAdIPsohsAqegG2UM3oCI5RJBRQ4YMicWLF/vGU5Zo0KBBdOzYMa6//noDBqgw33zzTVx//fVxzjnnxNZbb506DvHD8sk3n4CKpBtkF90ASEE3yD66ARXFIYKMWbRoUVx//fVx7rnnGi5Z5JJLLvHNJ6BCDR48OJYsWWLxlEW23nrrOOecc3zzCagwukF20g2AiqYbZB/dgIriEEHGGC7Zaeutt45zzz03rr/++li0aFHqOECe+/Hiaauttkodhx+55JJLYsmSJTF48ODUUYACoBtkJ90AqEi6QfbSDagIDhFkxMKFC+OGG26ITp06xZZbbpk6Dv+jb9++BgxQIQYNGhRLly6Nvn37po7C/9hqq60sn4AKoRtkN90AqCi6QfbSDagIDhFkxKBBg2LZsmVx8cUXp47CLygZMDfccEMsXLgwdRwgT1k8Zb++ffvG0qVLY9CgQamjAHlMN8huugFQEXSD7KcbkGkOEZS7hQsXxoABAwyXLNe3b99YtmyZAQNkzMCBA2P58uW+8ZTFttxyy+jUqZPlE5AxukFu0A2ATNMNsp9uQKY5RFDuSoaLbzxlt5IBM2DAAAMGKHcLFy6MgQMHRufOnWOLLbZIHYff0Ldv31i+fLnlE5ARukFu0A2ATNINcoduQCY5RFCuvv766xgwYEB06dLFcMkBF198cSxfvjwGDhyYOgqQZwYMGBArVqyweMoBW2yxRXTu3NnyCSh3ukFu0Q2ATNENcoduQCY5RFCuBgwYECtXrjRccsQWW2wRXbp0iQEDBsTXX3+dOg6QJ77++usYOHBgdOnSJTbffPPUcVgLF198caxYsSIGDBiQOgqQR3SD3KIbAJmgG+Qe3YBMcYig3CxYsCAGDhwYXbt2jc022yx1HNZSnz59YuXKlQYMUG5uuOGGWLlyZfTp0yd1FNbS5ptvHl26dImBAwdaPgHlQjfITboBUN50g9yjG5ApDhGUmxtuuCFWrVoVF110UeoorIMfD5gFCxakjgPkuAULFsSgQYMsnnJQyfLphhtuSB0FyAO6QW7SDYDypBvkLt2ATHCIoFzMnz8/Bg0aFN26dTNcclCfPn1i1apVBgxQZtdff32sXr3aN55y0GabbRZdu3aNQYMGWT4BZaIb5DbdACgvukHu0g3IBIcIysUNN9wQRUVFvvGUozbbbLPo1q1bDB48OObPn586DpCj5s+fH4MHD45u3bpF/fr1U8ehFPr06ROrV6+2fALKRDfIbboBUB50g9ynG1DeHCIos3nz5sXgwYOje/fuhksOu+iiiwwYoEyuv/76KC4utnjKYfXr149u3brFoEGDLJ+AUtEN8oNuAJSVbpD7dAPKm0MEZVYyXC688MLUUSiD+vXrR/fu3WPw4MExb9681HGAHDNv3rwYMmRIdO/ePTbddNPUcSiDiy66KIqKiuL6669PHQXIQbpBftANgLLQDfKHbkB5coigTObOnWu45JELL7wwiouLDRhgnV133XUWT3li0003je7du8eQIUMsn4B1ohvkF90AKC3dIH/oBpQnhwjK5LrrrotKlSoZLnnixwNm7ty5qeMAOWLu3LkxdOjQ6NGjR2yyySap41AOLrrooiguLo7rrrsudRQgh+gG+UU3AEpDN8g/ugHlxSGCUpszZ47hkocuvPDCqFSpkgEDrLVrr73W4inPbLLJJtGjR48YOnSo5ROwVnSD/KQbAOtKN8g/ugHlxSGCUrvuuuuiSpUqccEFF6SOQjkyYIB1MWfOnBg2bFicd955sfHGG6eOQzmyfALWhW6Qn3QDYF3oBvlLN6A8OERQKoZLfrvggguiSpUqce2116aOAmS5a6+91uIpT2288cZx3nnnxdChQ2POnDmp4wBZTDfIb7oBsLZ0g/ylG1AeHCIolWuuuSaqVKkSvXv3Th2FDCgZMMOGDTNggF/11VdfxbBhw6Jnz56x0UYbpY5DBvTu3dvyCVgj3SC/6QbA2tAN8p9uQFk5RLDOZs+eHcOHDzdc8lzJgLnmmmtSRwGy1DXXXBNVq1aN888/P3UUMuTHy6evvvoqdRwgC+kGhUE3ANZEN8h/ugFl5RDBOisZLr7xlN822mij6NmzZwwfPjxmz56dOg6QZWbPnh0jRoyweCoAvXv3jqpVq1o+Ab9INygMugHwW3SDwqEbUBYOEayTkuHSq1ev2HDDDVPHIcNKBowfuwP+1zXXXBPVqlXzjacCULJ8GjFihOUT8BO6QWHRDYBfoxsUDt2AsnCIYJ1cffXVUb16dcOlQGy44YbRq1cvAwb4iS+//NLiqcCcf/75Ua1aNd98An5CNygsugHwS3SDwqMbUFoOEay1L7/8MkaOHBnnn39+1KtXL3UcKkjJgLn66qtTRwGyRMniqVevXqmjUEF+vHz68ssvU8cBsoBuUJh0A+B/6QaFRzegtBwiWGv9+/ePGjVqRM+ePVNHoQLVq1cvzj///Bg5cqQBA8QXX3wRI0eOjN69e1s8FZhevXpF9erVLZ+AiNANCpVuAPyYblC4dANKwyGCtfL555/HqFGjfOOpQPXs2TOqV68e/fv3Tx0FSKx///5Rs2ZNi6cC9OPl0xdffJE6DpCQblDYdAOghG5QuHQDSsMhgrViuBS2evXqRe/evWPUqFHx+eefp44DJPL555/HjTfeGL17944NNtggdRwS6NWrV9SsWdPyCQqcblDYdAMgQjdAN2DdOUSwRiXD5YILLjBcCljPnj2jZs2afuwOClj//v2jVq1aFk8FbIMNNojevXvHjTfeaPkEBUo3IEI3AHQDdAPWnUMEa3TVVVfF+uuvH+edd17qKCS0wQYbxAUXXGDAQIH67LPPvl881a1bN3UcEurZs2fUqlXLN5+gQOkGROgGUOh0A0roBqwLhwh+06effho33XST4UJERJx33nlRu3btuOqqq1JHASpYyeKpR48eqaOQWN26db9fPn322Wep4wAVSDfgx3QDKFy6ASV0A9aFQwS/yXDhx0oGzE033RSffvpp6jhABfn000/j5ptvjgsvvNDiiYiI6NGjR6y//vqWT1BgdAN+TDeAwqQb8L90A9aWQwS/6pNPPvl+uNSpUyd1HLJE9+7dDRgoMFdeeWXUqVMnunfvnjoKWaJk+XTzzTdbPkGB0A34JboBFB7dgP+lG7C2HCL4VVdeeWXUrVvXcOEn6tatGxdeeGHcfPPN8cknn6SOA2TYrFmz4pZbbrF44md69OgRderUiSuvvDJ1FKAC6Ab8Et0ACotuwK/RDVgbDhH8olmzZsWtt94aF110keHCz3Tv3j3q1q3rm09QAK666qrYYIMNLJ74mTp16sSFF14Yt9xyi+UT5DndgN+iG0Dh0A34NboBa8Mhgl905ZVXxgYbbBDdunVLHYUsVKdOnbjooovilltuiVmzZqWOA2TIjxdP66+/fuo4ZKHu3bvHBhts4JtPkOd0A36LbgCFQTdgTXQD1sQhgp/5z3/+E7feemv06dPHcOFXdevWzYCBPHfFFVdEvXr1LJ74Veuvv35cdNFFceutt1o+QZ7SDVgbugHkP92ANdENWBOHCH7miiuuiA033DC6du2aOgpZbP31148+ffrErbfeGv/5z39SxwHK2ccffxy33XZb9OnTJ2rXrp06DlmsZPl0xRVXpI4CZIBuwNrQDSC/6QasLd2A3+IQwU98/PHHcfvttxsurJWuXbvGhhtuaMBAHrriiitio402ii5duqSOQparXbt29OnTJ2677bb4+OOPU8cBypFuwLrQDSB/6QasLd2A3+IQwU9cfvnlhgtrrWTA3H777QYM5JGZM2daPLFOunbtGhtttJHlE+QZ3YB1oRtAftINWFe6Ab/GIYLvzZw5M0aPHh0XX3yx4cJa69KliwEDeeaKK66ITTbZxOKJtVayfBo9enTMnDkzdRygHOgGlIZuAPlHN2Bd6Qb8GocIvnf55ZfHJptsEp07d04dhRxSu3btuPjii+P22283YCAPfPTRR98vnmrVqpU6DjmkS5cusfHGG1s+QZ7QDSgN3QDyi25AaekG/BKHCCIiYsaMGXHHHXdE3759DRfWWefOnWOTTTaJyy+/PHUUoIwuv/zy2HTTTS2eWGe1atWKiy++OEaPHh0fffRR6jhAGegGlIVuAPlDN6C0dAN+iUMEEfHDcOnUqVPqKOSgWrVqRd++feOOO+6IGTNmpI4DlNKMGTNizJgx0bdv36hZs2bqOOSgzp07x6abbmr5BDlON6AsdAPID7oBZaUb8L8cIojp06cbLpRZp06dDBjIcX//+9+jfv36ce6556aOQo6qWbNmXHzxxTFmzBjLJ8hRugHlQTeA3KcbUFa6Af/LIYL4+9//HptttpnhQpnUrFkz+vbtG2PGjInp06enjgOsow8//DDGjh1r8USZderUKerXrx9///vfU0cBSkE3oDzoBpDbdAPKi27Aj1UqLi4uTh2CdD788MNo3rx5DBw4MHr06JE6Djlu6dKl0ahRozj44INj9OjRqeMA6+C0006LZ599Nj766KOoUaNG6jjkuMGDB8f5558f06ZNiyZNmqSOA6wl3YDypBtA7tINKE+6ASUcIgpchw4d4rnnnosZM2YYLpSLIUOGRK9evWLq1KnRtGnT1HGAtfDBBx/EjjvuGIMGDYru3bunjkMeWLZsWTRq1CjatWsXd9xxR+o4wFrSDShvugHkHt2A8qYbUMIhooCVDJfBgwdHt27dUschTyxbtiwaN24cBx54YIwZMyZ1HGAttG/fPsaPH2/xRLkaOnRo9OzZM6ZMmRLNmjVLHQdYA92ATNANIPfoBmSCbkCEQ0RBO/XUU+P555+Pjz76KKpXr546Dnlk2LBhcd555xkwkAOmTZsWLVq0iCFDhkTXrl1TxyGPlCyfDjjggBg7dmzqOMAa6AZkim4AuUM3IFN0AyIcIgrW1KlTo0WLFjF06FDDhXJXMmD233//uPPOO1PHAX7DKaecEhMmTIgZM2ZYPFHuSpZPkydPjh122CF1HOBX6AZkkm4AuUM3IJN0AxwiCtTJJ58cL730UkyfPt1wISOGDx8e3bt3j8mTJ0fz5s1TxwF+wZQpU2KnnXaKYcOGRZcuXVLHIQ8tX748GjduHG3bto277rordRzgV+gGZJpuANlPNyDTdAMcIgpQyXAZPnx4dO7cOXUc8tTy5cujSZMmse+++xowkKUsnqgII0aMiG7dulk+QZbSDagIugFkP92AiqAbFLbKqQNQMXr06BF33313RERcdtll0aBBgzjrrLMSpyKfVa9ePf7yl7/EPffcE1OmTIlVq1ZFu3btYvLkyamjQcGaPHlytGvXLlatWhWTJ0+Oe++9N/r166dokFFnnXVWbL311nHZZZdFRMTdd98d5513XuJUUNh0AyqabgDZRzcgBd2gsDlEFIhnn302XnvttZg8eXLcd9990a9fv6hWrVrqWOS5s846Kxo0aBCXXXZZLF68OMaNGxdTpkxJHQsK1uTJk2PcuHGxZMmSuOyyy6Jhw4Zx5plnpo5FnqtevXr069cv7r333pg8eXK8+uqr8eyzz6aOBQVNNyAF3QCyi25ACrpBYXOIKBAlfwLX3/72t2jYsGGcccYZ8fLLL8fcuXMTJyNfTZgwIb777rvo169f3HfffTF16tSI+OHXIlDxSt7flClT4v77749+/frFd999FxMmTEicjHw1Z86ceOWVV+LMM8/8fvkUYRZAaroBFU03gOyjG1DRdAMcIgpEcXFxzJ8/P+6///7o06dP9OrVK/bZZ5946qmnUkcjDxUXF8cZZ5wRrVu3jmbNmkXDhg3j6quv/v7/BqRR8v769+8fDRs2jKZNm0arVq3ijDPO8DbJiH/961+x9957x/nnnx99+vSJ+++/P+bPn+/XGySmG1CRdAPITroBFU03YL3UAag4r7zySmy11VZx4403xrRp02LEiBHRvn371LHIQ5UqVYrx48fHySefHO3atYsjjzwyHnzwwdSxgP/vkUceiWOOOSbatWsXe+21V9xzzz1RqVKl1LHIQ+3bt4/FixdHr169onnz5rHFFlvExIkTo0qVKqmjQcHTDagougFkN92AiqIb4CciCsSyZcti+vTpMW/evFi6dGlMmjQpOnfubLiQMQ0aNIjnnnsu+vTpEw8++GDUqFEjInzrCVIqeX81atSIBx98MPr06RPjx4+PrbfeOnEy8lWlSpWic+fOMXHixFi8eHEsWLAgpk+fHsuWLUsdDQqabkBF0w0g++gGVDTdAIeIArFgwYKIiDjmmGPi9ddfj1atWiVORCGoWrVqXHnllfHUU0/Feuv99wewPv/888SpoHB98cUXEfHft/nUU0/FlVde+f3bhExq3bp1vPHGG3HUUUdFRMT8+fPTBoICpxuQgm4A2UU3IBXdoHD5HaZAHHroobH99tvHlVde6ZtOVLhDDjkk3n///Tj66KNj//33Tx0HCtZ+++0Xbdq0iYceeii22Wab1HEoMHXq1Im77rortt1225g5c2bqOFDQdANS0g0gO+gGpKQbFKZKxX4WEgAAAAAAyBB/NBMAAAAAAJAx/mimcvbVV1/FW2+9FUuXLk0dZa3Uq1cv9txzz6hVq1bqKOSRDz74IKZPnx4rV65MHWWNKleuHJtvvnnstttuUaVKldRxyBOrV6+O119/PWbPnh1FRUWp46xR1apVo0mTJtGsWbPUUcgjS5YsiUmTJsXChQtTR1krNWvWjDZt2sRmm22WOgp5RDcA3QB0A9AN+C+HiHLy5ZdfRvv27WP8+PE5MVh+rGbNmtGhQ4cYOnRoVK1aNXUcctiECROiU6dOMXXq1NRR1ln9+vXj//7v/6JLly6po5Djhg8fHn/7299izpw5qaOss+bNm8eoUaOibdu2qaOQw1auXBndu3ePMWPG5MzytUTlypXjgAMOiLFjx8YWW2yROg45TDcA3QAidAPQDfgx/46IcvDtt9/GrrvuGnPnzo22bdvG9ttvH9WrV08da42Ki4tj8eLFMXXq1HjhhRfixBNPjDvvvDN1LHLUW2+9Ffvuu29ssskmsffee8eWW24Z662X/bfOoqKiWLBgQbzxxhvxzjvvxE033RQdO3ZMHYscddNNN8W5554brVq1il133TU22mijqFw5+/8UxFWrVsUXX3wRL7/8csybNy9efPHFaNOmTepY5KhTTz017r333thvv/2iefPmUbt27Zz4l+EuX748Zs6cGRMmTIj69evH66+/HnXq1EkdixykG4BuABG6AUToBvyUQ0Q5GDt2bHTo0CG6desWm266aeo4pfLaa6/FE088EZ988kk0aNAgdRxyUMeOHePhhx+OTp06RbVq1VLHWWfFxcXxwAMPxNKlS2PGjBk5MRjJLsXFxdGoUaOoVatWHHfccTn5a2jFihUxatSoOProo+Omm25KHYcc9Omnn0bDhg3jsMMOi9133z11nFKZM2dODB8+PMaOHRunnnpq6jjkIN0AdAPQDUA34Oey/xSbA5588slo0KBBzhaNiIiWLVtG5cqV45///GfqKOSoxx9/PHbYYYecLBoREZUqVYrWrVvHzJkzY/r06anjkIOmT58eH3/8cbRu3Toni0ZERLVq1WKHHXaIxx57LHUUctQ///nPqFKlSrRs2TJ1lFKrX79+NGjQIJ544onUUchRugHoBqAbgG7AzzlElIOvvvoq6tatmzpGmdSoUSNq166dk39uIdlh3rx5Ua9evdQxyqQkv3dAaZT8usmHdzBv3rzUMchRc+bMiVq1akWNGjVSRymTunXrxldffZU6BjlKNwDdAHQD0A34OYeIclBUVJSzF+4fq1y5cs79y/TIHvnwDkryeweURsmvm3x4B94ApVVUVJQTf/bxmngHlEU+/DNRhG5A2eTDO9ANKAvdAHQDfi73fzUAAAAAAABZyyECAAAAAADIGIcIAAAAAAAgYxwiAAAAAACAjHGIAAAAAAAAMsYhAgAAAAAAyBiHCAAAAAAAIGMcIgAAAAAAgIxxiAAAAAAAADLGIQIAAAAAAMgYhwgAAAAAACBjHCIAAAAAAICMcYgAAAAAAAAyxiECAAAAAADIGIcIAAAAAAAgYxwiAAAAAACAjHGIyAIzZ86MoUOHRlFRUak/Y/r06TFixIgyfQak5B2AdwDeAHgHEOEdQIR3AN5A/nGIyALPPPNM7LffflG5cun/39GkSZOoUqVKvPfee+WYDCqOdwDeAXgD4B1AhHcAEd4BeAP5xyEisVmzZsWCBQuiefPmZf6s1q1bx6RJk8ohFVQs7wC8A/AGwDuACO8AIrwD8Abyk0NEYu+//340atQoqlatWubPatq0aXzxxRexYMGCckgGFcc7AO8AvAHwDiDCO4AI7wC8gfzkEJHYJ598EltuuWW5fFa9evWidu3aMWvWrHL5PKgo3gF4B+ANgHcAEd4BRHgH4A3kJ4eIxBYuXBh16tQpt8+rU6dOLFq0qNw+DyqCdwDeAXgD4B1AhHcAEd4BeAP5ySEisVWrVsV6661Xbp9XtWrVWLlyZbl9HlQE7wC8A/AGwDuACO8AIrwD8Abyk0NEYrVq1YqlS5eW2+ctXbo0atWqVW6fBxXBOwDvALwB8A4gwjuACO8AvIH85BCR2Oabbx5z584tl89auXJlLFiwILbYYoty+TyoKN4BeAfgDYB3ABHeAUR4B+AN5CeHiMQaN24cn3zyyU/+3meffRZDhgyJb7755vu/N3r06Jg0adL3fz1p0qQYPXr0z/576623Xmy99daZDQ3lzDsA7wC8AfAOIMI7gAjvALyB/OQQkVjLli1j7ty5MW/evO//3sqVK2P+/PmxevXq7//eggULYsmSJd//9ZIlS2LBggU/+az3338/WrZsGdWqVct8cChH3gF4B+ANgHcAEd4BRHgH4A3kp/L7t35QKrVq1Yo99tgjXnnllTj88MMjImK77baL//u///vJf+7888//yV8feOCBceCBB37/14sXL44pU6bEueeem/HMUN68A/AOwBsA7wAivAOI8A7AG8hPfiIiC7Rt2zY22GCDKCoqKvVnLFy4MA477LDYcMMNyzEZVBzvALwD8AbAO4AI7wAivAPwBvKPn4jIAjVr1oz99tuvTJ+x1VZbxVZbbVVOiaDieQfgHYA3AN4BRHgHEOEdgDeQf/xEBAAAAAAAkDEOEQAAAAAAQMY4RAAAAAAAABnjEAEAAAAAAGSMQwQAAAAAAJAxDhEAAAAAAEDGOEQAAAAAAAAZ4xABAAAAAABkjEMEAAAAAACQMQ4RAAAAAABAxjhEAAAAAAAAGeMQAQAAAAAAZIxDBAAAAAAAkDEOEQAAAAAAQMY4RPC94uLi1BE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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing the results\n",
        "print(\"Decision Tree Regressor Model Metrics (Log-Transformed):\")\n",
        "print(f\"Mean Squared Error (Log Scale): {mse_log}\")\n",
        "print(f\"Mean Absolute Error (Log Scale): {mae_log}\")\n",
        "print(f\"R-squared (Log Scale): {r2_log}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ONLhuaYi1vR",
        "outputId": "758d0900-6bfc-4670-f0b1-36c1fd3f8722"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Regressor Model Metrics (Log-Transformed):\n",
            "Mean Squared Error (Log Scale): 0.0007849625097064305\n",
            "Mean Absolute Error (Log Scale): 0.01888947264407529\n",
            "R-squared (Log Scale): 0.9992361408051387\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Decision Tree Regressor performed really well on the log-transformed data, with a **Mean Squared Error (MSE) of 0.00078**, **Mean Absolute Error (MAE) of 0.0189**, and an impressive **R-squared (R²) of 0.9992**. This shows that the model is almost perfectly fitting the data, with very little error. When compared to the previous models, like **Linear Regression** and **Ridge Regression**, the Decision Tree clearly outperforms them. Its R-squared value is much higher, meaning it captures more of the variance in the target variable. The MSE and MAE are also lower, suggesting the Decision Tree is better at predicting outcomes. This makes sense because Decision Trees can handle more complex, non-linear relationships between features, which seems to work better for this particular dataset."
      ],
      "metadata": {
        "id": "GNSpcHoslm0l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Compared to Baseline\n",
        "Compared to the baseline model, the Decision Tree Regressor performs much better. The baseline model likely made basic predictions based on simple assumptions, leading to higher errors and a lower R-squared value. On the other hand, the Decision Tree's R-squared of 0.9992 shows it explains almost all of the variation in the data, which is a huge improvement. The Mean Squared Error (MSE) and Mean Absolute Error (MAE) are also significantly lower, reflecting its greater accuracy. This shows that the Decision Tree is much better at capturing the complexity of the data and making more precise predictions than the baseline model."
      ],
      "metadata": {
        "id": "DCqXNROYlqof"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest Regressor\n",
        "I chose to use a Random Forest Regressor for this dataset because it's an ensemble method that works by combining the predictions of multiple decision trees. This approach helps improve accuracy and makes the model more robust. Unlike a single decision tree, which can easily overfit the data, a Random Forest reduces this risk by averaging the results from many trees, leading to better generalization. For a dataset like the diamonds one, with a mix of numerical and categorical features, Random Forest is a great fit because it can capture complex relationships between variables while remaining stable. Additionally, it handles missing data well and gives useful insights into which features are most important."
      ],
      "metadata": {
        "id": "pJSLYFn-FLnI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# get data prepared\n",
        "X = diamonds.drop(columns=['price'])\n",
        "y = diamonds['price']\n",
        "\n",
        "categorical_columns = ['cut', 'color', 'clarity']\n",
        "numerical_columns = ['carat', 'depth', 'table', 'x', 'y', 'z', 'volume', 'price_per_carat', 'depth_table_ratio']\n",
        "\n",
        "# define preprocessor and pipeline\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('cat', OneHotEncoder(), categorical_columns),\n",
        "        ('num', 'passthrough', numerical_columns)\n",
        "    ]\n",
        ")\n",
        "\n",
        "pipeline = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('regressor', RandomForestRegressor(random_state=42))\n",
        "])\n"
      ],
      "metadata": {
        "id": "dFLplZUoFgMk"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split the dataset into training and testing\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_log, test_size=0.2, random_state=42)\n",
        "\n",
        "# define the grid of hyperparameters for Random Forest\n",
        "param_grid = {\n",
        "    'regressor__n_estimators': [20, 50],\n",
        "    'regressor__max_depth': [None, 6, 10]\n",
        "}\n"
      ],
      "metadata": {
        "id": "TRhkiLHdm9ra"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# do GridSearchCV for hyperparameter tuning\n",
        "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2)\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "id": "T579PtqJnI3s",
        "outputId": "611c5d71-eb86-49a2-889e-5b114c76cfc9"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=20; total time=   8.4s\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=20; total time=   6.5s\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=20; total time=   8.8s\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=20; total time=  15.4s\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=20; total time=   8.4s\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=50; total time=  22.4s\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=50; total time=  19.2s\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=50; total time=  18.1s\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=50; total time=  17.5s\n",
            "[CV] END regressor__max_depth=None, regressor__n_estimators=50; total time=  17.4s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=20; total time=   2.6s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=20; total time=   3.1s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=20; total time=   2.3s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=20; total time=   2.4s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=20; total time=   2.5s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=50; total time=   9.0s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=50; total time=   5.6s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=50; total time=   6.8s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=50; total time=   5.7s\n",
            "[CV] END .regressor__max_depth=6, regressor__n_estimators=50; total time=   6.9s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=20; total time=   3.7s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=20; total time=   3.9s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=20; total time=   4.5s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=20; total time=   5.5s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=20; total time=   3.7s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=50; total time=  10.3s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=50; total time=  10.3s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=50; total time=   9.3s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=50; total time=  10.5s\n",
            "[CV] END regressor__max_depth=10, regressor__n_estimators=50; total time=  10.5s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[('preprocessor',\n",
              "                                        ColumnTransformer(transformers=[('cat',\n",
              "                                                                         OneHotEncoder(),\n",
              "                                                                         ['cut',\n",
              "                                                                          'color',\n",
              "                                                                          'clarity']),\n",
              "                                                                        ('num',\n",
              "                                                                         'passthrough',\n",
              "                                                                         ['carat',\n",
              "                                                                          'depth',\n",
              "                                                                          'table',\n",
              "                                                                          'x',\n",
              "                                                                          'y',\n",
              "                                                                          'z',\n",
              "                                                                          'volume',\n",
              "                                                                          'price_per_carat',\n",
              "                                                                          'depth_table_ratio'])])),\n",
              "                                       ('regressor',\n",
              "                                        RandomForestRegressor(random_state=42))]),\n",
              "             param_grid={'regressor__max_depth': [None, 6, 10],\n",
              "                         'regressor__n_estimators': [20, 50]},\n",
              "             scoring='neg_mean_squared_error', verbose=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-5 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-5 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-5 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-5 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                                         OneHotEncoder(),\n",
              "                                                                         [&#x27;cut&#x27;,\n",
              "                                                                          &#x27;color&#x27;,\n",
              "                                                                          &#x27;clarity&#x27;]),\n",
              "                                                                        (&#x27;num&#x27;,\n",
              "                                                                         &#x27;passthrough&#x27;,\n",
              "                                                                         [&#x27;carat&#x27;,\n",
              "                                                                          &#x27;depth&#x27;,\n",
              "                                                                          &#x27;table&#x27;,\n",
              "                                                                          &#x27;x&#x27;,\n",
              "                                                                          &#x27;y&#x27;,\n",
              "                                                                          &#x27;z&#x27;,\n",
              "                                                                          &#x27;volume&#x27;,\n",
              "                                                                          &#x27;price_per_carat&#x27;,\n",
              "                                                                          &#x27;depth_table_ratio&#x27;])])),\n",
              "                                       (&#x27;regressor&#x27;,\n",
              "                                        RandomForestRegressor(random_state=42))]),\n",
              "             param_grid={&#x27;regressor__max_depth&#x27;: [None, 6, 10],\n",
              "                         &#x27;regressor__n_estimators&#x27;: [20, 50]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                                         OneHotEncoder(),\n",
              "                                                                         [&#x27;cut&#x27;,\n",
              "                                                                          &#x27;color&#x27;,\n",
              "                                                                          &#x27;clarity&#x27;]),\n",
              "                                                                        (&#x27;num&#x27;,\n",
              "                                                                         &#x27;passthrough&#x27;,\n",
              "                                                                         [&#x27;carat&#x27;,\n",
              "                                                                          &#x27;depth&#x27;,\n",
              "                                                                          &#x27;table&#x27;,\n",
              "                                                                          &#x27;x&#x27;,\n",
              "                                                                          &#x27;y&#x27;,\n",
              "                                                                          &#x27;z&#x27;,\n",
              "                                                                          &#x27;volume&#x27;,\n",
              "                                                                          &#x27;price_per_carat&#x27;,\n",
              "                                                                          &#x27;depth_table_ratio&#x27;])])),\n",
              "                                       (&#x27;regressor&#x27;,\n",
              "                                        RandomForestRegressor(random_state=42))]),\n",
              "             param_grid={&#x27;regressor__max_depth&#x27;: [None, 6, 10],\n",
              "                         &#x27;regressor__n_estimators&#x27;: [20, 50]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=2)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(),\n",
              "                                                  [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                                 (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                                  [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;,\n",
              "                                                   &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;,\n",
              "                                                   &#x27;price_per_carat&#x27;,\n",
              "                                                   &#x27;depth_table_ratio&#x27;])])),\n",
              "                (&#x27;regressor&#x27;,\n",
              "                 RandomForestRegressor(n_estimators=50, random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(),\n",
              "                                 [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                 [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;,\n",
              "                                  &#x27;volume&#x27;, &#x27;price_per_carat&#x27;,\n",
              "                                  &#x27;depth_table_ratio&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;, &#x27;price_per_carat&#x27;, &#x27;depth_table_ratio&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_estimators=50, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find best parameters from GridSearchCV\n",
        "best_params = grid_search.best_params_\n",
        "print(\"Best Parameters from Grid Search:\")\n",
        "print(best_params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qlCI2IenM78",
        "outputId": "46fc1c6d-22ed-4509-b9ee-cb8ba15c2966"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters from Grid Search:\n",
            "{'regressor__max_depth': None, 'regressor__n_estimators': 50}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make final model and fit it to training data\n",
        "final_model = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('regressor', RandomForestRegressor(n_estimators=200, max_depth=6, random_state=42))\n",
        "])\n",
        "\n",
        "final_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "cL0-8mtWnwp8",
        "outputId": "a42d96e8-d641-4994-ce93-5dcc0b5c8862"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('cat', OneHotEncoder(),\n",
              "                                                  ['cut', 'color', 'clarity']),\n",
              "                                                 ('num', 'passthrough',\n",
              "                                                  ['carat', 'depth', 'table',\n",
              "                                                   'x', 'y', 'z', 'volume',\n",
              "                                                   'price_per_carat',\n",
              "                                                   'depth_table_ratio'])])),\n",
              "                ('regressor',\n",
              "                 RandomForestRegressor(max_depth=6, n_estimators=200,\n",
              "                                       random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-6 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-6 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-6 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-6 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(),\n",
              "                                                  [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                                 (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                                  [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;,\n",
              "                                                   &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;,\n",
              "                                                   &#x27;price_per_carat&#x27;,\n",
              "                                                   &#x27;depth_table_ratio&#x27;])])),\n",
              "                (&#x27;regressor&#x27;,\n",
              "                 RandomForestRegressor(max_depth=6, n_estimators=200,\n",
              "                                       random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(),\n",
              "                                                  [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                                 (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                                  [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;,\n",
              "                                                   &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;,\n",
              "                                                   &#x27;price_per_carat&#x27;,\n",
              "                                                   &#x27;depth_table_ratio&#x27;])])),\n",
              "                (&#x27;regressor&#x27;,\n",
              "                 RandomForestRegressor(max_depth=6, n_estimators=200,\n",
              "                                       random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(),\n",
              "                                 [&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]),\n",
              "                                (&#x27;num&#x27;, &#x27;passthrough&#x27;,\n",
              "                                 [&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;,\n",
              "                                  &#x27;volume&#x27;, &#x27;price_per_carat&#x27;,\n",
              "                                  &#x27;depth_table_ratio&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;cut&#x27;, &#x27;color&#x27;, &#x27;clarity&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;carat&#x27;, &#x27;depth&#x27;, &#x27;table&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;, &#x27;volume&#x27;, &#x27;price_per_carat&#x27;, &#x27;depth_table_ratio&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=6, n_estimators=200, random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# make predictions and calculate\n",
        "y_train_pred_log = final_model.predict(X_train)\n",
        "y_test_pred_log = final_model.predict(X_test)\n",
        "\n",
        "mse_train_log = mean_squared_error(y_train, y_train_pred_log)\n",
        "mse_test_log = mean_squared_error(y_test, y_test_pred_log)\n",
        "\n",
        "r2_train_log = r2_score(y_train, y_train_pred_log)\n",
        "r2_test_log = r2_score(y_test, y_test_pred_log)\n"
      ],
      "metadata": {
        "id": "7RWrssGoHEga"
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print results\n",
        "print(\"\\nTraining and Testing Metrics:\")\n",
        "print(f\"Training MSE (Log Scale): {mse_train_log}\")\n",
        "print(f\"Testing MSE (Log Scale): {mse_test_log}\")\n",
        "\n",
        "print(f\"Training R-squared (Log Scale): {r2_train_log}\")\n",
        "print(f\"Testing R-squared (Log Scale): {r2_test_log}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxbeKJKmn-pZ",
        "outputId": "fa76b3b9-4199-4bfe-9da9-1d224cb1092f"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training and Testing Metrics:\n",
            "Training MSE (Log Scale): 0.005089569825236713\n",
            "Testing MSE (Log Scale): 0.0052065437292793705\n",
            "Training R-squared (Log Scale): 0.9950505591232778\n",
            "Testing R-squared (Log Scale): 0.9949334315309595\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Random Forest model demonstrated strong performance, with a training mean squared error (MSE) of 0.0051 and a testing MSE of 0.0052 on the log-transformed scale, showing that the model generalizes well to new data. The training R-squared value of 0.995 and testing R-squared value of 0.995 indicate that the model explained about 99.5% of the variance in both the training and testing sets, suggesting a highly accurate fit. The Random Forest model's R-squared of 0.995 is  slightly lower than the Decision Tree's 0.999. However, the Decision Tree model tends to overfit the training data, as it can memorize the data points too well, leading to poor generalization on unseen data. This overfitting can be seen in the high variance between training and testing performance. On the other hand, the Random Forest model, with its ability to combine multiple decision trees, is less prone to overfitting. This allows it to generalize better to new data, as reflected in its more consistent performance across the training and testing sets. Although the Decision Tree's R-squared is marginally higher, the Random Forest's ability to avoid overfitting and its better generalization capabilities make it a more reliable choice for making real-world predictions.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "eoVVLE-9sXqS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Comparing to Baseline\n",
        "When comparing the Random Forest model to the baseline, it is evident that the Random Forest significantly outperforms the baseline in all key metrics. The baseline model, with an R-squared of -0.00, essentially fails to capture any meaningful patterns in the data, while the Random Forest model achieves an R-squared of 0.9949, indicating it explains nearly all the variance in the dataset. Additionally, the baseline model has a much higher Mean Squared Error (MSE) of 1.03 and Mean Absolute Error (MAE) of 0.88, compared to the Random Forest’s much lower MSE of 0.0052 and MAE of 0.069. These results show that the Random Forest model provides a much more accurate and reliable prediction, making it a far superior choice for this particular dataset."
      ],
      "metadata": {
        "id": "gfsRN9ptuAQP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Next Steps & Discussion\n",
        "\n",
        "#### Summary of Findings\n",
        "In my analysis of the diamond price prediction task, all the models I tested outperformed the baseline, demonstrating their ability to capture meaningful relationships in the data. The models were ranked based on their performance, with Decision Tree Regressor, Random Forest Regressor, and Ridge Regression standing out as the top contenders.\n",
        "\n",
        "### Key Findings:\n",
        "\n",
        "1) **Success of Decision Tree Model**:  \n",
        "   The Decision Tree model demonstrated the best predictive performance, achieving the highest R-squared value on both the training and testing data. Its ability to model complex, non-linear relationships in the dataset was a major advantage, providing deep insights into feature importance.\n",
        "\n",
        "2) **Model Comparison**:  \n",
        "   While the Ridge and Lasso regressions were effective, they demonstrated lower predictive power, reflected in their lower R-squared and higher MSE values compared to decision tree-based models. This suggests that simpler linear models struggle to capture the non-linear patterns in the diamond price data, which may be better suited for tree-based approaches.\n",
        "\n",
        "In conclusion, the decision tree-based models, particularly the Decision Tree Regressor and Random Forest Regressor, were the most effective in capturing the non-linear patterns within the data. The emphasis on robust ensemble methods in Random Forest helps to mitigate overfitting.  "
      ],
      "metadata": {
        "id": "vU2bGf1bxmnY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Next Steps / Improvements\n",
        "\n",
        "To further enhance the predictive power of the models and gain deeper insights, I would explore the following avenues for model improvement:\n",
        "\n",
        "- **Incorporation of Additional Diamond Characteristics:**\n",
        "    - One potential improvement would be to include more granular features related to diamond quality, such as cut quality, polish, and symmetry. These factors could provide a more comprehensive view of how specific diamond attributes influence price. This may lead to better prediction accuracy.\n",
        "\n",
        "- **Geographical Data:**\n",
        "    - Introducing geographical information, such as the market where the diamonds are sold or their origin (e.g., country of mine), could offer valuable context. Different markets may have varying price trends due to factors like demand, import/export taxes, and regional preferences. Knowing and accounting for these factors could enhance the model's performance.\n",
        "\n",
        "- **Feature Engineering on Shape and Weight:**\n",
        "    - It could be helpful to create new features based on diamond shape (ex round, oval, princess) and weight (carat). For example, there may be relationship between shape and price. Looking at weight in different categories (ex small, medium, large) could reveal new patterns.\n",
        "\n",
        "- **Advanced Ensemble Methods:**\n",
        "    - While Random Forest showed promigoodsing results, exploring other advanced ensemble methods like Gradient Boosting Machines (GBMs) or XGBoost could improve model performance. These models often give better results.\n",
        "\n",
        "- **Time-Series Data for Market Trends:**\n",
        "    - Incorporating time-series data to track price trends and fluctuations over time could provide a good context to diamond pricing. This could help capture seasonal price variations or broader economic trends that affect the diamond market.\n",
        "\n",
        "By adding these additional features and exploring other advanced models, I can further improve the accuracy of predictions and offer more valuable insights. This approach would provide a deeper understanding of factors influencing price and offer practical applications for both buyers and sellers in the industry."
      ],
      "metadata": {
        "id": "HWdOg1RbyOHF"
      }
    }
  ]
}