data_clean.ipynb

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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/avisekksarma/Fraud-detection/blob/main/data_clean.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUNcgjsry7zM",
        "outputId": "2c36e224-146b-4f5d-a2b5-965270702e8e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive' , force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOekFShn2zLb"
      },
      "source": [
        "# import zipfile\n",
        "# zip_ref = zipfile.ZipFile(\"/content/gdrive/My Drive/Fraud_detection/ieee-fraud-detection.zip\", 'r')\n",
        "# zip_ref.extractall(\"/content/gdrive/My Drive/Fraud_detection/\")\n",
        "# zip_ref.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGPq-aUmzU1D"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import  preprocessing\n",
        "import gc\n",
        "\n",
        "%matplotlib inline\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lugSDnCFzifD"
      },
      "source": [
        "os.chdir('//content/gdrive/My Drive/Fraud_detection/')\n",
        "# !ls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOVuiQejzi9h"
      },
      "source": [
        "train_identity = pd.read_csv(\"train_identity.csv\")\n",
        "train_transaction = pd.read_csv(\"train_transaction.csv\")\n",
        "\n",
        "test_identity = pd.read_csv(\"test_identity.csv\")\n",
        "test_transaction = pd.read_csv(\"test_transaction.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhJf5ig_zns8",
        "outputId": "b9c98795-d3e8-4baf-b101-bbfc56c452a4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_df = train_transaction.merge(train_identity, how='left', left_index=True, right_index=True)\n",
        "test_df = test_transaction.merge(test_identity, how='left', left_index=True, right_index=True)\n",
        "\n",
        "print(train_df.shape)\n",
        "print(test_df.shape)\n",
        "test_df.columns = test_df.columns.str.replace('-','_')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(590540, 435)\n",
            "(506691, 434)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulxRGeclZ3xd"
      },
      "source": [
        "# train_df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpi1_WfOO4Iw",
        "outputId": "d4f84013-90d9-4ea6-c4d8-829d8484168e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(train_df.shape)\n",
        "print(test_df.shape)\n",
        "print(train_df.shape[0] + test_df.shape[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(590540, 435)\n",
            "(506691, 434)\n",
            "1097231\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mo5It7C4OtKE"
      },
      "source": [
        "del train_identity, train_transaction , test_identity , test_transaction"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Nl2NkM9UX_c",
        "outputId": "40fb52a3-65d1-4b0a-a701-067e6d3edb92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "train_df.head(200)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   TransactionID_x  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
              "0          2987000        0          86400            68.5         W  13926   \n",
              "1          2987001        0          86401            29.0         W   2755   \n",
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              "\n",
              "   card2  card3       card4  card5  ...                id_31  id_32  \\\n",
              "0    NaN  150.0    discover  142.0  ...  samsung browser 6.2   32.0   \n",
              "1  404.0  150.0  mastercard  102.0  ...   mobile safari 11.0   32.0   \n",
              "2  490.0  150.0        visa  166.0  ...          chrome 62.0    NaN   \n",
              "3  567.0  150.0  mastercard  117.0  ...          chrome 62.0    NaN   \n",
              "4  514.0  150.0  mastercard  102.0  ...          chrome 62.0   24.0   \n",
              "\n",
              "       id_33           id_34  id_35 id_36 id_37  id_38  DeviceType  \\\n",
              "0  2220x1080  match_status:2      T     F     T      T      mobile   \n",
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              "\n",
              "                      DeviceInfo  \n",
              "0  SAMSUNG SM-G892A Build/NRD90M  \n",
              "1                     iOS Device  \n",
              "2                        Windows  \n",
              "3                            NaN  \n",
              "4                          MacOS  \n",
              "\n",
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              "      <td>150.0</td>\n",
              "      <td>visa</td>\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-989bdb49-fbe5-4855-b197-aa09aabe0fd5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74zCut53YhQe"
      },
      "source": [
        "#DOn't drop isFraud value here\n",
        "# target = train_df['isFraud'].copy()\n",
        "# train_df = train_df.drop('isFraud', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBNi4FqX7EVM",
        "outputId": "d850b814-f9a0-4d90-fbbf-11401cf283d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_df.shape[0] + test_df.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1097231"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dl7Or4eEl9gh"
      },
      "source": [
        "# import datetime\n",
        "\n",
        "# START_DATE = '2017-12-01'\n",
        "# startdate = datetime.datetime.strptime(START_DATE, \"%Y-%m-%d\")\n",
        "# df_trans[\"Date\"] = df_trans['TransactionDT'].apply(lambda x: (startdate + datetime.timedelta(seconds=x)))\n",
        "\n",
        "# df_trans['_Weekdays'] = df_trans['Date'].dt.dayofweek\n",
        "# df_trans['_Hours'] = df_trans['Date'].dt.hour\n",
        "# df_trans['_Days'] = df_trans['Date'].dt.day"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dRWzRgocl3zf"
      },
      "source": [
        "# #Dropping all V columns\n",
        "# V_columns = [ 'V'+str(i) for i in range(1,340)]\n",
        "\n",
        "# # Train Data\n",
        "# train_df.drop(V_columns, axis=1, inplace=True)\n",
        "# # # Test Data\n",
        "# test_df.drop(V_columns, axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The V columns appear to be redundant and correlated. Therefore for each block of V columns with similar NAN structure, we could find subsets within the block that are correlated (r > 0.75). Then we can replace the entire block with one column from each subset"
      ],
      "metadata": {
        "id": "vXT4qVNM-WlP"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ifISBSl_8gPu",
        "outputId": "76802afc-c116-41ae-a307-e6e22d2e885b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Reducing Highly coorelated features\n",
        "# https://www.kaggle.com/cdeotte/eda-for-columns-v-and-id\n",
        "\n",
        "nans_df = train_df.isna()\n",
        "nans_groups={}\n",
        "i_cols = ['V'+str(i) for i in range(1,340)]\n",
        "for col in train_df.columns:\n",
        "    cur_group = nans_df[col].sum()\n",
        "    try:\n",
        "        nans_groups[cur_group].append(col)\n",
        "    except:\n",
        "        nans_groups[cur_group]=[col]\n",
        "del nans_df; x=gc.collect()\n",
        "\n",
        "for k,v in nans_groups.items():\n",
        "    print('####### NAN count =',k)\n",
        "    print(v)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####### NAN count = 0\n",
            "['TransactionID_x', 'isFraud', 'TransactionDT', 'TransactionAmt', 'ProductCD', 'card1', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'C13', 'C14']\n",
            "####### NAN count = 8933\n",
            "['card2']\n",
            "####### NAN count = 1565\n",
            "['card3']\n",
            "####### NAN count = 1577\n",
            "['card4']\n",
            "####### NAN count = 4259\n",
            "['card5']\n",
            "####### NAN count = 1571\n",
            "['card6']\n",
            "####### NAN count = 65706\n",
            "['addr1', 'addr2']\n",
            "####### NAN count = 352271\n",
            "['dist1']\n",
            "####### NAN count = 552913\n",
            "['dist2']\n",
            "####### NAN count = 94456\n",
            "['P_emaildomain']\n",
            "####### NAN count = 453249\n",
            "['R_emaildomain']\n",
            "####### NAN count = 1269\n",
            "['D1', 'V281', 'V282', 'V283', 'V288', 'V289', 'V296', 'V300', 'V301', 'V313', 'V314', 'V315']\n",
            "####### NAN count = 280797\n",
            "['D2']\n",
            "####### NAN count = 262878\n",
            "['D3']\n",
            "####### NAN count = 168922\n",
            "['D4']\n",
            "####### NAN count = 309841\n",
            "['D5']\n",
            "####### NAN count = 517353\n",
            "['D6']\n",
            "####### NAN count = 551623\n",
            "['D7']\n",
            "####### NAN count = 515614\n",
            "['D8', 'D9', 'id_09', 'id_10']\n",
            "####### NAN count = 76022\n",
            "['D10']\n",
            "####### NAN count = 279287\n",
            "['D11', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11']\n",
            "####### NAN count = 525823\n",
            "['D12']\n",
            "####### NAN count = 528588\n",
            "['D13']\n",
            "####### NAN count = 528353\n",
            "['D14']\n",
            "####### NAN count = 89113\n",
            "['D15']\n",
            "####### NAN count = 271100\n",
            "['M1', 'M2', 'M3']\n",
            "####### NAN count = 281444\n",
            "['M4']\n",
            "####### NAN count = 350482\n",
            "['M5']\n",
            "####### NAN count = 169360\n",
            "['M6']\n",
            "####### NAN count = 346265\n",
            "['M7']\n",
            "####### NAN count = 346252\n",
            "['M8', 'M9']\n",
            "####### NAN count = 76073\n",
            "['V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V29', 'V30', 'V31', 'V32', 'V33', 'V34']\n",
            "####### NAN count = 168969\n",
            "['V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52']\n",
            "####### NAN count = 77096\n",
            "['V53', 'V54', 'V55', 'V56', 'V57', 'V58', 'V59', 'V60', 'V61', 'V62', 'V63', 'V64', 'V65', 'V66', 'V67', 'V68', 'V69', 'V70', 'V71', 'V72', 'V73', 'V74']\n",
            "####### NAN count = 89164\n",
            "['V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94']\n",
            "####### NAN count = 314\n",
            "['V95', 'V96', 'V97', 'V98', 'V99', 'V100', 'V101', 'V102', 'V103', 'V104', 'V105', 'V106', 'V107', 'V108', 'V109', 'V110', 'V111', 'V112', 'V113', 'V114', 'V115', 'V116', 'V117', 'V118', 'V119', 'V120', 'V121', 'V122', 'V123', 'V124', 'V125', 'V126', 'V127', 'V128', 'V129', 'V130', 'V131', 'V132', 'V133', 'V134', 'V135', 'V136', 'V137']\n",
            "####### NAN count = 508595\n",
            "['V138', 'V139', 'V140', 'V141', 'V142', 'V146', 'V147', 'V148', 'V149', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V161', 'V162', 'V163']\n",
            "####### NAN count = 508589\n",
            "['V143', 'V144', 'V145', 'V150', 'V151', 'V152', 'V159', 'V160', 'V164', 'V165', 'V166']\n",
            "####### NAN count = 450909\n",
            "['V167', 'V168', 'V172', 'V173', 'V176', 'V177', 'V178', 'V179', 'V181', 'V182', 'V183', 'V186', 'V187', 'V190', 'V191', 'V192', 'V193', 'V196', 'V199', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216']\n",
            "####### NAN count = 450721\n",
            "['V169', 'V170', 'V171', 'V174', 'V175', 'V180', 'V184', 'V185', 'V188', 'V189', 'V194', 'V195', 'V197', 'V198', 'V200', 'V201', 'V208', 'V209', 'V210']\n",
            "####### NAN count = 460110\n",
            "['V217', 'V218', 'V219', 'V223', 'V224', 'V225', 'V226', 'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V235', 'V236', 'V237', 'V240', 'V241', 'V242', 'V243', 'V244', 'V246', 'V247', 'V248', 'V249', 'V252', 'V253', 'V254', 'V257', 'V258', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V269', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278']\n",
            "####### NAN count = 449124\n",
            "['V220', 'V221', 'V222', 'V227', 'V234', 'V238', 'V239', 'V245', 'V250', 'V251', 'V255', 'V256', 'V259', 'V270', 'V271', 'V272']\n",
            "####### NAN count = 12\n",
            "['V279', 'V280', 'V284', 'V285', 'V286', 'V287', 'V290', 'V291', 'V292', 'V293', 'V294', 'V295', 'V297', 'V298', 'V299', 'V302', 'V303', 'V304', 'V305', 'V306', 'V307', 'V308', 'V309', 'V310', 'V311', 'V312', 'V316', 'V317', 'V318', 'V319', 'V320', 'V321']\n",
            "####### NAN count = 508189\n",
            "['V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339']\n",
            "####### NAN count = 446307\n",
            "['TransactionID_y', 'id_01', 'id_12']\n",
            "####### NAN count = 449668\n",
            "['id_02']\n",
            "####### NAN count = 524216\n",
            "['id_03', 'id_04']\n",
            "####### NAN count = 453675\n",
            "['id_05', 'id_06']\n",
            "####### NAN count = 585385\n",
            "['id_07', 'id_08']\n",
            "####### NAN count = 449562\n",
            "['id_11', 'id_28', 'id_29']\n",
            "####### NAN count = 463220\n",
            "['id_13']\n",
            "####### NAN count = 510496\n",
            "['id_14']\n",
            "####### NAN count = 449555\n",
            "['id_15', 'id_35', 'id_36', 'id_37', 'id_38']\n",
            "####### NAN count = 461200\n",
            "['id_16']\n",
            "####### NAN count = 451171\n",
            "['id_17']\n",
            "####### NAN count = 545427\n",
            "['id_18']\n",
            "####### NAN count = 451222\n",
            "['id_19']\n",
            "####### NAN count = 451279\n",
            "['id_20']\n",
            "####### NAN count = 585381\n",
            "['id_21']\n",
            "####### NAN count = 585371\n",
            "['id_22', 'id_23', 'id_27']\n",
            "####### NAN count = 585793\n",
            "['id_24']\n",
            "####### NAN count = 585408\n",
            "['id_25']\n",
            "####### NAN count = 585377\n",
            "['id_26']\n",
            "####### NAN count = 512975\n",
            "['id_30']\n",
            "####### NAN count = 450258\n",
            "['id_31']\n",
            "####### NAN count = 512954\n",
            "['id_32']\n",
            "####### NAN count = 517251\n",
            "['id_33']\n",
            "####### NAN count = 512735\n",
            "['id_34']\n",
            "####### NAN count = 449730\n",
            "['DeviceType']\n",
            "####### NAN count = 471874\n",
            "['DeviceInfo']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfP82e4tlsG0"
      },
      "source": [
        "# Vc = ['dayr','isFraud','TransactionAmt','card1','addr1','D1n','D11n']\n",
        "# Vs = nans_groups[176518]\n",
        "# Vtitle = 'V1 - V11, D11'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rgvDCFrRlsEA"
      },
      "source": [
        "# def make_corr(Vs,Vtitle=''):\n",
        "#     cols = ['TransactionDT'] + Vs\n",
        "#     plt.figure(figsize=(10,10))\n",
        "#     sns.heatmap(train_df[cols].corr(), cmap='RdBu_r', annot=True, center=0.0)\n",
        "#     if Vtitle!='': plt.title(Vtitle,fontsize=14)\n",
        "#     else: plt.title(Vs[0]+' - '+Vs[-1],fontsize=14)\n",
        "#     plt.show()\n",
        "# make_corr(Vs,Vtitle)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIsVeFI_lr_K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "outputId": "6969189e-8984-4668-e26e-0cd290b16cb4"
      },
      "source": [
        "#V ALL\n",
        "cols = ['TransactionDT'] + ['V'+str(x) for x in range(1,20)]\n",
        "train2 = train_df[cols].sample(frac=0.2)\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(train2[cols].corr(), cmap='RdBu_r', annot=False, center=0.0)\n",
        "plt.title('V1-V339 ALL',fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tptJac5ZmG-i"
      },
      "source": [
        "#Similar procedure for other V columns resulted, taken refernce form a kaggle notebook :\n",
        "#TODO : find another way to find these V elements\n",
        "\n",
        "v =  [1, 3, 4, 6, 8, 11]\n",
        "v += [13, 14, 17, 20, 23, 26, 27, 30]\n",
        "v += [36, 37, 40, 41, 44, 47, 48]\n",
        "v += [54, 56, 59, 62, 65, 67, 68, 70]\n",
        "v += [76, 78, 80, 82, 86, 88, 89, 91]\n",
        "v += [96, 98, 99, 104]\n",
        "v += [107, 108, 111, 115, 117, 120, 121, 123]\n",
        "v += [124, 127, 129, 130, 136]\n",
        "v += [138, 139, 142, 147, 156, 162]\n",
        "v += [165, 160, 166]\n",
        "v += [178, 176, 173, 182]\n",
        "v += [187, 203, 205, 207, 215]\n",
        "v += [169, 171, 175, 180, 185, 188, 198, 210, 209]\n",
        "v += [218, 223, 224, 226, 228, 229, 235]\n",
        "v += [240, 258, 257, 253, 252, 260, 261]\n",
        "v += [264, 266, 267, 274, 277]\n",
        "v += [220, 221, 234, 238, 250, 271]\n",
        "v += [294, 284, 285, 286, 291, 297]\n",
        "v += [303, 305, 307, 309, 310, 320]\n",
        "v += [281, 283, 289, 296, 301, 314]\n",
        "v += [332, 325, 335, 338]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqYop2UgmG6i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "outputId": "31c2e6a6-2cb9-4c28-a358-085eb6d9ac09"
      },
      "source": [
        "# V Reduced\n",
        "cols = ['TransactionDT'] + ['V'+str(x) for x in v]\n",
        "train2 = train_df[cols].sample(frac=0.2)\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(train2[cols].corr(), cmap='RdBu_r', annot=False, center=0.0)\n",
        "plt.title('V1-V339 REDUCED',fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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v9wc6d+4cABAYMWLEIdv98MMPgRtvvDHQs2fPQMuWLQN2uz0QHx8f6N+/f2DWrFmBqqoqMc7DDz8cuOiiiwLt27cPOJ3OQHh4eKBLly6B6667LrBx40Z1PpMmTQp07do1EBkZGYiKigr06tUrkJWVddj1ohk/fnwAQGDt2rXq87W1tYH27dsH7HZ74Pvvvw8EAoHA1q1bj7h+AQRKSkoaptOpUyfTc06nM9CqVatAnz59Atdff33g448/Pmw/a2trA48//nigb9++gdjY2IDD4Qi0bds2cNlllwVWrFhx2HGrqqoCc+bMCQwaNCjQqlWrgMPhaNgmDz30UKC4uNjUPphl+/LLLxvaDxo0yPScw+EIJCQkBHr06BHIyMgILFu2LODz+Q7bRyIiajy8U0ZERERERNSE+J0yIiIiIiKiJsSLMiIiIiIioibEizIiIiIiIqImxIsyxcqVK2EYBkpLS5u6K0REREREdIz+85//ID09He3atYNhGHj99dePOM7KlStxzjnnwOVy4fTTTz/qNORjEfRFmWEYh33cd999jdjNxjN48GDcdNNNptqAAQOwa9cuxMXFhXRev15fUVFROOOMMzBhwgSsX7++oc2ECRMOu56Tk5ND2iciIiIiopNVVVUVevTogWeeeSao9lu3bsUll1yCIUOGYMOGDbjpppswceJEvP/++43az6DTF3fv3t3w9+LFi3HPPffg+++/b6hFR0cjOjoaABAIBODz+eBwOELc3dAbPHgwevbsiTlz5jT6vAzDQE5ODoYPH47a2lr88MMP+Oc//4nXX38d2dnZGDduHMrKylBTU9MwTlJSUsM4wIHfHvr1bwcREREREf2eeDweeDweU83lcsHlch12PMMw8Nprr+Gyyy47ZJvbb78d77zzDr755puG2hVXXIHS0lIsW7bsuPp9OEFfNR38AUoAiIuLg2EYDbWVK1diyJAhePfddzFjxgx8/fXX+OCDD9CxY0dMnz4dn3zyCaqqqpCSkoKZM2fiwgsvbJhWcnIyrrvuOhQWFiIvLw8JCQmYMWMGrrvuOgBAXV0dpk+fjiVLlqCkpARt2rTBpEmTkJmZCQCYPXs2cnJysGXLFrRo0QLp6emYNWtWwwUiAKxevRp33XUXPvvsM7hcLvTt2xcvv/wybr75ZqxatQqrVq3Ck08+CeDA1fG2bdswZMgQlJSUNPxA6JIlS3DPPfegsLAQSUlJuPHGG3HLLbcEvRwHxcfHN6y35ORkXHzxxRg/fjymTJmC9PR0JCQkiDt0vx7naEwykk3DT9R8J9rY6qrliMaxfao1YA8TNXvlXlHzxspl8fnlewMOwzJ9wxBt1H4obzPYAj45/bKdpuG6ePmDqXU+v6i5HMGtH22ZwnzmE4itukS08Ue3kv0w5KEaBtm3gM1uGjaUlaGtR5uvXtaq9omaL9r8hkCtX04r3KbM09KvQ7F2125ZXwBg1FXJ8Vwxsp2nQs7Asm/XueTdcEcQ6xUA7J5KUfO5okXNSlvXAeWYsynLCb9XliLMP8Bsqy07Yh8AwBcRL+dZXysbWvcpr9wm/rAIUdPWmaPiF1FbVWoet2OcfEE93bNd1OpbnhbUPJ17fzQNV7Y4XbSJKt8hajVxHURNOaQRWW0+x/miWoo2jpIiUTPqakQt4FTWoyPc3IeoFqJNnc0pauGVe2RnFQGbPLf4I4/8o97qfqZNSzk2recWn3LO044Tbd+rcUSJmt1yWrJp5zzlNcFQ5umzrH+No06eC7TXQ3VdG8pxUltqbqOsw19q5c7YKkJO3+Epl/2wHK/aOVU7P2jsNaWiVmqT/W0dZ95OdSW7RRt/uDwfB30etIzrVY5V6+svABj18n8g6zkVkPueY/t60cafKM9Jtlq5/lElX/ft3QfLds2A9f/I5qTtvRNw//33m2r33ntvSD65t3btWtO1CgAMGzZMfLIu1EL6nbI77rgDjzzyCDZt2oTU1FRUVlZixIgR+PDDD/Hll19i+PDhSE9PR1GR+QXq8ccfR1paGr788ktcf/31mDx5csNduLlz5+LNN9/EK6+8gu+//x4LFy40fYTPZrNh7ty5+Pbbb/HCCy9gxYoVuO222xqe37BhA4YOHYru3btj7dq1+Pjjj5Geng6fz4cnn3wS/fv3x7XXXotdu3Zh165d6NhR/mO+fv16jB49GldccQW+/vpr3Hfffbj77rvF50sPtxyHc/PNN6OiogLLly8/irVNRERERPT7k5mZibKyMtPj4A2b47V79260adPGVGvTpg3Ky8tNn2YLtZB+vvCBBx7ARRdd1DDcokUL9OjRo2H4wQcfxGuvvYY333wTU6ZMaaiPGDEC119/PYADtwyfeOIJfPTRR+jatSuKiopwxhln4LzzzoNhGOjUqZNpnr++ak1OTsb//d//YdKkSfj73/8OAJg1axbS0tIahgHgzDPPbPjb6XQiMjLysHeiZs+ejaFDh+Luu+8GAHTp0gUbN25EVlYWJkyYENRyHE63bt0AANu2bTtsu8PRbuP6EIAdwd1dIiIiIiL6LQjmo4q/NSG9U5aWlmYarqysxK233oqUlBTEx8cjOjoamzZtEnfKUlNTG/4++LHIX3458DGXCRMmYMOGDejatSumTp2KDz74wDTuv//9bwwdOhTt27dHTEwM/ud//gf79u1DdfWBW9IH75Qdj02bNmHgwIGm2sCBA/Hjjz/C5/vvRyAOtxyHc/BrfUaQH8/TzJw5E3FxcabHlwjuY0xERERERHTgK1t79pg/Ar5nzx7ExsYiIkJ+zDxUQnpRFhVl/szwrbfeitdeew0PP/ww8vPzsWHDBpx99tmoq6sztQsLM3/22jAM+P0HvtdxzjnnYOvWrXjwwQdRU1OD0aNH4y9/+QuAA3eWLr30UqSmpmLJkiVYv359Q7LKwXk05sqzOtxyHM6mTZsAAKeeeuoxz1u7jdsLoU2PJCIiIiI6mfXv3x8ffvihqbZ8+XL079+/UefbqL9Ttnr1akyYMAF//vOfcfbZZ6Nt27bH9BG92NhYjBkzBs899xwWL16MJUuWYP/+/Vi/fj38fj8ef/xx9OvXD126dMHOnebwhtTUVLFif83pdJrudmlSUlKwevVqsWxdunSB3R5cgMHhzJkzB7GxseJLhUfD5XIhNjbW9OBHF4mIiIjo96yyshIbNmzAhg0bABwI9duwYUPDJ/cyMzMxbty4hvaTJk3Cli1bcNttt+G7777D3//+d7zyyiu4+eabG7WfjZpZf8YZZ2Dp0qVIT0+HYRi4++67g7pz9GuzZ89GUlISevXqBZvNhry8PLRt2xbx8fE4/fTTUV9fj6eeegrp6elYvXo1nn32WdP4mZmZOPvss3H99ddj0qRJcDqd+Oijj/DXv/4VrVq1QnJyMj799FNs27YN0dHRaNFCJlvdcsst6NOnDx588EGMGTMGa9euxdNPP236nlqwSktLsXv3bng8Hvzwww/4xz/+gddffx0LFixoSHoMFWva4s0R3USbJ8s3iJqWGqWlUhmWBCRtPF9MG1GLCD9ymhUA1Fq+TKklCSIg96eISJnGZZ0WANQnmENdbMr046MjRa2mVibUaX2zpkcCgD/MvOyBmNaykZLEF6Pc8a2tlqlU1nXrqSgVbcJj4uW0amT/tZRM63LGR8t+Bbt+tBTIyAhz/2uUMEDDLpPmNAElQcvKrvQhgODeaPE75X4W1Hja8aWsHy1xLajpKwlmGvU4DOrYPLZ+HZj+KaJ0YVC/8HG2qAT9dlhH87jq2ok6Q5SCXsoo8zLJrQsgMiXYqR0T9VsVkcmNOk8o59mgRchkS0nbF+VWCe5s0MgigntNC356R05bPkW+NKlqceRfPQoo5wz19VahnW9aB/EJpWDPU8d6nrXblHO7IY+UgCO47yRZ29nPkl+LOf636Jsfa5Lpb9Xnn3+OIUOGNAxPnz4dADB+/Hjk5uZi165dpq9WnXrqqXjnnXdw880348knn0SHDh3w/PPPY9iwYY3az0a9KJs9ezYyMjIwYMAAtGrVCrfffjvKy5V40MOIiYnBrFmz8OOPP8Jut6NPnz549913YbPZ0KNHD8yePRuPPvooMjMz8Yc//AEzZ840Xe126dIFH3zwAe6880707dsXEREROPfcczF27FgABz5iOX78eHTv3h01NTXYunWr6MM555yDV155Bffccw8efPBBJCUl4YEHHjCFfATrmmuuAQCEh4ejffv2OO+88/DZZ5/hnHPOOeppERERERHRoQ0ePBiH+1lma5r6wXG+/PLLRuyVFPSPR9Nvj/WORWPfKdN+q0i76xMe5Pf8tLtbgnKnLDzIO2XWOzXaO4NaX4O9E6QR8/QrH50Ncp1pd8qsy67dKXOpd8qOvH4AuZzHs3606Vvv3hzPug5GsL99pwl2mY51Wo0t2OOQiH67gnodDbFgzi2N3a9gXr+Ox+/l/HmDLbmpu3BIz/i3NXUXQq5R75QREREREdFvj/YRf2o8jRr0QURERERERIfHizIiIiIiIqImxI8vnsRsddWmYe37Y9Nie4ra0188K2p7l70lalU/7zUNd5iVo/RBfu8JQX4W2/CZf88uYJO7q6F8p0xT7ZWfJY8KmD/THrZ7kxyxy0BR0j6XbvNUyHEt37kDgMqweNNwtF9+rt77Qbac1l9vEyV71T7ZzvKdMlutEqyjfafMJ5cp0ie3XcW/HjUNh0/JEm1s9cr3wOqrRc0fRDqix6ukawbqZEObzL0KKN/Nkx2T42n9V0f1ynbWxETtu5ja9yChfI8T2vcNFdp3QImIAMBWUyKLlnNjQEm01VIJw4q+EDVfkpIsGsRrvL1ij6j5w2NFzfrddUD/X8D6Wmer2i/Hc8p+aQnR1v89AMCwvi5EJIs2RMeLd8qIiIiIiIiaEC/KiIiIiIiImhA/vkhERERERCYny49H/1bwTtkJlJ6ejuHDh6vP5efnwzAMFBQUNNT27duHDh06wDAMlJaWnqBeEhERERHRicSLshPI7XZj+fLl2LFjh3guJycHaWlpSE1NNbX/9TAREREREZ18+PHFE+jSSy9FYmIicnNzMWPGjIZ6ZWUl8vLykJX13yS7efPmobS0FPfccw/ee++9Y5uhSFiSKW1a0uKUcyaJ2tyPZ4laQkWpadirXOOHKQlOwSr6m9s03PHxF0SboBL2ANiUW/CG35yCV9fxHNEmXJlWQPkxRWvqHgD4IdvZ/eaUw4ByCNqHX6fMVdp+/62idvrTr5j7EBEX1LT8MnwR/jCZVBU9/q4jTmvD5X8WtR5vvC1q2noMpl+1NrlPhUMmdHkCct9wGUdO69SOk4CS7mgo+7ZYJmX/1PcLmdKopZ8FM269IfcpB4JLKSWik4s3ooWo2S3JsXV2JWlROWfUnyJfI7V0xGD4ohPltJS0Wq9LJjLaAjKZ1prcGHAoiZJhkbKm/Y+ipfk6tP8GTn788egTi3fKTiCHw4Fx48YhNzcXgV/Fqufl5cHn82Hs2LEAgI0bN+KBBx7AggULYLNxExERERERncz4H/8JlpGRgc2bN2PVqlUNtZycHIwaNQpxcXHweDwYO3YssrKycMoppwQ9XY/Hg/LyctPD4/E0xiIQEREREVEI8aLsBOvWrRsGDBiA7OwDPxBcWFiI/Px8uN0HPqqXmZmJlJQUXH311Uc13ZkzZyIuLs70mPX4EyHvPxERERGd/OxG832cjHhR1gTcbjeWLFmCiooK5OTkoHPnzhg0aBAAYMWKFcjLy4PD4YDD4cDQoUMBAK1atcK99957yGlmZmairKzM9LjtlptPyPIQEREREdGxY9BHExg9ejSmTZuGRYsWYcGCBZg8eTKM//9lyiVLlqCmpqah7bp165CRkYH8/Hx07tz5kNN0uVxwucxfWPVU8Mv9RERERETNHS/KmkB0dDTGjBmDzMxMlJeXY8KECQ3PWS+8iouLAQApKSmIj48/rvlqyUZ7l70lalrS4tTzbhO1cX8wf+et98DRcqZKilGwTsmabxo2vlom2gRSLw5qWk67vCkcCJh3f1ttmRwxQiYQGgEZCWj7/A3Zrqf8TTq/NeVKSzbyy2QpTceZ/zxim4AtuEM83CHXj+GT30k06qstlXjRpueSJaLmX5ErZ3qhW9aC6JeWVKitM5dDLrutxryN/REJso2nQtQCShKldjyJ5EalX3blmHAUfSmnFddG1HwxsmZNeHQoy214lXVGRCc9NdnVcp7SE/aU1wSvfE3Q0mqDYQTkm8ZaEqLaNSWVVyTd2mX6ovr/iNIPKK/xx/O/DFGw+PHFJuJ2u1FSUoJhw4ahXbt2Td0dIiIiIiJqIrxT1kT69+9visU/lMGDBwfVjoiIiIiIfpt4UUZERERERCb88egTix9fJCIiIiIiakK8KCMiIiIiImpC/Pji74jh94pa1c97RS2holTUrEmLALDgP0Wm4TRl+oHjuPVtTQ4M9JBphmpiYlRMcDOwpCkFtLSmIAV6/VFOvrpE1mLaHnFaWirVsbKX75bFyEP/tMKRBFxHXrdqgtbQDNnuWPtgTdkCAC1dU9n3/OFxR26jLaM2T61v1rTLINMv65P7BNVOZembH3KZbMeYkEZEJx/ra4yhJQv65euQ9j+EHo8YBC1lWDnPGsHeO7C+bmqvo8o8DQSXdhxsKvLJ5mT9kebminfKiIiIiIiImhAvyoiIiIiIiJoQP75IREREREQmTF88sXinLETS09MxfLj8zhMA5OfnwzAMFBQUYOrUqejduzdcLhd69uwp2tbW1mLChAk4++yz4XA4cNlllzVux4mIiIiIqEnxoixE3G43li9fjh07dojncnJykJaWhtTUVABARkYGxowZo07H5/MhIiICU6dOxYUXXtiofSYiIiIioqbHjy+GyKWXXorExETk5uZixowZDfXKykrk5eUhKysLADB37lwAwN69e1FQUCCmExUVhXnz5gEAVq9ejdLS0mPuU8CSuGYdBoAOs3JEzatcq/ceOFrUrGmLU+PTRJs7930jajLHUVdebx52KjFAhitB1LQMxRfb9RS1i35cZxpuEREh2sgcQV2tIedqi24jai+07WEaTt+yXrSJcMjDsrUyzzK/3J7WdlXRSaJNvDItbf0MK/xc1GKc5n1DWz/VPu3jDjJrMSLsyB+LeK5ND1H7y/YvRC1MDWSU6VsuS0qjIyD75TdkEplN6X+9TW5zu828TMrk1bCyOp9saFPalXvkMkVbulFZJ1PCYl1yBYXLyRPRSaa4Th771rBau0+eV2KNelGrsEWKmks5UQXzurm3Xr7ORSsnx6p6pW/K+azWckar9MrzYIJDntv9yjk6TPlfw2dpGOz/BkRHg3fKQsThcGDcuHHIzc1F4Ff/ieXl5cHn82Hs2LFN2DsiIiIiImqueFEWQhkZGdi8eTNWrVrVUMvJycGoUaMQFxd3mDGPn8fjQXl5uenh8XgadZ5ERERERHT8eFEWQt26dcOAAQOQnZ0NACgsLER+fj7cbnejz3vmzJmIi4szPbIee6zR50tERERERMeHF2Uh5na7sWTJElRUVCAnJwedO3fGoEGDGn2+mZmZKCsrMz3+duutjT5fIiIiIjr52Jrx42R0si5Xkxk9ejRsNhsWLVqEBQsWICMjA8YJ+J0Hl8uF2NhY08Pl4ldRiYiIiIiaO6Yvhlh0dDTGjBmDzMxMlJeXY8KECabnCwsLUVlZid27d6OmpgYbNmwAAHTv3h1O54EYtY0bN6Kurg779+9HRUVFQxvtd80Ox1651zTsi5FpgLa6KlELcygXczaZWhSwXGxqSYsPtzxL1J6s+ErU5q7fK2rTzjYnPfmdStLirm9FrazNmaJ2zQ8fiNqXV480F158Q7R5+ZvtojbhzHhRq1OSELW0yOu2rTINf3vN5aJN0guvidrTa7eK2rWprUStttq8PSPrykSbimrZrwmbV4jamosvErXTP1huGl6k9OvKs+R+5lUirmxe+Z3Hsipzu8lF+aJNtUP2P7K+XNS001uNLcrchyDfMFFCD+EyZNHrN7/PZU1jBADDL1PB7IZ8f0xZTEQpiZXhMKeghttlalptQCaLEtHJL0I5kUTZzOcgo75GtKkJixE1p3JO0s7ti76UPw10Za8OpuFopzznhdvktCIN+T9KTUD2LdZuXqY4h1wmo7JC1CqiZEKx018narYay2tpdLA50kTB40VZI3C73Zg/fz5GjBiBdu3amZ6bOHGiKQikV69eAICtW7ciOTkZADBixAhs375dtAlo+dpERERERCFmPwGf9KL/4kVZI+jfv/8hL6BWrlx5xPG3bdsW2g4REREREVGzxe+UERERERERNSHeKSMiIiIiIhPlq/HUiHinjIiIiIiIqAkZAaZHnLRqamtNwxHh4Se8D55Kmf43LaaHqM2pkimKhiWdz1arJOwpSXbe+PZyWko7w29OrYNyKASUJMo5rVJF7ebd60TNVlMqav6olubpK6l7RkCm+vntMt3R5pMpe6+feq5p+M8//ke08bmi5Ty19aP0w7q+tfVj3W4Hpu8Vtac7DBC1yXsLjtgvKOtMpfXfMq41QRQADG0/OI52wYwXUkEsN6BvE59N7md2v2U/U6a/p06ms7ZxyeXUUt78TksiptYmTKZHavundjzJEZU2Qa4zlTaudVJBtAGOvf9+yP3OFlCOnSBZ+6v2K8h9SiOmF+w20aalJANbjzF1/UA5Do91Pwh2vCCXSYyrjOdV3lN3oJH7HyQtrTYuynwM19bI41x1jH1Tz9nH83piYfPIJEeVNn3lOHEmtD2mfjS2R6LOaOouHNIdVT82dRdCjnfKiIiIiIiImhAvyoiIiIiIiJoQL8qIiIiIiIiaEC/KQiQ9PR3Dhw9Xn8vPz4dhGCgoKMDUqVPRu3dvuFwu9OzZU7S97777YBiGeERFRckJExERERE1ArthNNvHyYgXZSHidruxfPly7NixQzyXk5ODtLQ0pKYeCIjIyMjAmDFj1Onceuut2LVrl+nRvXt3/PWvf23U/hMRERERUdPg75SFyKWXXorExETk5uZixowZDfXKykrk5eUhKysLADB37lwAwN69e1FQUCCmEx0djejo/6bjffXVV9i4cSOeffbZo+6Tz9/0wZpz1+8VNS1p8aaoM+W4+9aYhrXkNl98B1GzV+8Xtf/sk6lyX+40J0PeHFso2rx44c2yr3s3iJrjF5kCFHDJu5vLi6pNw5t+kQlOtyTtEbU5va8RtRs3fyBqfyr6wjRslO8WbWRWGfCfX+S+8q3StxsifzANP3H+VNHm5h/fETVbdYmoWZMWAcBuSaz8T7Hs7aC4KlGrjmojatp7TpEV5vXhi2ktx1JStWy1subV9j1PpWlYSw30G3KZnPu3ynYRcaJmeOtErT7GnNpV65PvIMbUyH3KqJPr0b7vZ9mPTj1Nw479RaJNdGJ3pa/VoqYmkYlETCVdUFlnPmX7au+d2izvqNZ6ZZpbuENJ8FMmVueTx4nN0l/1/VtlmbSzczDv/dqUViIhE0C9IV/eresC0FMardtASzjUQkQdSvJqwKb8m+Ew17SkPGfxFlEz6uX065Lka4c1sa9Srh5EhCnphUEkOQIyzVF7d7teWT9OJTFXOzdWRJrPZ5HKSduh7SzKPO0V8tj3h8eahmvt8jwVWSVfu32W9GAAsCuvMesvHitqFxR8dsR+eWNlAmFlvVzQeI/sm3Wf1ZJAPdHydcKl9N8f1ULUamD+HyJSO6aVlGRNsCml9PvDO2Uh4nA4MG7cOOTm5uLXvzKQl5cHn8+HsWPlSSoYzz//PLp06YLzzz8/VF0lIiIiIjosu9F8HycjXpSFUEZGBjZv3oxVq1Y11HJycjBq1CjExcl3vY+ktrYWCxcuhNvtPmJbj8eD8vJy08Pjke8qEhERERFR88KLshDq1q0bBgwYgOzsbABAYWEh8vPzg7qo0rz22muoqKjA+PHjj9h25syZiIuLMz0efyzrmOZLREREREQnDi/KQsztdmPJkiWoqKhATk4OOnfujEGDBh3TtJ5//nlceumlaNNG+76MWWZmJsrKykyPW2792zHNl4iIiIiIThxelIXY6NGjYbPZsGjRIixYsAAZGRkwjiG6c+vWrfjoo4+CvsvmcrkQGxtrerhcrqOeLxERERERnVhGIKBlKNHxmDhxIpYuXYry8nIUFRWhXbt2Dc8VFhaisrISzz77LD766CMsXrwYANC9e3c4nc6GdnfffTeys7NRVFQEu13Lyzuy2hqZVmhl+GSaW9Hf5IXgKVnzRc2aqlWuJFzF+8pELeAIl/3w1ora1JYDTMNz938i2vjXyaS/6v4yVMWmXBc77eb3JLQUMkNJy9L66nfFyHYBmfJW5jXPM86mrDSFUS+T7L7NmCBq3V/MMw2HKamQ5a1lUl697CqilHQyu6UfRp3s14arx4laj7ylcgbK+qkyzPtGjVdLu5OTSnDJvlYp49ZbEknjncr7Ukq/VFqSYLDjWieljKel51UqG8q6DB6lCzVK4mAL5dg0fvxM1H7pcpFpuE3tTtGmetm/RM1xxZ2i9q8Cmbg2sW2paXhf/Omizds/7hO1q8+IFDXNkxvM07+xT5Jo8+I3xaJ2eUqiqO2tkalpy340jzv5rFjRZmOl3JYrtshl0hJze7Uzfx/5Dwnyu8I2JZk29yenqJXUyPPN+ckyaS6llTmNL7+oXLRpFSmT5s6Jl/uZlr64yTK5bglyWtq5V+MPU15P/OZzeYVXnjT2Kduyqk72v3OCsh5rzdMv88jXjjCbPD+cESlfb7Vz6PObzf1wp0SLNvZSmZTqad1V1CrrZN9iLau7WklsjbQH96+h9rpvnT4AhEea04h/KZPpr7HK+Vg5jaNaOw/6LQm5PiXhUNlX9vpl8mQrh7JQljfXc78tFU16tJX/B/xSJbf5z+Xyf4jJ/ZLlPJuB2TFdmroLhzS94ocjN/qNYSR+I3C73Zg/fz5GjBhhuiADDlyw/ToIpFevXgAO3BlLTk4GAPj9fuTm5mLChAnHfEFGRERERHSsTtYfaW6ueFHWCPr3749D3YBcuXLlEce32Wz46aefQtwrIiIiIiJqjvidMiIiIiIioibEO2VERERERGRysv5Ic3PFO2VERERERERNiOmLJ7GaWnPCj6Ft6iDT4mxfLRM1f4/hpuFav3xLJVx5m8VeIRPYDCU9zB+ZYBqe2qKfaPPU7o9EzRfTWtQ8Afn+Q5glxs+upCray3eJWsAZJWreta+LmuPcdFGrijL/5pxdiRJ0KDW7p1L2wyF/8sDweo7YRktDq/XJfcPlkOvMZklEs1XK1Dp/RJyolT57r6glTMyU44abx9X2KbVfXplI51eW3VZv3sba+rEXvC9qvn1yP3CenirnGRlvmZhMbvO75P5TeNP/ilpy+vmiZqRPFTXrsmvbV2P/ermcVttTRc0fbk4TVNNHo2VSof3nb0StJvlcOU/LF8ntfpl85rPJODe7T25zn11uT7/lvBemjFevjKcdm8G8WmrnEcf+7aJWkxhcqpm1v9r50x/VUtS8TpnYZ10X2vQBIGA3r++ATQZOaevC4ZEpjRrrcRfQjpN3nxE1b7mcvnPM7bIfZeaEUJ+yf6r9siuxgUrKasCyz1rTHg81nr1yr6jZqktEzZqi6KjZL/ugJP5qr+c1kMtkPYdqqZ/a/l9aK5czziX3DS3J2Jq+WFEtX/PDlLsy1nUN6PuedZ6GX6YvaucHjdZ/6zGg9SGsYrecp7LvaX1zRcvXzebg6TiZ6NlcTCn7vqm7EHL8+CIREREREZkwffHE4scXiYiIiIiImhAvyoiIiIiIiJoQL8pCJD09HcOHD1efy8/Ph2EY+OqrrzB27Fh07NgRERERSElJwZNPPinaezwe3HXXXejUqRNcLheSk5ORnZ3d2ItARERERERNgN8pCxG3241Ro0Zhx44d6NChg+m5nJwcpKWlYf369WjdujVefPFFdOzYEWvWrMF1110Hu92OKVOmNLQfPXo09uzZg/nz5+P000/Hrl274PcHF8hBRERERES/LUxfDBGv14sOHTpgypQpmDFjRkO9srISSUlJyMrKwqRJk8R4N9xwAzZt2oQVK1YAAJYtW4YrrrgCW7ZsQYsWLY6rTyJ9UUmIMpS0poCSGqW1MzwV5vm5EkSbmD3fipovUrbzK4mJgdWvmIbtPS4QbW5sO0TU5lRvEjUt9UqkIgWZROkoKZLTimsn2+3dLGqVrVNMw1Flclr1LTqJmnaUhu8sEDVvy2TT8L5594s2CTc9Jmq2uipR8yspk8GsI21f0djLZVJVfUJH03CdlgppKPuiTyb2aUS6mrKvq8t4PO2soylfnFaPTSWhK5hkRW1daKly9opfZDslGTIQFmGevpZ0qewrWkpgsCl4xyqodRbstgxhH/S4OCXRUDv3WrenMp7a/+OZp7YM1vGUfVF9nVD2F5G+qE1LGU9jq5WJjNZlCiiJsMGuM+3YEX3TUhuVY1qdltoPS03pl88RLmpacmlQr+dKX/1hcvpqyqRCS+uMCDdPr7ZavuZogj2fWbeJ4auT4ynrTE3c1PphPSa0dM3SHaKm/W+grW9X7PH9v9dY5sV3a+ouHNLk0u+augshx48vhojD4cC4ceOQm5uLX1/n5uXlwefzYezYsep4ZWVlpouvN998E2lpaZg1axbat2+PLl264NZbb0VNjYyPJSIiIiJqDHaj+T5ORvz4YghlZGQgKysLq1atwuDBgwEc+OjiqFGjEBcn36lbs2YNFi9ejHfeeaehtmXLFnz88ccIDw/Ha6+9huLiYlx//fXYt28fcnJyDjlvj8cDj8f8TpE/EIDLFdzvchARERERUdPgnbIQ6tatGwYMGNAQylFYWIj8/Hy43W7R9ptvvsHIkSNx77334uKLL26o+/1+GIaBhQsXom/fvhgxYgRmz56NF1544bB3y2bOnIm4uDjTIysrK/QLSUREREREIcWLshBzu91YsmQJKioqkJOTg86dO2PQoEGmNhs3bsTQoUNx3XXXmb5/BgBJSUlo37696c5aSkoKAoEAduyQn1c+KDMzE2VlZabH3/72t9AuHBERERH9LjT1RxR/bx9f5EVZiI0ePRo2mw2LFi3CggULkJGRAeNXX+z/9ttvMWTIEIwfPx4PPfSQGH/gwIHYuXMnKisrG2o//PADbDabSHX8NZfLhdjYWNODH10kIiIiImr+mL7YCCZOnIilS5eivLwcRUVFaNfuQPrON998gwsuuADDhg0zfbTQbrcjMfFAKlllZSVSUlLQr18/3H///SguLsbEiRMxaNAgPPfcc0fVj+oac/piZIRMHmpsZVXyI5dO5S0Oe/V+UasMizcNR9qVXVVJQLopMkXUsqpkImNMZISonWgV1XL92JV0PkddpahV2yNFzW9ZRRFhcv082fJsUZuyVyY5KsGHCLOZ++bwyOSzSnu0qEU65DJpKYQPxp1pGr6z5BvRxm6T4xnKacwP2c4GczutD8HSzpzHMTk5LW0GSlKblmIpRlPS57TExHptm8M8z3rlvTxtn7UFZMJYuRKS6XKYpxfhlalsXqfcp/zK+lF2M3U/sNL6qqWsavyWREmtrz6lr9o6C0a99SAH4FVq0YaWWucUtWDWj9ZVh7Lf1Ua3OeK0ALns2vR9yjKp01LOB9bVbT3uAX25naU/yRkoSZS2WnPycH2brqJN2C8/ilpt0lly8sq+YbOsEG3/9CrHobOuQtR84bGiZqUGdSrz1FIVNba6alGzpgtq6YtqImyY8uaysh/7XPK4E9MK8pytnXutrxUVHuWcoeyLUZApkFWQ/W8RI1/Pm4P5LZpv+qJ7P9MXKQhutxslJSUYNmxYwwUZALz66qvYu3cvXnzxRSQlJTU8+vTp09AmOjoay5cvR2lpKdLS0nDVVVchPT0dc+fObYpFISIiIqLfIbthNNvHyYjpi42gf//+0G5A3nfffbjvvvuOOH63bt2wfPnyRugZERERERE1N7xTRkRERERE1IR4UUZERERERNSEeFFGRERERETUhJi+eBIT6UZKUmG1V0t+ktNy2o98/f5iu56ids0PH4iaL6qlqH38s0xrSksyp8MZyhc7I6v3ilptVKKo/S1KJjLOLf3cNLzXL9MYt5TUilqftjLFstoX3JdOcy3raMpPH4s2ewMyFa9wv0xp7NpK9jc+zDyspWVpqWb/bNND1G7Y+ZmoFXvNM9DWz2kJcv0oIZCIDZM1a6LYk61SRRstKVI7jWmpbNZF15JAtfWjnSSP9YvG2mger0xV1JK8tJQ967FZq0xLWxdxUU2fPkpEjau0Ur621lqidcOV86BWU/5dUFN6NxXL16t+nczpi/srZL+083GdMgPtfGY9h1bWy/NgywgZo6Cd78OC+BGs5pDefCLktpT/OzUXE/bJVO3fOgZ9EBERERGRycn6I83NFT++SERERERE1IR4UUZERERERNSEeFEWIunp6Rg+fLj6XH5+PgzDwKpVqzB8+HC0a9cOLpcLHTt2xJQpU1BeXm5q7/F4cNddd6FTp05wuVxITk5Gdnb2iVgMIiIiIqIm/4Fo/ng0HRO3241Ro0Zhx44d6NChg+m5nJwcpKWlITU1FSNHjsT//d//ITExEYWFhbjhhhuwf/9+LFq0qKH96NGjsWfPHsyfPx+nn346du3aBb9ffmmViIiIiIh++5i+GCJerxcdOnTAlClTMGPGjIZ6ZWUlkpKSkJWVhUmTJonx5s6di6ysLPz0008AgGXLluGKK67Ali1b0KJFC9H+qPq060fTcH1CR9HGVicTkIyAvAAM2JTrd0uy304ZuIS9V48UtR6vviZqc9fLFMUb+nYQNdEFJRcvPEKmItWVFYva1Pg00/Dd+78Vbd7+QY43oXucqNUYTlGLQL2oGfXmlfTtNeNEm/YvyPXzYsFuUZvcp72o2XzmedYbcrtpyVV2T6Woreo3TNS6r/mPafjVjXtEm/9JbStqWkJXrE2uH1e0ed3Wlf4i2thqykTNHya3ueGT0/dHJpj75ZBJkZo6nzwmwm1yoazpkdq6NpRTrh/KNvHKZEtbbbmoBZyR5ukr29IfHitqrtjjO78QUfNXXSPPI9Zzi62mVLSpiGgtx1POZ+HwitrCTSWilpF2imnYUynP4/D7REk75/kj4kUtYDfH+do8FXJaymuHL6aN7IfCXmn+H8WRdEZQ4/3WLUrs3tRdOKQr925s6i6EHD++GCIOhwPjxo1Dbm6uKZ47Ly8PPp8PY8eOFePs3LkTS5cuxaBBgxpqb775JtLS0jBr1iy0b98eXbp0wa233oqaGuWKh4iIiIioEdiN5vs4GfGiLIQyMjKwefNmrFq1qqGWk5ODUaNGIS7uv3cAxo4di8jISLRv3x6xsbF4/vnnG57bsmULPv74Y3zzzTd47bXXMGfOHLz66qu4/vrrDztvj8eD8vJy08PjqQv9QhIRERERUUjxoiyEunXrhgEDBjSEchQWFiI/Px9ut9vU7oknnsAXX3yBN954A5s3b8b06dMbnvP7/TAMAwsXLkTfvn0xYsQIzJ49Gy+88MJh75bNnDkTcXFxpsejTz3bOAtKREREREQhw4uyEHO73ViyZAkqKiqQk5ODzp07mz6eCABt27ZFt27d8Kc//Qn/+Mc/MG/ePOzatQsAkJSUhPbt25vurKWkpCAQCGDHjh2HnG9mZibKyspMj9tvlN9hIyIiIiKi5oUXZSE2evRo2Gw2LFq0CAsWLEBGRgaMw0R3HkxV9Hg8AICBAwdi586dqKz875f1f/jhB9hsNpHq+GsulwuxsbGmh8slwyeIiIiIiKh5YfpiI5g4cSKWLl2K8vJyFBUVoV27dgCAd999F3v27EGfPn0QHR2Nb7/9Fn/729/QokULfPzxxwAOpDWmpKSgX79+uP/++1FcXIyJEydi0KBBeO65546qH9bUJS2p0PnTF6JW1/EcUbPVytSigN180Vdrlwl4+2tkMlM7l0xYshd+ImreruebhrU0PWviEgCER0aJ2s8lVaJm9WCLM0XtyeX3iprRJ13Uiu6S3/nrePmlorb/nFGm4TolljBxxd9FzdH2FFHD2ReIknWb2Mt3iTb1LU+V/aqR28SnnBp8D082DXf46yjRxp/yB1Gre32uqDkvmypq1kRAbbvFuuR7SdrbHlriY5glPUw7JgyvR9b8cj/WEh+DYij9V+YZcLhEzassk83ypk+9XzayLjcAhBUsk9Pf+7OoOXuYt6dvm0wptZ16tqx55LarTzxd1Ooc5vUYXikTPY2f5Dx/eullUet0nfx0gLeVZX+3yzertPOImt7plOcWMa335cfGveXy/BnZd6io+ZJSRM1WYV4fRqlMYvV26i3Hq9on+xYhk2N9H/1L1BxJlnV2mnxNqI+W6XxbJsrzQccLeola9IV/NQ17WncVbYzl/5R9rSgVNfuo20TNmkJrL5WfMvG7YuQ8leNc2w98seaE2bA934s2dW3ltvS98YSolW+T5+iEmx4zDTt3yf3fG99O1Gy1MnHQr2xza7vaOPmGb3jJNjktS3otAASU86Dt249ELezcy0zDnvL9og2U47BOSRAOg0zDta1/yzypDl1EG18L+TqqJvcq52NfmDnl1rZqgWjjPKOnqAU8MuHar+zH1vXTXCxu3XzTF8f8wvRFCoLb7UZJSQmGDRvWcEEGABEREXjuuedw3nnnISUlBTfffDP+9Kc/4e23325oEx0djeXLl6O0tBRpaWm46qqrkJ6ejrlz5T+1RERERESNoal/IJo/Hk3HrX///tBuQA4ZMgRr1qw54vjdunXD8uXLG6NrRERERETUzPBOGRERERERURPinTIiIiIiIjI5WX+kubninTIiIiIiIqImxPTFk1hppTn1Jz468hAtG88/Pt0uauNTZWrXyx1kutfYbWtNw1qSoDdBpilp6Yv5W2QS2ca9labhCRUrRJtpF90vanOqZBKWc8cGUQvU1YraR65U0/CP+2RC3bURP4jaixdME7UrC2XClT/cnLTl2L9NtPG2SBa1dbtkQtT3xZWiNj7wpWn4+WF3iDYTf/xA1Gz75H5Qf4rc5tZtp22301uEi5pDSResrpcJXYmR5g8H2AIyddJWXSJrHplq5ouXiWVGnXl7WrcHAPiVrEjnvs1y+nHtRa3ML9PJXA7ze2tlHrlMLcLtcp7Vct069svtpCWiWXnjlCS4evlj977oRFHzh5m3p72yWE4/qtUR+wDoyZPWl7jD/URJKGhJl9p+VuuX7ZTuI8zyVrXTXycbKS/jtTaZ3qmJKtki+2HZTtZUVwDwOeRxGFYm0zthk/uedT8IKG2ce3+U0/LLY7qujZLcaFkfpXVyvHhncCmoUPYX6/rQ0ol9EfGi5qj4RU6+Tr4GlMd2Mg1H2ZR0XJs8Lu1+mRQJvxzXmjK5LyATCFvV7ZWTUpZJSy79uM9gUbvw23WmYWs6NAD4lf14f21w57Pw0iJRs6pL6HTENgBg98n9oNxy7m259xvRRkvMVdMvq0vlPJN7BtW3E+21tjKVurn48275v9jhPPPMM8jKysLu3bvRo0cPPPXUU+jbt+8h28+ZMwfz5s1DUVERWrVqhb/85S+YOXMmwsPluS9UeKeMiIiIiIhOSosXL8b06dNx77334osvvkCPHj0wbNgw/PKLfKMEABYtWoQ77rgD9957LzZt2oT58+dj8eLFuPPOOxu1n7woIyIiIiKik9Ls2bNx7bXX4pprrkH37t3x7LPPIjIyEtnZ2Wr7NWvWYODAgbjyyiuRnJyMiy++GGPHjsVnn33WqP3kRRkREREREf1meDwelJeXmx4ej/zoaV1dHdavX48LL7ywoWaz2XDhhRdi7dq1oj0ADBgwAOvXr2+4CNuyZQveffddjBgxonEW5mC/GnXqvyPp6ekYPny4+lx+fj4Mw0BBQUFDbd++fejQoQMMw0BpaWlDfdeuXbjyyivRpUsX2Gw23HTTTY3ccyIiIiIis6b+gejDPWbOnIm4uDjTY+bMmWIZiouL4fP50KZNG1O9TZs22L17t7rcV155JR544AGcd955CAsLQ+fOnTF48GB+fPG3wu12Y/ny5dixY4d4LicnB2lpaUhNTTW1//XwQR6PB4mJiZgxYwZ69OjRqH0mIiIiIvqtyczMRFlZmemRmZkZkmmvXLkSDz/8MP7+97/jiy++wNKlS/HOO+/gwQcfDMn0D4XpiyHi9XrRoUMHTJkyBTNmzGioV1ZWIikpCVlZWZg0aRIAYN68eVi8eDHuueceDB06FCUlJYiPjxfTHDx4MHr27Ik5c+YcU59qamW6kZU1pQoAAkralNYuGIZPJoU90SZN1G7au0HUrIl3hldOS0uucrRPEbXaatnO8JmTqqyJVADgD5OpVDdFyTSiuaWfi5qtUqZXaYl9QkAmhWnpZzZl2R9p2880nFkkEyW9MTL90ggioetAR8z7gZa8pSWRwZDv/9zaqr+ozfVtNQ17qmTqYUCZljZ9bT1a24VyXz/U9I55+lr/1Qlalj2I5Qb0Y1NLarOm51mPGwDwOqNFTUswU1n7pkxfSzVTJ6Usu9+yjzb2K56WtKj1S9tOPrtcTm16cgZB7v/aqNpxbp2UTflJ02D3KXWmluNQmX4w/Tpk3yy0xFMblB2hkY+5YKfvtbxf7oAcr155Tz1MaXfM/dDGC1KtT65ba/pzbY1MZ1UFu24be1qWcbXz4PHss66Y+OD6cYK9mXRWU3fhkP60SyZgaurq6hAZGYlXX30Vl112WUN9/PjxKC0txRtvvCHGOf/889GvXz9kZWU11F588UVcd911qKyshM3WOPe0eKcsRBwOB8aNG4fc3FxTBHNeXh58Ph/Gjh0LANi4cSMeeOABLFiwoNE2KhERERHR8bAZRrN9BMvpdKJ379748MMPG2p+vx8ffvgh+veXbw4DQHV1tfgf3W4/8AZlY97L4lVBCGVkZGDz5s1YtWpVQy0nJwejRo1CXFwcPB4Pxo4di6ysLJxyivx9reMR7BceiYiIiIh+L6ZPn47nnnsOL7zwAjZt2oTJkyejqqoK11xzDQBg3Lhxpo8+pqenY968eXj55ZexdetWLF++HHfffTfS09MbLs4aw5Hv+1PQunXrhgEDBiA7OxuDBw9GYWEh8vPz8cADDwA48PnXlJQUXH311SGf98yZM3H//eYfOr7zrrtMH6UkIiIiIvo9GTNmDPbu3Yt77rkHu3fvRs+ePbFs2bKG8I+ioiLTnbEZM2bAMAzMmDEDP//8MxITE5Geno6HHnqoUfvJi7IQc7vduPHGG/HMM88gJycHnTt3xqBBgwAAK1aswNdff41XX30VwH9vgbZq1Qp33XWXuKg6GpmZmZg+fbqp5ufXBYmIiIjoGBj24D8m2NxNmTIFU6ZMUZ9buXKladjhcODee+/FvffeewJ69qv5ntC5/Q6MHj0a06ZNw6JFi7BgwQJMnjwZxv//7OuSJUtQ86svt65btw4ZGRnIz89H586dj2u+LpcLLpf5S+LBBH0QEREREVHT4kVZiEVHR2PMmDHIzMxEeXk5JkyY0PCc9cKruLgYAJCSkmJKX9ywYQOAA8mNe/fuxYYNG+B0OtG9e/fj6puW+mZNOAQAvytGtvtcptMEev3RNFxryITAOr9M57t59zpRc/zyo6jVtz7DPL/8+bJfF4yX0xIVoNqnpG9Z+lt813WizWlTp4malrQ4NV4mSj75kbzNXdW7k2nY61dSqiC/C2gv2ylqvrh2onb7L+a+BZQ0NG0/qFLWDyC3Xfx2yw8txrQSbbwtk0Vty43/I2pZJeuVeZpV+uVnt6Pswd0B9htyXGsioJbq59i3RdRsdTIpzBctl90fHmuZoVyHWnqk8ckSUQs7pYuo1bVXfibDkgqmLbeW4BemHHNamqZI3Cz7RTRxtj5V1LTkUn9kgqxZ+uuol+va/+8XZb9syuf6h2aIkt1TaRo26qrl9K3bDYA/LFxOX2E9nuxK6mrx/CxRazH1EVGz18u+2av2mYbr170n2wy6UtS09a8lzdW/8aSohf1pqhzXQksarct7XNRcnU4XNXu3c83jJXQSbb6/apSo1ZbLc2Ovd5aJmmHZh8Jqy0WbgEO+XhnacR7bVo5rWXbnVvma5u3UW9S01znPFzIh1/HHyebhkiLZV+X8r9ESPQ3LseNTXofsyvYN9sM30RXyp4EQbX491xIxVcr5TGP3yzREq3pD/ndgCzKZU56nlJRhRSAsUtQM5TgH4oOaHp3ceFHWCNxuN+bPn48RI0agXbvgTpy/1qtXr4a/169fj0WLFqFTp07Ytm1bCHtJRERERETNAS/KGkH//v2DiswcPHiw2o4/HUdERERE9PvBSHwiIiIiIqImxDtlRERERERkYjuJ0hd/C3injIiIiIiIqAkZAX6B6aRVWW1OknIob3jYakpEzRvRQtTsXhmvb6spNQ3XRrcRbZRQJ0RW7RE1wyeTk6ypV7bKYtmHatl/+6nniNr+Cpl2FGVY5vmZTJh0tG4vaoEEGd7i+/ELUZs25C5Re6Rio2nYUBKuokq3iZqWmuaLThQ1myVpzvflctEm8IerRK2m3i9qdpvsW2S1JVnOL1P9/Eoqob18t6hpHEnmhK59ynaL8SvpeVrSXBACSoKf3bJfA4DhlSmWWmIfLNszYAvuwwjWhD0ACDhlapc2PWuCpOH3BjWeNbUR0JMD7RXmtEW/K0q0MXzKPJX+1yd0lO0s5wgt+cyxf5uoQVkm7ZgIhpo6qRxzWuKgYTkG1HVY+rOo+eLlucUfEa9M37xujRqZ+haIiBM1bfsG7DJxMGzXt6LmtSbfKuNpyaVhuzfJfijHmLeFOW3R55BJl67Nq4OaVv2p58pmdVXmgpZCqxzTAVe0rAVxzGnnN19Ma1FzKCm6hvLaWt/KnFgplgf6OU97HVXPB9Z9W9v/Ncr01QTbUpm+6GjX1TRcVyZfz7Vpqfuxsk1stebjIujtG+Q8ZRt5ntLWtTqqMk9XlEy9bg7eS1YSf5uJP277qqm7EHL8+CIREREREZkYdn6g7kTi2iYiIiIiImpCvCgjIiIiIiJqQrwoC5H09HQMHz5cfS4/Px+GYaCgoKChtm/fPnTo0AGGYaC0tNTUfuHChejRowciIyORlJSEjIwM7Nsnv3NCRERERNQYDLvRbB8nI16UhYjb7cby5cuxY4f8gmtOTg7S0tKQmppqav/r4YNWr16NcePGwe1249tvv0VeXh4+++wzXHvttY3afyIiIiIiahpMXwwRr9eLDh06YMqUKZgxY0ZDvbKyEklJScjKysKkSZMAAPPmzcPixYtxzz33YOjQoSgpKUF8fDwA4LHHHsO8efOwefPmhmk89dRTePTRR9ULvsPxVJSahv1hMuEq2NQ97U0JmyWJLLutTOm5btsqUQs4ZXrb8iKZqNe3nTkpyal0QksvjIuSqVSzY7qI2pSi/5iG9xky/ejbvbJf57eT67HKL1PBlNWIO2K6m4bn7lsj2uw1ZJJawZ5KUUttI5OkWlpCncplWBZiXLKv8xLlGwRTdsi+/eIzL7u2fnq1ldtXE+eQ+541geqRqDNEm2t3y8Qlbf90KBvAus8quw+8SmSoliIapm1gC236hnLKrfXJmlP5gnWNV66zCIe5Xa3WJkxOKyJc7sdEdHIpq6oRtWrL636kcn7Qzm8+5dxlV05y63bK16tBnc2pvFoisva/h3Y+1vpb5jGnoFbWyfNgi/DgXqe1/yusrzHRkceW+Ptb834XmWbdXAz7QaZe/9bxTlmIOBwOjBs3Drm5ufj1dW5eXh58Ph/Gjh0LANi4cSMeeOABLFiwADabXP39+/fHTz/9hHfffReBQAB79uzBq6++ihEjRpywZSEiIiIiohOHF2UhlJGRgc2bN2PVqv/eHcrJycGoUaMQFxcHj8eDsWPHIisrC6eccoo6jYEDB2LhwoUYM2YMnE4n2rZti7i4ODzzzDOHnbfH40F5ebnp4fF4Qrp8REREREQUerwoC6Fu3bphwIAByM7OBgAUFhYiPz8fbrcbAJCZmYmUlBRcffXVh5zGxo0bMW3aNNxzzz1Yv349li1bhm3btjV89PFQZs6cibi4ONNj1uNPhG7hiIiIiIioUfCiLMTcbjeWLFmCiooK5OTkoHPnzhg0aBAAYMWKFcjLy4PD4YDD4cDQoUMBAK1atcK9994L4MDF1cCBA/G3v/0NqampGDZsGP7+978jOzsbu3btOuR8MzMzUVZWZnrcdsvNjb/ARERERHTSsdmNZvs4GTmaugMnm9GjR2PatGlYtGgRFixYgMmTJzd8aXTJkiWoqfnvl27XrVuHjIwM5Ofno3PnzgCA6upqOBzmzWK3H/hy6uEyWVwuF1wuc8qDp0J+0ZWIiIiIiJoXXpSFWHR0NMaMGYPMzEyUl5djwoQJDc8dvPA6qLi4GACQkpLSkL6Ynp6Oa6+9FvPmzcOwYcOwa9cu3HTTTejbty/atWt3VH2xVZeYhgMxrWV//TKZKaDtFlqMnEX6lvWi9u01l4tayqLXRG3TLxWidlEH80Vmnc0l2kSUFolahSG/rzflp49l3yaYP0baYv5S0ebHfVWipqUvaglRLoe8EW1NW5zacoBo80jFRlHbtFemWV3YUn5n0O9MMA23LN0i2lS2kkmUN275t6h98Ze/ilqHV942DX9XLPvVp51MhazT4gsV1qSw6TvXijbVyq4YWVcqiza5H9eEmdMdtYQxLU3Mr6WO+eT699nN+6iWtIiAlngq9xVbwCdqjiDaacd0jU8mYv4+ssOIft98yrm3hd0cy2vU1Yo21WGxoqYlwtYr099WKs9BgyzDWtJiOLyiZtTL/w089gRRawHzPFvVy992tVXIaZW37i5qEco8rf9PIbKraEN0vHhR1gjcbjfmz5+PESNGHPWF1IQJE1BRUYGnn34at9xyC+Lj43HBBRfg0UcfbaTeEhERERGZGcobgdR4eFHWCPr373/YjxoeNHjwYLXdjTfeiBtvvLExukZERERERM0ML4GJiIiIiIiaEO+UERERERGRycmacthc8U4ZERERERFREzICwXz5iX6TPFXmBKGAkkbnf/spUbMPv05OTEmMMyy1UiXPTQvlT3DIatjPBaJWn3SmeVphMvVQo+3RJbUyyS7M8pZE9AfPiDbOLj1Fzdf5XFEzfPWiZqvcK2p7Is3JkFFh8l2oO2JkGtRTn8yR83TK9eE7vZ9p2F74iWhTnzJY1Mo82vqRfdtzwxjTcNfbbpJ9aJcialoSYiBM7i8+W5hpWNtuCeF2OX2FT0tMtCQr2hBcOqJ1XweAgBG697QCNrlMWmqaTUmGtJa0JFAtUVJbdnvZTtk5pW9WpeEy2TXWLrddmVeuM2sKm/a+rJbUFvauPHeFnzNY1Cranm0adhlyW3oCsl8uZfP6ld7V+czTC1/zkmhjT0gUtcDpfUWtxhknak7LO9U25Vzjt4eJms0r00HrbE45fX+dqDl2mM/HviR5TNeFyURP21tzRM2IkO0CFymvMRZ2v1xO7ZjTjh2rsH1bRc0fIde1N6KFqGkpqNZ5aq85WmKr88u3RM2R2F7Uytv1Mg1HBOQ2Cmjb3JoQCMAfKZMKw/aZU3m9LZJFG3uJTDb+avJNonbm4tdFzfhkiai5LhhnGq6plYmP2r6t/d8SUM5ngXeeNk9/1x7RJmHEGFHztDtL1Bxl8jdhA+Hm5N4yI1K0iQtUi5p2nFTXy3NQ6zjZrjlYkSrPU83FBQWfNXUXQo53yoiIiIiIiJoQL8qIiIiIiIiaEC/KiIiIiIiImhAvykIkPT0dw4cPV5/Lz8+HYRgoKCiAYRji8fLLL5vaP/PMM0hJSUFERAS6du2KBQsWnIhFICIiIiICABh2o9k+TkaMxA8Rt9uNUaNGYceOHejQoYPpuZycHKSlpSE1NbVh+NcXcPHx8Q1/z5s3D5mZmXjuuefQp08ffPbZZ7j22muRkJCA9PT0E7IsRERERER04jB9MUS8Xi86dOiAKVOmYMaMGQ31yspKJCUlISsrC5MmTYJhGHjttddw2WWXqdMZMGAABg4ciKysrIbaLbfcgk8//RQff/zxUfWporrGNBwTKdPuGtvTa2XqlbtXkqjNS0wVtSnbV5oLdpkc5nVGi5qjrlLU1hbLvq3fWWaeH9aJNosuu1/UrtjxhZzn/m1yBkpS2PLyeNPwpr2yrzc6v5a1fjeJ2lO7PhQ1X2zbI/bLH9NG1D7eK08D3xdXidpEY4Np+PEL/iba3Prj26Jm1Mukrbo2XUXN7jGvD61f58fKflVFyvQ/TVT1L6ZhX7RMxdOSNI16JVUrXEk1syTZBZR9Vkvwc9SWilrAqaTbKelq9dHmZa/1ymSvaM9+OX1Lmhig99dRusM0rK2zSsjxouxy22nrttbmMg17tP47ZcKeEsAWVGJlvdJGSxrVaIme1jG1pEhNsK+81hS/sIBXaSQTAuvtLtlO4VAych2WFE5fVEvRxhcm0+fCyn4WtYBD9sOaCKglKNrq5DGn0foRDG3/Udsp69baX0PZmPXK9g2vlImA2o5QG2M+jzuVbW5NqgX0xEpb1T45S4f5eK1xyXNZVPkOUTNqK0RNS3d8u8elovbn3d+ahuv3ynRH7dxSoazIOG+pqNnLzef2gFP+v+NNOEXU7BVym/iV/b3Wcg8j3C/TTbWdSkuP1IRHNs/0xY96ybTp5mLIl582dRdCjh9fDBGHw4Fx48YhNzcXv77OzcvLg8/nw9ixYxtqN9xwA1q1aoW+ffsiOzvb1N7j8SA83Bx1HhERgc8++wz19fKES0REREQUaobd1mwfJ6OTc6maSEZGBjZv3oxVq1Y11HJycjBq1CjExR34TZQHHngAr7zyCpYvX45Ro0bh+uuvx1NP/ff3doYNG4bnn38e69evRyAQwOeff47nn38e9fX1KC5Wbvf8fx6PB+Xl5aaHx6O8k0NERERERM0KL8pCqFu3bhgwYACys7MBAIWFhcjPz4fb7W5oc/fdd2PgwIHo1asXbr/9dtx2222mjyrefffd+OMf/4h+/fohLCwMI0eOxPjx4wEANtuhN9fMmTMRFxdnejz+WNYh2xMRERERUfPAi7IQc7vdWLJkCSoqKpCTk4POnTtj0KBBh2x/7rnnYseOHQ13tSIiIpCdnY3q6mps27YNRUVFSE5ORkxMDBIT5eetD8rMzERZWZnpccut8vs+RERERETUvPCiLMRGjx4Nm82GRYsWYcGCBcjIyIBxmG8Ub9iwAQkJCXC5zF+GDgsLQ4cOHWC32/Hyyy/j0ksvPeydMpfLhdjYWNPDOk0iIiIiImp+mL7YCCZOnIilS5eivLwcRUVFaNeuHQDgrbfewp49e9CvXz+Eh4dj+fLluPXWW3Hrrbfi/vsPpPz98MMP+Oyzz3DuueeipKQEs2fPxvLly7F+/XokJycfVT9qq2VKnZVdSWbafv+totZx5j+POK0yv0yDinPIZC+NlhD1zXXXmYa7L1wi2oTt3iRq5W3OErVan5Lg5DInaNlry2XHAkr/lTSlgF0uu5aIVuwzXyi39srlNnZslNNPOkPUbkwaKmpPlm8wd+Ej+Rt3NUMmiprGqfwOSMTeH8zTd8n0y29vuF7UUnJekjNQ0imrLCl+lfVy/Wv9ildWf7VPtvNakvdinbIPhl9Jt1MElP6Hkt+QiXSVdXKfsi6Dlu6ojRdnyO+cet/5u6jVXjrdPF75dtFm/0vPilr8tXeL2uwvZHrkrWeYEyu3OmVC2vYymd45JEwm/fni24naWz+Zt+elp8qUs5U/y3XRr4NMpyytlevxi13mRLpL28hAplJXK2U8mbxaXS+n3yHWHPzUM0Im4AXs8pz0n33yoNCm3z1Rro8OUeZ96rsSuUwRYXL/P6PyB1HTzqE/xnQzDXeKkX211ch9JdgEXuv7oNZUVwCosMnUxuIaeey3jZJ9q6wzL1ONkhiqOcUut52tpkzU3i0zJxoO66QksVb8ImpaaqD2uhwPczJzXZicfphPHhNhe74TteLEs+X06+W2c7Yy/1TQ3nKZrhnjCO7f0Srl3C7OS8U/yRETO4nS/qgOoqb1H5ZjLOsL+f9CWod4USvzyH3qy59KRW3mJd3lPJuBlWn9m7oLhzT487VN3YWQ4++UNQK324358+djxIgRDRdkwIG7X8888wxuvvlmBAIBnH766Zg9ezauvfbahjY+nw+PP/44vv/+e4SFhWHIkCFYs2bNUV+QEREREREdK9tJ+iPNzRUvyhpB//79od2AHD58uOlHozUpKSn48ssvG6trRERERETUzPA7ZURERERERE2Id8qIiIiIiMjEsPHjiycS75QRERERERE1IaYvnsRqas2JZRHh4Ydo2Xi0BMg3kvuI2p+KvhA1m9ec/mT4ZAKYluxVaZdpXJF2uZsb1nGVtMSAQ/6swKwWMm3q9l8+FzWbkubos6RjiT4cqh9KuqO2PqbF9jQNz923RvYhsoWcljJPtW+WWkBJQzO8MrVL207vdjlf1IYUrjMNh9uU01MIUw8Dys9VGMop8XjaBTNek1C2iVd5ny7MXydqVsX18kMXrZxy+oZHps/VuMxJc+F+uf+o6abHStt/tH092P1MG9c6KaVNY6d3Hg9rf9W+KrVjTi4NdpsEyzhyIqkNTXAcBrtM1vWh7T82mc6qnceD2o+D3f+DbOdRmsVFRZiGa2tqZKPj6dsJnpaaDqo2VD6Q5pXnVGs6ZXOR339gU3fhkM5fu7qpuxBy/PgiERERERGZ2OzN982jkxHXNhERERERURPiRVmIpKenHzLuPj8/H4ZhoKCgAACQm5uL1NRUhIeHo3Xr1rjhhhsa2t53330wDEM8oqLkjzsSEREREdFvHz++GCJutxujRo3Cjh070KGD+bPBOTk5SEtLQ2pqKmbPno3HH38cWVlZOPfcc1FVVYVt27Y1tL311lsxadIk0/hDhw5Fnz7ye1hERERERPTbx4uyELn00kuRmJiI3NxczJgxo6FeWVmJvLw8ZGVloaSkBDNmzMBbb72FoUOHNrRJTU1t+Ds6OhrR0f8Nqvjqq6+wceNGPPvssydmQYiIiIiI6ITiRVmIOBwOjBs3Drm5ubjrrrtg/P8Utry8PPh8PowdOxbvv/8+/H4/fv75Z6SkpKCiogIDBgzA448/jo4dO6rTff7559GlSxecf75MqjsSa8qbp6JUtNESAv0RcaIWUBKE7OW7TcNV0UmiTWRdmaj9+cf/yL5apgUA9spi03B9m66izf65d4ha/M2Pi5qWs+X1mz+9G16+Q2klZRatkNP3yTQl35fLRa383CtMwy1Lt8hp7d8lZ5oo94/6Lz4UNWva4tSWA0SbJ2q+EzUtybHeLpMnnR5z4pStfr9o449MEDUt4eqSjXL9eC1fKi6vk2lisTLwUU3VUhPvLPuxloSoJchpqWZqIuaxfiL8WFPZgp1WkAljrso9ouaLaWMattXKYzohPF7U6gNynk6lH3ZrMJ7Sf78hk+bCyuVxEnBGiJrX0je7T6Y7+pR9XUvn09I1AzD3zVn6kzKeXG6/JYkVAHwOmZBrC5j3PVudTLT1O+XH27X936/ts8q+bas2H+e+6EQ5LSXR0Fkqz6Ha8VTf6nTTsLZe7VVKup1dvg4FlGW37kMOZZ1pqXjaetRY+6ueH5R0REfFXlHTzr118ebzvT3IVEttWtp5ymZJQQ0o+x2UbRL2y4+iVtc2RdSiypTX0ijL63eQ57xgl8lRUmQer06mO2r/2/hi28p5KgnC4nhSjmktjfh4Ukrp94ffKQuhjIwMbN68GatWrWqo5eTkYNSoUYiLi8OWLVvg9/vx8MMPY86cOXj11Vexf/9+XHTRRairk//U19bWYuHChXC73SdyMYiIiIjod86wG832cTLiRVkIdevWDQMGDEB2djYAoLCwEPn5+Q0XVX6/H/X19Zg7dy6GDRuGfv364aWXXsKPP/6Ijz76SEzvtddeQ0VFBcaPH3/EeXs8HpSXl5seHo/ye1FERERERNSs8KIsxNxuN5YsWYKKigrk5OSgc+fOGDRoEAAgKenAx/u6d+/e0D4xMRGtWrVCUVGRmNbzzz+PSy+9FG3atBHPWc2cORNxcXGmR1ZWVoiWioiIiIiIGgu/UxZio0ePxrRp07Bo0SIsWLAAkydPbvh+2cCBB34Z/fvvv29IaNy/fz+Ki4vRqVMn03S2bt2Kjz76CG+++WZQ883MzMT06dNNtYA/yO+pEBERERH9ysn6McHmihdlIRYdHY0xY8YgMzMT5eXlmDBhQsNzXbp0wciRIzFt2jT885//RGxsLDIzM9GtWzcMGTLENJ3s7GwkJSXhj3/8Y1DzdblccLnMX1avrZFfdCUiIiIioubFCASU+DE6LmvXrsWAAQMwYsQIvPPOO6bnysvLcfPNN2Pp0qWw2WwYNGgQnnzySVP6ot/vR6dOnTBu3Dg89NBDx9yPmtpa03BEuJKw1MgqquWFocOmJG15KkWt2h5pGg4P8h2bOa1SRW3K3gI5T6UfVlo6n5YUFmw7qzqfHM+pLKe2fiptkaIWEXbkTyTfHNFN1B6r2iRq2onBuu2C7VekQ0mtU9ZPVsJZpuHp+74WbbTtpq1/LR0uiE3SbGhnZmsSHwA49m8zF5QEMJ+S9AclwUxLWbWm/dWFyYS6MCXRUFPslfOMdpr32Yj6CtHG64oVNZ+ygsKU7avtB1Za0mLYbnlMaKypsFpSpNZX+zHujPV+OS2fUtOOuWDWRbAcNTJ51ROuJK8qrMuurQptmTS2INajtn21deGoLRU1e+lOUQuU/mIarus2WLRx7t8qanUtTpX9UPYN6zJp/fcqqycsIFP9tMRNq2DPNdq+ra1+W32tqLli4k3DtdUyEdOaoAjoyZBaiqLfKV93xLSU5dT6H8zreWmtXD/aa3eMVyZcl9piRK11XHDJnyfa2gsGNXUXDqn/ilVHbvQbwztljaB///441LVubGws5s+fj/nz5x9yfJvNhp9+krHKREREREQngs3O6IkTiWubiIiIiIioCfGijIiIiIiIqAnxooyIiIiIiKgJ8aKMiIiIiIioCTF98STmqTKnmGnJarVK+p8WehXuOPL1+4vteorahM0r5PTDZXLSxz9Xi1qPNuY0Ii1YMNInx9PS4f7ZpoeoTdmxxjS81x8h2mwpkSlSfdrKNKgqn5L0JypArmUd3bjl36LNPqdMytu4Vy5nzzZyOSPt5o1n+OpFm3q7S9RujUoRtSfLN4iaNT1PWz+nJcj1o227WCUUzGt5n+jpRJmkOfkXmaSpnca0lEbrvu1S+qWlmmknyWNNz9OS1LTjUOu/lkjntHwRu9Yrf59Qm1ZclNzfiejkUlqpvEZazjcu5fVdOzeW18lzizbupmKZutyvUwvT8P4K2S8tvVBLKNbOZ9Z2FXUyHbF1pPwfSJt+WBBJzzGRv4/z56cXDTlyoyZy7vKPmroLIcf0RSIiIiIiMuGPR59Y/PgiERERERFRE+JFWYikp6dj+PDh6nP5+fkwDANz586FYRjq45df/vtjlB6PB3fddRc6deoEl8uF5ORkZGdnn6hFISIiIiKiE4gfXwwRt9uNUaNGYceOHejQoYPpuZycHKSlpeHaa6/F6NGjTc9NmDABtbW1aN26dUNt9OjR2LNnD+bPn4/TTz8du3btgt8vP8tNRERERNQYbMr396jx8KIsRC699FIkJiYiNzcXM2bMaKhXVlYiLy8PWVlZiIiIQETEf78cunfvXqxYsQLz589vqC1btgyrVq3Cli1b0KLFgS/GJicnH1OfbFX7TMPe2LaiTaSvStT8YfILrIbPc8T5DSv8XNTWXHyRqPVf9YGofftLhagN6BBj7oNybvDbZdiFT7l+vWHnZ6K2qu9Q03DX1atEm++LK0Wtb2vtsJGpFdqXka3hIl/85a+izelL31H6IbdT33bRomYEzF9u1kI9tNAKLdRjWmxPUXuwbKNpeONeuX56to0UtXrly9SGr07UfDZzf6fvXCvaBOCV06qX6wcBuSNYQ2b8sIs22mtQvRKwEQb5RfJ6y4cPgg0DcSgzDfPL9aMJBMz7Y7R2TCvHCRGd/LRzS0TAfG4x6mRgU41TBnJFKIlNYcopbst+GeJhDfqwhlIdoAQZ1ZfJVq4YUQs3zOfjeH+paGOUyAASW4tTRc3ulevDXrnXXIg8Q7QhOl78+GKIOBwOjBs3Drm5uaYkuLy8PPh8PowdO1aMs2DBAkRGRuIvf/lLQ+3NN99EWloaZs2ahfbt26NLly649dZbUVMjTyZERERERPTbxztlIZSRkYGsrCysWrUKgwcPBnDgo4ujRo1CXJx812n+/Pm48sorTXfPtmzZgo8//hjh4eF47bXXUFxcjOuvvx779u1DTk7OIeft8Xjg8ZjvZtk8Hrhc8k4JEREREdHhGHbeuzmRuLZDqFu3bhgwYEBDKEdhYSHy8/PhdrtF27Vr12LTpk3iOb/fD8MwsHDhQvTt2xcjRozA7Nmz8cILLxz2btnMmTMRFxdnejw6d15oF5CIiIiIiEKOF2Uh5na7sWTJElRUVCAnJwedO3fGoEGDRLvnn38ePXv2RO/evU31pKQktG/f3nRnLSUlBYFAADt27DjkfDMzM1FWVmZ63D51cugWjIiIiIiIGgUvykJs9OjRsNlsWLRoERYsWICMjAwYli/7V1ZW4pVXXlHvoA0cOBA7d+5EZeV/AxR++OEH2Gw2ker4ay6XC7GxsaYHP7pIRERERNT8GYFfp1JQSEycOBFLly5FeXk5ioqK0K5dO9Pz8+fPx5QpU7Br1y7Ex8ebnqusrERKSgr69euH+++/H8XFxZg4cSIGDRqE55577qj6UVttSWEz5DV41fx7RC16/F2iZtTLNCVrAlKVXybZVdfLBLzEMJmeZy/8RNR8nftaGsmEw4CyTH5D9qOkViblWdU9NEnUOvx1lJx+98Gi5ti+XrZrI9OZ9tjNCVTWC3YAKJt2haidcYO8gLe1lalR3pbJ5unXySQ+X2QLUdtfI9eP0y77dndcd9Pw3DWPizbGqT1Frea9F0TNNeom2bcwc3JjmUf2K8EpSuo29ymnNi2JzMpWL5O3DL/cZwPa/mgL4mu6yj6rJVGq09L2d5iXya8st5YE6tz7o5y8R0lujDB/H9aokx+j9raW+7q9VN7ZtynTr2hzpmk4wlMi2mjL/cucu0Wt3bhrRa2+bYq54KsXbQIO5Q0sZZ6agOUYNlbI7/7WbN8uanEXXy5qdUlnipq9fLdp2Fa6U7TxdkgVNVuNTK3zu2Riq92S0gsARsnPR5y+9VgFgF13TBC1xJ5dRC18pPlc642Q5yTndytFLaCkBHp7yN8HtVm2sb2kSE5LSfDzh8fKdupxbj7f2D0yhdanret1r8t2Zcr6H5phHq9MbnN/VEs5nvI6bfjkucuaTKslMztKfxY17TXYen4AAHz2hii5LhhnGq4r/UW08TtlSqx2brcF5OtC2Bbz/xC+ilLRxujcW9R80Yly+vXyHGc9R2iv+YiR28SnbCf7L5tlrdv5cnrNwPp0maDdXPR+a3lTdyHkeKesEbjdbpSUlGDYsGHiggw4cFF2+eWXiwsyAIiOjsby5ctRWlqKtLQ0XHXVVUhPT8fcuXNPQM+JiIiIiOhEY/piI+jfvz8OdwNyzZo1h3wOOBAYsnz5yfcOABERERH9NtiUT8xQ4+GdMiIiIiIioibEizIiIiIiIqImxI8vEhERERGRicGPL55QTF88iZVWmpOY4qNlWlZje3rtVlGb2FMmPT2V2EPUpm1bYRo2lNQ0X0xrUbMpiYOf7pcJTl/sMqeTXe8oEG2eH3aHqF2zU6Yuhe0tFDUtyWtFqTld6rtimdo1JU6us6xzZTLkLZuXiZovzhwsY01uA4BAuEwd+3SfPPFu3Cv7do3/c9Pw1AG3iDZP//y+qBn7ZRJf/Wn9RM3mqTANf7xPbrd+7WX/a70y5dPjk6c2LblR9EFJ3jKUdDUtscya3Kil+lnTEgEgrEwmnfmVfdtnk+lnVuV1MpksziXXo716v6xV7JXzjG9vGrZVyjblsZ1ELcqmJJ4G5Hby2c3rSEtW0xLYglXvP7aXuDAlsTKYaWnjKSGr0F55telbJxcGuQ7Vc6MjXNaUmTp9HjnPmlLTsD8yQU7LLvdtZ+lPouZ3KYl64ebEPmuaIaCn/2kppfUtZQqtdTG180OY8s9mWJD/f4rETb/cZ73KB5Gc1TJp0XrOA+TxFGmX2y3YVEItOda6v5QiQrRJqFXSEaNkSqZm5TlDRG3YD1+YhmtqZZKmplRJTtbOZ879ltdNJb22Lr6jqAW7zioD5nNvNOS+qI2nJUoaXnnMuWLiRa052DBqWFN34ZB6LpH/a/zW8eOLRERERERETYgXZURERERERE2IF2VERERERERNiBdlIZKeno7hw4erz+Xn58MwDBQUFGDdunUYOnQo4uPjkZCQgGHDhuGrr75qaLty5UqMHDkSSUlJiIqKQs+ePbFw4cITtRhERERERHSC8aIsRNxuN5YvX44dO2SgQU5ODtLS0nDaaadh+PDhOOWUU/Dpp5/i448/RkxMDIYNG4b6+gNfvF2zZg1SU1OxZMkSFBQU4JprrsG4cePw9ttvn+hFIiIiIqLfKcNua7aPkxHTF0PE6/WiQ4cOmDJlCmbMmNFQr6ysRFJSErKyspCWloY+ffqgqKgIHTseSAH6+uuvkZqaih9//BGnn366Ou1LLrkEbdq0QXZ29lH1qbbanEKoJVxZ0+IAYMPlfxa1nkuWiJo1Wa7aF1x0VZSSWmRXEt2+uOZ/TcM9XnlVTswu0+iq/HI5a7xyN49wmPsbFZDrwqak7gXsMtUpoCQswZAnjTKvueZQktqi60tlP2plQtc3N04VtZTcxbIfFlWQEYTa+ol1yf6Hl+80DWtpU1Pay7Smp3Z/JGpaCmSZYU4I9SppdC4lNS3cIfuqpRDGOs37hpaKpyWpGVpqoJKEaE3y0o457Yxr9yvpecr0tXQ+p2V9eJSkOafyAqYtu3PLJ6K2o02aabhd/R7RxvvZO6JWM2SiqGnbyfXL96ZhX0yiaFPmiBe1FiU/ipovqqWoFQXMSX8dw+X2LfHLdR0eZBR0rSXls1VlkWgTsMtjriRCpneWeGTfOkWb9yHnjg1y+mEyPa+sVTdR046nOiWltLXdfC7cWCnPebFKAl6nCrlNtON8f6Q5JTbWKfcLLTlWO9944zuImjXlU0sqrKqXx0m1UotTzoNV9cf2b1Nrj1wm7TVmf8IZ5j4EqkUbLRG2OqqNqFnPDwBgs6QvWl+XACDOJs9JAeX1Vk3OrJDJjWGJp5j7WqO83kKuV4/cJFA2CWw1JeaCX46o7Ys1kMsUEZD/o8CynLtr5XrV0k3tyonW+r8HALSOU/6HaAa+Gv3Hpu7CIfV45b2m7kLInZyXmk3A4XBg3LhxyM3Nxa+vc/Py8uDz+TB27Fh07doVLVu2xPz581FXV4eamhrMnz8fKSkpSE5OPuS0y8rK0KJFcFG0RERERET028KLshDKyMjA5s2bsWrVqoZaTk4ORo0ahbi4OMTExGDlypV48cUXERERgejoaCxbtgzvvfceHA79d7xfeeUVrFu3Dtdcc81h5+3xeFBeXm56eDzytzCIiIiIiI7EZjea7eNkxIuyEOrWrRsGDBjQ8DHDwsJC5Ofnw+12AwBqamrgdrsxcOBAfPLJJ1i9ejXOOussXHLJJaipkT9Y+9FHH+Gaa67Bc889hzPPPPOw8545cybi4uJMj6zHHgv9QhIRERERUUjpt2fomLndbtx444145plnkJOTg86dO2PQoEEAgEWLFmHbtm1Yu3YtbDZbQy0hIQFvvPEGrrjiiobprFq1Cunp6XjiiScwbty4I843MzMT06dPN9UCPvn5eyIiIiIial54pyzERo8eDZvNhkWLFmHBggXIyMiA8f+/6FldXQ2bzdYwDKBh2P+rL6WuXLkSl1xyCR599FFcd911Qc3X5XIhNjbW9HC5XEcekYiIiIjIwrAZzfZxMmL6YiOYOHEili5divLychQVFaFduwNJU9999x169uyJjIwM3HjjjfD7/XjkkUfw1ltvYdOmTUhKSsJHH32ESy+9FNOmTcPUqf9N13M6nUcd9lFTa043MpRNLRKLAPjD40QtsCJX1IyhGabhaiXBT0v7iof8qKajeIuo1bfuYhounXe3nNYND8u+2uQN4PI6mcRkWFKRwl+fJdpE9h0qar42Z4ja5uny4vmMhx8XNWvqmBIshRaBKlGzl+0SNW9CR2VsCyUB0pqaCejrR0uGdLz6iGk4qs8g0cafeJqo3dh2iKjN3bdGjhuRcMR+xSipb1piYr3ynpPTZ/6epbYuHCUyPU9Lv/TFtBa1gNOcHqnti9YULwAIrHpR1MJOO0vU6juliVowSXPWVEhATzw16uWx6Y82pyE69m0TbbyxMkkQYeFyWkpKoJXNI9c1lPVY8+ocUQsffauoWRPjbNXKOS8yQdSCTc60JsY5f/5KtKlas0zUnH++SU5fSbcL21toGq5d85ZsM2KSnJayb2v7o6NU/oyL35JS54uIl9NS1kXJrGmi1qKHTIEMO/dS03BdvDyXFd14haiVbS8TtR5vvitq1tc1m0eeU/0umXYXCIuU7ZT92LrsDk+5HE9J5A1T0k0r1nwoauFX32Uadu78WrTxtpLnWS3ls9Ym9wNremGwCYdauzDldcJZsl3UHO26moYrqpX/A5Rp+ZT/IexKOy1J2qrerhwTSrsw5ZXZel7Vzqm2Om0/k4mPWjtnXCulJ03vm6suaeouHNJZC2Xq728d75Q1ArfbjZKSEgwbNqzhggw48J2zt956CwUFBejfvz/OP/987Ny5E8uWLUNSUhIA4IUXXkB1dTVmzpyJpKSkhsfll1/eVItDRERERESNiN8pawT9+/fHoW5AXnTRRbjooosOOW5ubi5yc3MbqWdERERERNTc8E4ZERERERFRE+JFGRERERERURPiRRkREREREZnY7LZm+zhazzzzDJKTkxEeHo5zzz0Xn3322WHbl5aW4oYbbkBSUhJcLhe6dOmCd9+VwUKhxPTFk5g1fVGjJTIGjNBFjdq8HlF7JqmPqE3eWyDHras2DRteuTxaqla9kkqopfMZfsvvuClttASz22JkKl5WyXpRs1ftEzWfllIXBL+SyqalTb17xkDT8CUbl8s+RMoUT3X9+OplRyxJf9r6sSkJfvDVidLUlgNEbU71Jku/5G/tqYmGmoBM0NIS9ay0YyJYwRw7QU9f6b+Wpnms46nrNph2Sv+rIFPfogxl/1FYt6faLyVVTmsXzPQbnbb+FYa6fyp9DWJ6wSZuBsu6brV+afO0+49tmwfTh0PRzo1ifsohpx2qx3PsByXIfcN6ntLOz17lPXWHluernTOC6Ucw5xro57yaejn9FjHmZMvaGuV1Qp1BcMt0rOdebbxg9gNDeU3TXjO1RFVth3TFxB9xnk1h47j0pu7CIXVfIJNoD2Xx4sUYN24cnn32WZx77rmYM2cO8vLy8P3336N1a5mkXFdXh4EDB6J169a488470b59e2zfvh3x8fHo0aNHKBfDhEEfRERERET0m+HxeODxmN/4d7lc6m/0zp49G9deey2uueYaAMCzzz6Ld955B9nZ2bjjjjtE++zsbOzfvx9r1qxBWNiBC+vk5OTQL4QFP75IREREREQmht1oto+ZM2ciLi7O9Jg5c6ZYhrq6Oqxfvx4XXnhhQ81ms+HCCy/E2rVr1eV+88030b9/f9xwww1o06YNzjrrLDz88MPw+Y79EwjB4J0yIiIiIiL6zcjMzMT06dNNNe0uWXFxMXw+H9q0aWOqt2nTBt9995067S1btmDFihW46qqr8O6776KwsBDXX3896uvrce+994ZuISx4pyxE0tPTMXz4cPW5/Px8GIaBgoICrFu3DkOHDkV8fDwSEhIwbNgwfPXVV6b2r7zyCnr27InIyEh06tQJWVlZJ2IRiIiIiIiaPZfLhdjYWNNDuyg7Fn6/H61bt8Y///lP9O7dG2PGjMFdd92FZ599NiTTPxRelIWI2+3G8uXLsWPHDvFcTk4O0tLScNppp2H48OE45ZRT8Omnn+Ljjz9GTEwMhg0bhvr6A18Qfe+993DVVVdh0qRJ+Oabb/D3v/8dTzzxBJ5++ukTvUhERERE9Dtl2G3N9hGsVq1awW63Y8+ePab6nj170LatHr6WlJSELl26wG7/b+hOSkoKdu/ejbo6GfISKkxfDBGv14sOHTpgypQpmDFjRkO9srISSUlJyMrKQlpaGvr06YOioiJ07HggIfDrr79GamoqfvzxR5x++um48sorUV9fj7y8vIZpPPXUU5g1axaKiopgHEUyYnWNOZ0vMiL8OJfy6JVVyYQlp10ug72mVNQqHbGm4Ui7kpykpOk9GHemqN1Z8o2oRUdGiNqJpq2fMJtcP466SlGrtEWKmnVUp3LieqyFTI+cvu9rUfMpZwZr3xy1paJNmT1W1GLCgttvb4pMMQ1nVW0SbRzK+tHSsvxQ2oUuWDToRLdgBJv6Zq/cK8etsySQaql+YXJf0ZJA65VuOAPmFLxa5VPv2j6rpf/V+mU7v2We0fWloo2WGOq1jgggTEmfs54jtFXtLP1J1AxL+isABOxH/sR/bYvT5HhKO+2QCCYJzqOE0fmUdRFTWyyn75T7gdcZLedp6Ya2f4btlsdmZesUUVN2DdgtM9COG207+ZWiXZmBtZlN2QLa+cG1ebWcaUwrOe7ureb5ndFX9mv3D6LmObWfnJayTDbLCtH671XWj6t8p2wX3142tNDWtXb8BpNeCwB2j3y9csaZ16OnfL8cT0ks9ocpr9MOmcbqC5evO1YhPWcHmYipbZOAK0rUnAnHlszc2L6beFlTd+GQuj3/etBtzz33XPTt2xdPPfUUgAN3wk455RRMmTJFDfq48847sWjRImzZsgU224Ht+uSTT+LRRx/Fzp1ym4YK75SFiMPhwLhx45Cbm4tfX+fm5eXB5/Nh7Nix6Nq1K1q2bIn58+ejrq4ONTU1mD9/PlJSUhpSXTweD8LDzRdPERER2LFjB7Zv334iF4mIiIiI6Ddt+vTpeO655/DCCy9g06ZNmDx5MqqqqhrSGMeNG4fMzMyG9pMnT8b+/fsxbdo0/PDDD3jnnXfw8MMP44YbbmjUfvKiLIQyMjKwefNmrFq1qqGWk5ODUaNGIS4uDjExMVi5ciVefPFFREREIDo6GsuWLcN7770Hh+PAO7DDhg3D0qVL8eGHH8Lv9+OHH37A448/DgDYtWvXIeft8XhQXl5uelijQomIiIiIfk/GjBmDxx57DPfccw969uyJDRs2YNmyZQ3hH0VFRab/sTt27Ij3338f69atQ2pqKqZOnYpp06apd9VCiRdlIdStWzcMGDAA2dnZAIDCwkLk5+fD7XYDAGpqauB2uzFw4EB88sknWL16Nc466yxccsklqPn/P6R47bXXYsqUKbj00kvhdDrRr18/XHHFFQDQcAtVo0WDPsaAECIiIiL6nZsyZQq2b98Oj8eDTz/9FOeee27DcytXrkRubq6pff/+/fHJJ5+gtrYWmzdvxp133mn6jllj4EVZiLndbixZsgQVFRXIyclB586dMWjQIADAokWLsG3bNuTk5KBPnz7o168fFi1ahK1bt+KNN94AABiGgUcffRSVlZXYvn07du/ejb59D3xe/bTT5HcVDsrMzERZWZnpcevf/tb4C0xERERERMeFv1MWYqNHj8a0adOwaNEiLFiwAJMnT24I56iurobNZjOFdRwc9vvN39622+1o3/7AF3Rfeukl9O/fH4mJiYecr/Yr5tagDyIiIiKiYBiH+YQWhR7TFxvBxIkTsXTpUpSXl6OoqAjt2rUDAHz33Xfo2bMnMjIycOONN8Lv9+ORRx7BW2+9hU2bNiEpKQnFxcV49dVXMXjwYNTW1iInJwf//Oc/sWrVqoY7ZsHyVJaZhv0O+fsNHq+M8lKCvBDuOPKB+VybHqI2uShfTt8pk4c+/lkmnaW0MieFRSlxZVoapRLuiCdbpYra33aak7b2BmS/tpTIC9u+bWTyU6U/uFvaz7U1r6PpO+Wvye/1y7Sp7/fJ9XN6C9mudbh54cvrZR8iw+S2fDpRrh+tb/sC5nluKj7ydgP0bRLnkPtenWF+n+hvUTLN7dFKmfqmnca0lEZrqpmWvKWl+mmsCXLB0pLUapWoSy05s84n15m1ndZGWxfNIX2UiBqXlvBbY3ndj1Be37WUZC3lU1Pwi3xdGJDc0jS8v0K20eZZp54bZbvqevMyVdbL82DLCHkfQnvt0BI9rZXfy/nz++sub+ouHFLXfy5t6i6EHC+BG4Hb7UZJSQmGDRvWcEEGHPjO2VtvvYWCggL0798f559/Pnbu3Illy5YhKSmpod0LL7yAtLQ0DBw4EN9++y1Wrlx51BdkRERERET028CPLzaC/v37q+++AMBFF12Eiy666JDjtmrVCmvXyjsUREREREQniu0ofqSZjh/XNhERERERURPiRRkREREREVET4scXiYiIiIjIxODHF08opi+exOr27zQN+yMSRBvD6xG1WptMaQyHV9QChvlg3ScnpSYmhivJSfaKPaJW7DT/BEB8uEw4NPw+2S+bbKclOEWV7zAN18R1EG0q62SCU4IMX1TZ6mXqVYXNnEyopRJqqXthFbtFbZ9L/kRCvPPIJ9CAkhqorZ9gtrnfkOu6Vkn0jFASHw3l1GPtm9av26NlIuNTuz8StZJ/zRG1H1//wjR8zr+XH1O/gOD3vWCmrx2H/lULRe3mdPmD8E9WfGXuw5pXRZv8aX8XtYFfrBa1klq5TAmW486n9H9PldxX2kWHiVp2W5nQ+j87N5iGI2r2iTY3tz5P1LKqvhM1v9K3f7XrZRoev2uDaKP1a+KuL0TNVlsmaltuu940fMpTL4s2Vc9milrhW3L6Rd/sFbX0ZyaYho30qaKNtk/V5N4vaj9//LWofb5si6hd9e3b5unv2SzabH7mWVHrNOdfogZDHvubxo40DXd7+U3RxlYvk28Nv9zPfK5oOU8L185vRM1fVixq+5a/I2rxNzws+1Fj3g8+uWS0aNP/6TtErf6si2VNSTQs+z/zPtXqnnmizc5KGa2rHXNzW50tatO3m8+X3o8WiTa4VNnPlNerz4cMFbW+D/2vqLkuGGcavt0pf3f1gZKvRM0o+EDUPhr/kKj9oWCNeTzl/GzzVIja06f8QdQm7y0QNes+dH3nv4g2f/9enrO3zXtG1L56SS7nn3d/K2rNQeEUuW83F6c//UpTdyHkeAlMRERERETUhHhRRkRERERE1IR4UUZERERERNSEeFEWIunp6Rg+fLj6XH5+PgzDQEFBAT788EMMGDAAMTExaNu2LW6//XZ4vebPyb///vvo168fYmJikJiYiFGjRmHbtm0nYCmIiIiIiOhE40VZiLjdbixfvhw7duwQz+Xk5CAtLQ2BQAAjRozA8OHD8eWXX2Lx4sV48803cccd//1C8NatWzFy5EhccMEF2LBhA95//30UFxfj8ssvP5GLQ0RERES/Y4bd1mwfJyOmL4aI1+tFhw4dMGXKFMyYMaOhXllZiaSkJGRlZaGoqAjLly/HunXrGp5/6623MHr0aPzyyy+IiYnBq6++irFjx8Lj8cBmszW0GTlyJDweD8LCZLrSoXiqzElDAZv8BQQtzUqlJc3ZzX0pl2FQiPfLtCN/eJyoeWRgH1yWY87w1clGWlKeQ6ZH1iopfpG1+03DdZEtRZsarxwvRkmUVAXkQtX6zeNG1pWKNt6IFqJW55PTCrcph6416Uzpg5aGpq6f+nLZt/B483hK0mK4Q07fFpD7j9aPYPoVVSWTOm9sO0TUZr8sE8Cc511mGva2SJbdUpIQtX1KS/LyO6PM01LWvzXBEgDsVTJxUO2HXR7//m/+Yx4vIkq0sSWnyunXVYtaXZuuomZNHatNOku0KfPI7ZtYs1PUjFq5zioSu5mGtf3auUsmk1UnyVS58O9XiZp378+mYf/5V8nxdinpfBHyPKXV7Ds3mtu07CTaoEimHpZ/mi9q0d3lMoV1Mq+firayjcb54XOiZk9oLRva5P7os6wzZ095fPl+kumX3rTLgupb2DfmRD3v2TKVUEuU1M5nWuKpdVQtvdbwyRcsbf8MOCNkLcxcs+3/SbaJaytq/jA5LX+kTEV27jTvL54OPUUb9Zgr+VHUfLFymy/vYf5UzwXPTJT9qpLnf9ufbxW1sP3bRa2+hTwGIsLDzW1+2SbaBDavFzVD2b7GKWfKvlX8Yhr2JXQUbexl8pzkD48RNe11IWxvoblQI9fPg72uEbU73pDJqzblHO08/wpRaw42T2ue/QKAzk/KpNvfupPzUrMJOBwOjBs3Drm5ufj1dW5eXh58Pl/DhVa45cQUERGB2tparF9/4GTUu3dv2Gw25OTkwOfzoaysDP/6179w4YUXHtUFGRERERER/TbwoiyEMjIysHnzZqxa9d93a3NycjBq1CjExcVh2LBhWLNmDV566SX4fD78/PPPeOCBBwAAu3btAgCceuqp+OCDD3DnnXfC5XIhPj4eO3bswCuvHP73GDweD8rLy00Pj0f54TAiIiIioiMwbLZm+zgZnZxL1US6deuGAQMGIDs7GwBQWFiI/Px8uN1uAMDFF1+MrKwsTJo0CS6XC126dMGIESMAoOGjirt378a1116L8ePHY926dVi1ahWcTif+8pe/4HCfNJ05cybi4uJMj1mPPd7IS0xERERERMeLF2Uh5na7sWTJElRUVCAnJwedO3fGoEGDGp6fPn06SktLUVRUhOLiYowcORIAcNppB37d/plnnjlwQTVrFnr16oU//OEPePHFF/Hhhx/i008/PeR8MzMzUVZWZnrcdustjbuwRERERER03HhRFmKjR4+GzWbDokWLsGDBAmRkZMAwzOEOhmGgXbt2iIiIwEsvvYSOHTvinHPOAQBUV1c33DU7yG4/8EVXv18Jbfj/XC4XYmNjTQ+XS4YTEBERERFR8yLj+Oi4REdHY8yYMcjMzER5eTkmTJhgej4rKwvDhw+HzWbD0qVL8cgjj+CVV15puPC65JJL8MQTT+CBBx7A2LFjUVFRgTvvvBOdOnVCr169jqovhiUdLhAhU560JDhPQNZcDrmrBCwXm3abdtGo7GJaIqDy/kCVJfkwMkxJwKuvFTU/ZDqilp5qTd9Sgv5QXS/7GuOQ0/cbMiFKe8fDYbOMqyRi+oIMRK32yX5EWianpf/5lL6q6bLauJa+eZSVVqckdUaFyRmEKdO3rkclyBEl/5ojalrS4vQr/iFqT7xhSb26+Do5AyVpUd2nlKQ2vyvWNGxTji8tVdEfHitqWPemKFVulCl+MVfebBreNMkt2nS5+o+yHxddK2o/V8hEulNaJpuGtSTQncp4LeITRe2b666X01/4hmk40lcj2qz88xRR6/OJTFqsTxksah9fOdA0PPALmb74xZTbRe3sV5aKmlFXJWq7814yDSfc9qRoU/elTEL8OvdjUQNk7dz7rjYNe1vL9EuN/2eZNFfy0Sei1vKs00Qtarh5HQV2b5HTr5Hrwq+cu2yGPHZ8+2QaYlCCSGwNlq26RNQCHplI6kvsLLth2Q+8O7eKNvaYVqLmjZK1er9cZ1XLlpiGXRk9RJv9NfI8m9DiFFHzLc8WtQtfMKcovviXh0Wbq4vkJ3O0V+76L/4tavaBo2TDcHMaZfVbz4smEZfL43z73VNFrf3F54macaH5vKf9HxOlHL8Vb74gamHj7xU1p+W8/eGfbxJtbng4XdR2vvehqO356mdR67+i+aYc0onDO2WNwO12o6SkBMOGDUO7du1Mz7333ns4//zzkZaWhnfeeQdvvPEGLrvssobnL7jgAixatAivv/46evXqheHDh8PlcmHZsmWIiJBxukRERERE9NvGO2WNoH///ocM5VixYsURx7/iiitwxRV814SIiIiImoZhl5+socbDO2VERERERERNiBdlRERERERETYgfXyQiIiIiIhNDTQGjxsKLspPZMSZVuQyZsWSrKRM1f3iceTzl4K2xRYmaS4ZxIbJCpnHtC29t7oOStBiwh8m+Qn6fr175ip8/0pxGGWZNRgSQaI0zPDBXUbH7ZKJeQElWtCaR1YTFiDZhSlpZePUeUStxyXQ7WFICtT4ok4f2awvW7QvI9MgEpzKelu7oVVIyw8Jl3yzDNiX18MfXvxC1cxf/U9RE0iKAm0fONg3PqZYJhFo6ot0ml8kf1VLUbAFzIprh94o22j4bViSXqa6yVNS+e1mm56WNM6/HM++6WbT52C3TxPoPvUbUOkYoiZh283qM+eUH0SYuUib4+cPkcnpr5fqwWwNJK/aKNjansk8pO7JXSbKrKZXb0+p4/vHwlJpTOLUEwooiefxGJMj9v3S7PM/uXJ5vGk4aKtM1tXOezLDUFTz/kaj1u8S8b9hj4kWbb++bI2rd/yCTLTUvX/WEaXjMz+NEG6NeLoHhqxM17TwlziPVpaKNlrRY9an8zrerU29Rs/pp6dui1vEymY6IgaeKUjjkMVFabT5fxm+TSYjORNkvvzNS1Da/Kpep25N/Nw3/zzvyXOm1yxRa5aUDWX99QtTu+ECeG3G++Xvy3+etEU16jL5V1E6bOk3U/nOlbNfPkiYbUaek40bL18w1D78rasP6DRa1QIsk87SU5OGYrl1Erb5KvvaV7/hO1IgAfnyRiIiIiIioSfFOGRERERERmdhsvHdzInFtExERERERNSFelAUhPT0dw4cPV5/Lz8+HYRgoKCjA1KlT0bt3b7hcLvTs2VNtX1BQgPPPPx/h4eHo2LEjZs2aZXp+8ODBMAxDPC655JJQLxYRERERETUDvCgLgtvtxvLly7Fjxw7xXE5ODtLS0pCamgoAyMjIwJgxY9TplJeX4+KLL0anTp2wfv16ZGVl4b777sM///nfkIKlS5di165dDY9vvvkGdrsdf/3rXxtn4YiIiIiIqEkZgYASFUUmXq8XHTp0wJQpUzBjxoyGemVlJZKSkpCVlYVJkyY11O+77z68/vrr2LBhg2k68+bNw1133YXdu3fD6TwQW3fHHXfg9ddfx3ff6Wk8c+bMwT333INdu3YhKkomJB1OZbU5vcqupAsGy1B2k4Al/Uxr41fymrT0P8OvJFUdY3qktV+HbGfprpZghoBMowsoSXzHSjv6gu2Htn6C2Sba+jnWbadtN239BNuPY3Wsy3lTZIpo8/QXz4qav7pcTr/HMFH76Q5zAlhc5/aiTdz4v8l++WQCm09Jdwxm3Qa7fcMjIkSNiE4utTVHzuEM9pwRbDvt9So80vz/SzD9Oqp5BkF7TVNfb4PoR0S4TE89GRVlypTe5uKUmTlN3YWQ452yIDgcDowbNw65ubn49TVsXl4efD4fxo4dG9R01q5diz/84Q8NF2QAMGzYMHz//fcoKSlRx5k/fz6uuOKKo74gIyIiIiKi3wZelAUpIyMDmzdvxqpVqxpqOTk5GDVqFOLi5O+kaHbv3o02bdqYageHd++Wv9P12Wef4ZtvvsHEiROPOG2Px4Py8nLTw+M58u/zEBERERFZGXZbs32cjE7OpWoE3bp1w4ABA5CdnQ0AKCwsRH5+Ptxu+UOeoTJ//nycffbZ6Nu37xHbzpw5E3FxcabH449lNVrfiIiIiIgoNHhRdhTcbjeWLFmCiooK5OTkoHPnzhg0aFDQ47dt2xZ79uwx1Q4Ot23b1lSvqqrCyy+/HPRFX2ZmJsrKykyPW26V310hIiIiIqLmhRdlR2H06NGw2WxYtGgRFixYgIyMDBhHEVbQv39//Oc//0F9fX1Dbfny5ejatSsSEhJMbfPy8uDxeHD11VcHNW2Xy4XY2FjTw+VyBd03IiIiIqKDmvojir+3jy86mroDvyXR0dEYM2YMMjMzUV5ejgkTJpieLywsRGVlJXbv3o2ampqG9MXu3bvD6XTiyiuvxP333w+3243bb78d33zzDZ588kk88cQTYl7z58/HZZddhpYtZQJbsBwwJyAFIJPbbPW1ohawh8l2ngpR87tizMNGcKmEWnJSMNO3f71ctPGdfZEyA9kPn1+ZpzWp0Cu/g2d4KmW/lFQ8x74tQbXzuMzfP7RrF/VKcpXhq5fttBRIm/mQNvwy1U/bvl4tBVLpmnV/0abvD5Opfo5S+XMS3oRTZN8s68OrbLcwLb1T2XZwyDclrO20pMUp50wStYxhp4latzHfiFr7i88zDdui40UbW7UM9Sldmi1qsX0GiJq/5whRs+4vRpDBZHVlxbLoq5OTD4s0T1/bp2zypcReuVfOM77jEfulpaFpCWw25ZjwK/v2sY53rImh2jlVWxf1CUdeF1o/wvZ8L6fV+oygpqVOP5jtGUTSK6Avuxa3a52+lirqVJYTVfLYqT/1XDlLy36s7Z/6udEpasEk8AabQmurq5bTV/gsx5wtEFw6sXW5ASCgnQeDSC88ntRDbd0GM/3joiUUW9iC3I+17WlY7mHU1Mp9Pdj/p7RtxzRcAnhRdtTcbjfmz5+PESNGoF27dqbnJk6caAoC6dWrFwBg69atSE5ORlxcHD744APccMMN6N27N1q1aoV77rkH1113nWk633//PT7++GN88MEHjb9ARERERETUpHhRdpT69++PQ/2028qVK484fmpqKvLz8w/bpmvXroecBxERERFRYzNsJ+fHBJsrrm0iIiIiIqImxIsyIiIiIiKiJsSLMiIiIiIioibE75SdxLT0p2MdL6Ak6lkThLTUNI8SiOS0K6ldtUdOX/Tt23XEPhyK9g29Op+5c5FKYpSaCqmkKtrqauQ8XTIJy28J9/Ir3x0Mh5K+WC9TuwKW9QPIJCktRQpKGpS2fuqV5EOHZR1p6ZSGkvalbd9jpaZ2KfP0Q7azW/Ztf3W5aKMlLWa/L9M159x0hajV7TKnTEa0URImHeGi5oyJEjVbuKz5tGW3pI1qX0fVjk01rSzIlDrZSPbLG9dOaXhk2nZTc9qCSFtTRwv2nBHKdLhj7Ks2rro9lGXS1qPGjiC2r9Yt7aShLWeQqbzBzSBIQW7joLpxjK+jGuN49gMLdf9U0iODSREN+pwRZD+0hNNjpe4GWmKi9R6Dsq6DPbeo/wNZ+qEeXWrSaOj2Hzr58U4ZERERERFRE+KdMiIiIiIiMjlZf6S5ueLaJiIiIiIiakK8KAtCeno6hg8frj6Xn58PwzBQUFCAqVOnonfv3nC5XOjZs6doW1tbiwkTJuDss8+Gw+HAZZddJtqsXLkShmGIx+7du0O8VERERERE1Bzw44tBcLvdGDVqFHbs2IEOHTqYnsvJyUFaWhpSU1MBABkZGfj0009RUFAgpuPz+RAREYGpU6diyZIlh53n999/j9jY2Ibh1q1bh2BJiIiIiIiOjB9fPLF4URaESy+9FImJicjNzcWMGTMa6pWVlcjLy0NWVhYAYO7cuQCAvXv3qhdlUVFRmDdvHgBg9erVKC0tPeQ8W7dujfj4+OPqt92SjOd3yjQ3w1sra0qSnRFEmlK9TSY/uQwlbSogD3JvfAfZzsJ5eqqo1SkJSwHItCO7kooUZjOP67fJhMlAEP0CAF90K1Hzh8eKWpjN3A+7zyPnqSTg1YcniJpDS2m0REQFlKRFjbp+IJMbrdPzxbaVbbQkr5jg3lSwjqv1S0uUtNVViZpdS/S0JGcGegwTbbqN+UbUtKTFm/74sKg9s2Wpua/1MpWzNl++IeNMkvvZ1n8+L2odHz9f1KwcFb+Iml/ZPw2PXGd6IqMl3dEhj3MtndVRtlPUauPkcvos29x6jBwNbd841vQzLbUuuD4El2aoJqMqqYHWc68/Ik600RLw7Mo5W02eVJbTmhKorQmbNq0gUw+tyxT0NtLWWTDttH8ste2r7P+Gmv5ns7RRjhstaFFNpzzyOtNef/1KqqVNOd/4nZFKO/PrvrbPep3Rsh9BHpo1Dvm/hvWsoR5fyvqxa8ukpUFbp68skz2gHBNBvkbaLNPzB/l/knqcqGQqL/3+8BI4CA6HA+PGjUNubi4CvzqR5OXlwefzYezYsSGfZ8+ePZGUlISLLroIq1evDvn0iYiIiIioeeBFWZAyMjKwefNmrFq1qqGWk5ODUaNGIS5OvnN5rJKSkvDss89iyZIlWLJkCTp27IjBgwfjiy++OOx4Ho8H5eXlpofHI+/CEBEREREdic1ua7aPk9HJuVSNoFu3bhgwYACys7MBAIWFhcjPz4fb7Q7pfLp27Yr//d//Re/evRvmN2DAADzxxBOHHW/mzJmIi4szPWbNfjKkfSMiIiIiotDjRdlRcLvdWLJkCSoqKpCTk4POnTtj0KBBjT7fvn37orCw8LBtMjMzUVZWZnrcNn1ao/eNiIiIiIiODy/KjsLo0aNhs9mwaNEiLFiwABkZGTCC/ebrcdiwYQOSkpIO28blciE2Ntb0cLnkF1GJiIiIiKh5YfriUYiOjsaYMWOQmZmJ8vJyTJgwwfR8YWEhKisrsXv3btTU1GDDhg0AgO7du8PpPJA9tHHjRtTV1WH//v2oqKhoaHPwd83mzJmDU089FWeeeSZqa2vx/PPPY8WKFfjggw+Our8+l0xPsvK7YkRNS/LSEoqsKYF2JTXN65fX/VqinjUpEpAJS/7IeNFGTStTrpO1oCev5T0JNc1QSfXzR8h+aEmLwURV+ezywtkWkAljYf46UdNSGq3bzjiO913qlXHDLPO0pngBh9hXlAQwbT+zVtSALiWpTUsW9bvkNrGu2x23XSPatL/4PFGr27VD1KxJiwBww2mXm4ZvvvYc0abz5OtEzXDJ5K3ke4eKmldZdmuKmTdOvoGjJZ35o1qImsa6n2lJc1rqmJaoahNbGDjWsEVt/1fPB8eYoqjtnxqReKr0S0spDbof1sTT6ETZBzV18sjnh6Ap61WdVpBJdsHwxcrEVkN7DVBYk/fUhD3tde4YkzoR5LrWUkq1REwbjpyia/crSYJKIqDG2s6nJP8Fu6tox1d4QL5eAeZlV/cfJVEy2BRI6zGg7v9Bbl/teNLSFkUb5XXomI85+l3iRdlRcrvdmD9/PkaMGIF27dqZnps4caIpCKRXr14AgK1btyI5ORkAMGLECGzfvl20OZjqWFdXh1tuuQU///wzIiMjkZqain//+98YMmRIYy4WERERERE1EV6UHaX+/fubYvF/beXKlUccf9u2bYd9/rbbbsNtt912DD0jIiIiIgoNw8ZvOZ1IXNtERERERERNiBdlRERERERETYgfXyQiIiIiIhPjJP2R5uaKF2W/c1pqmpa0BSWNyJo4pX3VTktk1FiTFlV2Z1DTCpbomwyVgz88LsiJKYmDQSRDBpsMF9CWXUnBCyUtJdMSCqamfampY1pSXghpiYA2bf1bUtniOreX40XHi1pEm1PktOprRM2atvjEc1+INne2XCxq7f9nvJz+MW7f49mnbJ4KUfOHm7exUS/PGdb1CgB+Qya6Wfef46KdpzRBrEdtnQWdmmaZvrYugt7/g0i21JL4DK9HTiqYc+pxUPczbZtoy2RNR1RS8QIOZf/R1qM2fWuab5Dr4rj2gyCmpb6OBvFPr/Y6rSYtHus5I8QBgerrVTD9UNaZ1rWAWm16fqVfzbOn1FzxEpiIiIiIiKgJ8aKMiIiIiIioCfGijIiIiIiIqAnxoiwI6enpGD58uPpcfn4+DMNAQUEBpk6dit69e8PlcqFnz56i7cqVKzFy5EgkJSUhKioKPXv2xMKFC01tvv32W4waNQrJyckwDANz5sxphCUiIiIiIqLmghdlQXC73Vi+fDl27NghnsvJyUFaWhpSU1MBABkZGRgzZow6nTVr1iA1NRVLlixBQUEBrrnmGowbNw5vv/12Q5vq6mqcdtppeOSRR9C2bdvGWSAiIiIiosMw7LZm+zgZMX0xCJdeeikSExORm5uLGTNmNNQrKyuRl5eHrKwsAMDcuXMB4P+xd+ZhVVX7/3/vfQ7nMIMDKCg5pjijoAVlzmIp1+6lq1H3onLIq2lqNhhpaYORYamVVysVIiMVtbxl39RMjNLMVJzTICdUHJGZM//+8HextT9LOZgKej+v5znPw3qftddZe1qHffber41z585hz549pJ0XX3xRKE+cOBHr16/H6tWrMWTIEABA9+7d0b17dwDACy+88Kf6rWqMTQ6JtUtma5IZhHQSO5YWmcFJkdmmJIYuh0LbV53itA6jF6njqhlL2jet6UnWL8myUKWGLtcGCPKZLtqypP1wpX0XUV3V4mnmU9YvaVOS7ccV05m0X7JIZlqUGek0+4DfiOdIHbW8kE4nMcFVZq8iWauxo4WyzLT4xpubSPb+hBdJppacJZni35RkpI7dQjKp/U+2zUqNlZJ9WNu+bDrZ+v0fcJFJx4Ibrbcj7btoPbze32Fl45Sr9ss7kOsdZ282MmOrE5Lvbq0x1MXtQmrWlWR2xw1cPrLt2JXmXRyTXJ3WFVz/vrrzx0Hm+vjfHVVrgF6vR3x8PNLS0uD8w06dmZkJu92OuLi46267qKgI9evXvxHdZBiGYRiGYRjmNoQPylwkISEBeXl52Lx5c1WWmpqK2NhY+Pm5+CwrDStWrMD27dsxatSoP90/s9mM4uJi4WU20zMFDMMwDMMwDFMdiqrW2dedyJ05VzeB0NBQREVFYcmSJQCA3NxcZGdnw2QyXVd7mzZtwqhRo/DRRx+hQ4cOf7p/ycnJ8PPzE15vzX77T7fLMAzDMAzDMMzNhQ/KaoDJZMKqVatQUlKC1NRUtGrVCr169apxO5s3b0ZMTAzmzJmD+Pj4G9K3pKQkFBUVCa/nn33mhrTNMAzDMAzDMMzNg0UfNWDYsGGYOHEiMjIykJ6ejrFjx0Kp4Q2bWVlZGDJkCGbNmoXRo0dXP4GLGI1GGI1GITOXldyw9hmGYRiGYZj/HVRd9ZI35sbBB2U1wNvbG8OHD0dSUhKKi4sxcuRI4f3c3FyUlpaioKAAFRUVyMnJAQC0b98eBoMBmzZtwpAhQzBx4kTExsaioKAAAGAwGKpkHxaLBQcOHKj6++TJk8jJyYG3tzdat25do/5qLWBS85DEyKhzWEmmP76LZNbm3YWyxU7b10ksRrKNznDxCMnsGtNc7qR/kTotFy4jmVNi+jPbqMHJoFGq6myVtE7RSZLZGrQkmfITNfGpnXqTrMyjofiZkuui9So90NdXXCKZw+hDMoLUmkaXT6Vk3cn64eYQzX5upedpv3wCaTc2L6X96DOSdk2zjcr65eGkdkG1spj2w92XZG7Hd4p1GjQjdS6tXkIygw81fxqCqAlRMYqWxib/HEHqyEyL44MH0Owxelnz3Qs/JRlZx5J9TmaUVBw2kumKC0hmq3+XWKfkHKljDmxLMn3FRfqZMoulzkMoe9jKSB2n3kgzyXzK9n2dpUIoOwx0XcqWBWTGSgkyyyfpQ942kjlCOkoqGmhUfFqczuBN6tgl+5xaWUQyp5snbf/wj7Rvre+hfdOgv3CUZNaGdGyUrTv14GaxTke6/auHfiCZ+XAOyXSxz5PM4dVArFNKt1mnXrKsy+ljb2z1m5NMazl0O0lty3b/Ji59plpWQTLSf802AACW+i1IppOYUmWXQ0ltrNo+SEypsp+gdeV0P5f+G+8p7udaO/RV+3WdZl1ZHbtk/NFZy+lnykzVmn6oVrreZOvJrlmXAKCa6RiHoLtpxvzPwZcv1hCTyYTCwkJER0cjODhYeC8xMRFdu3bFBx98gMOHD6Nr167o2rUrTp06BQD4+OOPUV5ejuTkZAQFBVW9/va3v1W1cerUqarpTp8+jdmzZ6Nr165ITEy8pfPJMAzDMAzDMMytgc+U1ZDIyEhBi/9HsrKyrjltWloa0tLSrlmnefPmV22fYRiGYRiGYZg7Dz5TxjAMwzAMwzAMU4vwQRnDMAzDMAzDMEwtwpcvMgzDMAzDMAwjoOj43M2thA/K7mBUi2j4kdr6JLYmqenMr1H1nydRM+ldfGKAw8OPZFZF3Dybx/QkdaTWNJnxUdI5rW1RNt92P2rQkhkN3e5qQzKbgZrOtMZH1UmXv80hsedJjHEOiUVRRfX3I0olnC6YFgFqx5KZFu0qNVcZW1LTnFVqhhTnXbu8AMCxkRoIdeEDaVvb/0MiS+kl8eP6dyN1fLtHkUx1p8v/yIeLSNZ8ej+xfck8qiVnSSYzLb6fsZ9kc+LovKsPxAll3e8/kzr2c9Qiim7RJLIFUMOrYhHtZHafAFLnXDndD4Mk+/5vY/9JsuCFmWJgp9vd2ZQpJGs4bT7JZNt2wZvPCeWA6QtInZIPXyaZz79eJ5nMuGbf9IlYp38CbX/LdyRz2DeQrPICtYg2iOwhth/xIKkjG/OcOd/Svl6gdk1dS7rt0QnpOpEi6Ye8WvWabUVmKnSnY6oMYuaUGfzsdJuV7a/yD3ChnnSgpfPkNEqW2bbPxTqd+5AqhZX0u6OhTF64cy3NOvcXu3WJjg8yo6qUQ1tp+63Cq59OM48AgHtjSaQvOkUyx+/UBu3sFqMJ6DrSW0ppW1tXk0y5fzjJdGUXhPLPf6NjWffkcSQr3rWdZMc2UFtn+Jd0PGD+9+BDYIZhGIZhGIZhmFqEz5QxDMMwDMMwDCPAly/eWnhpMwzDMAzDMAzD1CJ8UOYCMTExGDRokPS97OxsKIqCPXv2YMKECQgPD4fRaERYWBipm5WVhaFDhyIoKAheXl4ICwvDp5+K94j07t0biqKQ1+DBg2/GrDEMwzAMwzAMU8vw5YsuYDKZEBsbi/z8fDRt2lR4LzU1FREREejcuTMAICEhAdu2bcOePfRGzi1btqBz586YMmUKGjVqhK+++grx8fHw8/PDkCFDAACrV6+GxXLlpuoLFy6gS5cu+Pvf/34T55BhGIZhGIZhrqCofO7mVqI4nTJFEPNHbDYbmjZtivHjx2PatGlVeWlpKYKCgpCSkoIxY8ZU5TNmzMAXX3yBnJycatsePHgwGjVqhCVLlkjfnzt3Ll5++WWcPn0aXl7UAHctLBdFa5HDox6po9jMJLPr3Umms5aTTGsrvECbgpcbVbC56yQmxJIzJLtgFC1vfkZq7FIk9kgZlQ76mV4V54Wy2Yta5cqt1ODkS+WCcruXZNmWQrRvyayQbrKslBr7Cg0NaN8MLgygEiuVbPl4wEon1djDHKDTWR10SJEJxmToSs8J5TKPhqTOCz7tSfbemSySFa+gdr5fl/0klLt9S41XimRIdCp0PmXbntMVq5ykfdm24thMTYtPx6SQ7N2L4jzZvk0ndb6dQLMBR34hWYmZzpO3ofp5OlVKt5UmPnRHSWscRrLHToomNa/ifFJnYpMBJJtVepBkqmQ9LQ0WP/Mfp3JInY8adSHZmHxqlVPN1N6W+/x4odzs3ytInfKPppIs78udJDu6m46DMfNHCmUlZgKpI9umKtJeIdnJH/aSrOgYNT72WC0aJXWl50mdHx+fSLLuG9eTTLZPFEx7Qig3fv0jUke1VpJMZtu1G71JpsXtwhHafmUJyfYmTSdZx1dfpP0weAjlXROTSB2/ZvT7NjiF7oey8bLo9SeFsswYerKE7nNNvenv7O8F0G174vEsoWz/YSWpg4GjaSZhZ3+6b0a8PoZkxt6PC+UphpakzmtnfyKZ8xdqj/x21ByS9Tu8TTOhxHxrpuv8w+a9SJZ4mu6bhvwcofxk28dJnX//toxkv899h2Rbl9H98J/n6XhWFyhOpWbauoLvqFdruws3HD4EdgG9Xo/4+HikpaXhj8ewmZmZsNvtiIuLu8bU16aoqAj169e/6vuLFy/Go48+WuMDMoZhGIZhGIZhbg/4oMxFEhISkJeXh82bN1dlqampiI2NhZ8ffcaWK6xYsQLbt2/HqFGjpO///PPP2LdvHxITE6tty2w2o7i4WHiZzZJTVwzDMAzDMAzD1Cn4oMxFQkNDERUVVXWZYW5uLrKzs2Eyma6rvU2bNmHUqFH46KOP0KGD/OGdixcvRqdOndCjRw/p+38kOTkZfn5+wuutue9fV98YhmEYhmEYhrl18EFZDTCZTFi1ahVKSkqQmpqKVq1aoVcvej1ydWzevBkxMTGYM2cO4uPjpXXKysqwbNkylw/6kpKSUFRUJLyenzS++gkZhmEYhmEYhqlV2L5YA4YNG4aJEyciIyMD6enpGDt2LBTJjeXXIisrC0OGDMGsWbMwevTVb6TNzMyE2WzGP/7xD5faNRqNMBpF8YbFRm9KZxiGYRiGYZjq4IdH31rYvlhDEhMTsXr1ahQXF+P48eMIDg6uei83NxelpaVYuHAhNm3ahOXLlwMA2rdvD4PBgE2bNmHIkCGYOHEiJky4YtAyGAxE9tGzZ080adIEy5ZRm4+rVFZUVF9JYihSJBlkpjmNfdEsmcwd1JblVOlvATKLnwpx01TsFlJH1pauuIBkVr8mJNNbxINWm4FavFw2CcqWmcTIqJ1P1emawU+2l2qXj6voL/xOMkuDVrR9Sd8cigt2QcnvFFJLpmT56C8eFftVvwWpo7NL7pX8eQ1tPqw/yZwas6jTzYPUka1L2TpxdZ5cQbG6sK9Cvg9MqH+vUH5pRjSpEzBpJsmckr7Klof2Mx2a/R4A7JL9xGAtI5lD0r52m5JtP6qdmuYcOmp31JVfJJnTzVPTB4ldVmJV3NCxL8kGrn6d9iP0AU1nJfuIZFuRjmcau6msPbtK51u2zNwKT9BuuPvQz5Stc8326DDS6ZQt1DLpuN816ZV2fcrWpcwoKUNqRtVOKxufJegLj5Psheb0GaGvrZoklNXekh9PZduBbJ+TfIdpLZOufme6FZ8mmd0nkNb7XbQc2pvQWyh0xdQEamnUlmQyS6ZsH/NwFzPLJWoUfiMoimST5g2jbT36LMkUi2iIdkqsnK7uc07J9uh2Llco2/3p/xSqxCL9bS+6bfTJ2UQyo48/yeoCJekzarsLV8UnfkZtd+GGw4fANcRkMqGwsBDR0dHCARlw+YCta9eu+OCDD3D48GF07doVXbt2xalTl9X0H3/8McrLy5GcnIygoKCq19/+9jehnUOHDuGHH3647vvVGIZhGIZhGIa5feCDshoSGRkJp9OJtWvpszOysrLgdDrJq3nz5gBQpdTXvrKysoR22rZtC6fTiQED6PM/GIZhGIZhGOZmo+jUOvuqKfPnz0fz5s3h7u6Oe+65Bz///LNL0y1btgyKouDhhx+u8WfWFD4oYxiGYRiGYRjmjmT58uWYPHkypk+fjp07d6JLly6Ijo7G2bP0Mto/cvToUTz77LPo2bPnLeknH5QxDMMwDMMwDHPbUJPn877zzjt44oknMGrUKLRv3x4LFy6Ep6dn1WOuZNjtdjz++ON45ZVX0LJly5s1GwJ8UMYwDMMwDMMwjICiqnX2JXs+b3JyMpkHi8WCHTt2oH//K/IvVVXRv39/bN269arz/uqrryIwMPCW+h1YiX8Ho1YWCWWHu59L01kVulno9dXbn0ot1DDmrqPWNJmBqtJOTVI6VcyMMkuVxMomo9JG7Vs+GuOaVe9F6hSZ6TwFutO+yqyEOgftW6VDrOftoNY9sxvth9VOTWTuerociZFRZh2T2KZky8fbTu15NoNoYSu10uncdXT5GHR0+cjsjtr+WuyS9respG1JzFUHx9CBtMPUp4Wyte0DpI4ikb7JRHD6EnrZg80vSNMWnVBmAJPtE7rf6fXulsM5JNPaFl+bsY7UmdeLzie6/4VEJVbaX1/NApGZFi9W0nXZWKXrzrJ8Fsmcw5KEsoeNbndlGbNJZhw5g7alp9a38qVvVjtdySe0X+0e6UgyxUDbd/6osRD2+iepo//1e5JV7N9OMoeV2mp9ej4olK1Nu5E6qsTEZ9tNDW9OCzXlGTtS453W2KezXSB1Kk4cIZlesp/IzJC6S/lC2dGAWlZdNdrK0BoZVSv99Vy2Hzp+302yNt50vHSUFQtl49nDtA9GyTjesDVtS2ZR3LdRKNu6DCJ1Cisk302SZaY/mEUyRTNenp37Em1rMv3nVob+2A6SWVveK6kpYv+emqXHvvIgyVa9sJpk/2zXmWTOruK0dh21xBpKz9F+HKLjrPPeWJJZD/0ilItyFpE6DfrR9XSxmG575pXvkMw46lWSMdcmKSkJkydPFjLto6EA4Pz587Db7WjUqJGQN2rUCL/++qu07R9++AGLFy9GTk7ODeuvK/BBGcMwDMMwDMMwtw2y5/PeCEpKSvDPf/4TH330ERo2bHjD278WfFDGMAzDMAzDMMwdR8OGDaHT6XDmjPgcuTNnzqBx48akfl5eHo4ePYqYmJiqzOG4fBZar9fj0KFDaNWKPtv1RsD3lDEMwzAMwzAMc8dhMBgQHh6OjRuvXBbscDiwceNGREZGkvqhoaHYu3cvcnJyql5/+ctf0KdPH+Tk5CAkJOSm9ZUPylwgJiYGgwbRa4UBIDs7G4qiYM+ePZgwYQLCw8NhNBoRFhZG6h46dAh9+vRBo0aN4O7ujpYtW2LatGmwWsV7jy5duoRx48YhKCgIRqMRbdq0wddff30zZo1hGIZhGIZh7lgmT56Mjz76CB9//DEOHjyIsWPHoqysDKNGjQIAxMfHIynp8v3N7u7u6Nixo/Dy9/eHj48POnbsCIOB3md6o+DLF13AZDIhNjYW+fn5aNq0qfBeamoqIiIi0Lnz5RtPExISsG3bNuzZs4e04+bmhvj4eHTr1g3+/v7YvXs3nnjiCTgcDrzxxhsALltiBgwYgMDAQKxcuRJNmjTBsWPH4O/vf9Pnk2EYhmEYhmEAQFGppOt2ZPjw4Th37hxefvllFBQUICwsDN98802V/OP48eNQ1do/T6U4nTKvGPNHbDYbmjZtivHjx2PatGlVeWlpKYKCgpCSkoIxY8ZU5TNmzMAXX3zhkrVl8uTJ2L59O7KzswEACxcuREpKCn799Ve4ubn9qX5bCguEstS+KDNcSZBZDp06sX9WyYlXiagNRsl2rys5Q7ILxgCh7O/i4lCt1GhYITErutvFenY3T1LHIdk99BKbmKumMK0VzCKxKrqp9AP0ZedJdsmtHsl8DdUPKopk+VTqPEhmVOg8OV0YoGUjitS0KDMOFovbrNmHXu+9Nfx+kvX5jpq8rNv/j2TbXv1MKN+b/R3tl4vrUnFQU55TYgh1pX21vJBWy9lAsnX/fJtkg37NEqfbRe2LE/tMJdmcCmqdcuXbQF95iWQn7XT/CvKky2x7v4Ek67RBnE/jd9RqtuDx90k2+gw15clY3DhMKD8hmW5di3CSPfTbjyRTK4tJtu+JRKF8d8YaUqcoZRLJ8r7eS7JjeXQ7ePDlh4Sy5xMzab+01lUARe89T7Kzu/JIdveIh2l794qZYikndXKnTCBZ8wUrSCbDuVZcn8rg8bQPMrOuZN9x6Ku/0V+nMe0CgCLJzi9OIVn98ZLlrdkOHPuzSZ1NJmoMfWDf1fXbf6R0/hSh7J9I9998Gx2zgyX73O6hQ0jWLeNjoezMpSZQW9hgkslMmjv7DyDZPfNeIJk+XGxvQzu6zz2wjY55ujxqR1zYeyLJTJr9WrVS06hszN7QsS/Jon9aTjLVLFphP+1BLauPrqXGyrPf0O+hWa+uJ9m7dmozrQuUL3fNwlkbeA5Pqr7SbUbtHxbeBuj1esTHxyMtLQ1/PIbNzMyE3W5HXFzcdbWbm5uLb775Br169arK/vOf/yAyMhLjxo1Do0aN0LFjR7zxxhuw2yX/1DIMwzAMwzAMc9vDB2UukpCQgLy8PGzevLkqS01NRWxsLPz8XHv+13+JioqCu7s77r77bvTs2ROvvnrl+RS///47Vq5cCbvdjq+//hovvfQS3n77bbz++uvXbLMmTzZnGIZhGIZhmGui6uru6w6ED8pcJDQ0FFFRUViyZAmAy2e5srOzr+tJ38uXL8fOnTuRkZGBtWvXYvbsK5c5OBwOBAYG4sMPP0R4eDiGDx+OqVOnYuHChddsU/Zk87fmvFfjvjEMwzAMwzAMc2th0UcNMJlMeOqppzB//nykpqaiVatWwqWHrvJfnWb79u1ht9sxevRoPPPMM9DpdAgKCoKbmxt0uiu/ArRr1w4FBQWwWCxXtb7InmyuSO5TYRiGYRiGYRimbsFnymrAsGHDoKoqMjIykJ6ejoSEBCiyO19rgMPhgNVqrXow3X333Yfc3NyqMgAcPnwYQUFB19RwGo1G+Pr6Cq+b8aRzhmEYhmEYhmFuLGxfrCGJiYlYvXo1iouLcfz4cQQHB1e9l5ubi9LSUixcuBCbNm3C8uWXDT7t27eHwWDAp59+Cjc3N3Tq1AlGoxG//PILnn76afTp0wdLly4FAJw4cQIdOnTAiBEj8NRTT+G3335DQkICJkyYgKlTqYHpWlRUivYhD3f3Pzn3NwZzWQnJdIXHSebwFO2Cym/UwoTmXUhk96XGPncPaqrSYt32BcmcrSJo5u5DMrezv5HM0rgd7YcntdRpqaygdkTFbqEVHVT+4nTBRKZIDGZGLzpPMuwnRGOc3asBreNZn2R6jVURABySabX2Qtl6K6+gVi3j2UMkswa2IZnWviWbb0sRNV3KrG+KxsYFAA4vcd6dOteeZyKzjzr1dFrZslW1Zjwd1ZQ6JNnTHqEkm7NmMslsly4KZY++f6d9lZhLT7xJx6ug5CW0fY2i1Q0Sw55C7x/QWakR0O387yST7YeuoJrpOOV0k4wjmv3w8L/+Qaq0T36DZHY/Ok45DXR80BpJHR6Se5gldlD1919oV5t1pp9ppPuAXrMcrQGt6Ufa6D3LMouobDxWK4vEOq5s1wB0pedIZqt3l2Razb4pMfFpv19k/QIAxUbHXoeHv1hHYnKEZP89Po1aJptNo3Y7h0+gUNZdyid1ZPPtXP8hzaLHksx4Xvy+sjak61cv+U6WYfNvSjKZrdno4y+Upd9zEjOwbDyTWW4L5z4rlAMGxdC2ApuRyFK/Bcn0Fomts0KzzUq2a0N+DslsjdqSTGuuBgCjd83cBLeK8sy3arsLV8Xz79Qwe7vDly/WEJPJhMWLF+Ohhx4SDsiAywdsfxSBdO3aFQBw5MgRNG/eHHq9HrNmzcLhw4fhdDrRrFkzjB8/Hk8//XTVNCEhIVi3bh2efvppdO7cGU2aNMHEiRMxZYqoyGUYhmEYhmEY5s6AD8pqSGRkJK52cjErK+ua0w4fPhzDhw936TN++umn6+kewzAMwzAMw/x56sADlf+X4KXNMAzDMAzDMAxTi/BBGcMwDMMwDMMwTC3Cly8yDMMwDMMwDCOg6O7MhzTXVfig7A5G1Rqn6oh90a5S85DuwkmSKSdFQ5TamFqS7EZqK7NKbvlzZc5t52gfjPUCaPsSm5vMpiQzhcEF+6Ku6BTJ7H7BNPvybdqPoc8IZWMptfrZfRpV24eroTUO6hzUlKcrOEz75Uc/0ykxxmnXnWy9FVZS62R9ieHqZAk1gIV40P4SZKZLie1LZmQkk0kMfjJTntawBwA2ifGuxEzn3dcgGgFLJDuAl+SaCJlp8emh75Bs/pEvhPKB554jddz9qX2x5ThqfTtQSI19Z0rF5X13fWo4NOrpfAcU0/3VrrHWAYDZKc68h5kaAiuM1MTnrpdsfZL7ibU203bPjqOTFZ0l2cV6d5PMW7Jt4JBondVpLHYAoEoyW0tqjrUZvGn7EhT/JkLZKrmoxmijRkP7niza1r1/JZn+omj2k9oXK6gJ0X6YGiWVCGrB05/LFcoyQ6BTlfyzKbGlOiRmUe14r7jRbUWR2COb/fMx+pEuGCutOzaQOoYukmekhvcn0VnJmOHXUNz2TpXSsfIuiR1Xa+UEAEXyfWKX7Tsaii10/PR1o/bgMjt97JCHSrPAvz4qlA+9Qa2BLT9YQbIi2fLZQC2x1oeeEspmK+2/XxNqNy1z0O3MU/fnHqXE3Lnw5YsMwzAMwzAMwzC1CJ8pYxiGYRiGYRhGRHZGmblp8JkyhmEYhmEYhmGYWoQPylwgJiYGgwYNkr6XnZ0NRVGwZ88eTJgwAeHh4TAajQgLC7tmm7m5ufDx8YG/v7+Qf/TRR+jZsyfq1auHevXqoX///vj555/ljTAMwzAMwzAMc9vDB2UuYDKZsGHDBuTn55P3UlNTERERgc6dL9/gmZCQUO0Doq1WK+Li4tCzZ0/yXlZWFuLi4rBp0yZs3boVISEhGDhwIE6epDe1MwzDMAzDMAxz+6M4nRKdFCNgs9nQtGlTjB8/HtOmTavKS0tLERQUhJSUFIwZM6YqnzFjBr744gvk5ORI25syZQpOnTqFfv36YdKkSbh06dJVP9tut6NevXp4//33ER8fX6N+m8tE85vRy6dG098stP0CAMVaQbJzTtFUGGihBjOHd0OSyax+7i5YD+0n9tJ+WWi/rBLTn/78ETqtxOKntupRbT+sZ49K2qJ2rCLvJiTzVsR6TolNTLWUkczgR5ejDNvJg0LZ4U3tlKdt1LwVpKemNpmxUmsmlG2z5RW0LffT+2hfGzQnmcMgbgfuHtT0Zy65RLvlsNHMXEoyu2Z5yAxvioPavtRKappT7PQzrd7ULqiza4yGkiFdZkOzZ75JMs8HHibZuBZi9t7pjaSObPmceo+27zft37QfDrG/OolZTSYr04PazwwFB0lWGdRRKKugy8cByWdK7ILSbVY73V5qylPu6kAziZ3PIhlbdOUXxT5IbICy+z705/JIZq9HLYQON7oP6As1dkTfIJc+E5JxyqmnRj3teOaQ2AtVSVuKZOyye/jTepp9TLZ/ubocFRs1hmq3A6fEzirbJ3R520jmaN6NZFpLpr7iIqnjcPcjmf5QNskq21JLo8cFjZ3Sj36X6IpPk0xmp7TWC6HVSs+TzNBQ3PZk4zgZywCXxzPDr1lCWalPt1nzz+to+0Mm0H5I9n2tTdniT+fbeGw7yeyN6T7tMNLvNdl3UV2g4j/v1nYXrorHX+i6u93hM2UuoNfrER8fj7S0NPzxGDYzMxN2ux1xcXEut/Xdd98hMzMT8+fPd6l+eXk5rFYr6tenymCGYRiGYRiGYW5/+KDMRRISEpCXl4fNmzdXZampqYiNjYWfH/3FSsaFCxcwcuRIpKWlwdfX16VppkyZguDgYPTvT58/8kfMZjOKi4uFl9ks+dWJYRiGYRiGYapD1dXd1x0IH5S5SGhoKKKiorBkyeWHCubm5iI7Oxsmk8nlNp544gk89thjeOCBB1yq/+abb2LZsmX4/PPP4V7Ng5+Tk5Ph5+cnvN6aTR8uzDAMwzAMwzBM3YIPymqAyWTCqlWrUFJSgtTUVLRq1Qq9etHrta/Gd999h9mzZ0Ov10Ov18NkMqGoqAh6vb7qYO+/zJ49G2+++SbWr19fJRG5FklJSSgqKhJezz/7TI3nkWEYhmEYhmGYWws/PLoGDBs2DBMnTkRGRgbS09MxduxYKIrkDvSrsHXrVtjtV25AXrNmDWbNmoUtW7agSZMrN9q+9dZbmDlzJtatW4eIiAiX2jYajTAaxRuqZUINhmEYhmEYhqkOReVzN7cSPiirAd7e3hg+fDiSkpJQXFyMkSNHCu/n5uaitLQUBQUFqKioqLIvtm/fHgaDAe3atRPq//LLL1BVFR07XrGDzZo1Cy+//DIyMjLQvHlzFBQUVH22t7doZKoOao2qG/ZFOKk1TX/xOMkaeWju1ZMZ8CRmLIsbNS1e++LPy9iP7qf9Cm5JP1NmGCuiZkhnA2q0ut7hTWv1AwC/4mO0nl+w+HkS65jFSO+BpE4tOVobpaoxUgFAsMTQpSsqJJk1oDXJKhVxSKLeNsAusXFpDXsAYLHT7czn7GExaNaF1JFZ02Q49XQ+Fc22rVjptiJDV0KXo92HrnMZDo3dTmszBABD5SWa9f07yQ489xzJtLbFp4L6kTqvzIohWfCEF0lWLOmbTZN5W2lfze71SKY1BAKANfBuWs8iWjKlpjyJ/c8V0yJArWxo1om2dYyaXSs7RZPMIDF6Hn7qCaHcZnwC7YTE6GnrPJBWk1kOJWOodhzRbmMAoL9EH9Niz6FmTjXybyQzr3lfbGvYC6SOrugUyWw539H276ePoFF2rxeDTn1JHdn6VSUWYJmdknye7DtNsn06gtrQz5SMoTpfcRvNf+1ZUqfZpCkkszejV9XI9NrmhuJ+Ihsr3RvQ7z7ZOjEU/EoyW8MWkk+tHtl2Jh3PZDbKluIP2Lb1S0gdJYba+mQ23JMvP0myxm/Q9rRYm9Ef0WVmV5lNE3XUvsjcWvgQuIaYTCYUFhYiOjoawcHiF1diYiK6du2KDz74AIcPH0bXrl3RtWtXnDpFB7KrsWDBAlgsFjzyyCMICgqqes2ePftGzwrDMAzDMAzDMHUAPlNWQyIjI3G1R7tlZWXVqK2RI0eSs21Hjx69vo4xDMMwDMMwzI3iDrUc1lX4TBnDMAzDMAzDMEwtwgdlDMMwDMMwDMMwtQgflDEMwzAMwzAMw9QifE/ZHYwr1qja4IyFXqPsHdCeZPqVbwpl979Rc5LDQE2LOontyBXUFtSaZnOnpkKbgVowDYHUNqVYyq+rH5fcA2nfJPXsny0kmdX0hlCu5+5P6rjZKiWtubat2DR2uzIHXZcemxaRzBlFDWxlEueju1r9ujtTRu2IXm50CZ0qoeZDP0/RKEb9j3I7HySPvnBK9i+tmVNmcnQqtK/mwLYkO1dOp7WW0nlq7CX292IltYnZHXQ/aepGTXPu/p4k086DzLQ4fcqXJHsvfhLJvj1yiWT5RWI/mvnT5do1iC6LZi5aMgvsonGwMej2f0GyrBt4SiyNkvuJy70aCWUPMzWNOtv3JtlJybr0lmxT7aeLFsuijXRZ672oVdHaeQjJKirotuGupxZCD724jVZaJXY+P2qXVSyysYXiPmikUJYZVaVI7m+RmQ9tBaL5UNeFrkubZFRVJWO7K1idtC33eneRTLGUkUxmca1URQth87HjSB3b8YO0I53pOpGNl9r1e6KYGjjDAum2WOjRmGTFOvp9ZS+n67OtZtGecmEsA+Tjmc1BTdJNDOJ2YLhvKKlTaKHbygXJPtHi1X+T7GSZ2F+JFBK+BrodVNhoRYeT9v/6fJXMnQafKWMYhmEYhmEYhqlF+EwZwzAMwzAMwzAibF+8pfCZMheIiYnBoEGDpO9lZ2dDURTs2bMHEyZMQHh4OIxGI8LCwqT1nU4nZs+ejTZt2sBoNKJJkyaYOXNm1furV6/GgAEDEBAQAF9fX0RGRmLdunU3Y7YYhmEYhmEYhqkD8EGZC5hMJmzYsAH5+fnkvdTUVERERKBz584AgISEBAwfPvyqbU2cOBGLFi3C7Nmz8euvv+I///kPevToUfX+999/jwEDBuDrr7/Gjh070KdPH8TExGDXrl03fsYYhmEYhmEYhql1+PJFFxgyZAgCAgKQlpaGadOmVeWlpaXIzMxESkoKAODdd98FAJw7dw579uwh7Rw8eBALFizAvn370Lbt5Rv7W7QQb++cO3euUH7jjTewZs0afPnll+jateuNnC2GYRiGYRiGkaKofO7mVsIHZS6g1+sRHx+PtLQ0TJ06Fcr/N7FlZmbCbrcjLi7OpXa+/PJLtGzZEl999RUGDRoEp9OJ/v3746233kL9+vWl0zgcDpSUlFz1/WvhrKPXAjcyUhuRYqOmQtujonVMd+QnOl3D5vQDvAOuq1+qmZqxoKNmLJ2dmqpkpsvrHcp8ddQGJTP2qU+8ROvpRbuUzAr2Z7YK3SXxbLGXf1NSp6xPIsm8HXT9einUvuV0Vj8kBXtTW5zh0gmS1fen24HDjU6rRVd6jmQ2v2CS6YtO0Xqa5eFQqBVPZvDTV1wkWZBERGn3oeOAzlwqlBur1DDmcKf2xRNTp5Ks5bixJDv5nmhBDZ7wIqkjMy0+FdSPZO+U/0oyq0Zj5u60kDp2HV1vNgM1zenP5ZEssJFotnQ6aFsBerpO1EsnSeb0oDZWD7vYX+u6JaSOoQ/9jmgpGZ8dbvVIpjWeesbT9QYHHTPw+Vsk8u1Hr+KwSSyBbmcPCWVdg+a0fYlVTr2HmjntLoyNTond1C4Zx/WdetJuSAy8+oEJmsboNuUm2abU0gs0K79E++YrGgf1DrrPycyrlz6dRzK/Ec+RzKiI7dn96bbuaBpGskMjHyHZ3R9/TjK3InHbbthQYvwtLiBZfck8+dQLIZl2TJLR1JuO9aq5hGSNJV+ksvHMrUDcZh2SfbV+4W8k85KYbyH5jm9+ab9QtgRTW7PbuVzaVyM1ejq8GtDPZBjw5Ysuk5CQgLy8PGzevLkqS01NRWxsLPz86M4v4/fff8exY8eQmZmJ9PR0pKWlYceOHXjkETqQ/pfZs2ejtLQUw4YN+9PzwDAMwzAMwzBM3YPPlLlIaGgooqKisGTJEvTu3Ru5ubnIzs7Gq6++6nIbDocDZrMZ6enpaNOmDQBg8eLFCA8Px6FDh6ouafwvGRkZeOWVV7BmzRoEBtJfsv6I2WyG2Sz+uuNwOmE0Gq8yBcMwDMMwDMNchTp6xdWdCp8pqwEmkwmrVq1CSUkJUlNT0apVK/Tq1cvl6YOCgqDX66sOyACgXbt2AIDjx8WHXS5btgyJiYlYsWIF+vfvX23bycnJ8PPzE17/vdeNYRiGYRiGYZi6Cx+U1YBhw4ZBVVVkZGQgPT0dCQkJVfeXucJ9990Hm82GvLwr9z0cPnwYANCsWbOq7LPPPsOoUaPw2WefYfDgwS61nZSUhKKiIuH13HP0WnWGYRiGYRiGYeoWfPliDfD29sbw4cORlJSE4uJijBw5Ung/NzcXpaWlKCgoQEVFBXJycgAA7du3h8FgQP/+/dGtWzckJCRg7ty5cDgcGDduHAYMGFB19iwjIwMjRozAvHnzcM8996Cg4PLNth4eHte8d81oNJJLFSsqK2/czDMMwzAMwzAMc1NQnE6JCoy5Klu3bkVUVBQeeughrF27Vnivd+/eggjkvxw5cgTNmzcHAJw6dQpPPfUU1q9fDy8vLzz44IN4++23q+yKV2tjxIgRSEtLq1FfrefESyLdAqhlqzawXDpLQ4ldMPVX0Yb4eCd6X50e1HpVTKV+CPSjtiYt5mJqwJP1C5Kzo06V/r4hs196uFMbn5azRdQC6afSmZrzC7UETu6imU9J/8/Bh2RN6lW/fADA8fsvQtmqMcMBgF1H72NUndQOJ7OTVUA0ovl7e5I67/tRW9bYnM9IljNhCslsleJn3rNhE6lTXuHajxkOydCp1uDM+R/RWamd8rex/yRZ9lpqFxx1aodQtiyfRers/nAjySI20Sy3kFrH7vIVDaR2B53vb49cItmQu6kpcrJnKMlmLfqHUHZY6HaR+eIXJHvsJH12o+zL7Kfw+4XyvTt+IHW2hN1Hsl67fySZaqH75tl3koRy/RfeI3WOPUlFTeZiuqyL86l9rvtLj4t96PU4qeOUmAQdX75LsvM5h0kWPExiZGwrWg4VOx1/9BLjqbkhHQ9kGM+IFk5LY7pdyFAl/bCrknnX7JsGq8SsK0FXfJpklgA6T4rGdqn/jW4rjpCOJLN5uGZRNqfNEMpuI6aTOgVldD+RmWk/Dgoj2eicZUL5oMTE2vyjlSTzLNhPsq3xT5OsQSi1C4Yu+kIoL/Cn61w7lgGAfc0ckv0wfTXJeu7eQjItxmPbSbbygX+R7KGjtB/HnhCFbLKxePTu5SS7uDqNZL+u+JlkPbfSbaguYP4uvba7cFWMfeNruws3HD5TVkMiIyNxtePYrKysaqcPDg7GqlWrrvq+K20wDMMwDMMwDHPnwAdlDMMwDMMwDMOIsH3xlsKiD4ZhGIZhGIZhmFqED8oYhmEYhmEYhmFqERZ93MF8+5soguh/d0At9USkslxy07VESOF2Vrwp3RxIBQ8ygYTZSdvy8/Kotl8l5RUk06tU3ODqzeYyPD2qF31cKKHSB1k/vAp/J1lZvZZCWSfxTtgle7wrywcAisrEZeQm6Zfx7CGSWQNau9R+uV1sr74PFX1o+wBAep+nlTpgyPKQte+q6MMqEV7Ilsf1tiUTaugk7Ws/U9aWbPnI2tp2spRk3RqLEhibpP2lewpIZuoWTDIsm0miKYlLhfIwidAnfCuVH7mKdjnK5lu2rA2SnUe277jSvmyZyZBtPdrmPJwWUkcm+tDuS1dD1l9XcPVfB9nYpV0eRj0ds2XLTL6e6LR2Td+skhXn6nzL9mmtSES2Xcial32im50KX4oc4vr0NtBLyNxK6D5n9WlMMxfGA9lY6eVGl2uZpKKr3zEBvuJYKxvHZcva1fFMu53JpEuVNtp/2XqSbbMW+/WNI7J6sv7LpFZ1AUv2suor1RKGno/WdhduOHymjGEYhmEYhmEYphbhgzKGYRiGYRiGYZhahA/KGIZhGIZhGIZhahE+KHOBmJgYDBo0SPpednY2FEXBnj17MGHCBISHh8NoNCIsLExa3+l0Yvbs2WjTpg2MRiOaNGmCmTPF+yzMZjOmTp2KZs2awWg0onnz5liyZMmNni2GYRiGYRiGYeoA/JwyFzCZTIiNjUV+fj6aNm0qvJeamoqIiAh07twZAJCQkIBt27Zhz5490rYmTpyI9evXY/bs2ejUqRMuXryIixcvCnWGDRuGM2fOYPHixWjdujVOnz4Nh0NyJy7DMAzDMAzDMLc9fFDmAkOGDEFAQADS0tIwbdq0qry0tBSZmZlISUkBALz77rsAgHPnzkkPyg4ePIgFCxZg3759aNv2skmwRYsWQp1vvvkGmzdvxu+//4769esDAJo3b35d/Q7xM17XdDcb1Sqx50nsixf8RWOfn4NaD2XTedgkdkdUbxd0Lz1D21fpLuI0UEuSXjJPDqNPtZ8pQ2boMkusUWcNd5HsLofG5OWk0xkcNsknuGZf9DAXCmWHux+pY/ehlk/VXEIypxv9TG+rdt3RZe1RcYFkDs96JPO003WilohGUvi0o3VAzVgOyVq5XtOiDOk2a6eWPdVM61nrhVTbln1zBsmU/iaS3V2frhOtPczbeonUaeZPp3OXWALLLXTb09oWV+w9S+r0LD9HMqs3tTRqrXgA4Fkhtlfp3YjU8ZDMk01fn2SyVe5ZJo4bNl9qwPMqzieZw+hFMsVOl49iEdenw4Puc3BSq6u3bD+XjJeQ/N5nN3rTUIPbQWrEtLXtSZuXfKZX6SlxOv8mpI7MgGdw0G3KDvo956aZ1OGCtRQA3E/vI5klqAPJtJMayLgFWL76N8n0f32GZDJ8NTJNXSndJ5ySdaSTfEfqK4pIph0vZWOlDbR9b4PEknzhCMnsPnQf047lniWnSA3ZdiAbz3Qn6Xoyt7hXKOvLztMeSL4ndKV0bJFh0BuEslpJv9Ns/k1Jplqo0db+Qyb9gCHjXOrHLUflC+puJby0XUCv1yM+Ph5paWmCyjQzMxN2ux1xcXEutfPll1+iZcuW+Oqrr9CiRQs0b94ciYmJwpmy//znP4iIiMBbb72FJk2aoE2bNnj22WdRUUEHTYZhGIZhGIZhbn/4oMxFEhISkJeXh82br/w6mJqaitjYWPj5SX65lPD777/j2LFjyMzMRHp6OtLS0rBjxw488sgjQp0ffvgB+/btw+eff465c+di5cqVePLJJ6/ZttlsRnFxsfCymOnzTxiGYRiGYRiGqVvwQZmLhIaGIioqqkq4kZubi+zsbJhM9BKgq+FwOGA2m5Geno6ePXuid+/eWLx4MTZt2oRDhw5V1VEUBZ9++il69OiBhx56CO+88w4+/vjja54tS05Ohp+fn/D64N13/txMMwzDMAzDMP+bqLq6+7oD4YOyGmAymbBq1SqUlJQgNTUVrVq1Qq9evVyePigoCHq9Hm3atKnK2rW7fE/L8ePHq+o0adJEOPvWrl07OJ1O5OfT+xL+S1JSEoqKioTXvyZMruksMgzDMAzDMAxzi+GDshowbNgwqKqKjIwMpKenIyEhAYri+s3+9913H2w2G/Ly8qqyw4cPAwCaNWtWVefUqVMoLS0V6qiqSsyPf8RoNMLX11d4GYx1U/TBMAzDMAzDMMwVFKdToqtirkpiYiJWr16N4uJiHD9+HMHBwVXv5ebmorS0FAsXLsSmTZuwfPlyAED79u1hMBjgcDjQvXt3eHt7Y+7cuXA4HBg3bhx8fX2xfv16AJeNju3atcO9996LV155BefPn0diYiJ69eqFjz76qEZ9tZ/YK5R1IZ3+5NzfGCrLqU3JodBT0cv2i8apRztQ25rqtLvUlqcHtZNpse1YSzJL+34ufabz28UkcwsfQDJ9E2r701JURi9TlZnCtuRT+9P9TUTDlWxZyHBl+QCApbBAKNvc/UmdMivVufk5y0nmMFD7nFPzI4eHO+3XOLU5yd77bQXJsv46nmSqQVwevX/ZSupUVFaS7Gaj2qk17WzyJJK9+cZGkr1TIu7n5ekzSZ3U51eTbMxZaogtttBt29tN/O1O9oVxrpya/hp7uZFsaXAYyYbn7xLKnhLT4lOBD5BsXnEOyRxudHtZ3KiLUB59lFoDU5pQa+Bzp+m2oUpMbfsmTBLKbT9dQ+qUL3qJZHlf7SDZge2nSTY8daxQVgY8Qepo9xsAMKe/SrJj3+4i2fqv80j29CFxHmz7t5A6hxatJFmHuXNJ5nCnFtoDT4wSyrJlJrP6KTZqX7Q0akvrOcTt2JCfQ6c7+ivJfv2AjiPt0ul86orFcfDLrg+TOv3f+jud7u8vkMzqoHvU748PFcp3Z9Dlc7KUjhkhPgaSTZUYZlNOrBfK2x9PJHW6rF1HMpmZNrvb/STrNq4vyRqMTxHKE3QtSJ2UYjomycaz9Cmfk+yJM7tJpsV49hD9zPaPkGzC+b0k2/fXh4Tyx+t/J3Xey6XbSv5H80m2dgEdW8ZeottjXcC67Yva7sJVcbvn4druwg2Hz5TVEJPJhMLCQkRHRwsHZMDlA7auXbvigw8+wOHDh9G1a1d07doVp05dVr+qqoovv/wSDRs2xAMPPIDBgwejXbt2WLZsWVUb3t7e2LBhAy5duoSIiAg8/vjjiImJqdLtMwzDMAzDMAxzZ8HPKashkZGRuNrJxaysrGqnDw4OxqpVq65ZJzQ0FBs2bLie7jEMwzAMwzAMc5vBZ8oYhmEYhmEYhmFqET4oYxiGYRiGYRiGqUX4oIxhGIZhGIZhGKYWYfviHYzWcujuSW13tYG5jFoDLQq9vdFoFfvv1FGbm1Ol09kkvzV4e3pU268jz/6TZI1eo8ZLqX3xuzSSuYUPJJk+6O5q+1Ge+RbJlJgJJDMUUFuTNVBsX1dyltSp9GlMMh8Xlg8A5E8XzW/1X3iP1JEZrkq/zSSZ+6NTSKbdDmT9Kq+gdkSHZBiTDWw6jaVOZp2UbZ9wUqOkDLI9Ki7+7iVp3yl5OKZdYmrTacycshFdtnzcbNTyqS86STJrg5ZincLjpI7ioPZFm18T2paerk+7pmsy06jORtf5RN8wks1ZQ5/NaOgsmhuPzH6D1Gk24h8kK9/zM8mOf7uTZK0fHyyUnRXULqsY6Xami/obyfQXj5HM4VVfKNsPbSd10GMoiRSJ0VP9/Rc6beNWJHIe3y/2oR21X8pY2DSSZKPmDSeZV99YoWwOpAZFa8brJDu36zDJQmZR863+7G9iINuXfAJIBh21Fzr19NEyTs1+rZrpmOHU03Wu5HxDMn1wS5JZj4jLX9eBGg4r/OgjcgySfdru5knrXRTNlpb61ISorQMAdt8gksmWj2w80/7/IRvHpd+tknUnG+PcT4nmRqeZGn/tga1JZvNqSDLZeKP9/0NmNna1/zJkpuG6gHX7f2q7C1fFrftfarsLNxw+U8YwDMMwDMMwDFOL8EEZwzAMwzAMwzBMLcJKfIZhGIZhGIZhBBQXL79kbgx8pswFYmJiMGjQIOl72dnZUBQFe/bswYQJExAeHg6j0YiwsDBSd8aMGVAUhby8vK5ca7169WpERETA398fXl5eCAsLwyeffHKzZo1hGIZhGIZhmFqGz5S5gMlkQmxsLPLz89G0qXhzbWpqKiIiItC5c2cAQEJCArZt24Y9e/aQdp599lmMGTNGyPr164fu3btXlevXr4+pU6ciNDQUBoMBX331FUaNGoXAwEBER0ffhLljGIZhGIZhGKY2YfuiC9hsNjRt2hTjx4/HtGnTqvLS0lIEBQUhJSVFONiaMWMGvvjiC+Tk5Fyz3d27dyMsLAzff/89evbsedV63bp1w+DBg/Haa6/VqN8VlaJBqK7YfbRWSEBuTDRo7Is2gzepo7ObSeaUGLTcPaq3CzoO/0iykibdaFugpjmZBVKx0b4Zffyr70cetb6VB3UimcFCjV8Og2i4klnBHO5+JHNl+cj6VhncmdTRl50nmdNI153MiKa1msmMoaXl1DDmfmgzyaztepPMprEXyuyO2v3maigOatpy2baowalQ46CrI7O+4qI4ncT6JrMeehbsJ5ndJ5BkWoOn1vB51X6dyyNZeaN2JNMKJXV0URDDJABg/YckenroOySbV5wjlN3O5ZI6Nl9qJFVP7KOfKcEZLJoDK75OJXU8ox8nmXLpNMnsTTqSTLtPy8x2ZqfEOHv2IO2rQWK/9KcWv92xos2xa/oS2i/PeiRTD9L9EHfRscuuWd4OSIybkrEdkn1OZhfU7k6qVWJsdaP7iayeruQMndbdR1OmY6oMmaVXf09MtdPZvRqQzCr5zvQ6tZtk5cFdSHb6GdE0fNcsui9926E3yfod2koymYVQhtZ0K7MvuhXTfcKpWdYAYHGj3wv6rDSxrXb3kDrWQ9Q+anuAWpf1sjFIsx1YJRZj2zJqdvUYkkjrScbZuvL/mRbbLmoMrSvou8qvYLud4csXXUCv1yM+Ph5paWn44zFsZmYm7HY74uLirqvdRYsWoU2bNlc9IHM6ndi4cSMOHTqEBx5wTUnMMAzDMAzDMMztBR+UuUhCQgLy8vKwefOVXwJTU1MRGxsLPz/XfiX7I5WVlfj0009hMpnIe0VFRfD29obBYMDgwYPx3nvvYcCAAddsz2w2o7i4WHiZzZJfGhmGYRiGYRiGqVPwQZmLhIaGIioqCkuWXL6MIzc3F9nZ2dKDKlf4/PPPUVJSghEjRpD3fHx8kJOTg+3bt2PmzJmYPHkysrKyrtlecnIy/Pz8hFdKSsp19Y1hGIZhGIZhmFsHiz5qgMlkwlNPPYX58+cjNTUVrVq1Qq9eva6rrUWLFmHIkCFo1KgReU9VVbRuffnJ82FhYTh48CCSk5PRu3fvq7aXlJSEyZMnC5mDbxdkGIZhGIZhmDoPnymrAcOGDYOqqsjIyEB6ejoSEhKgSG7Qr44jR45g06ZNLp9lczgc1V6KaDQa4evrK7yMRmON+8YwDMMwDMMwzK2Fz5TVAG9vbwwfPhxJSUkoLi7GyJEjhfdzc3NRWlqKgoICVFRUVNkX27dvD4PhihFwyZIlCAoKwoMPPkg+Izk5GREREWjVqhXMZjO+/vprfPLJJ1iwYEGN+2s495sYhFALVl1BlRzczsu5JJTHdqfGJVViPZSZvFzB1rAFyWRyUofejWQ6cymdVtI3VyhpTNeTm2T5fHmCWiAHtxLLNnf/6+rD1bA2Fu15Vq06D8ApJ73HsqlO8vuPxFQosxBq+SS4K8lGrXiOZD88dh/JKi6JP2785bRrhj0ZTok9UtFuLxJTngydhRolC96k8/TNQmo/G3H8J6FcvvRNUid9yuck+1fBTpLJLH5u3qIpTG+h23qBnZrDAhu1JdlP4feTLPznbKHsWXGW1Pngbvo4kLG7l5NsXnE8ySb6hgnlOV/S5frOsLkke+HQapJZD1IzanrfSUL5H6dySJ2L86eQ7NAqaoL79KeTJHvtnb8KZcNoangzKnQ7u7DqY5LlfrmLZIW/XyLZoOx0oWzZQref7cmZJLt37QqSOQ3UjrisqWi1HX6S9kstLySZzL4Ib7ofqmWikVStKCJ1bLn0M0988X8kC3nzI5Lpik4J5b2S2xACOlKrZcOX6fd4uWQM/aZFuFD+6+EsUud4Bd3nWjQJI9nnIXS8fHzLIqH8YzQ1QPY6tI1ksq/WL5tRQ3G/afR/G88JbwvlzyR9/cexn0hW+vFMkn029T8kSygQzZNWJ91W3CRj9nuB1E458eQWkv38N9HSuGffOdqH1S+S7PQ700m2dgEdx8de+pVkdQKVz93cSvigrIaYTCYsXrwYDz30EIKDg4X3EhMTBRFI166XB8MjR46gefPmAC6f9UpLS8PIkSOh09EBoqysDE8++STy8/Ph4eGB0NBQLF26FMOHD795M8UwDMMwDMMwTK3BB2U1JDIyUnr2BEC1Mg7g8v1iJ06cuOr7r7/+Ol5//fXr7R7DMAzDMAzDMLcZfFDGMAzDMAzDMIyAIrmii7l58MWiDMMwDMMwDMMwtQgflDEMwzAMwzAMw9QiivNqN0gxtz1FZaLRzc/Lo5Z6IlJZQU1z5Ta6GXrqxEyxW0kdp55q/2X2RU8PaqrSYik6T7IKPTU+uqm0fbdSaoxzuPuSzOjjX20/KsvLSCaz4rk7LSRz6gxCWbHTOnYdXWauLB8AMBeLVrNKHd2m3B308Q2KxE7p8GpAM826k/WrvKKy2n66iqx9c1kJyZwSU+QNxUUTpWy01lZzdURXJXYytZJa6hwe9cTPs0nWr4OaQJ06aim1qAaSaeVzkt0LRjPt1/GXJpCs5eTnSWY+IFrkno5JIXWS//0o/VAJWa98SbLG7RsK5a5ffU0nlG0/EjOnZfkskhmGi+bGcrtku6Ctwxt038eOtbRrnfvQ9txEY6JsXSqS/hd/+DLJ/HpE0mnv7iGUbb6NaVvvUkumpbicZI2eoXY+9fcdQtkZ0oHUcerptujU0/HA6Sb53tTMu3SfsNFxyu5ZnzYlWXm/J8YK5VaLV5E6Z8voPhfoRe9IuZH/4bn6BCBFYsl09xS/S2XjuCrZkl0x8gJ0PnUO+v+CVaHLR2Z+lk3r0OwDN/o/Z1e/g2819gNZtd2Fq6Jr37u2u3DD4TNlDMMwDMMwDMMwtQgflDEMwzAMwzAMw9QifFDGMAzDMAzDMAxTi/BBmQvExMRg0KBB0veys7OhKAp2796NuLg4hISEwMPDA+3atcO8efNI/aysLHTr1g1GoxGtW7dGWlqa8P6CBQvQuXNn+Pr6wtfXF5GRkfi///u/mzFbDMMwDMMwDMPUAfigzAVMJhM2bNiA/Px88l5qaioiIiKwY8cOBAYGYunSpdi/fz+mTp2KpKQkvP/++1V1jxw5gsGDB6NPnz7IycnBpEmTkJiYiHXr1lXVadq0Kd58803s2LEDv/zyC/r27YuhQ4di//79t2ReGYZhGIZhGIa5tbB90QVsNhuaNm2K8ePHY9q0aVV5aWkpgoKCkJKSgjFjxpDpxo0bh4MHD+K7774DAEyZMgVr167Fvn37quo8+uijuHTpEr755purfn79+vWRkpICk8lUs36f/k0o64PurtH0NwuZXdCh0AcUZuw9I5Qf7RhI6uhBDWAytOYnGeZSanirVKihS2Zf1ElMWzJjmSv90Fozr/aZ2ceLSdYrRGxftlxlhit3D9fMnNp1J7NCVtjoOvGni0JqpNNfEn/4kG2z7/u1JdmTuz4l2c7xU0im6MTPDP9yA6lTUXnj7I6Ki8OrzF4oM9llTKdmv7EnfhTKJZ9Qg9/3r9PpHvz9Z5LJ1qd225PJ0C6U0/4HGOm8Z3XtRbLuO34Qyt7WS6ROSlM63fOb3yaZs0U3Ou1dol3wybdjSZ2kJ5eR7L2f5pKsMHsTyb5OXi+Uh+XvInXwnzkkOrp2C8k+X/kryZ5ZLVoIK+//B6mjSFaKsiKZZDvn0+3dWkYtjX3Xpwll+1H6o+D2pPkk6/5lJslkRsPfJ8QL5eYLVpA6+hJqtJUZK+3eASTT2hD1F4+TOo4zR0m2/hG6zw3IeJF+ZqHYtw3/+pDUadm3Gc0WUYuiVasfBfBdqwihHH1kB6lz5BJdb6386Hif3iScZKMOihbOQ89ITKaSvsq+O3YNplcRdRxJjZ4+8TOEsmwcH30sm2Rln71DMpkFVbuMZH3VX/idZF9GDCPZg0d+IVlu4iNiH77KJXXG5nxGsjOffkSynf/+gWRDTu0lWV3A/itdJ3UFXWjP2u7CDYfPlLmAXq9HfHw80tLS8Mdj2MzMTNjtdsTFxUmnKyoqQv36VxS4W7duRf/+/YU60dHR2Lp1q3R6u92OZcuWoaysDJGRVCvMMAzDMAzDMMztDx+UuUhCQgLy8vKwefPmqiw1NRWxsbHw8/Mj9bds2YLly5dj9OjRVVlBQQEaNWok1GvUqBGKi4tR8Ydnd+3duxfe3t4wGo0YM2YMPv/8c7Rv3/6a/TObzSguLhZeZrPkOTUMwzAMwzAMw9Qp+KDMRUJDQxEVFYUlS5YAAHJzc5GdnS29pHDfvn0YOnQopk+fjoEDB9b4s9q2bYucnBxs27YNY8eOxYgRI3DgwIFrTpOcnAw/Pz/hNeu9hTX+bIZhGIZhGIZRVLXOvu5E7sy5ukmYTCasWrUKJSUlSE1NRatWrdCrl3ivw4EDB9CvXz+MHj1auP8MABo3bowzZ8T7pM6cOQNfX194/OGeHoPBgNatWyM8PBzJycno0qWL1OT4R5KSklBUVCS8pjxF73NjGIZhGIZhGKZuwQdlNWDYsGFQVRUZGRlIT09HQkKCcIP1/v370adPH4wYMQIzZ84k00dGRmLjxo1CtmHDhmrvF3M4HDCbzdesYzQaqzT6/30ZjVRSwTAMwzAMwzBM3YLtizUkMTERq1evRnFxMY4fP47g4GAAly9Z7Nu3L6Kjo5GSklJVX6fTISDgsh3qyJEj6NixI8aNG4eEhAR89913mDBhAtauXYvo6GgAl894Pfjgg7jrrrtQUlKCjIwMzJo1C+vWrcOAAQNq1NeSctHi5+PpmmHvZlNZQe2CTok9rMIqmrY83OhvCIrDTjJ9ITVt6Zu0q7ZflqLztA96akuUmRBlRjqZec8Vy6Fs+ThAP0Bm7TK68DOLW8FBkukk1jpX+ibrl8VODWlGvWTdSZaP29nDYr+adaF9kNg79UWnSGb3obZO0i9vej+oq9unrP+yeq4gtTRKTHNaqxwAfNuht1Bu90hHUqfJq/+WtCUxhkpMeaSOxCrqVKn1TX/pJMnsfsEks2lmXZUsQ72llGQVK+eSzKvb/SRTGjYRysVfUVOnd7d7SfbUvZNI9kh7avrrOV+sp3SNJnUcbnS/V610O9NfOEoyS2Nx7Cq3u7aN+V3KI5k9/zDJnJ36k0ynMR/K9iXnD8tJpvSk0iunxLKqtY069EZJHTq2y5Bte65Oq8Vwmlomn7qb2vmmTBHtgo1fSCF1ZMiWhVOybej2rBPK9s50myrT7jgA/IqPkcxW7y7afuk5oezwrEfqKJUlJLN7NySZKhmTnKqeZFrzsLn4IqmjHcsAIPRv9H76pjM/IBlBsqxlY6qsnmw9qWZxecjGSu1yBYBfn51Esrs/yCCZ7LuoLuA4/GP1lWoJtc19Nao/f/58pKSkoKCgAF26dMF7772HHj16SOt+9NFHSE9Pr7Klh4eH44033rhq/RsFnymrISaTCYWFhYiOjq46IAOAlStX4ty5c1i6dCmCgoKqXt27d6+q06JFC6xduxYbNmxAly5d8Pbbb2PRokVVB2QAcPbsWcTHx6Nt27bo168ftm/ffl0HZAzDMAzDMAzzv87y5csxefJkTJ8+HTt37kSXLl0QHR2Ns2clj94AkJWVhbi4OGzatAlbt25FSEgIBg4ciJMn6Y+NNxL6cwZzTSIjIyE7uThjxgzMmDGj2ul79+6NXbskz7H5/yxevPjPdI9hGIZhGIZhmP/PO++8gyeeeAKjRo0CACxcuBBr167FkiVL8MILL5D6n34qXlGxaNEirFq1Chs3bkR8fDypf6PggzKGYRiGYRiGYUQklwfXFcxmM/EtGI1GGI3i5dAWiwU7duxAUlJSVaaqKvr373/V5wRrKS8vh9VqFZ49fDPgyxcZhmEYhmEYhrltkD0KKjk5mdQ7f/487Ha79DnBBQUFLn3WlClTEBwcjP796T24NxI+U8YwDMMwDMMwzG1DUlISJk+eLGTas2Q3gjfffBPLli1DVlYW3N2rl2H9Gfig7A5GIuers1jstLPnKkRDV4ieKv6vz3Unx2GgpkVIZE2ucr0mPpnR0C65j/FSJTWMNfIULzW43j64iswAKUMmF5T1zKmrfkhSK4tI5vCg5irFQi2NFDrdzV5mriKz8ymWcpINXP26WMdAvzSclcUkky0z2YqS2RZJv2TTSdpXJevErjGcSuSmUCVWs+Pf7iRZO4l90XrwZ6Gc9cqXpM59z9JlLTMtrjxA+9F85Rqh3LTHw6SOIrO+Wan9snLHdyTTDWotlM12uj5ku6GPmyf9yKPUvGps3plOrLEjykx8tkJ6g7yr/1Co5YXix/k2dnHK60OxW2mmmUcAsB77lWT1DfTyrYoL4hikKz5NP9RB17k1oDXJJBJFnP1C3KYadh5E6pRa6Pjv49WAZNLx0t1XKCuSscbp7kM7JkE2zio62eN4NPu5mRpVB658lWSqF+2HXbI9OjTzLlu/MtOiUkGXj1Nmo7RZNJ2g7cvG1NBnn6L1pN9NddO+WJeRXaooo2HDhtDpdNLnBDdufO2xZ/bs2XjzzTfx7bffonNnyVh5g+HLFxmGYRiGYRiGEVHUuvtyEYPBgPDwcOE5wQ6HAxs3brzmc4LfeustvPbaa/jmm28QERHxpxajq/CZMoZhGIZhGIZh7kgmT56MESNGICIiAj169MDcuXNRVlZWZWOMj49HkyZNqu5JmzVrFl5++WVkZGSgefPmVfeeeXt7w9vb+6b1kw/KGIZhGIZhGIa5Ixk+fDjOnTuHl19+GQUFBQgLC8M333xTJf84fvw4VPXK2bcFCxbAYrHgkUceEdqZPn26S4+/ul748kUXiImJwaBB9JpuAMjOzoaiKNi9ezfi4uIQEhICDw8PtGvXDvPmzRPqnj59Go899hjatGkDVVUxadIk0l5aWhoURRFeN/vGQoZhGIZhGIYRqO1LFG/A5Yv/Zfz48Th27BjMZjO2bduGe+65p+q9rKwspKWlVZWPHj0Kp9NJXjfzgAzgM2UuYTKZEBsbi/z8fDRt2lR4LzU1FREREdixYwcCAwOxdOlShISEYMuWLRg9ejR0Oh3Gjx8P4PIzFQICAjBt2jTMmTPnqp/n6+uLQ4cOVZWVOiIeYBiGYRiGYRjmxsMHZS4wZMgQBAQEIC0tDdOmTavKS0tLkZmZiZSUFCQkJAjTtGzZElu3bsXq1aurDsqaN29edfZsyZIlV/08RVGqNcK4gme5xhTmddefbvOGIDGRqZJfPb757bxQfqJbsEvNO7ypNe1mI7PPyebTFSx2Op1eoqTbeZoaqB5sKVq1nKDmMGujtiRz9fGQ1ExI57tSYtJ01904Fejvzz9JsrsnTyJZQeZnJDNfEpdZyzkZN6xfgGQ7cHEbUGxmktk3fUKyo2s2kazlu+niR/64gtQ5OH02ydqnL6P9kPRXa1/USUyI5V6NSOZht5Ds7LzpJPN8VryiwLPsDKmzb8IkkrUd9Rfa12C6baf3Faft1JX29evk9SSLWzWNZFrTIgDMni/aHd9JoXuTbgtdJ4U7qT3y09fWkWzsp+KVEvreiaSOlxsdP63ffU6y31ZmkyzoBF3e9eMnCWXd2VxS5/BX9KGrdw+m+6YiGdvzXhTbb/Y+3RZl1kAZTollUtXaRh3UVCjbjo+s+ZpkL53+kWT6wnyhfO6zD0md7LepSfOhoztI5pR8d3z34U9C+bHnLpI6pRZ6X4vTm1oPjyfRddJi2mtCuXAF7b/XGPq8J9nPw8emTiDZXX+n+yb6xgvFvCQ6XYt36P9Fjp/pPndw+lska7NUrKeTrHOt9RMADk0eT7K2cxeQ7NwH4vIoPkqNm22ef45k5mPUeHrodfqjfNgquu8z/3vw5YsuoNfrER8fj7S0NGEAzczMhN1uR1xcnHS6oqKi63r6d2lpKZo1a4aQkBAMHToU+/fvv+6+MwzDMAzDMAxTt+GDMhdJSEhAXl4eNm/eXJWlpqYiNjYWfn70+RJbtmzB8uXLMXr06Bp9Ttu2bbFkyRKsWbMGS5cuhcPhQFRUFPLz8685ndlsRnFxsfAym+mv7wzDMAzDMAzD1C34oMxFQkNDERUVVXXZYW5uLrKzs2EymUjdffv2YejQoZg+fToGDhxYo8+JjIxEfHw8wsLC0KtXL6xevRoBAQH44IMPrjldcnIy/Pz8hNesef+u0WczDMMwDMMwDHPr4YOyGmAymbBq1SqUlJQgNTUVrVq1Qq9evYQ6Bw4cQL9+/TB69Gjh/rPrxc3NDV27dkVuLr2m/48kJSWhqKhIeE2ZSK8lZxiGYRiGYZjqcCpqnX3didyZc3WTGDZsGFRVRUZGBtLT05GQkCCYEffv348+ffpgxIgRmDlz5g35TLvdjr179yIoKOia9YxGI3x9fYWX0Wi8IX1gGIZhGIZhGObmoThl6h/mqiQmJmL16tUoLi7G8ePHERx82Qi4b98+9O3bF9HR0UhJSamqr9PpEBBwxQaYk5NT1U7btm3x3HPPwWAwoH379gCAV199Fffeey9at26NS5cuISUlBV988QV27NhRVcdVKsvLhLK7p9f1zPINR9svALBJfh8wWMV6TlUiC5U8LsCuowejnh7VP+ut4gtqRHJGjyWZ6nTN5CXDLbB5tXUq1y+m/XjgcZLpS86SzO7VQKxTdIrUsfiHkMyV5QMAlV+LVipHn1GkjuHiEZKpFdSkZgnuRDLtduDt6UHqlFdUksytmJqwrL70hwyHZriTtW8uuUQyGYrDRjLtNiqt4+ovfCq1+Mm2bZ3Des0+AIBNMsofMcWSrN2z40jmtIntoxldb04DXY7WddSkpjz8LK3nEDtn0Ln2CBDnl++SzFZaSjL9oy8KZdn+61DostZbaFsONzqf2mkne4aSOsn/fpRkPj16kszSvh/JtNu28/A2Ukdpcw/NTh0imSOkI8ngRvf9ytXisnV/mF514XCn91IXz3+BZA0GUROfrVm42JakD4YztP8yS6m5SWeSqXZxm1XM1FTrNPqQTGZpVGx0vHHqXRgvJfvvwfhHSNbptSSSWVreK5R15dS+KOu/28k9JKsMCSeZmv2pUFbuG0bqYBu1dyJiCInsEvul7Ck+HprnrcrGcZ1sWWvsr4B8fy2a+4xQrn8P3Sd0DajV2tLmAZLJxgitDdTuSSVusjFJ3+cxkjk865Gsrvx/psV+hFpi6wq6Ft1quws3HFbi1xCTyYTFixfjoYceqjogA4CVK1fi3LlzWLp0KZYuXVqVN2vWDEePHq0qd+3atervHTt2ICMjQ6hTWFiIJ554AgUFBahXrx7Cw8OxZcuWGh+QMQzDMAzDMMx1c4deJlhX4YOyGhIZGSl9rsiMGTNcetJ3dScm58yZc80HSzMMwzAMwzAMc2fBh8AMwzAMwzAMwzC1CJ8pYxiGYRiGYRhGRHaDIHPT4DNlDMMwDMMwDMMwtQifKbuD0RceFwPPdrXTEQ2K0yEJ6e8DB0rFzbOT7RipY6vfjGT26xSK2oqpIdBNYmGS9f/84hSSNYql1iW4YF/U1QsgGXX4AZeMDUnmq+mvzPR3vcsHACqOievAXfIjmlNnIFnZlm9I5vaIxOLnQh/KFlJbmf/9fUhm2fURyUqOnxHK3q9T06XUpCnbZiXYfUW7l6vGUF0eNeqVbPmOtm+1ksxn1EtCWf/r96TOha/+Q7L2yW+QzFlEjZ5KcBuxzrG9dLr2vUlm6BNHstwnqeUt8N1lQtmrOJ/UKVr1Ick875JYRO8bSrKL86cIZd9xb5I6+A+9j9cRM4Fksn1ft2WFUJaZFpOeXEayCf/cT7KgyGySnT0q2hd1bnSbakQSoGjXdpJ5BPxCMtWDWt88u90vlJXiM6QO9mWRqPQk3Xf8zheQzLb3fbF9iZWzcE06ycyF1IjZ8Jm3SKbTmGll9lfZPm2/SPvqCKV2PmJklCwL1Zfa+do/O5r2Q4LyvWhH1N3VhtQpDYmgn+nfhGTmtBkk87m3t1AuX0G/vzwGJ5DMITFKXphJja2NogeQDD3F/aJi8UukiveIqSTTH8wi2dn/+4pkjYf9Qyhbjv5K++Cg67zwrYkkC/zX87TeUtGsmPd1DqnT+Qk630WfvE2yvak/kqzvnp9JxvzvwWfKGIZhGIZhGIZhahE+KGMYhmEYhmEYhqlF+KCMYRiGYRiGYRimFuGDMheIiYnBoEGDpO9lZ2dDURTs2bMHEyZMQHh4OIxGI8LCwkjdGTNmQFEU8vLyunJNf1paGnnf3d39Zs0awzAMwzAMw1BUte6+7kBY9OECJpMJsbGxyM/PR9OmTYX3UlNTERERgc6dOwMAEhISsG3bNuzZs4e08+yzz2LMmDFC1q9fP3Tv3l3IfH19cejQoaqywkpShmEYhmEYhrlj4YMyFxgyZAgCAgKQlpaGadOmVeWlpaXIzMxESsplc9G7716285w7d056UObt7Q1vb++q8u7du3HgwAEsXLhQqKcoCho3bqydvMYoloo/3catQmbd++73C0K5dTg1ULk5qZdQd50HsZ49+pGsxEHb8gA1ONWfQI1u9rILJHPltx1n6x40kyygnaepiaxXiGhSc3g1IHWud/kAgN/AvwnlSkm/Cj3otuv310m0osR+5ubCr1+5X+4kWRuJkW5v2g8k86gnnnWm/j7AWk+WUhQHNXNeL46QjjSzbyBZnmTeu8ZbhHLFfmrdy/uaGhMDJtD1dLHe3bTepVyhXNkpmtQ5WUqtkC0lpjZzsZlkWhxGagPM+2oHycKWUmuacjaXZIdWicbB7k/S7e7o2i0kaz3oCdo5ayWJCneK66RBP3pVhcy0+O4ndJ0knC0jWUeT2J5+ILXi6S8cJdnB598jmXcjumwDu7YgmXv+KaHs2TSY1LEWFpKs2aQpJHN41iMZTh+hmQbfeNqW1JioN9JqfmJ/9XYLqWPPzSGZo5iO2bpWEsuhxuZYefwwqWNs3ZlOFxJKMmv95iS7kPmZUK7X83FSp6CEzlMrK/3O//1ruu/c7S4acnd/uJHUue+BISSTfZ/kb6H7nF8raoE09BTL0n36cbp/le+hZtq8tfT/q8CR/kJZ7UbHqcp1S0h2+IscktV7nvb/wDLRjui0022x9AS1lMqMoXbrjfvuYO4s7szzfzcYvV6P+Ph4pKWlwfmH/44zMzNht9sRF0fVz66waNEitGnTBj17iqNVaWkpmjVrhpCQEAwdOhT799MvdIZhGIZhGIa5WTgVtc6+7kTuzLm6CSQkJCAvLw+bN2+uylJTUxEbGws/P78at1dZWYlPP/0UJpNJyNu2bYslS5ZgzZo1WLp0KRwOB6KiopCfT5/d80fMZjOKi4uFl9lMf0ljGIZhGIZhGKZuwQdlLhIaGoqoqCgsWXL59Hdubi6ys7PJQZWrfP755ygpKcGIESOEPDIyEvHx8QgLC0OvXr2wevVqBAQE4IMPPrhme8nJyfDz8xNeby5Iva6+MQzDMAzDMAxz6+CDshpgMpmwatUqlJSUIDU1Fa1atUKvXr2uq61FixZhyJAhaNSo0TXrubm5oWvXrsjNpddt/5GkpCQUFRUJrxfGjrquvjEMwzAMwzD/4yhq3X3dgdyZc3WTGDZsGFRVRUZGBtLT05GQkHBdZsQjR45g06ZNLp1ls9vt2Lt3L4KCgq5Zz2g0wtfXV3gZjYZrTsMwDMMwDMMwTO3D9sUa4O3tjeHDhyMpKQnFxcUYOXKk8H5ubi5KS0tRUFCAiooK5OTkAADat28Pg+HKAdKSJUsQFBSEBx98kHzGq6++invvvRetW7fGpUuXkJKSgmPHjiExMbHG/XUaPGo8za1AdoOm7NDW7pA5Gatv63qxB7UjmawLMtuXzlpOp/Xwv65+VBjoPYpuknrlLhic7Pob+4w7S1AHoWyVLKBCM+2Xr7dkDiTrzunCjxzH950jWbdpnSQ1qX3x0rEiSb3rxJVtT2KLk6KjP6BUXigm2dHd1O4VppnWYaVG0mN51JR3j4Ga+Lwl82Rp1FYoG8zUJubtRscahxu17hXnl5BMu8YVO+3/ge2nSRZx8RjJbE2oxfLTn04K5S7LZ5E6n6/8lWRJM46SrHLHd7T919YJ5TFPpZA6QZHZJJOZFpes+51kyUMvCmWLzpPUMQTR+W7/eBTJDmVSk93e9J9JNmCjeOm7JWcTnW4xzbrHjCaZ00D7u2ZChlD+61+fJXUUCx1TITGeqpJtTy0Xt3fFRu2gsrZObaQWzqZ9R5AMGvti7uofSZUG7ej22WDqfJLJxtAN734vlB+bSMcCs41+D1nqU5PmyV/ovtN1rmhW1H+ymdQpD6Jjql4yPF/8jY4t9srq72c/vouOZV0k31dOBx1DT+TSzwxv0Eooq066fj3uH0qy38ZnkOw+Cx3jPBuK29mRnQWkTlSPe0l2fvP3JLt44fYxYzO3Fj4oqyEmkwmLFy/GQw89hOBgUbubmJgoiEC6du0K4PKZsebNmwMAHA4H0tLSMHLkSOh0VBldWFiIJ554AgUFBahXrx7Cw8OxZcsWtG/f/ubNFMMwDMMwDMMwtQYflNWQyMhIQYv/R7KysqqdXlVVnDhx4qrvz5kzB3PmzLne7jEMwzAMwzAMc5vB95QxDMMwDMMwDMPUInymjGEYhmEYhmEYkTvUclhX4aXNMAzDMAzDMAxTi/CZsjsY5w02791qugaLFkI3u9m1CfVUoOIKagm1QbnVb0nrSaxOurILJHN4UvucKxh0VHElM1E29a1+/cr66lCub/kAgK5YNE6pvsGkTjNv2r7bOfqcPWtAa5IpLvxOFDN/JG2/WSjJ7pnxD5Kd2kAteKQPsntGJRZFxU6Nbk6dW7V1ZOiKqSGtQWQPksW0b0UyqOLy9ulJra4PvkxtX9p1CQA4RE18Suc+QvnwU0+QOu2nv0gyW+DdJOv+0uMkM2s2d8VCrYTDU8eSzOFVn2YSo+Rr7/xVKBuGTyF1nglqSjJLY2pj1Q2i2+zYT8X9UC9Zl2eP0qyjaRDJtKZFAEh6cplQ/nf0I6SOeQ/drr1a0+Uf/hIVRuka0Met2P3F/Vof9VdSJ+LVBiTTF52ibXkHkKxFJzGT7XJq+SWSKbZKkjmNdJ07D/0klB2h1ESpduhJsuaN7yKZVaX/Jjk089T5rddIncocat3TS6x+Bsn3Ttzy54XywdHUANnuw49JBgsdbwZ8QI2Ydi9x3XV9nu6XDokJ1ymxL7YdSsdev34xtKKGh/4teY6qSr87ZOPZwJfoeKb9rtMXHid1ZN/Jf3/jYZJVrn6XZJ2eFOu1u0T3Vbfg5iQLHES3z/tJwjCX4YMyhmEYhmEYhmFE+PLFWwovbYZhGIZhGIZhmFqED8oYhmEYhmEYhmFqET4oc4GYmBgMGkSv/weA7OxsKIqCPXv2YMKECQgPD4fRaERYWJi0/rp163DvvffCx8cHAQEBiI2NxdGjR4U6n376Kbp06QJPT08EBQUhISEBFy7Qe5YYhmEYhmEYhrn94YMyFzCZTNiwYQPy8/PJe6mpqYiIiEDnzp0BAAkJCRg+fLi0nSNHjmDo0KHo27cvcnJysG7dOpw/fx5/+9vfqur8+OOPiI+Ph8lkwv79+5GZmYmff/4ZTzxBb65nGIZhGIZhGOb2R3E6Zd4j5o/YbDY0bdoU48ePx7Rp06ry0tJSBAUFISUlBWPGjKnKZ8yYgS+++AI5OTlCOytXrkRcXBzMZjNU9fLx8JdffomhQ4fCbDbDzc0Ns2fPxoIFC5CXl1c13XvvvYdZs2ZJDwqvhbm0SCgbvf2uUvPWUllBzUkOUK2Tvuy8UFYlVja7H7X/VUr8NX5eHtX2y75vI8lszSNI5pTYuJzfLiaZvsdgkrk1ltjzNJjLSqqtAwBqeSHJtFYt2TKrcPMhmSvLBwDsv4qWN+td3UgdQ34O/cwdm0imG/o0ycxO8XciWb8qKqmBzWKnw5hNYqz00Ivte3pQg6X9aA7JFIeNZA4Puj9pTXNaG+PV0JWcJZnTzUjb96TGQe0I7pAM6arEpKY3F9N6edvpZ7a5Tygrkv2kLIdO5xk/lWRwUBuoFsVaTifzoNY0ZWsmyZz3xpJMu03JTKaqxConqQazZDvTayb22/0fUufMug0ka/TcLJKV6zxJ5nv2gFB+shW1L76z4kmS6aITSWbR0/3JaKVjhFNzc79VMp0e1Eha8TG1EPr2oleZ2Bu3Eco2D7pd25a9QbLSk+dI1mDSm7RvF46Kn+dDDZBSO7HE/ufQ0/1Qu4vpJFZIyPY5Tb8AQDFTI+P5RmFCuX45tVrKTJS2etQeKeu/dmyssNJ16W24fkuvZNbh4S4ub9k4Lvtv1C4JdZIP0NoXVSv9P8Nm8KYfcJ3IzMbldtovN4lN2U2y77h7UktjXcB2+rfa7sJV0QdRw+ztDp8pcwG9Xo/4+HikpaXhj8ewmZmZ9GDHpwAA3FZJREFUsNvtiIuLc6md8PBwqKqK1NRU2O12FBUV4ZNPPkH//v3h5nb5n7fIyEicOHECX3/9NZxOJ86cOYOVK1fioYceuinzxjAMwzAMwzBanIpaZ193InfmXN0EEhISkJeXh82bN1dlqampiI2NhZ+fa2egWrRogfXr1+PFF1+E0WiEv78/8vPzsWLFiqo69913Hz799FMMHz4cBoMBjRs3hp+fH+bPn3/Nts1mM4qLi4WX2ezic70YhmEYhmEYhqk1+KDMRUJDQxEVFYUlS5YAAHJzc5GdnQ2TyeRyGwUFBXjiiScwYsQIbN++HZs3b4bBYMAjjzxSdQbuwIEDmDhxIl5++WXs2LED33zzDY4ePSpcHikjOTkZfn5+wuutt9+5/hlmGIZhGIZhGOaWwA+PrgEmkwlPPfUU5s+fj9TUVLRq1Qq9evVyefr58+dfPlh6662qbOnSpQgJCcG2bdtw7733Ijk5Gffddx+ee+45AEDnzp3h5eWFnj174vXXX0dQUJC07aSkJEyePFkMrZLr3BmGYRiGYRimOu7QywTrKry0a8CwYcOgqioyMjKQnp6OhIQEKLI7Wq9CeXl5leDjv+h0l2+mdTgc1da5lpPFaDTC19dXeBmN9AZfhmEYhmEYhmHqFnymrAZ4e3tj+PDhSEpKQnFxMUaOHCm8n5ubi9LSUhQUFKCioqLKvti+fXsYDAYMHjwYc+bMwauvvoq4uDiUlJTgxRdfRLNmzdC1a1cAl5+J9sQTT2DBggWIjo7G6dOnMWnSJPTo0QPBwdQ0eC0sqkEo15VDNJlpUeewkkxrT3JozIIAACe1GNmuUyhqaxYuaV/SluSXI12vx+ik19ULwCEx9qmSs55OXfW7r8NAjU52iUHOVWxNOwtlxU7Xm9ONmtrcHqKX39oUavey2+n61KL8CWGs6sJasQZKjE6Sde6U/CCjaOyCik1yX6ekLbtPoEv1pPOu6Ycq2b+k8y1pX/Xxp33TGukkBkW9l8RkJ6nnko3SSduSLuseQ0mmNS0CgFERt6kyyfKR/rgmWdYyI6OXm/iZSpt7SJ1GdDKpic8Q1JFk5j2i8VRmWpw87N8km7OGLkfPzg+QzBZAjbB2VVxPMmOlQWJt9In5J8kcRmp71ZWJz92U2Rc9B1KBlpfNQtsvPU+ysxkfCuV6E98idWTIjH2KzkAzF9qSGVVtDZrTtiSGXF/NbiIzLaKEPrtU8aFbmqz/su2Y1nFtnNVLzaXVLyHZWOaU7ZuSaWXmQzKeSf43kPZD8gHacRwAnMTMKfk/QHKaQ7ok+OwTcxX4oKyGmEwmLF68GA899BA5SEpMTBREIP890Dpy5AiaN2+Ovn37IiMjA2+99RbeeusteHp6IjIyEt988w08PC7/Izty5EiUlJTg/fffxzPPPAN/f3/07dsXs2ZRfTLDMAzDMAzD3BRqcDUY8+fhg7IaEhkZedXLCLOysqqd/tFHH8Wjjz56zTpPPfUUnnrqqevpHsMwDMMwDMMwtxl8DpVhGIZhGIZhGKYW4YMyhmEYhmEYhmGYWoQPyhiGYRiGYRiGYWoRxXktzzpzW2M9e1QouwU2r5V+aKksp7Ypm+T3gWX7RHvVY52oWUons1JJbkw1evtV2y9rQR7Jyn2o8VJrc7saqsSqZfCXWPY0mEuLaN9UatD66WQpye5v4imUFYmByqnSW0ndPagxUYb13HGx7E3np9JGP9NLldisZBZLjZXNLeAuUqdwwQskc29C11PlyVMk01Jv7Jt0Osn26ZCYImXGUNmypZXo8lEr6Tp35nxLsvJDB0jm87D4AHvb7k2kTtGevSRrMGAwyRwtI0imK8wX25cY5MpAt0+3z6nxTid5TEflQxOFsredbtflK+aSzHP4ZJLpLh4n2YVVHwtlv3+9QuqYV75DMq/+fyeZw82TZNYfPxfKhpbUoHgpewPJDn72E8naPx5F+9FatIEqUY/QfmUtJdnTQ+k8PTGEmkU7Pk2NiU6LOK4aWnYgdWxnTpBMCaX9dxroMlN+/UFsq8sgUkdnptuBbN+RGWa1FkVF0pZaIRlnD24jmdtdbWg/NCY+69GDtIoftQXrgluTzFa/Gcns34j2SN2g0aTOeRs1mSrznyWZd5MAkrmHdhHKlb/uJnU8ox+nffVvSrKyJTNI5ntPT5K5df+LUJaN49qxDJCPZyUH6fL2f2yCUJaZXpW8X0hWmE3b933yDZIdGSfaQAO60n3J6E9No06JUbjiXCHJGj3/HsnqAtr/I+sSdeV/2hsJiz4YhmEYhmEYhhFhff8thZc2wzAMwzAMwzBMLcIHZQzDMAzDMAzDMLUIH5S5QExMDAYNote8A0B2djYURcHu3bsRFxeHkJAQeHh4oF27dpg3bx6pn5WVhW7dusFoNKJ169ZIS0sT3i8pKcGkSZPQrFkzeHh4ICoqCtu3b78Zs8UwDMMwDMMwUpyKWmdfdyJ35lzdYEwmEzZs2ID8/HzyXmpqKiIiIrBjxw4EBgZi6dKl2L9/P6ZOnYqkpCS8//77VXWPHDmCwYMHo0+fPsjJycGkSZOQmJiIdevWVdVJTEzEhg0b8Mknn2Dv3r0YOHAg+vfvj5MnT96SeWUYhmEYhmEY5tbCog8XGDJkCAICApCWloZp06ZV5aWlpcjMzERKSgoSEhKEaVq2bImtW7di9erVGD9+PABg4cKFaNGiBd5++20AQLt27fDDDz9gzpw5iI6ORkVFBVatWoU1a9bggQceAADMmDEDX375JRYsWIDXX3/9Fs3xrUeVGBMLK0S7nUMiCtWp1IrnkgFPgtOjekPjVZH8auPUUSOdK1gkpkW6dIByKzUaanFIDFR/xrfqMHoLZbukMZuDZk69ZJ3IzJASU5uWkz9Qk2CbMWEkK9xE7XZa6lVb4+rIfqlzSrZjLYrktzCnxOpnv1BAMum8Pyoav7TmPAA4u4uaResldiaZzeBNMtQTjWsON3dSpaKCbou+/YaT7NT71Mjo/ZAmkCzXY9/uIlmH7tSk5gykJrvcL8VpwyPWkjo751M7YpTGUAfILXu/rcwWyu0/eJTU8QigffVuRK2BhzKp/S/8pfZC2aKnplTPzg+Q7IkhX5Lso69+I9mLHal9LrBXpFC25tPt5+I22tcGHWg/ZPtJyfYfhbKnxL74Z9B+B2htjADgvHiaZMX79pOsocQ8qd3HLmzbQerUa9+KTtehD8msOmokzV22USi3jZlA6pRWWEjW+p9jSPbba9NJ1qanuNMVrf2a1DH6UNuxbN+89Bu1cHq1pmOX9pvo9Fa6rL0eldgLJePZmV8Ok8x3dEPNhPT7RRdIbb6nf5a0NY7Op5uXOO4VbPuV1GkzhppMK3OpMffc7t9JJlnazP8gfKbMBfR6PeLj45GWloY/PkEgMzMTdrsdcXFx0umKiopQv379qvLWrVvRv39/oU50dDS2bt0KALDZbLDb7XB3F3d+Dw8P/PCDqBBmGIZhGIZhmJuGqtbd1x3InTlXN4GEhATk5eVh8+bNVVlqaipiY2Ph50fPsGzZsgXLly/H6NFXni9SUFCARo3E30MaNWqE4uJiVFRUwMfHB5GRkXjttddw6tQp2O12LF26FFu3bsXp0/RXvT9iNptRXFwsvMxm85+ca4ZhGIZhGIZhbjZ8UOYioaGhiIqKwpIlSwAAubm5yM7OhslEH3a4b98+DB06FNOnT8fAgQNr9DmffPIJnE4nmjRpAqPRiHfffRdxcXFQq/lVIDk5GX5+fsJr1rsLavTZDMMwDMMwDMPcevigrAaYTCasWrUKJSUlSE1NRatWrdCrVy+hzoEDB9CvXz+MHj1auP8MABo3bowzZ84I2ZkzZ+Dr6wsPj8v3CbRq1QqbN29GaWkpTpw4gZ9//hlWqxUtW7a8Zt+SkpJQVFQkvKZMGHsD5pphGIZhGIZhmJsJH5TVgGHDhkFVVWRkZCA9PR0JCQlQ/nBj//79+9GnTx+MGDECM2fOJNNHRkZi40bxBt4NGzYgMjKS1PXy8kJQUBAKCwuxbt06DB069Jp9MxqN8PX1FV5GI72BmGEYhmEYhmGYugXbF2uAt7c3hg8fjqSkJBQXF2PkyJFV7+3btw99+/ZFdHQ0Jk+ejIKCy/YhnU6HgIAAAMCYMWPw/vvv4/nnn0dCQgK+++47rFixAmvXXrGBrVu3Dk6nE23btkVubi6ee+45hIaGYtSoUTXu7/VaCGsD1UntbT2b1xfKbnZ6j5xTYhd0SF2F1WPf9AnJvCLo5ad2/6Yks66hz6Tz7N6PfojPfdX2w+CgVi3ZumwfQO1tWhQHXa5OhRorXUVXdkEoG7wDSJ0iO+2r/hJ9nITNL5hmGvufzF/5yzfUXNXmSfr7UoOO9OzynkWiaU52/llx2EimA83+lMZS2/7hH2kmsb4VHaNGPaebaOMzdowide4eQY2JTiM1nclwaNpXbXQ/dNfT/dBWj5rOgodRI2OZqtlfqTQN67+m9r8O86jdzu7bmGSFv18SykpniQGv7B2SOTv1J5mxOTVWBp0Qr36AxE6petB9NbBrC5LtTf+ZZLoGQWIfrGWkji2ALouOT1MTnMy0+MabNJvbs4dQPrrsC1JH1dF9rtH5IySr2EHbz10j2ig7J5IqsK1fQjJDmzCS2Y9RC54+pI1QdlTSZXYs/TOafX+MZA8kTCFZ5ZqFQvmTt74jdUyvU6Oqn42aBA12Ot5v3nBUKLezlJM67hK7r60+tY96NW5AMq0ZuEGXtpI6dJ+WGQ1L8gtJZmjTjU6rwS6xBzsNdD+RjWfNLl2kDWq+62TjuENiWDYX0/FMkawTrybid92p7fQ7DZJx0LunVi8LtG4URDKGAfhMWY0xmUwoLCxEdHQ0goOv/FO5cuVKnDt3DkuXLkVQUFDVq3v37lV1WrRogbVr12LDhg3o0qUL3n77bSxatAjR0dFVdYqKijBu3DiEhoYiPj4e999/P9atWwc3N8kAyTAMwzAMwzA3A0Wtu687kNvnVEodITIyUtDi/5cZM2ZgxowZ1U7fu3dv7NpFn7nzX4YNG4Zhw4b9mS4yDMMwDMMwDHMbcWceajIMwzAMwzAMw9wm8JkyhmEYhmEYhmFE7tDLBOsqvLQZhmEYhmEYhmFqEcUpu0GKuSOoLBeNU+6e1dv6bgXmshKSOSW/xpidYmZUqPlJZliy6+ijADw9qBFNi3XbF7Rfban5yamXPGpAshvJDE4G/8Bq+2H/NZs270etcjJ7oaKxY6nl1Ixl9aZ9cGX5AID9QJZQdjRsTuo4jd4kg2Q9aa2BAODQLFsPd9ovawE18dl++g/J3LoPop+psY65NabWOu1+czW0yxqQb8euoForXPtMcyn9TI3tT7FJ7J0SI6Cu6DTJ7P5NaN9Kz4l1JNudbJ8znj1EMmtAa5Jp93ODjtpT3S5Qq58zn7a/a/q7JAtfKJoVbQ2akzr6Sydo+3rJPiHbjjXbVMX/fUzqeHa7n2RF2RtI5j/kcZLZ/cXlLTOx2t2o6U/dnE4yKRJD66TBs4TymL9RO1/Hd+eSrGLjcpKd+mEPyVpPnS6UrYFtSJ2KtFdI5tuL7tPHlywmWYMOotnSK3IAqVOwfCnJ3LzoOm/4F3qP95H57wnloPupbVD18SeZ+eRxkvn0fJBklbu+F8q6IeNJnWI7tej6H/qWZM677yVZ7gSTUA6dMZ3Uyf9oPskCX3qfZPqiUyRzGOn/Gob64nZsPUeXhRSZQdiNjjfOnevEft1N14n14DaS6btQG6vsu1V/MEsMGlN379lUOv4EjppAMpll0i2A2mrrApZLZ2u7C1fFlf+nbjf48kWGYRiGYRiGYUT48sVbCi9thmEYhmEYhmGYWoQPyhiGYRiGYRiGYWoRPihzgZiYGAwaRK9lB4Ds7GwoioLdu3cjLi4OISEh8PDwQLt27TBv3jyh7urVqzFgwAAEBATA19cXkZGRWLdunbRdAHjzzTehKAomTZp0I2eHYRiGYRiGYZg6BB+UuYDJZMKGDRuQn59P3ktNTUVERAR27NiBwMBALF26FPv378fUqVORlJSE99+/cmPs999/jwEDBuDrr7/Gjh070KdPH8TExEgfJr19+3Z88MEH6Ny5802dN4ZhGIZhGIZhahe2L7qAzWZD06ZNMX78eEybNq0qLy0tRVBQEFJSUjBmzBgy3bhx43Dw4EF89913V227Q4cOGD58OF5++WWh3W7duuHf//43Xn/9dYSFhWHu3Lk17nddtS/K7HZOlZqkNh4pEsr9WviROqrNTDKtwQ+QW/y0yGxQDndfkjl1btW2BQCK3Uoyo8TIpcVSdJ5+puRm24MlNAutp+mbZDoHqN3OVfuiufiiGEjW28EiOqS0bUDbVyRWLW1/3T2oodG2ez2dzI9amJwXTpJMa0RT29xH6lRWuGZChMS+6NJN0ZLppPZFib1Tf4maziyN2wllXdkFlz7T7h1AMqvkdzqDXdzHHBKTY4WVtu+l0O1fZimtVMX9VWZfVL7/lGb3/pVkamUxyWxbPhenGziaTrfjS9rVrtSKp1RSc6zubK5QtgeF0jrFZ0hm2XX174U/oo8S59Ps3YjUsUu+xX1O7iSZNZ+aS48u+4JkFRfEMXrhamq6/HfeSpI5LXQ7Tot6gmQjNs4Vyra2PUmdfY/8hWRdZ79EspTutP2H4zoI5TbPP0fqpEcmkizAk47t0Vs/I9lHHWOFco+eIaSOf8sGJAt5ldoLHRIL7f7hDwvl9pl0+zxbRk2gQQrd/lXJNqsdD5RCOq7YQ7qQzCGxBhpO7iaZrT41CWrti/ZjdDqZnVWnsb8C8u9W7Xgm+56WjY26QmpetTQNI5khP0cMJP9nyMyoDk9/kslwC2zuUr1bjez/kbqCwa9hbXfhhsNnylxAr9cjPj4eaWlp+OMxbGZmJux2O+Li4qTTFRUVoX79+ldt1+FwoKSkhNQZN24cBg8ejP79+9+YGWAYhmEYhmGYGuBU1Dr7uhO5M+fqJpCQkIC8vDxs3ry5KktNTUVsbCz8/OgZnC1btmD58uUYPZr+KvtfZs+ejdLSUgwbduU5KMuWLcPOnTuRnJxco/6ZzWYUFxcLL7OZnkViGIZhGIZhGKZuwQdlLhIaGoqoqCgsWbIEAJCbm4vs7GyYTCZSd9++fRg6dCimT5+OgQMHStvLyMjAK6+8ghUrViAw8PKlVydOnMDEiRPx6aefwt2Fy+3+SHJyMvz8/IRXyuzZNZxLhmEYhmEYhmFuNXxQVgNMJhNWrVqFkpISpKamolWrVujVq5dQ58CBA+jXrx9Gjx4t3H/2R5YtW4bExESsWLFCuERxx44dOHv2LLp16wa9Xg+9Xo/Nmzfj3XffhV6vh90uuQfn/5OUlISioiLh9dyzz96YGWcYhmEYhmH+t1DUuvu6A6F3JTJXZdiwYZg4cSIyMjKQnp6OsWPHQlGu3JS+f/9+9O3bFyNGjMDMmTOlbXz22WdISEjAsmXLMHjwYOG9fv36Ye/evUI2atQohIaGYsqUKdDpqFThvxiNRhiN4o2nMqEGwzAMwzAMwzB1Cz4oqwHe3t4YPnw4kpKSUFxcjJEjR1a9t2/fPvTt2xfR0dGYPHkyCgoKAAA6nQ4BAZetQBkZGRgxYgTmzZuHe+65p6qOh4cH/Pz84OPjg44dOwqf6eXlhQYNGpDcFdRK0V6IOmJflP3CIXOANtSYsGR1ZLYjfXEBrejevNpuWb2pwU+GCtoRp0KNcdf7S47Fja4nVdK+hxu182k/U2Za1FdcJBk8gmkmwe7mWW0dXyM1Y0nXnUJ/ZDAWHBCDFt1Inbz5C0nW4p/DSOaooD9K7J8xVyiHf7lB0i+6zGT9V2XbsWZaRTahZDr9haO0noQfH59Ish6viRa5ihNHSJ2T3+eQrOW8NJIZbZUkcxp9hLL+ErVauvs1IRlsdN71l6jpzFmvlVB2O7iZ1Nm/iJr+QiX2RYdnPZJtT84UyvcMoNa97UnzSRY+TWJyLDxLssNfbRXKrSV9xb4sElkLC0m2d/EmkkW8Klr89PfGkjoGK93WbWfosr64bRvJVB3dHju+O1co/zuFtv9kq0dINq84h2SP5+8g2d7HxHUXuqw3qdP5s+UkszuocXDSOfqZqlk0DtrNtP/x2z8h2elPPqKf+evPJPvXzqVC+dzKj0kdc2EpyWSmRdVC++ZRT7x9we0C3ad9fJuRDA4DiWx76P7k1jZCKNsLqeHQ0ZL2VTY2Hpj+Gsnavv0u7ZuG39+k090VO5hkZb//RrKS49RmGjBVNFvKlqtMDnFk/nu0H1PfpP34Sfyu2DWfWoDvfWcSycr355DsxCaadfrsa5Ix/3vcmef/biImkwmFhYWIjo5GcPCVf2RXrlyJc+fOYenSpQgKCqp6de/evarOhx9+CJvNhnHjxgl1Jk6k/2gxDMMwDMMwDPO/AZ8pqyGRkZGQPdptxowZmDFjxjWnzcrKqvHnXc80DMMwDMMwDMPcPvCZMoZhGIZhGIZhmFqEz5QxDMMwDMMwDCMiu1+euWnwmTKGYRiGYRiGYZhaRHHKbpBi7ggsl0RTmMHfNbvgzcZcVkIyxWaufkKng0YSm5VFoSeAfTxpPS37HqfmJ5lFyuHdkGTmzLdJ5tX7YZLpJDZBLeXLk0nm1ovaBXXl1N5mbdRWrHMpn9SpqNecZK4sHwDIm/ioUG72AjVoKZV0/Rakf0Cyhs/Sh5tXak7e+3nRfske9eCQmBwdkqFNp4q/+nlIHtJuLrlEMtm2J7WI6tyqrSNDsVZcX/sAnKo471Lho+THTplF0b4ni06rsRw6t1C7oNNCrY36e2JIZvUNIpnFLi5bd1VmbaTb8fzQv5DsyY0ptG+afa7oE7qv+o2cQqczuGirtYu20aIPXyFVSk9Su12zSfQz7V4NSKYvOiW2v341qeMT80+Saa2ZAODUSWy156nZT2ua8+zRj9SxNelEsom+YSR7aUY0yYL++YRQNgdTu/DJZ+NJdibnNMnuXfQG7dspcZ4UN2olVNypSVbXgFpobfXvIpn+4nGhLLP6OY10+znz0TskKzlBTYJ3vyRuQ7LxwRbQmmSKmRofnUZvkulOHxTK1rvo95Lh1F6SyT5TZkBWKy6RzC2wuVCWjbOy8U06hkrGY3XXWrGKmY5JurbdSWarR9evYqcGYe1nKpZyWkeVbAeScUTWvsGP/l9RFzCXFlVfqZYwevvVdhduOHz5IsMwDMMwDMMwInfoQ5rrKry0GYZhGIZhGIZhahE+KGMYhmEYhmEYhqlF+KDMBWJiYjBo0CDpe9nZ2VAUBbt370ZcXBxCQkLg4eGBdu3aYd68eULd1atXY8CAAQgICICvry8iIyOxbt06oU5JSQkmTZqEZs2awcPDA1FRUdi+fftNmzeGYRiGYRiG0eJU1Dr7uhO5M+fqBmMymbBhwwbk59ObzVNTUxEREYEdO3YgMDAQS5cuxf79+zF16lQkJSXh/fffr6r7/fffY8CAAfj666+xY8cO9OnTBzExMdi1a1dVncTERGzYsAGffPIJ9u7di4EDB6J///44eZLelM8wDMMwDMMwzO0P2xddwGazoWnTphg/fjymTZtWlZeWliIoKAgpKSkYM2YMmW7cuHE4ePAgvvvuu6u23aFDBwwfPhwvv/wyKioq4OPjgzVr1mDw4CsmwPDwcDz44IN4/fXXa9TvygrR2OTu4Zph72Yjsy/CYa92OsVukYT0dwWzG7UduWIXLFo8jWSeMYkkc3jWoxP/vIZE+lZdSKZrRjMtFf+hxkdd1N9o+xorGwBYG7cT61w8SuqUN6AGLVfti8WpLwtl96F0u1fN1I5o3fYVrTeALlutOVPWL+12DQAOuPYsFa2FUGpf/BO2KZmJ7EaiNS3+GRTJPiezoDr1RqGsykyREhwSM6orlkxVoorUV1wkWeWahSTzeGgUbd9DNHQpe78ldZydB9JMZoKToDWp6Y/tIHVs5wtIpnboSTKHxJSnVojbo3rhGJ0uoCXJnAZqF5T9umxb+2+S5W/aKZR/WHmA1Hk8n87nhZTJJHttxjqSvXcmSyjbvQNIndXNqClvyFuP0LbGfUaywQ+Ky6PjtKdJnVXRtK/NuzQiWfjHdDtbFSYaSe8bex+po3OnxsdGw+n2Cck+V7TxS6Hs9egkUqdU70sybwc1Asr2Q+0+7EodgI4FgHybktkFjT7+Qllm0f0zUgldqWg4lY1lsu9uh8yO6LCRzKUzM3+i/3Xl/zMt0vVUR3D3dNGQexvBZ8pcQK/XIz4+HmlpafjjMWxmZibsdjvi4uKk0xUVFaF+/fpXbdfhcKCkpKSqjs1mg91uh7vmH0UPDw/88MMPN2BOGIZhGIZhGIapa/BBmYskJCQgLy8PmzdvrspSU1MRGxsLPz/6rIQtW7Zg+fLlGD169FXbnD17NkpLSzFs2OVnUPn4+CAyMhKvvfYaTp06BbvdjqVLl2Lr1q04fZo+n+WPmM1mFBcXCy+z2YVnfzEMwzAMwzAMU6vwQZmLhIaGIioqCkuWLAEA5ObmIjs7GyaTidTdt28fhg4diunTp2PgQHpZDABkZGTglVdewYoVKxAYeOWhzp988gmcTieaNGkCo9GId999F3FxcVAlDyX8I8nJyfDz8xNeKSn0QaoMwzAMwzAMw9Qt+KCsBphMJqxatQolJSVITU1Fq1at0KtXL6HOgQMH0K9fP4wePVq4/+yPLFu2DImJiVixYgX69+8vvNeqVSts3rwZpaWlOHHiBH7++WdYrVa0bEnvGfgjSUlJKCoqEl7PPffcn5thhmEYhmEY5n8TRa27rxoyf/58NG/eHO7u7rjnnnvw888/X7N+ZmYmQkND4e7ujk6dOuHrr7++3qXoMnxQVgOGDRsGVVWRkZGB9PR0JCQkQPnDTen79+9Hnz59MGLECMycOVPaxmeffYZRo0bhs88+E2QeWry8vBAUFITCwkKsW7cOQ4cOvWbfjEYjfH19hZfRSG/KZRiGYRiGYZj/FZYvX47Jkydj+vTp2LlzJ7p06YLo6GicPXtWWn/Lli2Ii4uDyWTCrl278PDDD+Phhx/Gvn37bmo/2b5YQxITE7F69WoUFxfj+PHjCA4OBnD5ksW+ffsiOjpauGxQp9MhIOCyWSojIwMjRozAvHnz8Le/XbHpeXh4VN2Xtm7dOjidTrRt2xa5ubl47rnn4O7ujuzsbLi5uWYD+y+W86LC39Cw6XXN843GVevSrxfFe+La+UkMe5Lp7CpdTp4e1LJHpju2m2TWAGoqVJwOkukkJkS7DzV5aQ1UMsorKkmmOqkp71gpzZp5a+x2kuUjMxW6snwAwHJRnE+HO72fspiKt1C/nC4fmz/dHrV9k/Vr18P0kuCOU8eTzH6BGu+WPT5HKI+8cJDUcdU2JTOMaY19MouXDPXgZpIpEtPiic+WkSz4FdGep7tEH91h+WktyfR9HqPZxeMkszYKFdv6fC6p4z5oJMmcEpOgrpD2raRhG6HsVUrvn90/7kmSdXrnLZJZA9uQbFnTbkL58YP/R+rkTplAspbvfUIytbyQZHkvThLKLeakkTq2r94nmYw1EzJI1qKTaCYMX0ltg7qyCyRznMojWfH2H0mWu+YXkkUsmiuUnRfpOtn7yjsk6/LOGySzezck2VONegvld8p/JXWMZw+RDBXFtP2GLUimnhD/cVIl466jiC6zspytJHP/O7U0ak1/J96hZuRGkRL77oAEkunz95Ds//o/JZTbDm1L6oS8/THJZMZE+ya6HRvCxSt07Ef3kzq2iIdJJhGj4j93dSNZ7K5VJNM3Ec3AZ2dPJHX8xtLlKDMIX1qTTjIv06vidJJx0GGktr69I0aQLPSxXiRzWMWx/NQPdL21NMWTTG3UnGSVW6mN2CtOfmVVbSOzHdcVFFUl7gSj0Sg9IXHPPfege/fuVY+pcjgcCAkJwVNPPYUXXniB1B8+fDjKysrw1VdX1tW9996LsLAwLFxIjaw3Cj5TVkNMJhMKCwsRHR1ddUAGACtXrsS5c+ewdOlSBAUFVb26d7+i9f3www9hs9kwbtw4oc7EiVcGp6KiIowbNw6hoaGIj4/H/fffj3Xr1tX4gIxhGIZhGIZhrhenotTZl8ylkJycTObBYrFgx44dwu1Cqqqif//+2LqV/hADAFu3biW3F0VHR1+1/o3i5j5U5w4kMjISspOLM2bMwIwZM645bVZWVrXtDxs2rMrGyDAMwzAMwzCMSFJSEiZPFs9ky86SnT9/Hna7HY0aiVcvNWrUCL/+Ss/SA0BBQYG0fkEBvQLnRsIHZQzDMAzDMAzD3DZc7VLF2xk+KGMYhmEYhmEYRuBOsE40bNgQOp0OZ86cEfIzZ86gcePG0mkaN25co/o3Cr6njGEYhmEYhmGYOw6DwYDw8HBs3LixKnM4HNi4cSMiIyOl00RGRgr1AWDDhg1XrX+jYPviHUxFpWjx83B3zbB3s5HZfJwSrZPi0NgFXXwuhawtV+ZdZj2U2aYUyS4jMxrKcMVy+Gf6oUW2LGS4um1o+/Znlo8KWk/bX1m/tNv1n+Fmt+8qrqxLwPX1eauR9f9m99XVb666sMj+TF+v9xv6z8w3WZ8S46xTYgeVtqUdxwE4FHHayZ6hpM7ccmpGdel7AiDfFa5O5+o83Uhk61fbXVkdq4OGBp1rK13bnqvbnavblGw8cPfwEMo3e5y90fvczR5H6sr/Z1pk/4/UFVy1RgOXlfgjRozABx98gB49emDu3LlYsWIFfv31VzRq1Ajx8fFo0qRJlShky5Yt6NWrF958800MHjwYy5YtwxtvvIGdO3eiY8eON2uW+PJFhmEYhmEYhmHuTIYPH45z587h5ZdfRkFBAcLCwvDNN99UyTyOHz8OVb3yY05UVBQyMjIwbdo0vPjii7j77rvxxRdf3NQDMoDPlN3R8JmyK/CZsmvDZ8puTvuuwmfKag6fKat5Wy5Py2fKbhl8puzmwGfKbgx3ypmy2wW+p4xhGIZhGIZhGKYW4YMyF4iJicGgQYOk72VnZ0NRFOzevRtxcXEICQmBh4cH2rVrh3nz5gl1s7KyoCgKef3xuQd2ux0vvfQSWrRoAQ8PD7Rq1Qqvvfaa9NloDMMwDMMwDHMzcDiddfZ1J8L3lLmAyWRCbGws8vPz0bRpU+G91NRUREREYMeOHQgMDMTSpUsREhKCLVu2YPTo0dDpdBg/frwwzaFDh+Dr61tVDgwMrPp71qxZWLBgAT7++GN06NABv/zyC0aNGgU/Pz9MmDDh5s4owzAMwzAMwzC3HD4oc4EhQ4YgICAAaWlpmDZtWlVeWlqKzMxMpKSkICEhQZimZcuW2Lp1K1avXk0OygIDA+Hv7y/9rC1btmDo0KEYPHgwAKB58+b47LPP8PPPP9e436rdqknqyPW3kvsTDOd/J5nNP0Qo29cvInV0AxPpdLrre5igsuFD2q+ufWj79ZuT7NDjsSTr/PoU+iEd+1XbD51Du94AkHUJKDZ6rbfD3U9sq6yQ1LF4Nqi2D1fD8GuWUHYG3U0rSe79Ozr1aZK1mPcJyRyqW7V9UK10vp1619a5YtXczyi5jt9w5hCd0MVf5ey+gULZqXdtn1MP/UAyRW+g7be+l3ZNO++S/UuRZLJtSq0oIpnDu6FQ1hWdom1JsHsH0LYMniSzae6N0av05g23C0dIVvbNpyRzH/4cydRycR+4mPY2qVN/FJ3O7uXafqJWistMV3KO1Clck04y33g6PiiWctp++SWhXLZpNanjOTCOZA7J8pdhW7+EZJXnLwrlvC+3kzqdP1tOsuNTnyTZz8v3kWz4L58JZdn9Y5M825Hs4Zb1SOYVSLepxmFBQrlZPF0+u15+n2QBHYNIFvLCTJLtf3KMWKdPZ1LHvYEfzbrcTzJL7h6SOcqKhbLxAfr9ojRoQTJdZTHJnG4eJLO7MM7KkN0rptgtkorVX4Sl2swkc0jGcen9trLxTPOZqqRfdsn/BvT/JMAp67+mG6r2uwSAU0eXq1Ol/2Yrks+UfRcx/3vw5YsuoNfrER8fj7S0NOEywszMTNjtdsTF0QEfAIqKilC/fn2Sh4WFISgoCAMGDMCPP/4ovBcVFYWNGzfi8OHDAIDdu3fjhx9+wIMPPngD54hhGIZhGIZhro6zDr/uRPhMmYskJCQgJSUFmzdvRu/evQFcvnQxNjYWfn70F7EtW7Zg+fLlWLt2bVUWFBSEhQsXIiIiAmazGYsWLULv3r2xbds2dOvWDQDwwgsvoLi4GKGhodDpdLDb7Zg5cyYef/zxa/bPbDbDbNb88mSzwGi8vrNGDMMwDMMwDMPcGvhMmYuEhoYiKioKS5ZcvtQjNzcX2dnZMJlMpO6+ffswdOhQTJ8+HQMHDqzK27Zti3/9618IDw+vaisqKgpz5sypqrNixQp8+umnyMjIwM6dO/Hxxx9j9uzZ+Pjjj6/Zv+TkZPj5+Qmvt2bTy3QYhmEYhmEYhqlb8JmyGmAymfDUU09h/vz5SE1NRatWrdCrVy+hzoEDB9CvXz+MHj1auP/savTo0QM//HDlfpLnnnsOL7zwAh599FEAQKdOnXDs2DEkJydjxIgRV20nKSkJkydPFkOb5FpvhmEYhmEYhqkGyePwmJsInymrAcOGDYOqqsjIyEB6ejoSEhKg/OGJgvv370efPn0wYsQIzJxJbw6WkZOTg6CgKzcXl5eXC08VBwCdTgeHQ3Jj6x8wGo3w9fUVXnzpIsMwDMMwDMPUffhMWQ3w9vbG8OHDkZSUhOLiYowcObLqvX379qFv376Ijo7G5MmTq549ptPpEBBw2YI1d+5ctGjRAh06dEBlZSUWLVqE7777DuvXr69qJyYmBjNnzsRdd92FDh06YNeuXXjnnXeI3dEVFGI38qlxG7cKxUpNTFpsxdQspbuBfbCXXKJhNQfD/6WyWNJ/9fp6JzM/UR8dAB2189E6N3YXd1pE86HUvCWxTRUdo1Y/Ga48e0Rx2Gi/nBKbmGw5yvqrRWKslOKw0/Y9/cVAsixkmA/nkEx1p1Y5fcsIkjmh+fFFZg6TLDNdKbUE2g//QjKli2gMteV8R9uXbOv6Tj1pPf+mJLI7xWkNDrqOFMlZ/3O7DpMs5O90nWjXk6WYGg6lNrfrhI67gLmw1LXPlG1TGstq6Um63rxkV0W4OE+GNmEkM3YQx5auvehzOu2SbepMzmmSDXnrEfqhFeJY7lToCCczLX7xO90356+ZTTLL0V+FsiLZPrvOGEsyXb1Aktnd6fdm5+SXhbLTJrHpedH+OwqoRfTCjr0kC358pBiUXSR1dDK7pmSdSLcpiSXwupHaESX7oSvTuYjMJnvdrUmWj9ONLh+tpVFXTLd1rf0YABxGb9qWxJIJH/9rdJL5X4EPymqIyWTC4sWL8dBDDyE4OLgqX7lyJc6dO4elS5di6dKlVXmzZs1w9OhRAIDFYsEzzzyDkydPwtPTE507d8a3336LPn2uaNffe+89vPTSS3jyySdx9uxZBAcH41//+hdefln8EmAYhmEYhmEY5s6AD8pqSGRkpKDF/y8zZszAjBkzrjnt888/j+eff/6adXx8fDB37lzMnTv3T/SSYRiGYRiGYZjbBb6njGEYhmEYhmEYphbhM2UMwzAMwzAMwwjIrgxjbh58poxhGIZhGIZhGKYWUZx8GHzHUlRWIZT9vDxqqSciFZWVJFMkm6HWyKVILEm6sgsks/lQg5aHu/t19UuGrK+u4u5R/TqQ9cPVj1Q11iuZpcquUrOUp0f1y+dqfdPi6vKRGde0yNabq+tJhrZvsvVRWVFBsuvGRQOe8zpNnX8G2XqS2SkdetHuqFqovVC2nTkMXjSTe0SrRbapaG1ogHzb1tlFG6JipevXYaSGPVfXiWoVt0eHG91mVYmRUbtcr1ZP21+nG91m1dLzJLP7NqadleD8djHJCr7fLpSXL/iJ1Jl0Lodkbsd3kuzNbtQc/NzxTULZ4UlNhdv7DSRZ1EevkmxcJxPJ/nlfiFC+99P3SZ3/i/wHybyDqSnv/jVLSDan7VChPPyp+0gdGQHTF5BM9r1WukgUe/kkUNFXpULtu+6g9kWHxLSo3fVl+5dsGJfVk40jZsmwp/3/40aO4wAdW1SJj1E2/kjnSbJOHIo4HvyZZSbDlf9RaoNLpRJbbR3B35saim93+PJFhmEYhmEYhmEE+OHRtxa+fJFhGIZhGIZhGKYW4YMyF4iJicGgQfThmQCQnZ0NRVGwe/duxMXFISQkBB4eHmjXrh3mzZsn1M3KyoKiKOT13wdNA0BycjK6d+8OHx8fBAYG4uGHH8ahQ4du6vwxDMMwDMMwDFN78OWLLmAymRAbG4v8/Hw0bdpUeC81NRURERHYsWMHAgMDsXTpUoSEhGDLli0YPXo0dDodxo8fL0xz6NAh+Pr6VpUDA6/cA7V582aMGzcO3bt3h81mw4svvoiBAwfiwIED8PKi92gwDMMwDMMwDHN7wwdlLjBkyBAEBAQgLS0N06ZNq8pLS0uRmZmJlJQUJCSINzO3bNkSW7duxerVq8lBWWBgIPz9/aWf9c033wjltLQ0BAYGYseOHXjggQduzAwxDMMwDMMwDFNn4IMyF9Dr9YiPj0daWhqmTp0K5f/rdDIzM2G32xEXFyedrqioCPXr1yd5WFgYzGYzOnbsiBkzZuC++65ubioqKgIAaTvVobs+0dlNR2rnk9jbFE01fdEpUschMZFdrxxRZnNzqq7tIjKjm8xIBxfsi64itzpprkh20f7nKtplJLPdyeZbrSwimd2TbtPXu+5cnc6VXUJmIIQiudJbYuhSHKL9TDGXutQvh1cDkjklnykzAmr3J1eslgCgWspIpj+XSzJLcCfx83avJ3VsBcdpWwOpdc/mRs/2a1edm6z7DrpN6c/+RqsFtSeZWnZRKCv5B2gf7o6knymxI8pQK4s1AV1HupKz9DP9gmlb5YW03iHRfKi/qx2pczbjQ5I1ePIV2pZkPNOHtKHTdhBtjg/HldC+mmlmO3WEZIMfbEmnPbFPKDtCe5E6jcOCSGY5+ivJtKZFAPjkxxNCOVIytnf8ZzeSeTTwJZlTRy2HMY+K21lAZFc6nZnaBWV2Te32CQBed4lX5Lidobcw6A30u8QuGUcgGWe13GgHt1pH//dwSGZU56oekWFuAXxPmYskJCQgLy8PmzdvrspSU1MRGxsLPz8/Un/Lli1Yvnw5Ro8eXZUFBQVh4cKFWLVqFVatWoWQkBD07t0bO3dSjTAAOBwOTJo0Cffddx86dux442eKYRiGYRiGYSQ46/DrToTPlLlIaGgooqKisGTJEvTu3Ru5ubnIzs7Gq6/S56bs27cPQ4cOxfTp0zFw4JVnrbRt2xZt27atKkdFRSEvLw9z5szBJ598QtoZN24c9u3bhx9++KHa/pnNZpjN4q9wVrsDRqNrv/YyDMMwDMMwDFM78JmyGmAymbBq1SqUlJQgNTUVrVq1Qq9e4mUXBw4cQL9+/TB69Gjh/rOr0aNHD+Tm0kuGxo8fj6+++gqbNm0ichEZycnJ8PPzE15vz05xfeYYhmEYhmEYhqkV+KCsBgwbNgyqqiIjIwPp6elISEiour8MAPbv348+ffpgxIgRmDlzpktt5uTkICjoyrXzTqcT48ePx+eff47vvvsOLVq0cKmdpKQkFBUVCa9nnn2uZjPIMAzDMAzDMLj88Oi6+roTUZzOG32L551NYmIiVq9ejeLiYhw/fhzBwZdv1t63bx/69u2L6OhopKRcOUOl0+kQEBAAAJg7dy5atGiBDh06oLKyEosWLcJ7772H9evXo1+/fgCAJ598EhkZGVizZo1wqaOfnx88aiiJKK8QbzT29HC/rnm+0Wj7BQClFok0QXMDrq8iETBIbqqX3czu7sKys508SLLz3neRzNdAP9OtpIBkTj29QdxQn97c70o/LPXpwbneQiUSNoO3WKfyEqnjcKf3QLqyfADAlr9fKBf70X656+lvPYZLJ0hm8ac36KuaK8Vl/XL8/gvJ7N4NaWclqOWXhLKueRipU1lOBRg3G13pORpKRB+KRM5hqyduo4pEJiCVzljpfihF1fRDsn/J9jmpMEVSr1T1FMo6iSXA5yS979Zp9CaZrX4zkukvHBXKDk9/2i3NdgEA1sC7SaZIhBFaOYfTjV4yrr8kkRRJRA2Kjbbv8BJFDU4dXYayfVq2TcmERI7jVHyiqxcolBXtNgDA7tuYZM7ftpNM9fanmY+YmZuG0T7sWksyRTLeq43pGKRdT0+2eoTUeWVWDMkaDoklma1pZ5LpC8XxzHH2GO1Dk7Y0k+wTipXur85yUYzkaES3Rat3IMn0FVQaoljoOncaNcIdu43UkcmHZOgv/E6n9Q4gmcFf7K/tNBX12CTyG9k+p9jo2KUVssikOQ4fusx0l/JpPUn/lQpxnSiS/qt+9HvI5i/5zpeIiwwNq78iqjY4V1xe2124KgG+ntVXus3gM2U1xGQyobCwENHR0VUHZACwcuVKnDt3DkuXLkVQUFDVq3v37lV1LBYLnnnmGXTq1Am9evXC7t278e2331YdkAHAggULUFRUhN69ewvtLF++/JbOJ8MwDMMwDMMwtwYWfdSQyMhIyE4uzpgxAzNmzLjmtM8//zyef/75a9bhE5cMwzAMwzBMbcP/k95a+EwZwzAMwzAMwzBMLcIHZQzDMAzDMAzDMLUIH5QxDMMwDMMwDMPUInxP2R2M6qRGw7qA1rAHAB5u9PeBkyWidUkq2pFY5Ryg9jZXcBh9SOZvkP1uQfsvMy0qNol9zpV+eFCTmiKZpRKVLhAvbd8ktrvrXT4A4NQsIzcdbavMSteJXmv7gnyeHM7q++YoOk/bkiwzmX3Laa7eJKU4qIlMiuxae9lMudKUbPuRGNHUypJq25Ib3qitzC4xjDlldjvNtE6dG6ljk/y+5yapJzNDam2LbhL7ouXor7RfEQ+SzOFGDbO23F1iH7pF0+nOHCUZZPZFybahtRxaG7ejbUnGKXtuDq3noGO22qGn2JTEOilD1djiAMB58TTJjqV/RjLPxqJ5b8O735M68ds/IZndnY5Jq6Inkyz2/8RnaCrBdL53vfw+ybrOGEuy/4v8B8k6/rObUJaZFqdP+ZJkj63aQbKoLz4m2aaHEoVyQAdq3Qvo3JxkDZ96jWSy/enIpBFCucU8uqzPlFErYRM32Tgi2Q4gjse6krOkjs2LzpPsu/v/sXfmcVFV//9/3ZmBYd8UBQTFHVwzNQMz99CMtC+lkbkE6scSlyxNy1IrsyIzTbOyBA1Nc0lLzbJUxNQsXBI1d3PHjX0ZmJnz+8Ofk3feBxkGFdD38/GYh57XnHPP+5577hnOXV5H5lwqHOl4T+Iy2OZyqxTTMVs2Dlq70JpdJe61MmfarHSiCS/qhKizdll1pvtolvzOyZDFwTAA3yljGIZhGIZhGIapUPhOGcMwDMMwDMMwKiQrXDJ3EL5TxjAMwzAMwzAMU4HwpMwGIiMj0aNHD+l3KSkpUBQF+/btQ3R0NIKCguDs7IzQ0FDMmjWL5DcYDHjjjTdQp04d6PV6BAcHY8GCBdJtL126FIqioE+fPrdzdxiGYRiGYRiGqUTw44s2EBsbi6ioKJw9exaBgeoXQBMSEtCmTRukpqaiRo0aSEpKQlBQELZv345hw4ZBq9UiLi7Okr9v375IT0/H119/jQYNGuDChQswm+kN4lOnTuHVV19Fhw4dyHcMwzAMwzAMcyfhtaPvLjwps4EnnngCvr6+SExMxKRJkyx6bm4uli9fjvj4eMTExKjK1KtXDzt27MCqVassk7INGzYgOTkZJ06cgI+PDwAgODiY1GcymdC/f39MnToVKSkpyMzMtCtuxUTdmSoFEicyncT1La9InU+4S9zcJPvomHmG1ulMndTItiTOajKXJJlbllJUQPNJHAFtwejsQ7cvyXelgMbratVGZokLlq4wk27M2c+m2MxOHqq0gySwaxL3RQ9H6sqmSEZ7x+Pb1UKzriTP1Y3riFb9mReIJnNazPtjkzquJp1oOS11MJMi6cfW7oWyfZShzT9LNEWy/f0TJxOt6cyPVWnziX0kz5VtKUSr9r83JIHQfRc6vSqtKaZ9XeNIHQE1uVepZpC4R/o0UCWdLqSRLH9/8R3RmrTrTbcvcZk8s/onVTroIVpu49NvEa3nlgCiFf9LXSBPrlmvSge++znJY7p2kWjmbNo+53/bTrRgPytXudqtSB7ZMSk+9AfRstMOEO3frf8SrcULtVRpXxc65l34Zj7R/AcMJVpwy5pEy9u7Q5V2bErPc99m/kTTetcgmlsA7XvO1dTjVPUnokgemdPikl3niRZ+8TjRCjPU/WzLhhMkj98W2q59RkkeTpI4bp63iqOe5Pg6O9CxXWPlBArIxwOl8cPqtMSxVererND4C/f9TjRdV+qIaU160hdEq/HCKFqlxB2x6Mgemu+xYWpBMn7KNMOBXUTTBrYgWvGpg6r0ySWrSJ6Gb04lmu7KSaJd+YGOZzVepU9WMfcf/PiiDeh0OgwcOBCJiYkQN/2RtXz5cphMJkRHR0vLZWVlWSZfAPDDDz+gTZs2+PDDD1GrVi00atQIr776KgoK1APu22+/jRo1aiA2NvbO7BDDMAzDMAzDMJUGvlNmIzExMYiPj0dycjI6deoE4Pqji1FRUfD0pHdEtm/fjmXLlmHduv+u6p84cQLbtm2Dk5MTvv/+e1y5cgUvvfQSrl69ioSEBADAtm3b8PXXX2Pv3r1lis9gMMBgsLqrU2SAXq+XF2AYhmEYhmGYEjDz44t3Fb5TZiMhISEIDw+3mHIcO3YMKSkp0rtZaWlp6N27NyZPnozHHnvMopvNZiiKgsWLF+Ohhx7C448/jo8//hgLFy5EQUEBcnJyMGDAAMyfPx/Vq9OFG2/F9OnT4enpqfp8OGNm+XaaYRiGYRiGYZg7Dt8pKwOxsbEYOXIk5s6di4SEBNSvXx8dO3ZU5Tl48CC6du2KYcOGqd4/AwB/f3/UqlVLdWctNDQUQgicPXsWeXl5OHXqFCIjIy3f3zAB0el0OHz4MOrXry+NbeLEiRg7dqxalLznxDAMwzAMwzBM5YLvlJWBvn37QqPRYMmSJVi0aBFiYmKgKP85HRw4cACdO3fGoEGDMG3aNFK+ffv2OH/+PHJzcy3akSNHoNFoEBgYiJCQEOzfvx979+61fJ588kl07twZe/fuRVBQUImx6fV6eHh4qD786CLDMAzDMAzDVH4UIdjwsiwMGTIEq1atQnZ2Nk6fPo2AgOsOXWlpaejSpQsiIiIQHx9vya/VauHr6wvgultjaGgoHn74YUydOhVXrlzBkCFD0LFjR8yfT52sAGDw4MHIzMzE6tWryxxrfoHaIcrF2anM27gTFBbYdgev0KTumk5aidWfzMkx4zTVaoWWWp/xwlGimdyp25fQ2HaDWeYMqXf3KrVcYX6eTds3CHpNRW/DZRaHi4eIpq37oE11kthkblwmOqTodTSfzJnQ4dIRdVx1WpI8hjzq4KfNpO6FJk/qnmcdr97VnWSRtb+1qyIAKBLXNFk+W5BtS4bD6d1Ee635IFW6kRt1UBx0eifRNAWZRDM7e0liU7t8mh2caTmF7rc+nToVFvmFEM26G2hA+4XMGVWGNou655k81M6iMndNzV9riPZyxwlE83Gk+/nmBeo+Z43Q0bFXKabuoELilmo93kj7ouxcOkndFxUddVE0Vq9LNGvHOLOLF8lj+oe61iltehFNk59By7r5qrcvcWfVZV0gmnCi56vMcdD6GJtdvEkebR51v4TEaTHuweFEm/pBpCpd7fk4kkc2Nlo7mQKAkDnTFqn7hlni5Fusof3Y6dAmohlDOxFNm612AzW70dclNLlXiCYbU2XOjbJzzMlZPW6YT/xF8oxv3JdoAU7093bEReqcSVwsZWOxzJFa4qYsJMfOur9YtyEAmPX0/P286f8Rbeg5eu7o3exza77TnMuw7e+RiqCWN23vqg7fKSsjsbGxyMjIQEREhGVCBgArVqzA5cuXkZSUBH9/f8unbdu2ljxubm7YuHEjMjMz0aZNG/Tv3x+RkZGYPXt2RewKwzAMwzAMwzCVAH6nrIyEhYVBdnNxypQpmDJlSqnlQ0JCsHHjRpvrS0xMLEN0DMMwDMMwDFN++GG6uwvfKWMYhmEYhmEYhqlAeFLGMAzDMAzDMAxTgfDjiwzDMAzDMAzDqKBWasydhCdlTKXADOqsmFGodlzzc6MuSZpiA9UKqTufLVi7tAG2Oy0KReIMKXFwsmlbNjr45RZQxz69k7pOWVwi85JdcQG2xZZXTIdxiWmd5IgD5otq1zeZ+6JSkEXjkjgCKkU2uEZJ3BdtdlC08/jKnPIUiYuozFlUcaT7+c7KMaq0OS+b5NEUUk3mtCgkTmSQ9W0bMHlQ51KZy6TJ6oENjaQ6mdOZUeIEZ5a481k7Mhq9a9MYMug58dprnYlWcJX2PV2G2vmz2LcByaMYC4mmkfRjSDSzlVOh0dGNbp9uSeo+J4poHIVrPifa5b1qF9RfFu4lef63O4nWeY063y5/eCDRovZ+r0qbfeqQPAdeoq6HLaa/RbQZD75AtMhnm6jSDd+cSvJsfnwI0QozaPtYOy0CwOTXflSlXz52juTROVMHwsB3aVubHagzZ+Znk1Vp7zEfkTz5RXR8cPKrRzTFSH8jrV0slWLqiGx2rUbLScYCjWScNbuUvhyPWTaWff8KrdNAY5P9BsgcNkk5yZitGOjfC8JJ4oRo5dwoG2vMeqoN+XYMrVPmAskw4McXGYZhGIZhGIZhKhS+U8YwDMMwDMMwjAo2X7y78J0yhmEYhmEYhmGYCoQnZTYQGRmJHj16SL9LSUmBoijYt28foqOjERQUBGdnZ4SGhmLWrFmqvIMHD4aiKOTTtGlT6bbff/99KIqCMWPG3O5dYhiGYRiGYRimksCPL9pAbGwsoqKicPbsWQQGBqq+S0hIQJs2bZCamooaNWogKSkJQUFB2L59O4YNGwatVou4uDgAwKxZs/D+++9byhqNRrRs2RLPPPMMqfPPP//EF198gRYtWtzZnWMYhmEYhmEYpkLhSZkNPPHEE/D19UViYiImTZpk0XNzc7F8+XLEx8cjJiZGVaZevXrYsWMHVq1aZZmUeXp6wtPzP1ef1atXIyMjAy+8oHaPys3NRf/+/TF//ny8++67dsetK8pVC87U5alCkLjKyW7ZZhnUTm3+LpJcEre44pqNiVa6FxTgkH6YaEbvQKLJHJYcT/5J81WjjmJwrltqHLJnuDWgYoFRZlarbiOZ211RSCei2ToQaA3qPmWWuB7K0Bkk7n+SdhQNHyp1Wzt79SVa+4UfEM14/iTRzqxaq0o3mPMdySNrMxlSx0Qb3TqtcTj3t0359rwyhWjNFi9TpfWXjpA8xQdSiKY060g1iROc9TGW7XexoOemzkzz6Y7+TjRDgw6qtGMxdXP7sVUfovU69BuNVeKatn/QIFXaur0A4Jf/fUm0Hv9sIZo2+wLRLn+rLuv14jskD9LotgpP0+N0bBVtnxYfqrcnajSi25dQfOoQ0a7+kUq0bz7cRLRRiUNV6YdOXCV5Lq9YSLTqz1AnxPYvtifamY/Vv2t+784neYI60wuSwkhd6/qNpNv3DWulSpsv/UvzNK1OtC0bThCtx/NxRLN2W5w5fzfJM35MONEUs5Fo1mMqAOyctUWVfnwULVdspuec0FLHx/zFHxLN7Sn18cW/+0kec4sIosmcYy9/Sp0taw57lWjwb6hKnpw2mWSp/eFXRNNdo8euaPsqGlu3WHVa4jopc0G9GP8G0Wq8OYdohb98o0ofWU7P1VYzphBNNKX9wJi6nmjoQl1KmfsPfnzRBnQ6HQYOHIjExESImwal5cuXw2QyITo6WlouKysLPj4+JW7366+/Rrdu3VCnjvqP9xEjRqBXr17o1q3b7dkBhmEYhmEYhmEqLXynzEZiYmIQHx+P5ORkdOrUCcD1RxejoqJUd79usH37dixbtgzr1q2Tbu/8+fP46aefsGTJEpW+dOlS7N69G3/+Se+83AqDwQCDQX1lSDEYoNfbco+IYRiGYRiGYf7DzPaLdxW+U2YjISEhCA8Px4IFCwAAx44dQ0pKCmJjY0netLQ09O7dG5MnT8Zjjz0m3d7ChQvh5eWFPn36WLQzZ85g9OjRWLx4MZycyvao4fTp0y2PR974fPjxrNILMgzDMAzDMAxTofCkrAzExsZi5cqVyMnJQUJCAurXr4+OHdXvZRw8eBBdu3bFsGHDVO+f3YwQAgsWLMCAAQPg6PjfM+Cpqam4dOkSHnzwQeh0Ouh0OiQnJ2P27NnQ6XQwmUp+12XixInIyspSfcaPHX17dpxhGIZhGIZhmDsGP75YBvr27YvRo0djyZIlWLRoEV588UUoimL5/sCBA+jSpQsGDRqEadOmlbid5ORkHDt2jNxl69q1K/bvV79w+8ILLyAkJASvvfYatFptidvU6/XkUcUiUVSW3WMYhmEYhmEYAJBYjDF3Ep6UlQE3Nzf069cPEydORHZ2NgYPHmz5Li0tDV26dEFERATGjh2LixcvAgC0Wi18fX1V2/n666/Rrl07NGvWTKW7u7sTzdXVFdWqVSO6LQiJM2GlQKE3aIslZ76DxiqfpBxM1I3L4fIxms+tTalhFfmFEk1o6ERY5kBlrNOalpXEa8sRkT3DbZsfIEUWv+NV6kqIWnTfZZj0buq0WTZkU9c9s6OrTdvXXrRypPMJIHnC5kwgmvD0o9typ+5qQX1Kb0lZm0mRmC+Kmy7SAPK+IsPkVUuyMVrWs443zWcVr9DTtt4c+xHRuqR2J5pSlE81q3FEl3Ga5HHyrk3LSZzmzEF0HNOomwxFaz8jebp9SJcNETrbHvH2baZ2UJU5sNXrInFKlSFxlEyZoXYvjBzxHsmj8aCGT/oG1F2wWih1mivcu1WV1kZQd1ltziUaqmc1onk3qU+02HddiKZx91KlverRbRkyqGugrO9pnagjoE8Lug/WOFWj72rDVdL/JQiD+hgrtWh9vi2Ciea3hba/7HdH56zeJ5nT4oefbCfarHfoL4Ai+Q1rGd1SXd/l4ySPa3XqwqkU0v7pHjmAaGYntfOtpjZdK9UMhWgKlVC9A3W/NHnQ8dj6j03/RyTL/UgqEI60f57/jbatf/dh6vokLrHCgW4r4OmnaRySMc6x/eOqtPce6p4q22/r8RkAtgz/hGgRR9h9keHHF8tMbGwsMjIyEBERgYCA//5gXLFiBS5fvoykpCT4+/tbPm3btlWVz8rKwsqVK6XvojEMwzAMwzAMc//Bd8rKSFhYmMoW/wZTpkzBlClTSi3v6emJ/Hx6NboktmzZUoboGIZhGIZhGKb8SB+GYe4YfKeMYRiGYRiGYRimAuFJGcMwDMMwDMMwTAXCkzKGYRiGYRiGYZgKRBGyF6SYe4LC/DxV2snFNge8O411XIDcgcravU3mDGdypa5gZoW6Hbk4l+7Ulvftu0Rz7EYdkWR1OqQfJpqwcrgCAJ1/w1LjKPr9O6IpDR+i29dRVzOzk9qxTJt7meQpdqcOUba0DwAUpSxVC6GPkDyK0UA0zZVTNI56DxPN+tjJ4sovoO55urwrRDO6UvdFa2TbN2RfI5oicfKC1N3LWS2YbfPNVIol75lq6fE1OrrRbGb1uSM09FVhmZPa+XG0b9cZ8ByNzUntWCb8qeubrC9mLYwnmuvw6TQ2q58gncy9U3JO6/5aTTRT1lUaW1e1qZLMQU72K6iVHBOh0xPNOrYjz/cmeZq8OoxomqAQohVVo+6IuiK1y6E28xzJY6wWTMtdPUXzeQcRTeZGWfC92gHT+ekxJI/Zuq8DuBo/lmj+A4YSrbh6PfW2JA57jid20jpzMolmbNmDaBqrMUjmGGry8CeazDFXY6Auk2YnD1Va5jQqcz8e7d6SaJ/89DrRih9VOyY6Z521afumvb/RWDv0J5r1uSMe7EXyKLvX0TpbPkbrlDgaWvdZAHD0VI/HsnFcK+mLsv2UjQfGpWrXU5cW7Ugea1dRACgMos7JGkHHbY0hRx2DE3UH1fy1hmiy30izC3URrSx/n1lz7HJO6ZkqiAa+9G+sqg7fKWMYhmEYhmEYhqlA2H2RYRiGYRiGYRgV/Czd3YXvlDEMwzAMwzAMw1QgPCmzgcjISPToQZ9bB4CUlBQoioJ9+/YhOjoaQUFBcHZ2RmhoKGbNmqXKu2rVKnTv3h2+vr7w8PBAWFgYfv75Z1WerVu3IjIyEgEBAVAUBatXr75Tu8UwDMMwDMMwTCWAJ2U2EBsbi40bN+LsWfqybUJCAtq0aYPU1FTUqFEDSUlJOHDgAN544w1MnDgRc+bMseTdunUrunfvjvXr1yM1NRWdO3dGZGQk9uzZY8mTl5eHli1bYu7cuXdl3xiGYRiGYRjGGjNEpf3ci/A7ZTbwxBNPwNfXF4mJiZg0aZJFz83NxfLlyxEfH4+YmBhVmXr16mHHjh1YtWoV4uLiAACffPKJKs97772HNWvW4Mcff0SrVq0AAD179kTPnj1vS9wyh6LKiiY/QyJqS80jczEy23mtIfvUBaL5FlGnSEjcFw27NxFN/1CEXXHofGsRzSR5sFtTkEU0a0comaultdtdWbB2t3MwUGcmxVhEtJzt1BXMSea+aENsxWZJW0j6gSyfE6xd0mxznZQicWoj7m1a2/qiJq+AbktPy0r33cptUea0KKPOJOqEqJWcY9bOfhqJo6fMfdFz0Di6LUkcDibq1mlNsYa2hVNAPaIpIeFEy7dqMwcNbR9pu0rcBY2S7mltYNz8nYk0k4Rin2Cb4nDMSVelFYkboCIZp4w+dej2tdQ90tFEz1f3DurfoCJJW2gkdeacSSeav8SNVXf2b1XaUJeOBUXH/iba1dT9RKvevDuNLU/toKoU0xhkrn4yt1QhcYY0O6jHDa3smEjGXpnT4pie7xHtg1y1+2LG0s9IHhc/+jukONLxTNandE0fVaULJX8KOlvlAeTuozKnQmnb2hCXxkanRRku3fupt1VEx1SZS6nst9Uh+yIt66X+XS4ySX5fJC6WJk3pbcEwN+A7ZTag0+kwcOBAJCYmqn6Aly9fDpPJhOjoaGm5rKws+Pj4lLhds9mMnJycW+ZhGIZhGIZhGObehidlNhITE4Pjx48jOTnZoiUkJCAqKgqennS9iu3bt2PZsmUYNoyuT3ODjz76CLm5uejbt2+54zMYDMjOzlZ9DIbSr0AzDMMwDMMwDFOx8KTMRkJCQhAeHo4FCxYAAI4dO4aUlBTExsaSvGlpaejduzcmT56Mxx6jiy0CwJIlSzB16lR89913qFGjRrnjmz59Ojw9PVWfj+Lp4q0MwzAMwzAMw1QueFJWBmJjY7Fy5Urk5OQgISEB9evXR8eOHVV5Dh48iK5du2LYsGGq989uZunSpRgyZAi+++47dOvW7bbENnHiRGRlZak+r46j73QwDMMwDMMwDFO54ElZGejbty80Gg2WLFmCRYsWISYmBory30vjBw4cQOfOnTFo0CBMmzZNuo1vv/0WL7zwAr799lv06kVfCrUXvV4PDw8P1Uevpy/lMgzDMAzDMExpCFF5P/ciirC2jmJuyZAhQ7Bq1SpkZ2fj9OnTCAgIAHD9kcUuXbogIiIC8Tc9NqjVauHr6wvg+iOLgwYNwqxZs/B///d/ljzOzs6W99Jyc3Nx7NgxAECrVq3w8ccfo3PnzvDx8UHt2rXLFGtRhtpByNHbr+w7fAcoLKCuSPkSW7NlBy6p0oNa0vi1Euc2k8RhzMW5dJc9W+Ny1FL3Nh3MdIMSdz4nZ+piZk1mbj7RtBLHuM2nqPviY3XV7zfa6sRnS/sAtI1k7SMxpYK7jopCQ121rEcjWVynJ75AtMDY/xEtY8NKohnzC1XpmuM/JXnyCwqJZisaO216FYlDHf74nkjpv20lWs1Js9UxpFGny4yUzUTzfPFdohH3SFD3M62Rtk+hhp5zeoWeEwUL3yFa8XNvqtIeErOyQ889RbQmcdRgSdOyK9FWNVWvMdm0e12S5+hvp4j2xBp6Ye3C6jVE2/TlTlW637k9JI+ydTHRru7YRbSNs+nxjV42XpXOakvfQZa1mXkdXVblyFLaN5I3niLa0PmDVelDSdRd1tmbnpuN36F9Knv9t0T7/Z11qnTXE3+SPObvP6J1tu5MtGsbVhHNtXagKu3YoAXJc/QTeu6f33WeaB1+W0G0K199oErvnLWF5GkZ3ZJovtO+JprEhBCvuYWq0rNyqetkynl6HrYPcifa1ekjiVbzxQmqdMFPC0keTT/qIir7Hbr2wWii+UcPpNtr1F6Vlo3j1mMZIB/PLm34iWje49XrwspcIWUOzmenjSda4BQaR/6yj1Xp7JPUrTlw6AiiFZ+gxy57P3UW9X15JtEqA/+kZ1d0CCUSUtOjokO47fCdsjISGxuLjIwMREREWCZkALBixQpcvnwZSUlJ8Pf3t3zatm1ryfPll1/CaDRixIgRqjyjR/83qP31119o1aqVxSJ/7NixaNWqFd566627t5MMwzAMwzAMw9w1eJ2yMhIWFkbWpQGAKVOmYMqUKbcsu2XLllK336lTJ+n2GYZhGIZhGOZuIbuTy9w5+E4ZwzAMwzAMwzBMBcKTMoZhGIZhGIZhmAqEH19kGIZhGIZhGEYFv01zd2H3xXuYwvw8VdrJxbWCIlFjHVdJaIqt3BBNRSSPcHChmpZakdmy7+YTfxGtuGZjun2JQ50u4zTdnpsv0Rw9q5cahyEnk4oSp0IZ1rEpZiPNo3Ukmi2ukABQfPG4Km3yoI6Y5LgB0F0+RrSigOa0Aqv4ZXHJ3BEVicmkSfIwvPOpP1RpbZNOJI+t/VMxFRPNuu/J8sjQZlMnL+FA990oaW/FaggXksbQFlIHLU3eVaIVp26kZR95WpU++86rJE/wi9R1zORVi2gGt5o0Dqt4HXIvkTwGV3ouOWWdJZrRM4BomiL18TQ5Uccu2a+gzElT5maqK7hGC1uhvXScaIY6bYmmM9DjdGjYIFW66SzqDKdIHDGNXkFEMztQx0RNEXV7hVU/NkvGVIerJ+m2CqgjrLFaMNH+fT1Ola71cRLJ43hNsv082tbFAc1obOmHVWnZOGV28SaaIhm7hI46i1qPobJxVneZHnOhp79DGUs/I5rHsLdV6dFudKz8dBftB9pqtP8X+tP2scYsOQFkTosy9KdTiVYU+ADRrH+DZeO4zeec5DwxO6q373DpKI3LL4TWWUzjuPTBK0SrPkntZiodMySOj9butYB8zHD0oceuMnDgQuV1X2zqz+6LDMMwDMMwDMMwzG2EJ2UMwzAMwzAMwzAVCE/KGIZhGIZhGIZhKhCelNlAZGQkevToIf0uJSUFiqJg3759iI6ORlBQEJydnREaGopZs9QrzG/ZsgWKopDPxYsXVfnmzp2L4OBgODk5oV27dti1a9cd2zeGYRiGYRiGYSoWdl+0gdjYWERFReHs2bMIDAxUfZeQkIA2bdogNTUVNWrUQFJSEoKCgrB9+3YMGzYMWq0WcXHql5oPHz4MD4//XlCsUaOG5f/Lli3D2LFj8fnnn6Ndu3b45JNPEBERgcOHD6vyMQzDMAzDMMydwiwxX2HuHOy+aANGoxGBgYGIi4vDpEmTLHpubi78/f0RHx+P4cOHk3IjRozAoUOHsGnTJgDX75R17twZGRkZ8PLyktbVrl07tG3bFnPmzAEAmM1mBAUFYeTIkZgwYUKZ4j59LVeVru3jVqbyd4rcfIk7n8ToyeHSEVXaWL0eyWPSUFcwmZOUm0vp7oJF184TzejsQ+uUbN/RLHGG1NBrHra4QMrc/2QOTg5Z54hmtHK8U8zUDcoouUFuS/sA1BnSpKNubg45F4kmHKlLZpGje6n1yeI6djmH5nOk+3StgO67o1bd0Rr40hgKCyQObBJHQ9nIqTWr3RZlfUCGzGEso5DGX2A0Ey3ATX0OyMoZTJJyLrTNHCQumcW+DUrNYzp9iGjm1pFEO5tH46gj1C6QQk/HqZMG6hhqvd8lcTpLfUxkfSW3iMbl50aPnSyftdbAgx7LQsn1z4t51JnTYKSdKsTFoEprc9JJHuRQJ82L/tTdMbeY9g0nLW0PFwe1ll9M99td0o5uZurkmKuh576LVr2fBkG3ZX2uAoDWkEu0Ah0dU92uqfuooXpDkidd0v7ODpJ9kmjW7SFpHrg60Phd86mzaNFv1HlyR5v/qdIdM3eQPCMfGkW0Oeepe+opHXWe9LA6dpfzab+o60XPORnnc2k7OkmOXaDV3x+ycVx2Tl8roM6WhSZ6ngRana+KxK05V9DtX5Fsv44b/b09k6s+yHmSc8nbiZ7nhZKxt0gSf3N/T6JVBvZfoI6qlYXK2mblgR9ftAGdToeBAwciMTERN89hly9fDpPJhOjoaGm5rKws+PjQP+ofeOAB+Pv7o3v37vj9998telFREVJTU9GtWzeLptFo0K1bN+zYQQdlhmEYhmEYhmGqPjwps5GYmBgcP34cycnJFi0hIQFRUVHw9KSz9e3bt2PZsmUYNmyYRfP398fnn3+OlStXYuXKlQgKCkKnTp2we/duAMCVK1dgMplQs6Z6LZ+aNWuS986sMRgMyM7OVn0MBsMtyzAMwzAMwzCMDCEq7+dOce3aNfTv3x8eHh7w8vJCbGwscnPpXfqb848cORKNGzeGs7MzateujVGjRiErq+x3GXlSZiMhISEIDw/HggULAADHjh1DSkoKYmNjSd60tDT07t0bkydPxmOPPWbRGzdujP/9739o3bq1ZVvh4eGYOXNmueObPn06PD09VZ/PPplR7u0yDMMwDMMwzP1A//79ceDAAWzcuBFr167F1q1bVTdYrDl//jzOnz+Pjz76CGlpaUhMTMSGDRuk84PSYKOPMhAbG4uRI0di7ty5SEhIQP369dGxY0dVnoMHD6Jr164YNmyY6v2zknjooYewbds2AED16tWh1WqRnq5+ZyA9PR1+fvS58JuZOHEixo4dqy4neWaeYRiGYRiGYRg1hw4dwoYNG/Dnn3+iTZs2AIBPP/0Ujz/+OD766CMEBASQMs2aNcPKlSst6fr162PatGl4/vnnYTQaodPZPtXiO2VloG/fvtBoNFiyZAkWLVqEmJgYKDcZABw4cACdO3fGoEGDMG3aNJu2uXfvXvj7+wMAHB0d0bp1a/z222+W781mM3777TeEhYXdcjt6vR4eHh6qj16vt2MvGYZhGIZhmPsdsxCV9nMnXtvZsWMHvLy8LBMyAOjWrRs0Gg3++OMPm7eTlZUFDw+PMk3IAL5TVibc3NzQr18/TJw4EdnZ2Rg8eLDlu7S0NHTp0gUREREYO3as5R0wrVYLX19fAMAnn3yCunXromnTpigsLMRXX32FTZs24ZdffrFsZ+zYsRg0aBDatGmDhx56CJ988gny8vLwwgsvlDne6s6V8/DqILGqkjwfbKjRWJXWmujJZu12BwC6YuqeBxvcBTWF1A1Kq6fufBrFtmsZism+O5Wa/Ay6LWf63qLZtRrNZ+W2KIvBQUOdpWxFKVa7q2m01M0q37Um0ZzM9NjJ+oE22+rdSZf6JI/MoUuf/g/RvH1qE80scYG0RubaBS11ItNI+pnQWV0IEXQfFYmmlbhkVpecvsKJXmjRZl9QpWtItm/yoHfaxS9f0gpadyOS7nCKelt1WtByLWoR6fDgp4nWcOH3RCuGOjbZOR3kSI+5tpg6/Vk7tgJA3VoPqNISI03UdKbtr8v4l2juknNOuKn7hsO5AySPxou2T31J/ynyqUuDK1K3h9Gb9mvFnZ5zypxXidZgAHUJNvrUIZrmn62qtGf9NiQPzPScUCTuiG6ya4NWXdRR4ripLcyWlKNOeU7W5xwAk9Vx0hVcI3lqOUjO6dzLtM6LJ2idfmonYCEZH5RCeh4a92+l+Rypg237IPXvjlZLr87LnBbjAroT7eN8OjZaj5de3oEkj/YK7f/F1RsQrZY7PTc1xYVEs8baLRGgYxkA1JT8XlkfXwDQZZxWpclYDMArj/YDZ78mRFMkf2vUz0xTpYuCHiR5HP79i2jCgy5lZPSi7c2UnenTp2Pq1KkqbfLkyZgyZYrd27x48SJZfkqn08HHx6dUb4cbXLlyBe+8884tH3ksicr5V3slJjY2Fl9//TUef/xx1W3MFStW4PLly0hKSkJS0n8Wt3Xq1MGpU6cAXHdXfOWVV3Du3Dm4uLigRYsW+PXXX9G5c2dL/n79+uHy5ct46623cPHiRTzwwAPYsGEDMf9gGIZhGIZhmPsR2Ws7JT0hNmHCBHzwwQe33N6hQ3Rpl7KSnZ2NXr16oUmTJnZNDnlSVkbCwsIgW9ptypQppR6A8ePHY/z48aXWERcXRxacZhiGYRiGYRjm+gTM1td0XnnlFdXTbTLq1asHPz8/XLqkXk/QaDTi2rVrpXo75OTkoEePHnB3d8f3338PBwfb1tK8GZ6UMQzDMAzDMAxzT+Lr62t5lehWhIWFITMzE6mpqWjdujUAYNOmTTCbzWjXrl2J5bKzsxEREQG9Xo8ffvgBTk70sWRbYKMPhmEYhmEYhmHua0JDQ9GjRw8MHToUu3btwu+//464uDg8++yzlleWzp07h5CQEOzatQvA9QnZY489hry8PHz99dfIzs7GxYsXcfHiRZhM9H3lW8F3yhiGYRiGYRiGUWGS+LLd6yxevBhxcXHo2rUrNBoNoqKiMHv2bMv3xcXFOHz4MPLzr5tN7d692+LM2KCB2gzn5MmTCA4OtrluRchekGLuCYoy1c/FmmVOggXU6c/o7EM0mSOaNVeK6I1XZx21OnORaNqcdKJl6NW3mh21tJyThnZfIXFrMhjpyOKoVcerK8yk29LRW9AyVyczaGy6IupEVqBzVaVNZhq/i5ZqWokrmHX7AICHo9UxkDjx2do+Og3dJ32eOg6zzBVS0j5FJrpPesl9emFljScrN68Gdf8beyaZaMbNS4h2fMUmVbrhgpUkj3UMAKBIhklZPnvRSFwyld3riDar51tEG3lpjyqtO7SF5Emd8BHRmv/wE9GyDPSqnrtVn5L9YKTnUVc8mUvmQv8HiPbcOXX8zgVXSZ7X/DoQbVoOfSnbLDlO39dWu6RFndlD8iQF0LgGnkslmqYwi2j/vjFKla71cRLJY0icQrQT6+n2z/1F3ee6f2Hl4PUYdfSS9c+ipdOJdv73v4nm6ked7Gq+qi4rc4Q1/k3POU3Y/xHN7ECdb4nzZ48X6bZk7rUSl1KzQ+mPCekyzxFN5rJqPrGPaq0jaVmj2p0vf/GHJI975ACiFVWjbrLFkt+AnA/Vfcpnwqckz/lc2j6yc26sSwjRZmdauQT+/SvJY2zTh2pL3yPaibXUJrzRc9QF0iVK7QY6SkudRj/MSSOaw6k/ibZz2JtEa73xF1VakTh1Wh83AFjR4FGi9Tm9m2j6S4dV6RHBvUmez46vINrpL+ixS/uGbv+J8/uJVhlIPZNZ0SGUSOsgr4oO4bbDjy8yDMMwDMMwDMNUIPz4IsMwDMMwDMMwKmRPHzB3Dr5TxjAMwzAMwzAMU4HwpMwGIiMj0aNHD+l3KSkpUBQF+/btQ3R0NIKCguDs7IzQ0FDMmjWL5F+8eDFatmwJFxcX+Pv7IyYmBlev0ncoAGDp0qVQFAV9+vS5nbvDMAzDMAzDMEwlgidlNhAbG4uNGzfi7Nmz5LuEhAS0adMGqampqFGjBpKSknDgwAG88cYbmDhxIubMmWPJ+/vvv2PgwIGIjY3FgQMHsHz5cuzatQtDhw4l2z116hReffVVdOhAX3BnGIZhGIZhmDuJSYhK+7kXYfdFGzAajQgMDERcXBwmTZpk0XNzc+Hv74/4+HgMHz6clBsxYgQOHTqETZuuO7599NFHmDdvHo4fP27J8+mnn+KDDz5QTfhMJhMeffRRxMTEICUlBZmZmVi9enWZ4zbk5ajSQkNfIZS5iUkdB7XU1UmxcvbLMNI5vrdO5v5H41CKCyRxqF38ZK6BMocusyRWmauct0ZdVuZwaHbxpprMTUwWW1E+0a6Z1fvko6Xx6zLPEM3kSh3SoHWksTmq3R1lx9fk7EW0HEn7eGpobJqCTFVa6N1IHpk7peyY2+IMKTtuPsXUCW5jq8eJ1m3hq0TTNFM7bZnca9K4JMicFjXFhTSfZN9pJonHsELPHU1RHs0ncYzTnle7ECqO9Pw1eQfR7Uv6hqF6Q6LprxwtNU9GIT1ONQwXJXXmEC2neiNVWuaoqss6TzRI3NVOvktd2eq//LIqXRjUmuRxyKLufJCc02YnD6JZO8caD2wneXS+tYiWv5fmc3v0CaJZn/uFHgE0LsmvuPPf622KQ0jGkcOTJ6vSjaZTd0GliI7ZMNA+a/IPJZo2W+0yKetTMmSGp7b8BSNz1pVuX3JOyxwfhZPayViR9GvrPACA/Zuo1pS6/1m3bbEnPW65RTSuahlHiWbyDiTaKK82qvTM70eTPOd+3UG0wPiFRNNKzk0hceV19KyuShddoRe5xd6NRNO4099g1H2ASIaNi1Rp50eoO6Lp/DG6/TrNiGb0om3mkK52X5T9zfJhq8FEe/lHOiYpzq5EcwyLIlplYOe/1yo6hBJ5uA51Cq/q8J0yG9DpdBg4cCASExNx8xx2+fLlMJlMiI6OlpbLysqCj89/nSYsLAxnzpzB+vXrIYRAeno6VqxYgccfV/9B+fbbb6NGjRqIjY29MzvEMAzDMAzDMEylgSdlNhITE4Pjx48jOfm/tVkSEhIQFRUFT096VWj79u1YtmwZhg37b02Z9u3bY/HixejXrx8cHR3h5+cHT09PzJ0715Jn27Zt+PrrrzF//vwyxWcwGJCdna36GAx0TQ6GYRiGYRiGYSoXPCmzkZCQEISHh2PBggUAgGPHjiElJUV6NystLQ29e/fG5MmT8dhjj1n0gwcPYvTo0XjrrbeQmpqKDRs24NSpU5ZHH3NycjBgwADMnz8f1atXJ9u9FdOnT4enp6fq8+FHM8qxxwzDMAzDMAzD3A14nbIyEBsbi5EjR2Lu3LlISEhA/fr10bFjR1WegwcPomvXrhg2bJjq/TPg+sSpffv2GDduHACgRYsWcHV1RYcOHfDuu+8iPT0dp06dQmRkpKWM2Xz9/ROdTofDhw+jfv360tgmTpyIsWPHqkUjff+EYRiGYRiGYZjKBU/KykDfvn0xevRoLFmyBIsWLcKLL74I5aY3jw8cOIAuXbpg0KBBmDZtGimfn58PnU7d5Frt9ZfJhRAICQnB/v37Vd9PmjQJOTk5mDVrFoKC6Mv6N9Dr9dDr1SYD1kYfDMMwDMMwDGMLvHj03YXdF8vIkCFDsGrVKmRnZ+P06dMICLjuhJWWloYuXbogIiIC8fHxlvxarRa+vr4AgMTERAwdOhSzZ89GREQELly4gDFjxkCj0eCPP/6Q1jd48GC73ReLMtTuZ2Yn+u6bzEGoSEfdBbUS2ytrKbeIusp5mnOJZu0QCAAGQZ+kNVlZijlqaQwOJvrenEniHllkorG55l9Sx+BGnfhyJPvkTc3KYJQ8CewgqDtcodV1EOciifulpH2sywGApDmggzpeReJOKWufYol9myw2a+dGmWujDL2Oto9eoW1rVtSOd7K4HNZ/SjRFT/dpyeDPiDZgnfpiialVLxqsBNkoqSuifdvoqHajlLnFKTYOuQ6XjhCtaF8y0bQd+qrSlz6hbl81JOssmh+gjpVnc2mfDdKr+1CBhrb1P1epa12r6tQF9eiwZ4lWc95yVdpTUNfS1KeoM1nLdT8TTVtMy+6KiFSlW2/6jeQ5MYRuv+EXS4gmGy+zEj9QpT2GvU3yGL6LJ9q+L2kcOmd6nrca31+Vzg7rT/LIcFr1AdGyjlOXyWotG9Oyrbuo0iJfMhZkSNxqW0cSTYYm9Uf1ttr2samcrVifYrq8KySPNucS0RSJu2mxH3WPtO4HypGdNE/tpkQzefgRTTa2K99NV6U1/SaSPP9m0bG9oYvk6Zi/f6VxXFW7X778FF1TdXbmX0ST/XZr9lKXTzTpSCRr98WC1TNJHu2j/Yi2f9AgojV7ZTCts7V6LC/WUidc5/RDRDPsouOIOXIM0VzO7laltw0YR/I0HdCeaHnnaN+7eoieh61W/0K0ysD2U/J1dCsD4cESV+oqDr9TVkZiY2ORkZGBiIgIy4QMAFasWIHLly8jKSkJ/v7+lk/btm0teQYPHoyPP/4Yc+bMQbNmzfDMM8+gcePGWLVqVUXsCsMwDMMwDMMwlQB+fLGMhIWFQXZzccqUKZgyZUqp5UeOHImRI0faXF9iYmIZomMYhmEYhmGY8iN5yIi5g/CdMoZhGIZhGIZhmAqEJ2UMwzAMwzAMwzAVCE/KGIZhGIZhGIZhKhB2X7yHKShUO6JJXd+EjQ8MK5L5uyjd6a9QQx2Q9JJNaXOpk5fJVe2sIzRakkeGYpY4Akrizzeq28Mj+1+SRzhQJ0qTO3VpFBKbPY3M+VCjdqSTuUK6ZZ4kGrTU8tHk5kvj0KnbW+YmJjSSV0kl7WOQdA2XnPOlljO7eJcaV4lY9SnZMZd249UfEU0XSd/dNFk5cmlANyY7lrJzx4zSHUll2LotaVlJNsf0w6q0yasWyWPtCgkAjhm0v5tdqZuVJiddva1q9UoLEwCgzb5INIM7dZ/T2NJoEhyv0fPk54epe1vHw2pnW1vrkzn2CSd3oln3Ke2O70geTbNHiSZz/8v3b07L2hCvzLbaKfM00aRjl5a6ZF56J06V9ptA3SPNkrHR8fx+ohl9G1BN0h/vJLLzXHbOaQT97ZD1Y+vzRDa+ybav+2s1Da4p7RtFTuoxVKuh25KNg/orR4lmqN6QaOfHDVSla0+jTrWjvNoQ7ZN86l4oGy+1BupMS9wXC6ljq/50KtGMNWj8JicPomm2q8872TmnybtGNINfE5pP0l80BRnquJx9SJ5LU/5HtFpjpxDN6OlPNGcn6mpbGUg5UXndFzvUY/dFhmEYhmEYhmEY5jbC7osMwzAMwzAMw6jgxaPvLnynjGEYhmEYhmEYpgLhSZkNREZGokePHtLvUlJSoCgK9u3bh+joaAQFBcHZ2RmhoaGYNWsWyT937lyEhobC2dkZjRs3xqJFi1TfHzhwAFFRUQgODoaiKPjkk0/uxC4xDMMwDMMwDFNJ4McXbSA2NhZRUVE4e/YsAgMDVd8lJCSgTZs2SE1NRY0aNZCUlISgoCBs374dw4YNg1arRVzc9Zem582bh4kTJ2L+/Plo27Ytdu3ahaFDh8Lb2xuRkZEAgPz8fNSrVw/PPPMMXn755bu+rwzDMAzDMAxj4scX7yrsvmgDRqMRgYGBiIuLw6RJkyx6bm4u/P39ER8fj+HDh5NyI0aMwKFDh7Bp0yYAQHh4ONq3b4/4+P+crF555RX88ccf2LZtGykfHByMMWPGYMyYMXbFbYv7osO/fxGtuPaDRJM5Kypmoyqdo3EheRy11JnJQWYmZoMLZAY1EoQ3NSWUOvaZzHTfTVaSc1EW3ZaOOiLZ6rSlLc4nWpFO7VimlThXHeobSbQHZn9ANJmzYlGA2r3N8SJ1yyr0b0a03CLqOubsQG+kO+WpXTLNzp4kj8zd0SDotmQunNZOXrLjJnMiu51IXUolyFzHqhIO16j7oqaAngPWbppC4riZ4UxdFX3yzxNNtv2r1dXuZ26OdPuyY6IYDUSTOQlaO4Ta6q5p7/HVFNHzHjLnWEk7ys4d6zhk3VP23ocOkjHVBhddKbJYbXTdk46XsuNkXeVtPA9t3ZatWNdpa/+R9Q1Z+9jiNCzbJZlrYNHS6URzfHZiqduXMcYllGgyR0aHk38QTdukkypd/OcPJI+x+WNEs/vcl/V1CZo86i4oc6G1RnckhW7LlbpCSh2KczPp9lrSfa8MbDpGnbErC10aUAfqqg4/vmgDOp0OAwcORGJiIm6ewy5fvhwmkwnR0dHScllZWfDx+c821WAwwMnK9tTZ2Rm7du1CcTGd9DAMwzAMwzAMc+/DkzIbiYmJwfHjx5GcnGzREhISEBUVBU9Perdg+/btWLZsGYYNG2bRIiIi8NVXXyE1NRVCCPz111/46quvUFxcjCtX6Jo4ZcFgMCA7O1v1MRjo1SSGYRiGYRiGKQ2zqLyfexGelNlISEgIwsPDsWDBAgDAsWPHkJKSgtjYWJI3LS0NvXv3xuTJk/HYY//dkn7zzTfRs2dPPPzww3BwcEDv3r0xaNAgAIBGU75DMX36dHh6eqo+Nz8myTAMwzAMwzBM5YQnZWUgNjYWK1euRE5ODhISElC/fn107NhRlefgwYPo2rUrhg0bpnr/DLj+qOKCBQuQn5+PU6dO4fTp0wgODoa7uzt8fcv3bOzEiRORlZWl+owbN65c22QYhmEYhmEY5s7Dk7Iy0LdvX2g0GixZsgSLFi1CTEwMlJte5j1w4AA6d+6MQYMGYdq0aSVux8HBAYGBgdBqtVi6dCmeeOKJct8p0+v18PDwUH30evoCMcMwDMMwDMMwlQu2xC8Dbm5u6NevHyZOnIjs7GwMHjzY8l1aWhq6dOmCiIgIjB07FhcvXgQAaLVay12wI0eOYNeuXWjXrh0yMjLw8ccfIy0tDQsXLrRsp6ioCAcPHrT8/9y5c9i7dy/c3NzQoEGDMsWrLchUpc1O9N03kz91U7J2VQRKcjVTu0vpJa54RsmDvw4SR0Zt9kWiWTu6eeoljlSCugbKsHZaBAAnqPdTODiTPDI3NFuRuWpZuy0WS9qn6bLVRDPnXCJahlMNonlYOT0V+UmOr8SsTK+jFwVkLpkyt0VrZM5hDpL2F3aaF/7VuSvRwlYuIFrx7l+JFv/MTFV6/LX9NtVpqxOfLS5vUte6/Gs04+EdRPpz3CdEa/PTj6q07t9Ukmfn8ClEa/3LBqIp7jWJZnZQmxM5XvyH5MnW0r7o7h1EtN3PxBAt9KefVWmHqydJns3dnidah93UsVbmgvpjHbWb7JOnd5M8e3rRdSgf/IG6wylFeUT7941RqnStj5NInqvTRhDt7PZjRLt2NINojXuHqNIB8YtIHp2ke+Z9PYVomUfPEC3nLK2z2Zw5qrTMoe7g5HeI1vDzxUSTuXX+ULv0YyJzl5VaDkrGWZLl6gmiafIziVa473eiaXv+j5a16geXP51K8lTv0J5oprC+dFuS37BrH4xWpf2jB5I8J7xbEK2WO/2dPrGWOiE27al2RhUn99JYW/UimsxpUebIOGszvSitVZusYvfrM0meB1Y9SjSHCweI9lvvUUTrkLpVlZb1WThQN+V1zSKI1nNBHC3aNFyV/qnnKyRPt69eItqVP2jf3reAHpNeZ/4mGnP/wXfKykhsbCwyMjIQERGBgIAAi75ixQpcvnwZSUlJ8Pf3t3zatm1ryWMymTBjxgy0bNkS3bt3R2FhIbZv347g4GBLnvPnz6NVq1Zo1aoVLly4gI8++gitWrXCkCFD7uZuMgzDMAzDMAxzl+A7ZWUkLCwMsqXdpkyZgilTptyybGhoKPbs2XPLPMHBwdLtMwzDMAzDMMzdQrZWKHPn4DtlDMMwDMMwDMMwFQhPyhiGYRiGYRiGYSoQfnyRYRiGYRiGYRgVZn6d5q7Ck7J7mEyNuypdw5m6C0Km2YnMA2vJnrNE69WoGtFSH4smWvjOzWpB4lIlc/rTFOUT7RA1GMPBy7mq9AATdUlKjqEuUm3+SCaaWw7dTwgzkX4vUO/7qcwCkufZrM1EW/vcB0TrlfYz0Qp06u27ZtG4TBKHvUPXaKwnrtF2jMrYpEpvGf4JydPl99VEU4rpfma7U3c+N7O6zj0Z1FbuoWnUDa3Ypw7RtO2jiDbhF3X7mCXHSOY+qjEVE61A50o0J6F2jBNaR5JH9oy+xFcUSv3WRGvz7nCiWbsjFtd7mORpN2sCjUOyTyYddSfT5V5RpY3V69Jy+ZJ9MuQSrVoIPfetnVFl/fPBEV2IJju/oNCW7DqppzqLmY4jzQZ3ppuXOK8qkuNZ+5knVWmzxAmxZkR3onnWr0U0UyF1HPTsGqlKG2SuqBIrU492HYjm2oC63Do2epBoRr26b5v1tK83njGbBmLl+AsAZmcvokXtWamuT/aHn8S1UfYbYJB0A2sjYI0bXQdUONJ90nWlLp9myTE3u6h/7WoOe5XkMXn4EU1XRM8JmbOxtdtiUeADJI9TIW0zTXEh0Ro9R/uesHbRbdKR5JGdv9pzaUSTOS2O7vwG0T4X/VXpli9TJ0qZY3F+IO2fnRLfpGXz1T/y2gL6o29wbUi0nomjiaZp9yTRiqz6cbUQH5JHcaJ9Su/lRrQ6j9LfPoYB+PFFhmEYhmEYhmGYCoXvlDEMwzAMwzAMo0K2xitz5+A7ZQzDMAzDMAzDMBUIT8psIDIyEj169JB+l5KSAkVRkJycjB49eiAgIAB6vR5BQUGIi4tDdna2Kv+WLVvw4IMPQq/Xo0GDBkhMTFR9P2XKFCiKovqEhITcqV1jGIZhGIZhGKaC4UmZDcTGxmLjxo04e5aaJiQkJKBNmzZo0aIFevfujR9++AFHjhxBYmIifv31Vwwf/t+L+SdPnkSvXr3QuXNn7N27F2PGjMGQIUPw889qw4amTZviwoULls+2bdvu+D4yDMMwDMMwDFMxKEKw32VpGI1GBAYGIi4uDpMmTbLoubm58Pf3R3x8vGrydYPZs2cjPj4eZ86cAQC89tprWLduHdLS/nMwevbZZ5GZmYkNGzYAuH6nbPXq1di7d++d3SmGYRiGYRiGKYG1h9IrOoQSeSKUOvVWdfhOmQ3odDoMHDgQiYmJuHkOu3z5cphMJkRHUzv38+fPY9WqVejY8T+r2R07dqBbt26qfBEREdixY4dKO3r0KAICAlCvXj30798fp0+fvs17xDAMwzAMwzBMZYEnZTYSExOD48ePIzn5vzWqEhISEBUVBU/P/9b8iI6OhouLC2rVqgUPDw989dVXlu8uXryImjXVM/uaNWsiOzsbBQXX13Fq164dEhMTsWHDBsybNw8nT55Ehw4dkJOTc8v4DAYDsrOzVR+DwXA7dp1hGIZhGIa5zzALUWk/9yI8KbORkJAQhIeHY8GCBQCAY8eOISUlBbGxsap8M2fOxO7du7FmzRocP34cY8eOLVM9PXv2xDPPPIMWLVogIiIC69evR2ZmJr777rtblps+fTo8PT1Vn+nTp5dtJxmGYRiGYRiGuevwpKwMxMbGYuXKlcjJyUFCQgLq16+vejwRAPz8/BASEoInn3wSX3zxBebNm4cLFy5YvktPVz+fm56eDg8PDzg7O0vr9PLyQqNGjXDs2LFbxjZx4kRkZWWpPhMnTizH3jIMwzAMwzAMczfgSVkZ6Nu3LzQaDZYsWYJFixYhJiYGiqKUmN9sNgOA5THCsLAw/Pbbb6o8GzduRFhYWInbyM3NxfHjx+Hv73/L2PR6PTw8PFQfvV5v664xDMMwDMMwjAWTWVTaz70Iuy+WkSFDhmDVqlXIzs7G6dOnERAQAABYv3490tPT0bZtW7i5ueHAgQMYN24cfHx8LJb2J0+eRLNmzTBixAjExMRg06ZNGDVqFNatW4eIiAgAwKuvvorIyEjUqVMH58+fx+TJk7F3714cPHgQvr6+ZYq1KOOiKm128iR5tDnUWcfkRutRhJlWYDapkpeLdSSLmyOd9ztp6URWFkeGXh2HswPdlgMkcUnIN9E6XZVitWC1PwBgcnAhmgb0lDGDbl9XlEu0XI16e1oNLecoaR9dziWiZeqrE83dwaqs7LhJkLWPi5bup6YoT5U2OXmQPLIRxSQRrUMFAKW4QJXOU5xIng+qNSPau2c3ES3/x6+Idnj5dlW6xffrSB5xiwstN6NI9snWstZoTMVU/ON7Ir3VYwrR3rm0U5U2bV1K8mx7bSHROuymS21kF9H+4uaopbFZcT6Xxh/oRseDBP9WRHvu3B5V2iXnPMnzSq1uRHs/91CpcQHAt7UeUKWjz+0leRb4tSTa0DM7iKYx0HP6+MRRqnTtT2n7F3z9Ji23NpVop/fQcfDxz15QpZVecSSPrC8WJE4l2oUdB4hmKqbjXsjsz1VpTX4GyXPi/XeIVvtDes4JrQPRsj97XZV2j/uA5NEYJe9ES/bT7EDHCGscMs4QTTHkES096Qui+T0XSzSzo/rJlpPTJpM8/o+0IJrm/8YTrVjyx2XWuy+p0tXfmkfy2HrOjXdrQrSP0lNUadO25SQPerxIJN3+X4i2+/WZRGv5cl+iOT2u3t5wJZjkmX1tJ9HMO+g4uPmlz4nW+aB6bFfMRpJHKaLH/Mu6nYk25MJuojmc+lOVHtF0MMnz2VF67p/45GOipX6XRrR+lw4SrTLwfdqFig6hRJ5qduubFVURvlNWRmJjY5GRkYGIiAjLhAwAnJ2dMX/+fDzyyCMIDQ3Fyy+/jCeffBJr16615Klbty7WrVuHjRs3omXLlpgxYwa++uory4QMAM6ePYvo6Gg0btwYffv2RbVq1bBz584yT8gYhmEYhmEYhqka0MsqzC0JCwuD7OZi586dsX37dkkJNZ06dcKePXtK/H7pUnqlhWEYhmEYhmHuJveqy2Flhe+UMQzDMAzDMAzDVCA8KWMYhmEYhmEYhqlAeFLGMAzDMAzDMAxTgbD74j1MYUFBqXms3e6ui3SuLnQSe30rZ79CM3Wec9JIupds+xLXOpmjWGkxAIBW4lRocPcjmoPRat8lLmHFCn3tUicx2JO5L2rN1B2r0Oo1TidQhygZQiN5/VPmrGjdtpI8uozTNC7vYJrPBmdLoaHOfLa6Esry6TLPquPyDCR5HITEVetv6gqGkPY0Dp2TVZr2a5vdFyVundbtYVMfLgnJsbN2vwSA9/zDVekXp/YkebyGUyc+aZWS9rB2MTNL8sjsiR2LcohmdnSlmmLVZpLml7lTCsk4Ijv3zS7e6nKS+DUGGuvG5tTx8bEVb9Pthz6qFiRxyVCMhUSz7p/Xg1O3j0lDxykZjpnUcdDs5E7rlBwTa7dFk2s1kkfZ/h3dVjh13ZO1B+lTkrH3diI7V2XIxsYJwb2I9s73r6jSmo79JZVKOrJsbJTsu7XbsWz8l/3mOGRTpzyTew26fatjpzwYQfLorpwkmqF2a6JpimX9mJ5jTlZrsVq7QwPAKJ+HifbOx08RzSOWuplaOzibZS7SRflEM8schCXHxPH8flXa6NuA5NFIXKR/6zKAaJ1SqVuw3t2LaJWBlfupG25lIap5QOmZqhh8p4xhGIZhGIZhGKYCYfdFhmEYhmEYhmFUmPhZursK3yljGIZhGIZhGIapQHhSZgORkZHo0aOH9LuUlBQoioLk5GT06NEDAQEB0Ov1CAoKQlxcHLKzsy15L1y4gOeeew6NGjWCRqPBmDFjpNtcvnw5QkJC4OTkhObNm2P9+vV3YrcYhmEYhmEYhqkE8KTMBmJjY7Fx40acPXuWfJeQkIA2bdqgRYsW6N27N3744QccOXIEiYmJ+PXXXzF8+HBLXoPBAF9fX0yaNAktW7aU1rV9+3ZER0cjNjYWe/bsQZ8+fdCnTx+kpaXdsf1jGIZhGIZhmJsxC1FpP/ci/E6ZDTzxxBPw9fVFYmIiJk2aZNFzc3OxfPlyxMfHw9vbGy+++KLluzp16uCll15CfHy8RQsODsasWbMAAAsWLJDWNWvWLPTo0QPjxo0DALzzzjvYuHEj5syZg88//7xMcVs7tcmcz6xdsADAqKduRDIjKcVqTp9XTN3iXBTqFieLI7eYVuBsZXOo1dA8GqPElc2BOj8VSR6M1hdmq9KFbjVJnoxC6tpVw0nWGNSFUOaeZ7QaSJRiiUOdszfRcopp/O6ys9eqTkXmWidxeJO1j2NxFtGMzj6qdJakfVwcJK54isRpUeLuaHawcuiSxZVGnRYViQvkv2+OIlq9UaNVaZmbmAzZ+K+VOJcaHd3UcUk3ZoNrJgBdFnW9Mh3YRrQxs9SOdysnrCJ5BnWnDnKmRtSdMs9EI3aDeudlTovXJP3AT3LJz7RmJtGMT6qd7JyNdMzIW/IR0ZxemEI0IXEXzF04TZV2GfIO3f63HxMt5P+aEE3jSrdv3rVGHYPEgVB3aAvR8v/+g2jCTPuGewe1m2ZR4IMkj6yfGfdtptsvok55+mbhRLN2W9TmXiZ58k4cJZpTe9uu8+qunVKli3wbkjy2Opfa4pYqGweVYurEp2RR97wAJzrQCoP63Ndd+5fmcXQhmtGTusVZu48CgMP+jap0cQv6pM61AvrbXVMyDjqc+pPW6a7+jfl70CCSp/k3i4kmOyYOFw4QLV/SR0kMO74nmsxp8c2xNN/sh8Lo9pp1Uaclf2c4ZNAL6+LITrqt1k8SLXvTD6q0IZP+dvt07k60fy/Tflbw7YdE0w97j2jM/QffKbMBnU6HgQMHIjExETevILB8+XKYTCZER0eTMufPn8eqVavQsWPHMtW1Y8cOdOumtmKOiIjAjh077AueYRiGYRiGYZhKDU/KbCQmJgbHjx9HcnKyRUtISEBUVBQ8PT0tWnR0NFxcXFCrVi14eHjgq6++KlM9Fy9eRM2a6js2NWvWxMWLdE2PmzEYDMjOzlZ9DAZDmepmGIZhGIZhGObuw5MyGwkJCUF4eLjlscNjx44hJSUFsbGxqnwzZ87E7t27sWbNGhw/fhxjx469K/FNnz4dnp6eqs+HH8+6K3UzDMMwDMMwDGM/PCkrA7GxsVi5ciVycnKQkJCA+vXrk8cT/fz8EBISgieffBJffPEF5s2bhwsXLthch5+fH9LT1c+1p6enw8/P75blJk6ciKysLNVn/NjRtyzDMAzDMAzDMEzFw5OyMtC3b19oNBosWbIEixYtQkxMDJRbvGRs/v8vbZflMcKwsDD89ttvKm3jxo0IC6Mvtt6MXq+Hh4eH6qPXU8MLhmEYhmEYhikNs1lU2s+9CLsvlgE3Nzf069cPEydORHZ2NgYPHmz5bv369UhPT0fbtm3h5uaGAwcOYNy4cWjfvj2Cg4Mt+fbu3QvgunPj5cuXsXfvXjg6OqJJk+tOX6NHj0bHjh0xY8YM9OrVC0uXLsVff/2FL7/8suwBS5wVrREa2gU0gjqpwYb+76Gnc/wCQd3K9JJ5rJeBuntlCLUDmJsjdZYSWgeiKWZHosmcG83OXqq0g8QN0MdJ4qpYutnX9dgkbeuiUbeRQUudFnWSib6nMYNoOYoX0VytHCtl7WN29iSarH2Enh47677hqafto5F0FqlDmpBcE9Kpj52jlpbbPGga0bomLyNarcceIdrW515VpR9O2URjkCGJ39opUpZNSDqLxIgSipFeuDGf2EO0X1+g7oXdDmxRpQeEtiB5Pu9E75oPubiXaM6SfmCC2q3TseAayWM0S1wJnaj72bbJ1BmyXaT6EW/tObr8x6LXqAPb0MFTiFbkQOv89g21a1qsxH1xy9Qfifb4se1EMxVSx7VDk9VOao3aU+OnSz+tJdrxdX8T7cwxep4/9qba6c9lKHUMlY3ZOYcOES39ryNEq5NJj6f+iWHq7RuoI2bOaepU6GSjs2jmmkWqtMwRU+ZSqsi2LxlnSTkjdZ3USI5l0RF6zo24mEq3V6B2pi3aTvv1+d9o//F/P/FWYVq4tOEnVdq3MXXILDTR8cfaNRMAdj3Vn2gPrfpGlW72ymC6LSfqwqwppu34W2/qctsp8U2ioV0fVXLzS9RNusvf1DFU5rQ46pHxRPskX93frd2nAcDkHUi0ZW3o+dp/Z22i6TupHWy/eCiW5Ilt34Fo3WPbEm3BKyuINpbdFxnwnbIyExsbi4yMDERERCAg4D97W2dnZ8yfPx+PPPIIQkND8fLLL+PJJ5/E2rXqH+NWrVqhVatWSE1NxZIlS9CqVSs8/vjjlu/Dw8OxZMkSfPnll2jZsiVWrFiB1atXo1mzZndtHxmGYRiGYRiGuXvwnbIyEhYWprLFv0Hnzp2xfTu9MmaNrKw1zzzzDJ555hm74mMYhmEYhmGY8iJZKpS5g/CdMoZhGIZhGIZhmAqEJ2UMwzAMwzAMwzAVCD++yDAMwzAMwzCMCrMNr9wwtw9F2PKSE1MlKSwoKDWPNoc6aJklrksyBy1rd6xsK5c2APDQUlcwqeNj3lVJcOp8ZokboNS1ztGFaDkGGoeHlTGhJpU6sGnqtyKa0Zs6MykSp0vFQN29rmnVzoc+oMfI+Gsi0fStuxFNONAlD4w+waq07topkqe4Wl2i5RZRVzMPpYhoun/VTmTCO4Dkkbk7Cgd6TMwOtL9YIztu7hLHR/1p6pBWFPgA3aCsH1tjo4OcNN/txJZYQc8d4SRxQtTRvpI58xWi1XjqWaKJArWLmbleG5pH4vLpcOko0Qr9qWGR9Y++5hbLjNyM03nqXlh0mPYDc+cXbNqeNVoTHVuEljq7mq0cNrM+oe1aM+o5Ws7Fi2hF1eoTTeqGa42kr2izL9I63arTsmbJ9v+wcrts9xTJIiR9SrObjqFKo4eJZnLzVW9LcswV2Z8mknPOrEgccq3QFufTTUncU6WOj6Zimk3S322Jy7iUOuy5dO9HNOt+IO0DkmOuyzhN65T8XhlXxavSDk9SB0X8+QORxMNRNJ8ETT51EXWsrnY+LMyn7oiy+M1WfQUAzI7UZXWMS6gqPfWDSJLHLagmjfWpV4mmk5w72pxLqrShFnW5xa9f03Id6PE16d2I5uxU+u9hRbAw9UxFh1Aig1oHVXQItx1+fJFhGIZhGIZhGKYC4UkZwzAMwzAMwzBMBcKTMoZhGIZhGIZhmAqEJ2U2EBkZiR49eki/S0lJgaIo2LdvH6KjoxEUFARnZ2eEhoZi1qxZJP/ixYvRsmVLuLi4wN/fHzExMbh69b93QlatWoU2bdrAy8sLrq6ueOCBB/DNN9/csX1jGIZhGIZhGKZiYfdFG4iNjUVUVBTOnj2LwED1y6oJCQlo06YNUlNTUaNGDSQlJSEoKAjbt2/HsGHDoNVqERcXBwD4/fffMXDgQMycORORkZE4d+4chg8fjqFDh2LVqlUAAB8fH7zxxhsICQmBo6Mj1q5dixdeeAE1atRARETEXd93hmEYhmEY5v7DxF6AdxWelNnAE088AV9fXyQmJmLSpEkWPTc3F8uXL0d8fDxiYmJUZerVq4cdO3Zg1apVlknZjh07EBwcjFGjrjsd1a1bF//73//wwQcfWMp16tRJtZ3Ro0dj4cKF2LZtW5knZUarc0mroQ5XmrxrRBM66jAGieuYtVNVrpE6RHnqqLugzN1R5l5oPRRIHQ5N1CEQoE5/ucXUVcvLrHZ/0gY2KjWGsqAYaWy5JnUc1Yup62TBBeqI6egocQq7/C/VrNwXlaLSHTgBIKeIHjsvcybRTDlqTVONOnvBfPtcCWXHzdo1EwBM3tSFySDogwDORWpHTJmjpxSZK5usz0qcRWlBGpfMRVRWp8zxTlg5eZm0NI+2uJBovj2oO9nh9z4kWuN33lWljb8sIHkc2/cmmsyFU4a126Iu7wrJY3SlroHCQB31HELbEa3YyrlOkbWr7LjJjpPkmGut3At92tEYik79QzTNg3Q8l7nsWTvSydzopH1F5hBoYz/WNnxQlS6WbEtTRN3zhIH2M42kb+syz6rSRV70/NVIx3aKRlv6Az8yN0Czq42OqjY4LcrOX51kW44taN/QSMZo634gczI1VqcuurLxQXZ8nR9Rn68FkjFD3+xRopkk54TMOVlbQNsbUF/QlsVlq9OirO9Zuy1Ofo06gY4fE060gN70nDMf/J1owsGqH0jcF3VtHyeaUeK0qM2nf3fBiToZM/cf/PiiDeh0OgwcOBCJiYm4eQWB5cuXw2QyITo6WlouKysLPj4+lnRYWBjOnDmD9evXQwiB9PR0rFixAo8/Tk9kABBC4LfffsPhw4fx6KN0gGQYhmEYhmEYpurDd8psJCYmBvHx8UhOTrbczUpISEBUVBQ8PekV4e3bt2PZsmVYt26dRWvfvj0WL16Mfv36obCwEEajEZGRkZg7d66qbFZWFmrVqgWDwQCtVovPPvsM3bt3v2V8BoMBBoP6al2xyQy9nl4BYxiGYRiGYZhbYTbz44t3E75TZiMhISEIDw/HggXXH905duwYUlJSEBsbS/KmpaWhd+/emDx5Mh577DGLfvDgQYwePRpvvfUWUlNTsWHDBpw6dQrDhw9XlXd3d8fevXvx559/Ytq0aRg7diy2bNlyy/imT58OT09P1WfGR/G3LMMwDMMwDMMwTMXDd8rKQGxsLEaOHIm5c+ciISEB9evXR8eOHVV5Dh48iK5du2LYsGGq98+A6xOn9u3bY9y4cQCAFi1awNXVFR06dMC7774Lf39/AIBGo0GDBg0AAA888AAOHTqE6dOnk/fNbmbixIkYO3asSis23b53exiGYRiGYRiGuTPwnbIy0LdvX2g0GixZsgSLFi1CTEwMlJteUj9w4AA6d+6MQYMGYdq0aaR8fn4+NBp1k2u1WgBQvatmjdlsJo8mWqPX6+Hh4aH68KOLDMMwDMMwjD2YROX93IvwnbIy4Obmhn79+mHixInIzs7G4MGDLd+lpaWhS5cuiIiIwNixY3Hx4kUA1yddvr7XHYUiIyMxdOhQzJs3DxEREbhw4QLGjBmDhx56CAEB1513pk+fjjZt2qB+/fowGAxYv349vvnmG8ybN6/M8TqY1BM5oUic2ySufsKBuhdCo6WaleuYt47mUXJzqCZxUzK41SSaY7HaYUnqKieZzMrmt9WcaVfXZGWpt+9DnQSNjtQ5SeaQVqzQ7TtKXJd8rNpRk0Pbx/vxfkQr8qaxSU9eK8cvmQOerH1quNCtKRkS58b6rdVxufuRLFoTvYBQrKHunTpJIGaonfikx82QRTRt1nmiuUocuqzdvazrAwCNxGFMlk8riokmZOeJFYpkv006J6LpinJpWQPtL9YugY65l0kWs4s3LVejDpHqffEd0QxrP1VXFzmK5MkoonflfTKoY5z+3z+JllVL3adcJLHqLx0mmqlGA6KZ/1pPNAerY1LgS11Wna6eoNv3CiSazJHR2tlPqUbPCZkjaeHP1MXS2hUPoMdONibJcDi8gWjaGnQckY0RxYf+UJeTHBMhaQtt47Z0+7K+Z4Ozomy8txWz1TmmuNcgeWx2PJVsX7Had8VIXSdlv6Mady+iGasF03xWfarIL4TkyZc5CsvclCW/Q6bzx1RpZzP9TZNhcqe/03CgY5fBtSHRrFtDkYzPiok6MjpknCWayZuem25B6thkTosffrKdaLMn0zjQojORzn8wUZWu5Uj3u+jY30RzbEBdGhWZw7UPuy8yPCkrM7Gxsfj666/x+OOPWyZSALBixQpcvnwZSUlJSEpKsuh16tTBqVOnAACDBw9GTk4O5syZg1deeQVeXl7o0qWLyhI/Ly8PL730Es6ePQtnZ2eEhIQgKSkJ/frRP9QZhmEYhmEYhqn68KSsjISFhUkfNZwyZQqmTJlSavmRI0di5MiRJX7/7rvv4t133y3xe4ZhGIZhGIZh7i34nTKGYRiGYRiGYZgKhCdlDMMwDMMwDMMwFQg/vsgwDMMwDMMwjApr0xzmzqKIW3mxM1WaomtqRzqzM3XBUoqpw55woI6MMlcqxaR2n8uDxFFIgotO4mSXfZFolxzVjlneTtTZTuaEKHPAK5A4VblC7QAmtA4kj1mR1Cnx45K685mpO1+BKN2dT6+jN7B1kvbJ0PsSzcPRqqzMTczG9nGSxCFrb2sKzZK20FBNJ9GsMRhpXAv8HyDaqEPfEy3nx2+Itv09tTtf1yN/kDxCKT0uAFAkjmX2ui9Kz8PtK4j2Vb8ZRBt61mofUteRPL8O+IBonY/sIlqWge6Tp97KMVTSB07lUK22Bx0PfqzzINEiTvylSrvmXyJ5ZtTtTrRRV/YTTfYHxLwaLVXpEZepQ9rPdVsT7fHjO4mmFFDnzyOj/6dK1/tqJcmT8eFoWm71XqIdPUW3/8x7fVRp/eApNC5Jl82Z8xrRLuw6QjRDNnUhfDDhC1Vak0OPycm5nxKt1vSvaSASl8YDfZ9UpZss/5Hk0Zjo+Gnt+AsAZon7nzUOV0/SsLLSiWY4QM8J7eMv0rJWLqgX498geQKefppoRU26Es0k6bOX34hVpWu9/RnJc6qAtmuQOz3nVtem51zfferxsihlFcmDJ18mki7vCtHWNYsgWs9E2t+degxTped4NiZ5yFgGQPn7F6It6z2ZaE+f3aMuJ+krGonj4yjvh4j26aWtRNNanQNvN32W5Jnw/XiinV37K9F2LUsj2nOXDxKtMjBv56mKDqFEXnw4uKJDuO3w44sMwzAMwzAMwzAVCD++yDAMwzAMwzCMCtmdXObOwXfKGIZhGIZhGIZhKhCelNlAZGQkevToIf0uJSUFiqIgOTkZPXr0QEBAAPR6PYKCghAXF4fs7GxV/i1btuDBBx+EXq9HgwYNkJiYqPp+69atiIyMREBAABRFwerVq+/QXjEMwzAMwzAMUxngSZkNxMbGYuPGjTh79iz5LiEhAW3atEGLFi3Qu3dv/PDDDzhy5AgSExPx66+/Yvjw4Za8J0+eRK9evdC5c2fs3bsXY8aMwZAhQ/Dzzz9b8uTl5aFly5aYO3fuXdk3hmEYhmEYhrHGZBaV9nOnuHbtGvr37w8PDw94eXkhNjYWubm5NpUVQqBnz55231Rh90UbMBqNCAwMRFxcHCZNmmTRc3Nz4e/vj/j4eNXk6wazZ89GfHw8zpw5AwB47bXXsG7dOqSl/ee88+yzzyIzMxMbNmwg5RVFwffff48+ffrYFXdhAXV0s0ZTSN2+5O6LpXeTIq2eaI7mIqIJLXWIUkw0n3WdMndEKRK3r2JJ+I4mK9cxiVMhJG56svilYUj2qVBRl3UuziF5FAN1iDK7Vaf5JE5wZtdq6jw2umva1D4AFLNRvS3ZMZH0FWk+yXGydj40SgZemWujzGnLKLnm5HQ4WZ0npCONy0bHSlmdNiHZb+t2LTmO0l8DlsUqc58zu1H3TuMvC4imtXJNO/fWSySPzB1OZglYpJGc+1ZpfS51xSt296NxGQuJJnR0DLJ2XJPlkbarbDyQoM27qkqbrM5BQO4uW+xZi2i6IvrDX7hqtjrPs6+TPLa4ogKQ9z3JOGVLP9PmXiaa2dmLZpT0R+MPs9Qx9HmVFrPRWdeW81AjGwcl/QASx0dpPhvQZZwmmtG7NtFkfUM4e6rS6fHUSdP39dlEk40jJsnvsnXbysZKiUmynM2JdPthTxHN0Vt9Dhfm0985GbJjrj+7l2gmD/X2zQd/J3mUFp2pZqJtNrLGo0SbfU3txmp08iJ5bHVmdsi+QLWadYlWGZj1+4mKDqFERrevd0e227NnT1y4cAFffPEFiouL8cILL6Bt27ZYsmRJqWVnzpyJjRs34qeffrLr73e+U2YDOp0OAwcORGJiIm6ewy5fvhwmkwnR0dGkzPnz57Fq1Sp07PjfH307duxAt27dVPkiIiKwY8eOOxc8wzAMwzAMwzC35NChQ9iwYQO++uortGvXDo888gg+/fRTLF26FOfPn79l2b1792LGjBlYsIBe3LQVnpTZSExMDI4fP47k5P+utCckJCAqKgqenv9d1YqOjoaLiwtq1aoFDw8PfPXVV5bvLl68iJo1a6q2W7NmTWRnZ6PAhrtat8JgMCA7O1v1MRjonQ6GYRiGYRiGqcrcib97d+zYAS8vL7Rp08aidevWDRqNBn/8QdfRu0F+fj6ee+45zJ07F35+9MkOW+FJmY2EhIQgPDzcMgM+duwYUlJSEBurXuRx5syZ2L17N9asWYPjx49j7NixdyW+6dOnw9PTU/WJj4+/K3UzDMMwDMMwzN1C9nfv9OnTy7XNixcvokaNGipNp9PBx8cHFy/SR41v8PLLLyM8PBy9e/cuV/28TlkZiI2NxciRIzF37lwkJCSgfv36qscTAcDPzw9+fn4ICQmBj48POnTogDfffBP+/v7w8/NDerr6fYn09HR4eHjA2VnyHlcZmDhxIpkACrNt70QwDMMwDMMwTFVB9nevXi9/B3TChAn44IMPbrm9Q4cO2RXHDz/8gE2bNmHPnj12lb8ZnpSVgb59+2L06NFYsmQJFi1ahBdffBGK5GX2G5j//6Toxu3UsLAwrF+/XpVn48aNCAsLK3dser2edEZbjD4YhmEYhmEYxpo76XJYXmR/95bEK6+8gsGDB98yT7169eDn54dLly6pdKPRiGvXrpX4WOKmTZtw/PhxeHl5qfSoqCh06NABW7ZssSlGgCdlZcLNzQ39+vXDxIkTkZ2drTrA69evR3p6Otq2bQs3NzccOHAA48aNQ/v27REcHAwAGD58OObMmYPx48cjJiYGmzZtwnfffYd169ZZtpObm4tjx45Z0idPnsTevXvh4+OD2rWpc9OtUIzqZ2tlLlJKsczBzIluTOKABCvXK9nJq5E4BJokrm8FoO58zoraCUvmwqQx0ueHzTK3LIkjoLV7WJFXEMmTV0zvNrprqGZWaGySFoPWag6vyc+g25K4txVKTlVnbemnryJxkDPLLiTY0D4AdbjKNdO9dNNQNzRZ+8icqqyRXfLQn08jmszd0VHSN4SPvzotaQtF8lS3zHxUI3E6k/Y9W5C44lm7+gGANvMcjU3vpkoXH/6L5NE0aEnLSVxEix8fSTTHbPVSIH7v0ZeYz+VR17rgzANEOzr5HaIFfL5cXZ+OxpX21ONEa7HqR6Jpc6hz467/G6BKP/jrRpLneGwU0RrN+ZpoipE6FV7+Qv24TPWX3iR5MpKoU97BpbuI5lKdPjHR/KU+qrTsrJGNjade7Es0B1c6trvWouOx72Pq9ta4e5E8eTtpOzr2GUVjU+i5aS5WnztSpz/ZOSdxRzRpSnfl1Up+h3SSsbf41EFatnknukGrOAp/+YZkcWxP+6zGQN12jV7UhdOw6G1VuvokukTOmWzaFvUz6diosxofAJDfbkeJO6WxOnUDNDt7E82haTjRimQunNblTv1JNMWR9s/sTT8QTd+pF9G0Oeo/pIUD7RfnP5hItNr/o2OetdMiAIzyeViV7v8wPW4hfR8iWs5pOiZdTqPui+02biYac/vw9fWFry8d66wJCwtDZmYmUlNT0bp1awDXJ11msxnt2rWTlpkwYQKGDBmi0po3b46ZM2ciMjKyTHHyO2VlJDY2FhkZGYiIiEBAQIBFd3Z2xvz58/HII48gNDQUL7/8Mp588kmsXbvWkqdu3bpYt24dNm7ciJYtW2LGjBn46quvEBERYcnz119/oVWrVmjVqhUAYOzYsWjVqhXeeuutu7eTDMMwDMMwDHMfERoaih49emDo0KHYtWsXfv/9d8TFxeHZZ5+1/M1/7tw5hISEYNeu6xfW/Pz80KxZM9UHAGrXro26dcu21AHfKSsjYWFhkC3t1rlzZ2zfvr3U8p06dbrlc6edOnWSbp9hGIZhGIZh7haV+fHFO8XixYsRFxeHrl27QqPRICoqCrNn//e0Q3FxMQ4fPoz8/PzbXjdPyhiGYRiGYRiGue/x8fG55ULRwcHBpd48sffmCj++yDAMwzAMwzAMU4HwpIxhGIZhGIZhGKYC4ccX72F0/6aq0tpmXWkm5+DbVp/Ud86NOkaKzEtEc5G4zyX8k6dKD2hRk25fUi7HQN3/ani60rIuDVVJ44YvSZZqDR8gWnGNRkTTFVN3L+HgQjQ3Fyt3NZfGJM/lbPqcsoeZOgnG784m2rhWVm6LkvbJLKTt4+9VevsAgHJwiyrtFvQAyXOxkA4rNZ1pnYrEvRAadVk3FxrX8PpPE23uIep+9ttTY4hmNqkfKYg4spvkKSikjqQyczi7nRYlaCTuZ9augQDwze9niDY7U+22mLX3K5Lnl+c+Itozp6lLo0HiNuogcSW1RvbaQVFAc6KlrDtOtOc06tbVFFKHuoW/nCBavMTR0+xOLYv/TlO7iLaSxLpl7TGiNfxc4kJron02+5TaSc3bxYfkOb5+L9GEibb1yd10cdLQzGuqtIOg55Ls+qpvK3r+XvzjH6Kd//Ms0XwHxqkFQx7Js2fuL0R7+PEhRJO5/p7f9rcqXZsaRUrPCW02da0z+9L9tEa5cJTG5UzHlpNLVhGt0cQQWqeTuyp9ZPnvJI/3niNEC3iPOnoWmWiHzD6p3s/qkj6bV0z7QVHQg0Qb4xJKtM8OL1altw0YR/K02bKJaI5HthLtp56vEK1aCD0HwjYlq9Ijmg4meazHMgAwZNLx4IuHYok2+OJetVCrBclTS+Lu+HbTZ4k2/hp1sbR2W1y8kzrhfvZNP6JlffIx0Q5JznO5rx9zv8F3yhiGYRiGYRiGYSoQvlPGMAzDMAzDMIyK+9F9sSLhO2U2EBkZiR49eki/S0lJgaIoSE5ORo8ePRAQEAC9Xo+goCDExcUhO/u/R8xWrVqF7t27w9fXFx4eHggLC8PPP/+s2t6UKVOgKIrqExJCH59gGIZhGIZhGObegCdlNhAbG4uNGzfi7Fn67H1CQgLatGmDFi1aoHfv3vjhhx9w5MgRJCYm4tdff8Xw4cMtebdu3Yru3btj/fr1SE1NRefOnREZGUnWLWvatCkuXLhg+Wzbtu2O7yPDMAzDMAzDMBWDInil4lIxGo0IDAxEXFwcJk2aZNFzc3Ph7++P+Ph41eTrBrNnz0Z8fDzOnKEv59+gadOm6NevH9566y0A1++UrV69Gnv37i133MXpJ1Vph5plW1n8TlEkMfqQvQy+56r6RebWztTYQpG8DJ7tUYdoPu7UdMMa8/FdNsVl8qpFNJiKqKZ1JJKjZ/VS4yjKukLj0DoQbcs5av7R2VdtRCB09MXmXIVq1WxoHwAwndmvTnv4kzxni2mbBerocZKZkBQ5qF++J8YoAMxH6Ev1b7ccRLQR70USzb2x2qTF6fEXSR5DTibRoFCrD8VUTGNzVMdvlliEaECHXIcr1GjCdOoA0XT+wbROK9MBpJ8ieYSRxqr19iVaseTleN2Zveo8ddqQPNlF1LSiWibdJxlZPmqjBhctbR/Hi4eIVuRHDQyKln1ANH2dBqq0KYy6SugvHiSasG5XAGZnT6LpLqlNJAr30f4pjHR8yD2TTjSvhx4mmkNAsCqd6d+K5NFqJP1spaQt6kqeutDRseXKb7+q0jWf/x/JYzpHjSwQ+giRzM7eRNP+rX5CxNiSPomiMdLxTVNADZWK3WrQOKxwukCNG8x6avShSI6T0NK3PMxO6n6gu/YvyWPyoKYz4uifVHuwF9Ec0g+r0rK+nl5Az5PAy3uIpjjSMfTD5tGq9NAP+pA8OWfo73TtoXS8LDoiqdOJtq2+y0BV2nxsJ8lDxjIA4gI1BxJF1IzJlKE29NG1fZzGumkx0bTekv7TkZos5Xz+uipdLZKaerzUkJqGfPLT60ST1enQrg+NoxIw7TfJeV5JeKMrNV2r6vCdMhvQ6XQYOHAgEhMTVQvCLV++HCaTCdHR0aTM+fPnsWrVKnTs2LHE7ZrNZuTk5MDHR+1UdPToUQQEBKBevXro378/Tp8+fft2hmEYhmEYhmGYSgVPymwkJiYGx48fR3Lyf7auCQkJiIqKgqfnf1fNoqOj4eLiglq1asHDwwNffUXtqW/w0UcfITc3F337/nfltl27dkhMTMSGDRswb948nDx5Eh06dEBODrWFvRmDwYDs7GzVx2CgVxoZhmEYhmEYhqlc8KTMRkJCQhAeHo4FCxYAAI4dO4aUlBTExqrXy5g5cyZ2796NNWvW4Pjx4xg7dqx0e0uWLMHUqVPx3XffoUaN/25l9+zZE8888wxatGiBiIgIrF+/HpmZmfjuu+9uGd/06dPh6emp+nwwe14595phGIZhGIa5HzGZRaX93IvwpKwMxMbGYuXKlcjJyUFCQgLq169PHk/08/NDSEgInnzySXzxxReYN28eLlxQLwS5dOlSDBkyBN999x26det2yzq9vLzQqFEjHDt26/czJk6ciKysLNXntVH0+W+GYRiGYRiGYSoXPCkrA3379oVGo8GSJUuwaNEixMTEQJEYANzAbL7+8vvNjxF+++23eOGFF/Dtt9+iVy/6gq81ubm5OH78OPz9qaHCzej1enh4eKg+ej01XGAYhmEYhmEYpnLBi0eXATc3N/Tr1w8TJ05EdnY2Bg8ebPlu/fr1SE9PR9u2beHm5oYDBw5g3LhxaN++PYKDgwFcf2Rx0KBBmDVrFtq1a4eLFy8CAJydnS3vpb366quIjIxEnTp1cP78eUyePBlarVZqJlIamkLqVlgpkLjuybiUp3bCMvlStzjFbCSaq5A4IaJ0d0FhyCea2b10Zy8AEA50+7LYbMHagRAAdKDudlmGPFpYY3VKS9ra3SjrFza6L7pWU6Wt3QYBQGs0Ec2sp65aisSxUp993iqs+iTPqXlziTZhzUSinf/pN6IV56lduwKoQZfU6VJotETTSNpWWF2kkV6ykTx1Yd2uAJC9hzq1ZS5bRbRjP6nd/65l03dJO8Z1IJrvmGlEyzPT/XTza6xKyxwlC4x0p8x6N6JlfjuHaI7D3lOlNUW5JM/Z+fSY+06mj2c7PzGEaBc+nqxKV5e4L6Yvnk+0a4eowVLIqyOJZvhX7Qyp6/wcyZP1zQxaLoPu55XkrUSr0UN9jjkESpxAiQKYTXTMKDxGXSbdOtCToMYLo9TbcqAOfvkH9hLNuWWEJBKKpmZwqXmE9VgGeZ+6xXVRC0avAFvCgu7KSaLNaxtDtCHfjlGlRdNwujHJmCFzpzRp6HiDE2qXW7N/M5KlUDJ+Cg/6e3Vm1ntEe/nHN1Xp9I2bSZ5aY6fQuHIvE+nKH7uJpveix8nXyn3xxCcfkzzHf6FPBP17mf4ud49tS7Rab85UpY2SvuLYgLrL/rswiWgBHQcSLee02i01SxK/zGlxTE/a/pPe6k60wErqvsjcXfhOWRmJjY1FRkYGIiIiEBDw30Dv7OyM+fPn45FHHkFoaChefvllPPnkk1i7dq0lz5dffgmj0YgRI0bA39/f8hk9erQlz9mzZxEdHY3GjRujb9++qFatGnbu3AlfyYSEYRiGYRiGYZiqD98pKyNhYWGQLe3WuXNnbN++/ZZlt2zZUur2ly5dam9oDMMwDMMwDMNUQXhSxjAMwzAMwzCMinvV5bCywo8vMgzDMAzDMAzDVCA8KWMYhmEYhmEYhqlA+PHFe5m8jIqOQI7ElVDmVHguu9AqD3UAg5k6/eUpTkSzZXEAc04m0TQuVDM6exFNKaYOUULiOGgL+cXUNc1DYtC150wm0foEeKjjkrRrptaTaLZ5TALaS8dVaVGrOcnjrKPBaoqoU6TMXU3oqZujNfu+3Ue0wP97kmjp+84RLfvsP6p0wCRJBTa6g9qLtUMjAGgkTpr/bvybaAd3nCVa35M7VGnDCuoKNmnIN0SLf+VDorloaWxmnbof6wqu0TyC9nWzxFHyn+92Ea35UPXjMaZty0medfN2EG3wZCLBKHFLtS476C1abvdn24jW/QjVzJJ+fPhdtetbyGPDSJ79Cb8TzVRMx65rVwuIZu3XV605dW6T9dmsy3T8v7zvBNEa1KTLrWjbWi3XIuiYdGbzXqI17ldMNKF1JFrhjrWqtOapEJJHMdFtyRyFzY42OMeaafyKkbqUXvnhO6INPUf7rHVsxtT1JM+W4Z8QrePelFtFaSF7v/rc93yQuloWmehYafQKJFraN9QdMaCPery8eoiOlb6etF8oF48Sbd+CP4hW59Eguj2rdOp3aSRPn+P0vfyCb+k4teCVFUR78V2126I2n45Tio72xV3LaBzPvHmBaJfT1Nqh3RdJnv4DqPOqzGnx3bc3Eu3zqUSqFPDji3cXvlPGMAzDMAzDMAxTgfCkjGEYhmEYhmEYpgLhxxcZhmEYhmEYhlHBjy/eXfhOGcMwDMMwDMMwTAXCkzKGYRiGYRiGYZiKRDD3NIWFhWLy5MmisLDwrpTjOitfufulzqoU6/1SZ1WKtSLqrEqx3i91VqVYK6LOqhRrVauTYXhSdo+TlZUlAIisrKy7Uo7rrHzl7pc6q1Ks90udVSnWiqizKsV6v9RZlWKtiDqrUqxVrU6G4ccXGYZhGIZhGIZhKhCelDEMwzAMwzAMw1QgPCljGIZhGIZhGIapQHhSdo+j1+sxefJk6PX6u1KO66x85e6XOqtSrPdLnVUp1oqosyrFer/UWZVirYg6q1KsVa1OhlGEELwyHMMwDMMwDMMwTAXBd8oYhmEYhmEYhmEqEJ6UMQzDMAzDMAzDVCA8KWMYhmEYhmEYhqlAeFLGMAzDMAzDMAxTgfCkjGEYhmEYhmEYpgLhSdk9Qr169XD16tWKDoNhGIZhGIZhmDLCk7J7hFOnTsFkMt3WbU6dOhVXrly5rdu8ExgMBhw/fhwGg+GulCsv6enpuHjxok15TSYT0tPTcfny5TscFeVuts/p06fxxx9/4M8//yzTxQXrPr9r1y7s3Lnzrh/TqoLRaKzoEGyios7NqkRZxpHbxf2ygs7JkyftOleqUvvYu48Mw9w5eFLGIDs7m3yysrIwbdo0nDhxwqKVlX379kGr1RLd3d0dsbGx2L59e5m3mZiYiB07dgAACgsLERsbC1dXVzRq1Ahubm4YPny49A85e8vZwqFDh1CvXj2iX7t2DU8//TRq166NF198ESaTCUOGDIG/vz9q1aqF8PBwXLhwQbrNdevW4dFHH4WrqysCAgLg5+cHLy8vDBgwAKdPny4xFnvbtrzts379egwZMgTjx4/HP//8o/ouIyMDXbp0kZb77LPPUKdOHdStWxfh4eF4+OGHUaNGDTzyyCNITU0tsb5///0Xbdq0gV6vR8+ePZGdnY3u3bvj4YcfRnh4OJo0aYIjR46QcpGRkfjmm29QUFBgS7NYuHTpkiq9d+9eDBo0CO3bt8fTTz+NLVu2lGl7N3P8+HFp+9gbKwBs2LAB+/fvBwCYzWa88847qFWrFvR6PQIDA/H++++X+AfkhQsXkJSUhPXr16OoqEj1XV5eHt5++21puX379uHdd9/FZ599Ri7mZGdnIyYmRlquPH1v48aNmDx5MjZt2gQA2Lp1K3r27IkuXbogISGhhNa5zqFDh5CQkGDpr//88w9efPFFxMTEWLZXGufPn8fkyZPRv39/vPrqq6Tv346y5RlHLly4gLfeegtdunRBaGgomjZtisjISHz99dd2XcTT6/U4dOjQLfOUp845c+Zg4MCBWLp0KQDgm2++QZMmTRASEoLXX3+9xElEQUEBtm3bhoMHD5LvCgsLsWjRIhv38DqNGzfG0aNHy1QGsK19bsWZM2dKPE8A4MqVK/jwww/x1FNPISwsDGFhYXjqqacQHx9f5gt39u7jzZQ0dlmTl5eHhIQEvPHGG5gzZ84tL7wdPHgQL730Elq1agV/f3/4+/ujVatWeOmll6TH92bKMx6URGnH5GbKsp8MI0Uw9wSKoohFixaJNWvW3PIjQ6PRSD+Koqj+LSt79+4ViqJIY23atKlQFEWEhISIjz76SFy6dMmmbdatW1fs3LlTCCHEq6++KoKDg8WqVavEoUOHxOrVq0WjRo3EuHHjbls5W/dT1j4xMTGiWbNm4tNPPxUdO3YUvXv3Fi1atBDbtm0T27dvF23bthUDBw4k5RYtWiTc3d3FK6+8It544w3h5+cnJkyYIObNmyc6duwoqlevLo4cOSKNxd62LU/7LF68WGi1WtGrVy/xyCOPCCcnJ5GUlGT5/uLFi9L2iY+PFwEBAeLTTz8V8+fPF6GhoeLtt98WP/30kxgwYIBwcXERf/75p7TOqKgo0bFjR/Hjjz+Kvn37ivbt24tOnTqJs2fPivPnz4uIiAjRp08fafvodDrh6ekphg8fLv76669S20aI6+dIenq6EEKI33//XTg4OIiOHTuKcePGie7duwudTieSk5Nt2pY1JfUfe2MVQojGjRuLrVu3CiGEeO+990S1atXExx9/LH766SfxySefiJo1a4r333+flNu1a5fw8vISHh4ewtnZWTRo0ECkpaVZvi/pWP7888/C0dFRNG3aVNSuXVtUq1ZNbNq0qdRyQtjf97755huh0+nEgw8+KNzc3ERCQoLw8vISQ4YMETExMcLR0VEsX75cWudPP/0kHB0dhY+Pj3BychI//fST8PX1Fd26dRNdunQRWq1W/Pbbb6Scs7Oz5Xw6cOCA8PT0FA0aNBDPPPOMCAkJES4uLmLfvn3SOu0ta+848ueffwpPT0/RunVr8cgjjwitVisGDBgg+vXrJ7y8vER4eLjIzs6Wxvryyy9LPxqNRgwcONCSvp11vvPOO8Ld3V1ERUUJPz8/8f7774tq1aqJd999V7z33nvC19dXvPXWW6Tc4cOHRZ06dSy/U48++qg4f/685ftb9b2nnnpK+tFoNKJbt26W9O1qn9IoaSwQ4vq56e3tLWrVqiUGDRokxo8fL8aPHy8GDRokAgMDhY+Pj3S8tHcfyxNvaGiouHr1qhBCiNOnT4vg4GDh6ekp2rZtK3x8fESNGjXEiRMnSLn169cLR0dH8fDDD4vJkyeLzz77THz22Wdi8uTJIjw8XOj1erFhwwZpLOUZD+zZx/LsJ8OUBE/K7hEURSn1U9LAUqtWLdGrVy+xadMmsWXLFrFlyxaxefNmodVqRUJCgkWzpqTB/sanS5cuJf6xmZ6eLvbu3Svi4uKEj4+PcHR0FP/3f/8n1q9fL8xmc4n7qdfrxb///iuEEKJRo0bip59+Un2fnJwsateufdvKCVHyD/CNz/PPPy/dT39/f/H7778LIa7/YaAoivjll18s32/btk3UqlWLlAsJCRFLly61pP/8808RGBhoaZd+/fqV+CNqb9uWp30eeOABMWvWLEt62bJlwtXVVXz11VeWfZe1T3BwsFi/fr0lffjwYVGtWjVRXFwshBBi1KhRonv37tI6fX19xZ49e4QQQmRmZgpFUURKSorl+9TUVFGzZk1STlEUceDAATFz5kzRvHlzodFoRMuWLcWnn34qrl27Jq3rRrkbk7Lu3buLmJgY1fejR48WXbp0kZadNWvWLT/jx48v8TyxJ1Yh1MezWbNm4rvvvlN9v3btWtGgQQNSrlu3buKFF14QJpNJZGdnixdffFFUq1ZN7N69WwhR8rEMCwsTr7/+uhBCCLPZLD744APh5uZm6Ue3+sPY3r53c7/79ddfhbOzs/j4448t33/00Ueiffv20jrDwsLEG2+8IYQQ4ttvvxXe3t6W+IUQYsKECdK+d3M/6N27t4iMjLT0V5PJJJ599lnxxBNPSOu0t6y940j79u3FlClTLOlvvvlGtGvXTgghxLVr18QDDzwgRo0aVWKsDzzwgOjUqZPqoyiKaNu2rejUqZPo3Lnzba2zfv36YuXKlUKI638Ia7Va1cWdVatWSftsnz59RK9evcTly5fF0aNHRa9evUTdunUtfepWfU9RFNGxY0cxePBg1Uej0Yg+ffpY0rerfUq7aDpz5swSY23Xrp0YNmyYdAw3m81i2LBh4uGHH75t+yhE+cauG329f//+Ijw8XGRmZgohhMjJyRHdunUT0dHRpFyLFi3Em2++KY1FCCEmT54smjdvLv3O3vGgPMfE3v1kmJLgSdk9ws2DQ1m5evWq6NOnj+jcubM4e/asRdfpdOLAgQMlltPpdKJnz55ksL/xefLJJ0sdsIUQorCwUCxZskR07dpVaDQaERgYWOLAXKdOHcsV+Fq1apErgwcPHhSurq63rZwQ1++SPPjgg+QH+ManTZs20v10cXERp06dsqQdHBzE/v37LekTJ05I63R2dhYnT55UaTqdTpw7d04IIcQff/whvLy8pLHa27blaR9XV1dyNXDTpk3Czc1NzJs3r8Q/ilxcXFT7aTabhU6ns1zl3rt3r3Bzc5PW6e7ubqnTZDIJnU4n9u7da/n+6NGjwt3dnZSzbp8//vhDDBs2THh6egpnZ2cRHR0tvUNyczl/f3+xY8cO1fdpaWmievXq0lgVRREBAQEiODhY+gkICLDpPLE1VusYa9asaZlU3eDIkSPC2dmZlPP29haHDx9WadOnTxfe3t5i165dJR5LDw8PcezYMZW2ePFi4erqKn788cdb/mFsb9+z7ncODg6qO02HDh0S1apVk9bp4eEhjh49KoT4r//c3Eb79+8vcVJ/45gEBQVZ7kbeYPfu3cLf319ap71lyzOOHD9+3JI2mUzCwcFBXLx4UQghxC+//CICAgKksU6fPl3UrVuX9K/SfhPKU6ezs7NlInVjP2++S3vq1Cnh4uJCytWoUUP8/ffflrTZbBbDhw8XtWvXFsePH79l3/v2229FYGCgWLBgQZn20972ufnpk7JePHVychKHDh0qcduHDh0STk5Ot20fb8Rb3rGrXr16qosIQlx/2iAoKEi6j//880+J8fzzzz/SfRTC/vGgPMfE3v1kmJLgSdk9ws2PV9nLZ599JgICAsSSJUuEEKUP2s2bN7fcDZGxZ88e6WB2q1hPnjwpJk2aVOJA9vrrr4uwsDCRkZEhJkyYICIjI0VOTo4QQoi8vDzRt29f8dhjj922ckJcv3r/zTfflHk/W7ZsKebMmSOEuP5Yhru7u5gxY4bl+3nz5olmzZqRcqGhoarHLFJTU4Wjo6MwGo1CiOsTjltNIO1p2/K0j2ySIoQQW7ZsEW5ubuKNN96Qts8DDzwgvvzyS0v6t99+Ey4uLpYrwf/88490YiWEEA8//LCYNGmSEEKIBQsWiJo1a4oJEyZYvn/77bdF69atSbmSLl7k5eWJhIQE8cgjj5T4R8axY8dEVlaWqFu3LpnkHDt2TPoHoxDX7wguW7ZM+p0QJfcfe2MVQoiXXnpJPPHEE8JoNIphw4aJIUOGqK6wjxw5UoSFhZFy3t7e0kfo4uPjhZeXl1i1apW0Tl9fX+njld9++61wcXER8+bNKzFWe/uel5eX6g84Nzc31YTgxIkTJR4T60mkddlTp05J//jTaDSWRxDr1KlD2urEiRMl/tFob1l7x5E6deqIbdu2WdLnz58XiqKI/Px8IcT18aCkWIW4/rhco0aNxCuvvCKKioqEEKX/JpSnzrp161rukh45ckRoNBrVHd5169aJ4OBgUs7d3V0cPHiQ6CNGjBCBgYFi69att3z8/uTJk6J9+/bi//7v/yx3oG2ZsNjTPgEBAWL16tUlfl/SWCDE9XFk4cKFJZZduHChqFOnjvQ7e/exPGPXjb4eEBCguoggRMnnV0hIiKpvWzNjxgzRuHFj6Xf2jgflOSb27ifDlARPyu4RynOn7GYOHDggWrZsKaKjo0sdtAcPHixeeumlEr8/ePCg9EfUllhLeszOYDCIJ598Unh7e4vu3bsLJycn4eLiIho2bChcXV1F7dq1yZX+8pQTQojnnntOjBkzpsRYS3p3LikpSWi1WtGgQQOh1+vF8uXLRUBAgOjbt6949tlnhaOjo+WPrZuZM2eO8PT0FOPHjxdvvfWWCAgIELGxsarttmrVShqLvW1bnvbp3bu39F0PIYTYvHmzcHV1lf6oLVu2TDg4OIi+ffuKgQMHCjc3N9XE6vPPP5dOHIQQYsOGDcLJyUk4OjoKJycnkZycLBo1aiQeeugh8fDDDwutViv9Y8KW9pHt542rpTeuqN48mRTi+iMwskerhLj+/tv48eNLrO9W717aE6sQ1x/pbNOmjWjQoIEYMGCAcHJyEnXq1BHdu3cXdevWFZ6enpb3uG6mQ4cOYt68edJtfvDBB0Kv10uPZffu3UV8fLy03JIlS4SDg0OJf9jY2/fatGmj+mMqKytL1bc3btwoGjVqJK2zRYsWqsck9+/fb3mUUAghtm7dKurWrUvKKYoivLy8hLe3t3BwcCAXa3755RfpmFeesvaOI6NHjxbNmjUTP/30k9i0aZPo3Lmz6NSpk+X7DRs2iPr160tjvUFOTo4YOHCgaNGihdi/f79wcHC45W9CeeqcNGmS8PX1FUOGDBF169YVEyZMELVr1xbz5s0Tn3/+uQgKCpK+p9W2bVuxaNEi6TZHjBghvLy8Sn0n2mQyibfeeksEBQWJDRs2lLqfNyhr+0RGRt7y8bySxgIhrv8u6PV6MWrUKLFmzRqxc+dOsXPnTrFmzRoxatQo4ezsLObOnXtb97E8Y1fz5s1Fq1athJubm1ixYoXq++TkZOkjt999953Q6XQiMjJSzJo1SyxdulQsXbpUzJo1Szz55JPC0dGRbOsG9o4H5Tkm9u4nw5SErqKNRpjbw6BBg+Ds7GxX2bS0NDRr1gwA0KRJE+zatQsTJkxAs2bNbrnNzz///JZuWqGhoTh58iTRJ0+eDDc3t1vGpCiKVHd0dMSaNWuwYcMG/Pjjj9BqtTCbzfD390f79u3x3HPPwdXV9baVA4AZM2bc0nmwZcuWMJvNRO/fvz+Cg4Oxc+dOhIWFWVwB33//feTn5+PLL7/EoEGDSLkRI0ZAo9EgKSkJBoMBgwcPxptvvmn5/qGHHsKSJUuksdjbtuVpn5dffrlEt8dOnTrhxx9/lLqf9e3bF+7u7khKSkJeXh4+/vhjDB061PL9008/jaefflq63YiICBw6dAipqalo3bo1goODsXXrVsydOxf5+fl477330LlzZ1KuY8eOcHR0lG7zBo0aNSLa5s2bVWl/f39V+uTJkxg2bJh0e2+//Tby8/NLrK9JkybS88TeWAHA09MT27dvx9dff40ff/wRwcHBMJvNKCoqQnR0NF588UUEBgaScgMHDkRycjKGDx9Ovhs/fjyEEPj888/Jdy+++CK2bt0qjSU6OhpCCMyfP1/6vb197/XXX4e3t7cl7eHhofr+r7/+Qt++faV13nAxvMGN8e8GP/30k9RVztrBrUGDBqr0zp078dRTT0nrtLesvePIu+++iwsXLiAyMhImkwlhYWH45ptvLN8rioLp06dLY72Bm5sbFi5ciKVLl6Jbt26luifK6kxKSrKpzqlTp8LZ2Rk7duzA0KFDMWHCBLRs2RLjx49Hfn4+IiMj8c4775ByTz31FL799lsMGDCAfDdnzhyYzWZpn70ZjUaDqVOnonv37hg4cKDNzpRlbZ9x48YhLy+vxO8bNGhAxpobjBgxAtWrV8fMmTPx2WefWerSarVo3bo1EhMTS+zvgH37aO/YNXnyZFXa+jfpxx9/RIcOHUi5Z555BrVq1cLs2bMxY8YMy5IPfn5+CAsLw5YtWxAWFiaNxd7xoDzHxN79ZJiSUISoQgtrMKWSlZWFjRs34tSpU1AUBXXr1kW3bt3IAHUzGo0Gbdu2xZAhQ/Dss8/C3d39LkbMMAzD3CkKCwthNBpLvVhTGmfPnkVqaiq6detW4gWa211nRZCbm4vjx48jJCQEer3e5nJlaZ/yUlxcbFl2onr16nBwcChTeXv3kWGYOwtPyu4hkpKSEBcXR9YU8/T0xOeff45+/fpJy6WkpCAhIQErVqyA2WxGVFQUhgwZUu4rPEajEefPn0ft2rXvSjl7SE9Ph8FgsKuuqVOnWq5e3o1yxcXFZf7xNRqN2Lx5M06fPo06deqgc+fO0rXjrly5UuZ4bsZkMuHff/9FcHAwNBoNDAYD1qxZA7PZjM6dO6NmzZq3tdwN8vLykJqaigsXLkCj0aB+/fpo1apViXdabyYrK0t1JdbT07PsO17FEELAbDZL+8CtSExMxFNPPVWmNjp69Kil31nfFbqTZe3hxp3wyvwHqslkUh23Xbt2wWw2o1WrVjbFXRX2sTJgMBhw9uxZBAYG3tNtlZ6eDiEE/Pz8bMpvMplw5coVaDQa+Pr6lqmuiux7Zd1PPk+YCqXCHpxkbiupqalCp9OJQYMGib1794rCwkJRUFAgUlNTxYABA4SDg4PKnU5Gbm6uWLBggXj00UeFoiiiYcOG4v333xcXLlywK6Zbre9RnnJz584VXbt2Fc8884z49ddfVd9dvnxZ+i5Idna26N+/v6hdu7YYOHCgMBgM4qWXXlKtbZOVlSWtLysri3wyMzOFg4OD+OOPPyza7Sq3bNkyYTAYLOlPP/1U1K5dW2g0GlGtWjUxderUEtsmLi5O/Pjjj0IIIc6cOSNCQkKEVqsVNWvWFFqtVjRv3lzlsHkDjUYjunTpIhYvXiwKCwtL3L6Mffv2CT8/P6HRaESzZs3E6dOnRbNmzYSrq6twc3MT3t7e4o8//rCr3K5du6R1mkwmMW7cOOHs7Kx610tRFFGnTh3xww8/lBjvjTXRrNflCw0NvaVxza0orc+uW7dOxMbGinHjxhEHtWvXrknts8tTrri4WLzxxhvi0Ucftbzv9+GHHwoXFxfh6OhoOQdsxcHBQWqmcIP33nvPci5eu3ZNdO3aVeVc1qNHD5GRkWFT2S5duthU1s3NTcTExFjs4svKL7/8Inr27Gl550ij0QgvLy/Rs2dPsXHjRru2efDgQen4U554T506JVq3bi20Wq3o0aOHyMrKEt26dbO0Ud26dUt8t7A8+7h3717xzjvviLlz54rLly+rvsvKyhIvvPDCbS1n/f7knj17xMCBA0V4eLiIiooSmzdvvq3lhBAiISFBbN++XQghREFBgYiJiRFarVZoNBqh0+nE//73P+l42KxZM/H222+L06dPl7jtkrjb7XP16lURFRUlgoKCxPDhw4XRaBSxsbGW8yssLEy1rps1a9euFR06dLC8T6rRaISnp6d4/vnnVW6Z1tzt86s8+1kR5wnDyOBJ2T3C4MGDxdNPP13i91FRUWUaHI4ePSpef/11ERQUJBwcHERkZGSZY7oTk7JZs2YJFxcXMWLECPH8888LR0dH8d5771m+L8n+OC4uToSEhIjZs2eLTp06id69e4tmzZqJbdu2ieTkZNGkSRPVOkU3Y+/i2uUpd+MHeMGCBcLJyUm89dZbYt26deLdd98Vrq6uYv78+dJYa9asaXGA6tu3r+jWrZvlh+Lq1aviiSeekPYTRVFEjx49hKOjo/D29hZxcXGWdcBKIyIiQjz99NNi//79YvTo0SI0NFQ888wzoqioSBQXF4vnn39edOvW7baVE0KI1157TYSGhooff/xRbNy4UTz66KPigw8+EIcOHRJvvvmm0Ov14ueffyblbkxMJkyYIDZv3iwOHjwoDh48KDZv3iwmTpwoXF1dSzSsuBW3ehnc3sW17S0nxHXThJo1a4qxY8eKJk2aiOHDh4ugoCCRlJQkFi5cKGrVqiU++OADUs7b21v6URRFeHp6WtLWBAYGWhwphwwZIlq1aiV2794tCgoKxN69e8XDDz+sMqu5HWXLswh9YmKi0Ol04tlnnxUJCQli/fr1Yv369SIhIUFER0cLBweHEs0jbsWtxi5747V3ofTy7KO9i4GXZxHxm8e9sizQbm85IexfuFxRFFGtWjWh1WpFRESEWLFihcoopiQqon3sXXxcCCEWLVok3N3dxSuvvCLeeOMN4efnJyZMmCDmzZsnOnbsKKpXry6OHDlCylXE+WXvflbEecIwJcGTsnuEhg0b3vKKzsaNG0XDhg3LtM3c3FzxxRdfCB8fH+nA0qpVq1t+QkJCbms5IYRo0qSJWLx4sSX9+++/C19fX4t7UkmDYFBQkGWwPHfunFAUxXJHSYjrVwNLstq1d3Fte8vd7Lr30EMPiQ8//FD1/WeffVai+6KTk5NlrZbAwEByh2r//v3S9bRu1Hn58mXx0UcfiSZNmljWZ/vss89KvIsoxPU/5G/cRcnPzxdarVZVb1pamnR9GHvLCXHdhv/mdZ7Onj0r3NzcLFe13377balzY+3atW9p8bx06VLpkgH2LpQuhP2La9tbTojra+bc6N9Hjx4VGo1GtSD5smXLpDbqbm5uolevXiIxMdHySUhIEFqtVkybNs2iWaPX6y1raQUHB5M/Dv/6668S1++yt2x5FqFv2LCh1LHwBnPnzpW6adq7kHx54rV3oXR791EI+xcDL88i4vYu0F6ehd3tXbhcURRx7tw58f3334vIyEih0+mEr6+veOWVV255R7ki2sfexceFuG5Rf/O48eeff4rAwEBLX+3Xr5946qmnSLmKOL/s3c+KOE8YpiR4UnaP4OrqestHCf79998S1+yxJjk5WQwaNEi4ubkJDw8PMWTIEOk6VHq9XgwaNEhMmTJF+vnf//4nHZDsLSeEfGHlGwu9TpgwocRBUK/Xqx41cXFxUT3yU9LCpELYv7i2veVuXvukevXq5LHTY8eOlbh+V4sWLSw/oqGhoWSivn37duHj4yOt0/rxmO3bt4uYmBjh7u4uXFxcxIABA6R1enl5Wa6WFhUVCa1WK1JTUy3fHzp0SHp3xd5yQlxfm8h6kVqdTmd51PbAgQPS4+nk5HTLP5oOHDggXVTZ3oXShbB/cW17y93Yz5v7u/XCsydOnJD2oaNHj1quKN9YK+zG/t+qzzZq1EisXbtWCHH97oP1I3p79uwRHh4et7VseRah1+v1di1Sa+9C8uWJ196F0u3dRyHsXwy8PIuI27tAe3kWdrd34XLrY3n+/Hnx3nvviYYNG1oelfv6669JuYpoH3sXHxdC/nur0+nEuXPnhBDXF7T38vIi5Sri/LJ3PyviPGGYkuBJ2T1CaWsalTY4nDt3TkybNk00bNhQKIoi2rdvLxYsWCByc3NLLNO6dWvx2Weflfh9SYsu2ltOiOt3vG6+Q3KDAwcOiJo1a4qBAwdKywYEBKj+4I+Ojla1V1paWokTgBuUdXFte8spiiIWLVok1qxZIwIDAy3vPNwca0l/4CYkJIjAwECxefNmsWjRIhEaGip+/fVXce7cObFp0ybRvHlzMWTIEFLuVotO5+bmiq+++kqEh4dLv+/atauIjY0VZ8+eFVOnThUNGjRQPSr70ksviQ4dOty2ckIIER4eLt59911L+ttvv1X9cbB//37p8ezQoYMYOHCg9FEjo9EoBg4cKB599FHynb0LpQth/+La9pYT4vpjrH///bclHR4errowcOjQoRL7UHFxsRg/fryoX7++ZSHg0vpsfHy8CA0NFUePHhUzZswQYWFhlj9WTpw4ITp16lTi49X2lrV3oXQhhHjwwQelj6XdYPz48eLBBx8kur0LyZcnXnsXSrd3H4WwfzHw8iwibu8C7eVZ2N3ehctvdSw3b94snn/+eekEoCLax97Fx4W4fmFv+fLllnRqaqpwdHQURqNRCHH9goBsPyvi/LJ3PyviPGGYkuBJ2T3CzX/Iyz4LFy4scXDo0aOH0Ol0ws/PT4wfP/6WV41uZtSoUWL06NElfn/s2DHVwqHlLSfE9clUSQs5p6WlCV9fX+l+9ujRQ3z++ecl1pmQkFDipONmyrK4tr3lbrzAf+Nz8+RDCCG++uqrEh9fFEKIGTNmCBcXF+Hs7CwcHR1V77P16dNHdQfk5jrtXXz8zz//FNWqVROKoghfX1+RlpYm2rVrJ/z8/ERAQIBwdnYmhizlKSeEEL/++qvQ6/XioYceEo8++qjQ6XRi5syZlu/j4+Olj/LcMBepVq2aeOqpp8Tw4cPF8OHDxVNPPSWqVasm/P39VVdXb2DvQulC2L+4tr3lhBCic+fO0scMb/Ddd99J/5C/md9++03Url1bTJw40aaFZkeOHCkcHBxESEiIcHJyEhqNxtL/2rRpc0vDIHvKlmcR+hvt17x5c/Hyyy+L999/X7z//vvi5ZdfFi1atBBubm7S93PsXUi+PPHau1C6vfsohP2LgZdnEXF7F2gvz8Lu9i5cbsuxlD3yXRHtY+/i40JcX7Da09NTjB8/Xrz11lsiICBA9X5nUlKS9LeoIs4ve/ezIs4ThikJtsS/R9BoNKXmURRFumDkk08+idjYWDzxxBNlssq+edHpsmBvOQDYv38/UlNTMXjw4BK3vXLlSrKoY0pKClq0aFGipfdPP/0EZ2dndOrUqdR4i4qKMGHCBGzevBmrVq1C3bp1S4zldpSzZu3atXBwcEBERESJZTMzM/HLL7/g5MmTqoV4GzZsKN3mnDlzMHToULtsgNPS0lC3bl38888/aNy4Mdzc3FBYWIjFixejoKAA3bt3R+PGjW9buRtlzWYzli1bBoPBgIiICHTv3t2meHNycpCUlISdO3eSxUmfe+456Zp+BoMBJpMJLi4uZWiZ6yQnJ2P79u2YOHGi9PvNmzdj0aJFZHFhe8sBwJEjR+Dg4FBiH1uyZAl0Ot0tF5sFgKtXr2Lo0KHYvHkzdu7cWeLxuMGhQ4ewdu1anDhxQtXvunXrVuoyBWUtO3XqVIwbN86uYwIAp06dwrx586T9YPjw4QgODiZlLl68CIPBgDp16pS5vvLEe+rUKdVC6enp6ZaF0nv16iVdKP1GubLuIwB8//332Lp1K2bOnCn9fsmSJZg/fz5ZVNfecsD1/n4z/v7+qsXRZ82ahaKiIowbN+62lLuZGwuXW/e9khYuf+GFFzB79uwyr+lZEe0DAL///rtq8fGDBw9aFh+PjIyULj5+g3nz5iEpKckyzr755ptwcnICcH3pCpPJhJCQEFLubp9f5dnPu32eMExJ8KSMsRuNRoOHHnoIsbGxZVp02t5yN8ras9C1veUqos6b2yc6OrpMC7BWVPtUVD+40Ua84DnDMAzDMFWZ0m+vMEwJJCcno0mTJnjllVfg7++PQYMGISUl5Y6Vu1G2WbNmdtXZtGlTu+u0p2x5yt1oHz8/v3LFOnjw4LvSPhXRD5o2bYpXX321TPtZGsXFxTh9+nSZyxmNRrvKladsVarT3nYtT9nytE9FUBH9gGHuJunp6Xb1VXvLlYfy1FkR8TL3CBX79CRzJzhy5Ij44osvxDvvvCOmTp2q+twJ7F10ujyLVd8PdValWKtanSVxpxY8v9/rrIyx2rMIfXnKlTdee8qVJ9aKaJ/7oc6qFKu9ZbOzs0X//v1F7dq1LQvVv/TSS5b34h599FHpO3f2lquIWG9HvAxjDU/K7jG+/PJLodVqRc2aNUXLli3FAw88YPncyhzidmHvotPlWaz6fqizKsVa1eq8mXtp0lGZ6qxssdq7CL295cobrz3lyhNrRbTP/VBnVYq1PGXj4uJESEiImD17tujUqZPo3bu3aNasmdi2bZtITk4WTZo0sazvdTvKVUSs5S3LMDJ4UnaPUbt2bfH+++9XaAylLTp9u8vdL3VWpVgra50VseD5/VBnVYpVCPsXobe3XEXsZ3lirYj2uR/qrEqxlqdsUFCQZf23c+fOCUVRLIvZCyHE2rVrRePGjW9buYqItbxlGUaGrqIfn2RuLxkZGXjmmWcqpO6tW7diwYIFWLlyJTQaDfr27YvY2Ng7Vu5+qbMqxVrZ6zx48CCeffbZEl0JL1y4gCNHjty2cvdLnVUpVgA4efIkwsPDLenw8HBs2rQJ3bp1Q3FxMcaMGXNby5UnXnvLlSfWimif+6HOqhRrecpeunQJDRo0AAAEBATA2dlZ5RTZrFkznDlz5raVq4hYy1uWYaRU9KyQub3ExMSIefPm3bX67Fl0ujzl7pc6q1KsVanOiljw/H6osyrFKoT9i9DbW6488dpbrjyxVkT73A91VqVYy1M2ICBApKamWtLR0dGqdd3S0tKEt7f3bStXEbGWtyzDyOBJ2T3Ge++9J6pXry4GDRokPvroIzFr1izV53Zi76LT9pa7X+qsSrFWtTorYsHz+6HOqhSrEPYvQm9vufLEa2+58sRaEe1zP9RZlWItT9kePXqIzz//XFpOCCESEhJEeHj4bStXEbGWtyzDyOBJ2T1GcHBwiR97XcFKIjIyUqxevVoYjca7Uu5+qbMqxVrV6ty/f3+Z6ypPufulzqoUqxBC/P333yIhIeGW254yZcptK3fjO3uwt1x5Yq2I9rkf6qxKsZan7NatW0VmZmaJ5davXy82b95828pVRKzlLcswMnhSxjDMfYOiKKJdu3biyy+/FNnZ2Xe83P1SZ1WK9UbZhx56yK467SlXnnjLU648sVZE+9zrdValWCuizqoUa3nLMowMnpTdw5jNZmE2mys6DIapNGzdulW88MILwt3dXbi6uoqBAwdK30O4XeXulzqrUqw3ysbExNhVpz3lyhNvecqVJ9aKaJ97vc6qFGt566xK5/TdHrsYpiR4UnYPsnDhQtGsWTOh1+uFXq8XzZs3F4sWLarosBim0lCVFrquSnVWpVjvlzqrUqz3S51VKdaKqLMqxVresgxzMzwpu8eYMWOGcHFxEePHjxdr1qwRa9asEePGjRMuLi7i448/rujwGKbSUZUWuq5KdValWO+XOqtSrPdLnVUp1oqosyrFWt6yDMOTsnuM4OBgsXDhQqInJiaK4ODgCoiIYSo/lXGh63uhzqoU6/1SZ1WK9X6psyrFWhF1VqVYy1uWub/hSdk9hl6vF0ePHiX6kSNHhF6vr4CIGKbykpycLAYNGiTc3NyEh4eHGDJkiNixY8cdK3e/1FmVYr1f6qxKsd4vdValWCuizqoUa3nLMowQPCm752jatKmYNm0a0d955x3RrFmzCoiIYSoXVWWh66pWZ1WK9X6psyrFer/UWZVirYg6q1Ks5S3LMNbwpOweY8WKFUKr1YqIiAjx9ttvi7fffltEREQInU4nVq1aVdHhMUyFUpUWuq5KdValWO+XOqtSrPdLnVUp1oqosyrFWt6yDCNDB+aeIioqCn/88QdmzpyJ1atXAwBCQ0Oxa9cutGrVqmKDY5gKxsHBAStWrMATTzwBrVZ7x8vdL3VWpVjvlzqrUqz3S51VKdaKqLMqxVresgwjQxFCiIoOgmEYhmEYhmEY5n6F75TdA2RnZ8PDw8Py/1txIx/DMAzDMAzDMJUDvlN2D6DVanHhwgXUqFEDGo0GiqKQPEIIKIoCk8lUAREyDMMwDMMwDFMSfKfsHmDTpk3w8fEBAGzevLmCo2EYhmEYhmEYpizwnbJ7jNOnTyMoKIjcLRNC4MyZM6hdu3YFRcYwDMMwDMMwjAyelN1j3Pwo481cvXoVNWrU4McX/1979xeadfmGAfx6p9lqOV2F9IdS1wZJtiKhhAKDJDCoIFCksw6TMGhK7MDA6qSCSUdJdWQHlVAHFSWaBaFRRLqxAwVpWQsU0VFg/7S3/Q6i4d6mLYgefX6fDwj6fTy4Ti/u535eAAA4z7SVDsC/68/dsVYnT55Me3t7gUQAAMC52CmrxBNPPJEkaTQa2bRpUy699NLJs2azmc8//zy33nproXQAAMDZKGWV2L9/f5I/JmUjIyOZM2fO5NmcOXNyyy23ZMOGDaXiAQAAZ2GnrDKPPPJIXnzxRb9HBgAAFwilrDI//PBDms3m5BP5fxofH8/s2bOVNQAAOM946KMya9euzRtvvPGX79u3b8/atWsLJAIAAM7FpKwyl19+efbu3ZslS5ZM+X7w4MHceeedOXHiRKFkAADAdEzKKvPrr7/mt99++8v306dP5+effy6QCAAAOBelrDK33357Xn755b9837p1a5YtW1YgEQAAcC6exK/Ms88+m5UrV2Z4eDj33HNPkmT37t354osvsnPnzsLpAACAVnbKKjQ0NJQXXnghQ0NDueSSS9LX15eBgYH09vaWjgYAALRQygAAAApyfbFiv/zyS06dOjXlm98pAwCA84uHPirz008/5bHHHsuCBQvS0dGRrq6uKX8AAIDzi1JWmY0bN+ajjz7KSy+9lIsvvjivvvpqNm/enGuuuSbbtm0rHQ8AAGhhp6wy119/fbZt25a77747nZ2d2bdvX3p6evLaa6/l9ddfz/vvv186IgAAcAaTssqMj4+nu7s7yR/7Y+Pj40mSu+66K5988knJaAAAwDSUssp0d3fn66+/TpLceOON2b59e5Lk3Xffzfz58wsmAwAApuP6YmW2bNmSWbNmZf369fnwww9z//33Z2JiIqdPn87g4GAef/zx0hEBAIAzKGWV++abb/Lll1+mp6cnfX19peMAAAAtlLL/A99//72riwAAcJ6yU1aZ5557Lm+++ebkv9esWZMrrrgi1157bYaHhwsmAwAApqOUVWbr1q257rrrkiS7du3Krl278sEHH2TVqlXZuHFj4XQAAECr2aUD8O86evToZCl77733smbNmtx7771ZtGhR7rjjjsLpAACAViZllenq6srY2FiSZMeOHVm5cmWSZGJiIs1ms2Q0AABgGiZllXnooYfy8MMPp7e3NydOnMiqVauSJPv3709PT0/hdAAAQCulrDJbtmzJokWLMjY2lueffz6XXXZZkuTIkSNZt25d4XQAAEArT+IDAAAUZFJWoUOHDuXjjz/OsWPH8vvvv085e+qppwqlAgAApmNSVplXXnkljz76aK688spcddVVaTQak2eNRiP79u0rmA4AAGillFVm4cKFWbduXZ588snSUQAAgBlQyirT2dmZoaGhdHd3l44CAADMgN8pq8zq1auzc+fO0jEAAIAZ8tBHZXp6erJp06Z89tlnufnmm3PRRRdNOV+/fn2hZAAAwHRcX6zM4sWLz3rWaDQyOjr6H6YBAAD+jlIGAABQkJ0yAACAguyUVei7777LO++8k2+//TanTp2acjY4OFgoFQAAMB2lrDK7d+/OAw88kO7u7hw8eDBLly7N4cOHMzExkdtuu610PAAAoIXri5UZGBjIhg0bMjIykvb29rz11lsZGxvLihUrsnr16tLxAACAFh76qMzcuXMzNDSUG264IV1dXdmzZ09uuummDA8P58EHH8zhw4dLRwQAAM5gUlaZjo6OyT2yq6++Ol999dXk2fHjx0vFAgAAzsJOWWWWL1+ePXv2ZMmSJbnvvvvS39+fkZGRvP3221m+fHnpeAAAQAvXFyszOjqakydPpq+vLz/++GP6+/vz6aefpre3N4ODg1m4cGHpiAAAwBmUsoo0m83s3bs3fX19mT9/fuk4AADADChllWlvb8+BAweyePHi0lEAAIAZ8NBHZZYuXZrR0dHSMQAAgBkyKavMjh07MjAwkGeeeSbLli1LR0fHlPPOzs5CyQAAgOkoZZV4+umn09/fn7lz505+azQak3+fmJhIo9FIs9ksEQ8AADgLpawSs2bNypEjR3LgwIFz/r8VK1b8R4kAAICZUMoq0dbWlqNHj2bBggWlowAAAP+Ahz4qcuZ1RQAA4MJgUlaJtra2zJs372+L2fj4+H+UCAAAmInZpQPw79m8eXPmzZtXOgYAAPAPmJRVwk4ZAABcmOyUVcI+GQAAXJiUskoYeAIAwIXJ9UUAAICCTMoAAAAKUsoAAAAKUsoAAAAKUsoAAAAKUsoAAAAKUsoAAAAKUsoAAAAK+h8yRld7lAsTDQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXzdQI7uoyFX",
        "outputId": "ffa4e95b-1469-42b5-9a46-0f214f48f73f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "len(v)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "128"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets tally with the heatmap of previous V's and see how well we did it\n",
        "\n",
        "#V ALL\n",
        "cols = ['TransactionDT'] + ['V'+str(x) for x in range(1,340)]\n",
        "train2 = train_df[cols].sample(frac=0.2)\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(train2[cols].corr(), cmap='RdBu_r', annot=False, center=0.0)\n",
        "plt.title('V1-V339 ALL',fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "id": "61BigK17v13J",
        "outputId": "418db6e3-9d3e-4565-a972-3ea82460b329"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkR4Iw8WmNdk"
      },
      "source": [
        "total_v =[i for i in range(1,340)]\n",
        "v_col_to_remove = [i for i in total_v if i not in v]\n",
        "v_reduced = [ 'V'+str(i) for i in v_col_to_remove]\n",
        "\n",
        "#Train Data\n",
        "train_df.drop(v_reduced, axis=1, inplace=True)\n",
        "#Test Data\n",
        "test_df.drop(v_reduced, axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAo8_7aIp12_",
        "outputId": "1a2095e0-ca8d-4749-be15-a49f683153a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID_x  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
              "0          2987000        0          86400            68.5         W  13926   \n",
              "1          2987001        0          86401            29.0         W   2755   \n",
              "2          2987002        0          86469            59.0         W   4663   \n",
              "3          2987003        0          86499            50.0         W  18132   \n",
              "4          2987004        0          86506            50.0         H   4497   \n",
              "\n",
              "   card2  card3       card4  card5  ...                id_31  id_32  \\\n",
              "0    NaN  150.0    discover  142.0  ...  samsung browser 6.2   32.0   \n",
              "1  404.0  150.0  mastercard  102.0  ...   mobile safari 11.0   32.0   \n",
              "2  490.0  150.0        visa  166.0  ...          chrome 62.0    NaN   \n",
              "3  567.0  150.0  mastercard  117.0  ...          chrome 62.0    NaN   \n",
              "4  514.0  150.0  mastercard  102.0  ...          chrome 62.0   24.0   \n",
              "\n",
              "       id_33           id_34  id_35 id_36 id_37  id_38  DeviceType  \\\n",
              "0  2220x1080  match_status:2      T     F     T      T      mobile   \n",
              "1   1334x750  match_status:1      T     F     F      T      mobile   \n",
              "2        NaN             NaN      F     F     T      T     desktop   \n",
              "3        NaN             NaN      F     F     T      T     desktop   \n",
              "4   1280x800  match_status:2      T     F     T      T     desktop   \n",
              "\n",
              "                      DeviceInfo  \n",
              "0  SAMSUNG SM-G892A Build/NRD90M  \n",
              "1                     iOS Device  \n",
              "2                        Windows  \n",
              "3                            NaN  \n",
              "4                          MacOS  \n",
              "\n",
              "[5 rows x 224 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c537fe38-85f9-4748-a547-15ed956a5fd7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID_x</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>...</th>\n",
              "      <th>id_31</th>\n",
              "      <th>id_32</th>\n",
              "      <th>id_33</th>\n",
              "      <th>id_34</th>\n",
              "      <th>id_35</th>\n",
              "      <th>id_36</th>\n",
              "      <th>id_37</th>\n",
              "      <th>id_38</th>\n",
              "      <th>DeviceType</th>\n",
              "      <th>DeviceInfo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987000</td>\n",
              "      <td>0</td>\n",
              "      <td>86400</td>\n",
              "      <td>68.5</td>\n",
              "      <td>W</td>\n",
              "      <td>13926</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>discover</td>\n",
              "      <td>142.0</td>\n",
              "      <td>...</td>\n",
              "      <td>samsung browser 6.2</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2220x1080</td>\n",
              "      <td>match_status:2</td>\n",
              "      <td>T</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>mobile</td>\n",
              "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987001</td>\n",
              "      <td>0</td>\n",
              "      <td>86401</td>\n",
              "      <td>29.0</td>\n",
              "      <td>W</td>\n",
              "      <td>2755</td>\n",
              "      <td>404.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>mobile safari 11.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1334x750</td>\n",
              "      <td>match_status:1</td>\n",
              "      <td>T</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>mobile</td>\n",
              "      <td>iOS Device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987002</td>\n",
              "      <td>0</td>\n",
              "      <td>86469</td>\n",
              "      <td>59.0</td>\n",
              "      <td>W</td>\n",
              "      <td>4663</td>\n",
              "      <td>490.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>visa</td>\n",
              "      <td>166.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987003</td>\n",
              "      <td>0</td>\n",
              "      <td>86499</td>\n",
              "      <td>50.0</td>\n",
              "      <td>W</td>\n",
              "      <td>18132</td>\n",
              "      <td>567.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>desktop</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987004</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.0</td>\n",
              "      <td>H</td>\n",
              "      <td>4497</td>\n",
              "      <td>514.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1280x800</td>\n",
              "      <td>match_status:2</td>\n",
              "      <td>T</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>desktop</td>\n",
              "      <td>MacOS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 224 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c537fe38-85f9-4748-a547-15ed956a5fd7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c537fe38-85f9-4748-a547-15ed956a5fd7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c537fe38-85f9-4748-a547-15ed956a5fd7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9ccdbe29-a8d0-48bb-9f5c-1867eb31def8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ccdbe29-a8d0-48bb-9f5c-1867eb31def8')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9ccdbe29-a8d0-48bb-9f5c-1867eb31def8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMI-s-_gptZH",
        "outputId": "f5573123-e6af-41b8-f3a2-84360f9f8231",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(train_df.shape)\n",
        "print(test_df.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(590540, 224)\n",
            "(506691, 223)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdoVtkAnmGyC"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddqRMADzqGEr"
      },
      "source": [
        "# Changing T/F to 1/0 in  id_35,id_36,id_37,id_38\n",
        "\n",
        "id_column=['id_35','id_36','id_37','id_38']\n",
        "\n",
        "for col in id_column:\n",
        "    #Train Data\n",
        "    train_df[col].replace(['F','T'],[0,1],inplace=True)\n",
        "\n",
        "    # Test Data\n",
        "    test_df[col].replace(['F','T'],[0,1],inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tl6lhU4QqF8t",
        "outputId": "22606b38-142a-4162-aa66-1e533abe2ff4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID_x  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
              "0          2987000        0          86400            68.5         W  13926   \n",
              "1          2987001        0          86401            29.0         W   2755   \n",
              "2          2987002        0          86469            59.0         W   4663   \n",
              "3          2987003        0          86499            50.0         W  18132   \n",
              "4          2987004        0          86506            50.0         H   4497   \n",
              "\n",
              "   card2  card3       card4  card5  ...                id_31  id_32  \\\n",
              "0    NaN  150.0    discover  142.0  ...  samsung browser 6.2   32.0   \n",
              "1  404.0  150.0  mastercard  102.0  ...   mobile safari 11.0   32.0   \n",
              "2  490.0  150.0        visa  166.0  ...          chrome 62.0    NaN   \n",
              "3  567.0  150.0  mastercard  117.0  ...          chrome 62.0    NaN   \n",
              "4  514.0  150.0  mastercard  102.0  ...          chrome 62.0   24.0   \n",
              "\n",
              "       id_33           id_34  id_35 id_36 id_37  id_38  DeviceType  \\\n",
              "0  2220x1080  match_status:2    1.0   0.0   1.0    1.0      mobile   \n",
              "1   1334x750  match_status:1    1.0   0.0   0.0    1.0      mobile   \n",
              "2        NaN             NaN    0.0   0.0   1.0    1.0     desktop   \n",
              "3        NaN             NaN    0.0   0.0   1.0    1.0     desktop   \n",
              "4   1280x800  match_status:2    1.0   0.0   1.0    1.0     desktop   \n",
              "\n",
              "                      DeviceInfo  \n",
              "0  SAMSUNG SM-G892A Build/NRD90M  \n",
              "1                     iOS Device  \n",
              "2                        Windows  \n",
              "3                            NaN  \n",
              "4                          MacOS  \n",
              "\n",
              "[5 rows x 224 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-203cae46-7f6f-4c97-bc22-b9b1ec7ff99b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID_x</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>...</th>\n",
              "      <th>id_31</th>\n",
              "      <th>id_32</th>\n",
              "      <th>id_33</th>\n",
              "      <th>id_34</th>\n",
              "      <th>id_35</th>\n",
              "      <th>id_36</th>\n",
              "      <th>id_37</th>\n",
              "      <th>id_38</th>\n",
              "      <th>DeviceType</th>\n",
              "      <th>DeviceInfo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987000</td>\n",
              "      <td>0</td>\n",
              "      <td>86400</td>\n",
              "      <td>68.5</td>\n",
              "      <td>W</td>\n",
              "      <td>13926</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>discover</td>\n",
              "      <td>142.0</td>\n",
              "      <td>...</td>\n",
              "      <td>samsung browser 6.2</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2220x1080</td>\n",
              "      <td>match_status:2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>mobile</td>\n",
              "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987001</td>\n",
              "      <td>0</td>\n",
              "      <td>86401</td>\n",
              "      <td>29.0</td>\n",
              "      <td>W</td>\n",
              "      <td>2755</td>\n",
              "      <td>404.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>mobile safari 11.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1334x750</td>\n",
              "      <td>match_status:1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>mobile</td>\n",
              "      <td>iOS Device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987002</td>\n",
              "      <td>0</td>\n",
              "      <td>86469</td>\n",
              "      <td>59.0</td>\n",
              "      <td>W</td>\n",
              "      <td>4663</td>\n",
              "      <td>490.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>visa</td>\n",
              "      <td>166.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987003</td>\n",
              "      <td>0</td>\n",
              "      <td>86499</td>\n",
              "      <td>50.0</td>\n",
              "      <td>W</td>\n",
              "      <td>18132</td>\n",
              "      <td>567.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>desktop</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987004</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.0</td>\n",
              "      <td>H</td>\n",
              "      <td>4497</td>\n",
              "      <td>514.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1280x800</td>\n",
              "      <td>match_status:2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>desktop</td>\n",
              "      <td>MacOS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 224 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-203cae46-7f6f-4c97-bc22-b9b1ec7ff99b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-203cae46-7f6f-4c97-bc22-b9b1ec7ff99b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-203cae46-7f6f-4c97-bc22-b9b1ec7ff99b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-223890f6-c1bc-4321-8b01-546a4f1bd159\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-223890f6-c1bc-4321-8b01-546a4f1bd159')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-223890f6-c1bc-4321-8b01-546a4f1bd159 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzDzMGpbqFy_"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wr74-bM58gLx"
      },
      "source": [
        "#Additional Features\n",
        "# Bank type\n",
        "train_df['bank_type'] = train_df['card3'].astype(str)+'_'+train_df['card5'].astype(str)\n",
        "test_df['bank_type'] = test_df['card3'].astype(str)+'_'+test_df['card5'].astype(str)\n",
        "\n",
        "\n",
        "# Full address\n",
        "train_df['full_addr'] = train_df['addr1'].astype(str)+'_'+train_df['addr2'].astype(str)\n",
        "test_df['full_addr'] = test_df['addr1'].astype(str)+'_'+test_df['addr2'].astype(str)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQjBMAqlLMj-",
        "outputId": "cfe0693b-1046-4270-8852-8e5767753b59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID_x  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
              "0          2987000        0          86400            68.5         W  13926   \n",
              "1          2987001        0          86401            29.0         W   2755   \n",
              "2          2987002        0          86469            59.0         W   4663   \n",
              "3          2987003        0          86499            50.0         W  18132   \n",
              "4          2987004        0          86506            50.0         H   4497   \n",
              "\n",
              "   card2  card3       card4  card5  ...      id_33           id_34  id_35  \\\n",
              "0    NaN  150.0    discover  142.0  ...  2220x1080  match_status:2    1.0   \n",
              "1  404.0  150.0  mastercard  102.0  ...   1334x750  match_status:1    1.0   \n",
              "2  490.0  150.0        visa  166.0  ...        NaN             NaN    0.0   \n",
              "3  567.0  150.0  mastercard  117.0  ...        NaN             NaN    0.0   \n",
              "4  514.0  150.0  mastercard  102.0  ...   1280x800  match_status:2    1.0   \n",
              "\n",
              "   id_36  id_37 id_38 DeviceType                     DeviceInfo    bank_type  \\\n",
              "0    0.0    1.0   1.0     mobile  SAMSUNG SM-G892A Build/NRD90M  150.0_142.0   \n",
              "1    0.0    0.0   1.0     mobile                     iOS Device  150.0_102.0   \n",
              "2    0.0    1.0   1.0    desktop                        Windows  150.0_166.0   \n",
              "3    0.0    1.0   1.0    desktop                            NaN  150.0_117.0   \n",
              "4    0.0    1.0   1.0    desktop                          MacOS  150.0_102.0   \n",
              "\n",
              "    full_addr  \n",
              "0  315.0_87.0  \n",
              "1  325.0_87.0  \n",
              "2  330.0_87.0  \n",
              "3  476.0_87.0  \n",
              "4  420.0_87.0  \n",
              "\n",
              "[5 rows x 226 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-42e6513a-3731-4550-9251-77bcff7d9620\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID_x</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>...</th>\n",
              "      <th>id_33</th>\n",
              "      <th>id_34</th>\n",
              "      <th>id_35</th>\n",
              "      <th>id_36</th>\n",
              "      <th>id_37</th>\n",
              "      <th>id_38</th>\n",
              "      <th>DeviceType</th>\n",
              "      <th>DeviceInfo</th>\n",
              "      <th>bank_type</th>\n",
              "      <th>full_addr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987000</td>\n",
              "      <td>0</td>\n",
              "      <td>86400</td>\n",
              "      <td>68.5</td>\n",
              "      <td>W</td>\n",
              "      <td>13926</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>discover</td>\n",
              "      <td>142.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2220x1080</td>\n",
              "      <td>match_status:2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>mobile</td>\n",
              "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
              "      <td>150.0_142.0</td>\n",
              "      <td>315.0_87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987001</td>\n",
              "      <td>0</td>\n",
              "      <td>86401</td>\n",
              "      <td>29.0</td>\n",
              "      <td>W</td>\n",
              "      <td>2755</td>\n",
              "      <td>404.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1334x750</td>\n",
              "      <td>match_status:1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>mobile</td>\n",
              "      <td>iOS Device</td>\n",
              "      <td>150.0_102.0</td>\n",
              "      <td>325.0_87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987002</td>\n",
              "      <td>0</td>\n",
              "      <td>86469</td>\n",
              "      <td>59.0</td>\n",
              "      <td>W</td>\n",
              "      <td>4663</td>\n",
              "      <td>490.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>visa</td>\n",
              "      <td>166.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "      <td>150.0_166.0</td>\n",
              "      <td>330.0_87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987003</td>\n",
              "      <td>0</td>\n",
              "      <td>86499</td>\n",
              "      <td>50.0</td>\n",
              "      <td>W</td>\n",
              "      <td>18132</td>\n",
              "      <td>567.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>desktop</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0_117.0</td>\n",
              "      <td>476.0_87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987004</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.0</td>\n",
              "      <td>H</td>\n",
              "      <td>4497</td>\n",
              "      <td>514.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1280x800</td>\n",
              "      <td>match_status:2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>desktop</td>\n",
              "      <td>MacOS</td>\n",
              "      <td>150.0_102.0</td>\n",
              "      <td>420.0_87.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 226 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42e6513a-3731-4550-9251-77bcff7d9620')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-42e6513a-3731-4550-9251-77bcff7d9620 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-42e6513a-3731-4550-9251-77bcff7d9620');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-76fbe505-4c77-4ace-8c3f-1d976456d9c0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-76fbe505-4c77-4ace-8c3f-1d976456d9c0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-76fbe505-4c77-4ace-8c3f-1d976456d9c0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpFVj8Q9K6vz"
      },
      "source": [
        "# New feature - log of transaction amount.\n",
        "train_df['TransactionAmt_Log'] = np.log(train_df['TransactionAmt'])\n",
        "test_df['TransactionAmt_Log'] = np.log(test_df['TransactionAmt'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZaUONyh-K6lz"
      },
      "source": [
        "# New feature - day of week in which a transaction happened.\n",
        "train_df['Transaction_day_of_week'] = np.floor((train_df['TransactionDT'] / (3600 * 24) - 1) % 7)\n",
        "test_df['Transaction_day_of_week'] = np.floor((test_df['TransactionDT'] / (3600 * 24) - 1) % 7)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPezNjugK6ic"
      },
      "source": [
        "# New feature - hour of the day in which a transaction happened.\n",
        "train_df['Transaction_hour'] = np.floor(train_df['TransactionDT'] / 3600) % 24\n",
        "test_df['Transaction_hour'] = np.floor(test_df['TransactionDT'] / 3600) % 24"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZoPegxfK6fz"
      },
      "source": [
        "# New feature - decimal part of the transaction amount.\n",
        "train_df['TransactionAmt_decimal'] = ((train_df['TransactionAmt'] - train_df['TransactionAmt'].astype(int)) * 1000).astype(int)\n",
        "test_df['TransactionAmt_decimal'] = ((test_df['TransactionAmt'] - test_df['TransactionAmt'].astype(int)) * 1000).astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUKtqgbZxcg5"
      },
      "source": [
        "#Looks like the data has a time split… so make sure you are not using time as a feature.\n",
        "#Dropping id31 and id13 as per this kernel: https://www.kaggle.com/tunguz/adversarial-ieee\n",
        "\n",
        "#Train Data\n",
        "# train_df.drop('id_31', axis=1, inplace=True)\n",
        "train_df.drop('id_13',axis =1 , inplace=True)\n",
        "#Test Data\n",
        "# test_df.drop('id_31', axis=1, inplace=True)\n",
        "test_df.drop('id_13',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWo5YhbI5Aax",
        "outputId": "16ffc859-c1ca-4b02-ed21-df661ba3e5ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID_x  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
              "0          2987000        0          86400            68.5         W  13926   \n",
              "1          2987001        0          86401            29.0         W   2755   \n",
              "2          2987002        0          86469            59.0         W   4663   \n",
              "3          2987003        0          86499            50.0         W  18132   \n",
              "4          2987004        0          86506            50.0         H   4497   \n",
              "\n",
              "   card2  card3       card4  card5  ... id_37  id_38  DeviceType  \\\n",
              "0    NaN  150.0    discover  142.0  ...   1.0    1.0      mobile   \n",
              "1  404.0  150.0  mastercard  102.0  ...   0.0    1.0      mobile   \n",
              "2  490.0  150.0        visa  166.0  ...   1.0    1.0     desktop   \n",
              "3  567.0  150.0  mastercard  117.0  ...   1.0    1.0     desktop   \n",
              "4  514.0  150.0  mastercard  102.0  ...   1.0    1.0     desktop   \n",
              "\n",
              "                      DeviceInfo    bank_type   full_addr TransactionAmt_Log  \\\n",
              "0  SAMSUNG SM-G892A Build/NRD90M  150.0_142.0  315.0_87.0           4.226834   \n",
              "1                     iOS Device  150.0_102.0  325.0_87.0           3.367296   \n",
              "2                        Windows  150.0_166.0  330.0_87.0           4.077537   \n",
              "3                            NaN  150.0_117.0  476.0_87.0           3.912023   \n",
              "4                          MacOS  150.0_102.0  420.0_87.0           3.912023   \n",
              "\n",
              "   Transaction_day_of_week  Transaction_hour  TransactionAmt_decimal  \n",
              "0                      0.0               0.0                     500  \n",
              "1                      0.0               0.0                       0  \n",
              "2                      0.0               0.0                       0  \n",
              "3                      0.0               0.0                       0  \n",
              "4                      0.0               0.0                       0  \n",
              "\n",
              "[5 rows x 229 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1917c158-41d9-4d43-8baf-e036e89dafaf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID_x</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>...</th>\n",
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              "      <th>DeviceType</th>\n",
              "      <th>DeviceInfo</th>\n",
              "      <th>bank_type</th>\n",
              "      <th>full_addr</th>\n",
              "      <th>TransactionAmt_Log</th>\n",
              "      <th>Transaction_day_of_week</th>\n",
              "      <th>Transaction_hour</th>\n",
              "      <th>TransactionAmt_decimal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987000</td>\n",
              "      <td>0</td>\n",
              "      <td>86400</td>\n",
              "      <td>68.5</td>\n",
              "      <td>W</td>\n",
              "      <td>13926</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>discover</td>\n",
              "      <td>142.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>mobile</td>\n",
              "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
              "      <td>150.0_142.0</td>\n",
              "      <td>315.0_87.0</td>\n",
              "      <td>4.226834</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987001</td>\n",
              "      <td>0</td>\n",
              "      <td>86401</td>\n",
              "      <td>29.0</td>\n",
              "      <td>W</td>\n",
              "      <td>2755</td>\n",
              "      <td>404.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>mobile</td>\n",
              "      <td>iOS Device</td>\n",
              "      <td>150.0_102.0</td>\n",
              "      <td>325.0_87.0</td>\n",
              "      <td>3.367296</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987002</td>\n",
              "      <td>0</td>\n",
              "      <td>86469</td>\n",
              "      <td>59.0</td>\n",
              "      <td>W</td>\n",
              "      <td>4663</td>\n",
              "      <td>490.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>visa</td>\n",
              "      <td>166.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "      <td>150.0_166.0</td>\n",
              "      <td>330.0_87.0</td>\n",
              "      <td>4.077537</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987003</td>\n",
              "      <td>0</td>\n",
              "      <td>86499</td>\n",
              "      <td>50.0</td>\n",
              "      <td>W</td>\n",
              "      <td>18132</td>\n",
              "      <td>567.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>desktop</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0_117.0</td>\n",
              "      <td>476.0_87.0</td>\n",
              "      <td>3.912023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987004</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.0</td>\n",
              "      <td>H</td>\n",
              "      <td>4497</td>\n",
              "      <td>514.0</td>\n",
              "      <td>150.0</td>\n",
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              "      <td>102.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>150.0_102.0</td>\n",
              "      <td>420.0_87.0</td>\n",
              "      <td>3.912023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 229 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1917c158-41d9-4d43-8baf-e036e89dafaf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-1917c158-41d9-4d43-8baf-e036e89dafaf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1917c158-41d9-4d43-8baf-e036e89dafaf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-57182b5b-0b4a-494f-b3e7-a5fe154e378f')\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-57182b5b-0b4a-494f-b3e7-a5fe154e378f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBKWfVOBbUr9"
      },
      "source": [
        "\n",
        "train_df[\"lastest_browser\"] = np.zeros(train_df.shape[0])\n",
        "test_df[\"lastest_browser\"] = np.zeros(test_df.shape[0])\n",
        "\n",
        "#TODO ; improve this code\n",
        "\n",
        "def setBrowser(df):\n",
        "    df.loc[df[\"id_31\"]==\"samsung browser 7.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"opera 53.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"mobile safari 10.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"google search application 49.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"firefox 60.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"edge 17.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 69.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 67.0 for android\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 63.0 for android\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 63.0 for ios\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 64.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 64.0 for android\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 64.0 for ios\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 65.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 65.0 for android\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 65.0 for ios\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 66.0\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 66.0 for android\",'lastest_browser']=1\n",
        "    df.loc[df[\"id_31\"]==\"chrome 66.0 for ios\",'lastest_browser']=1\n",
        "    return df\n",
        "\n",
        "train_df=setBrowser(train_df)\n",
        "test_df=setBrowser(test_df)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdaUdRtL9urK",
        "outputId": "40f358fb-870d-4564-ff42-ea077c8c8c4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "train_df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TransactionID_x  isFraud  TransactionDT  TransactionAmt ProductCD  \\\n",
              "590535          3577535        0       15811047           49.00         W   \n",
              "590536          3577536        0       15811049           39.50         W   \n",
              "590537          3577537        0       15811079           30.95         W   \n",
              "590538          3577538        0       15811088          117.00         W   \n",
              "590539          3577539        0       15811131          279.95         W   \n",
              "\n",
              "        card1  card2  card3       card4  card5  ... id_38  DeviceType  \\\n",
              "590535   6550    NaN  150.0        visa  226.0  ...   NaN         NaN   \n",
              "590536  10444  225.0  150.0  mastercard  224.0  ...   NaN         NaN   \n",
              "590537  12037  595.0  150.0  mastercard  224.0  ...   NaN         NaN   \n",
              "590538   7826  481.0  150.0  mastercard  224.0  ...   NaN         NaN   \n",
              "590539  15066  170.0  150.0  mastercard  102.0  ...   NaN         NaN   \n",
              "\n",
              "        DeviceInfo    bank_type   full_addr TransactionAmt_Log  \\\n",
              "590535         NaN  150.0_226.0  272.0_87.0           3.891820   \n",
              "590536         NaN  150.0_224.0  204.0_87.0           3.676301   \n",
              "590537         NaN  150.0_224.0  231.0_87.0           3.432373   \n",
              "590538         NaN  150.0_224.0  387.0_87.0           4.762174   \n",
              "590539         NaN  150.0_102.0  299.0_87.0           5.634611   \n",
              "\n",
              "       Transaction_day_of_week  Transaction_hour  TransactionAmt_decimal  \\\n",
              "590535                     6.0              23.0                       0   \n",
              "590536                     6.0              23.0                     500   \n",
              "590537                     6.0              23.0                     949   \n",
              "590538                     6.0              23.0                       0   \n",
              "590539                     6.0              23.0                     949   \n",
              "\n",
              "        lastest_browser  \n",
              "590535              0.0  \n",
              "590536              0.0  \n",
              "590537              0.0  \n",
              "590538              0.0  \n",
              "590539              0.0  \n",
              "\n",
              "[5 rows x 230 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ebf2b61d-c3cb-4e01-857f-b9d90dca6314\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID_x</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>...</th>\n",
              "      <th>id_38</th>\n",
              "      <th>DeviceType</th>\n",
              "      <th>DeviceInfo</th>\n",
              "      <th>bank_type</th>\n",
              "      <th>full_addr</th>\n",
              "      <th>TransactionAmt_Log</th>\n",
              "      <th>Transaction_day_of_week</th>\n",
              "      <th>Transaction_hour</th>\n",
              "      <th>TransactionAmt_decimal</th>\n",
              "      <th>lastest_browser</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>590535</th>\n",
              "      <td>3577535</td>\n",
              "      <td>0</td>\n",
              "      <td>15811047</td>\n",
              "      <td>49.00</td>\n",
              "      <td>W</td>\n",
              "      <td>6550</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>visa</td>\n",
              "      <td>226.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0_226.0</td>\n",
              "      <td>272.0_87.0</td>\n",
              "      <td>3.891820</td>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590536</th>\n",
              "      <td>3577536</td>\n",
              "      <td>0</td>\n",
              "      <td>15811049</td>\n",
              "      <td>39.50</td>\n",
              "      <td>W</td>\n",
              "      <td>10444</td>\n",
              "      <td>225.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>224.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0_224.0</td>\n",
              "      <td>204.0_87.0</td>\n",
              "      <td>3.676301</td>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>500</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590537</th>\n",
              "      <td>3577537</td>\n",
              "      <td>0</td>\n",
              "      <td>15811079</td>\n",
              "      <td>30.95</td>\n",
              "      <td>W</td>\n",
              "      <td>12037</td>\n",
              "      <td>595.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>224.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0_224.0</td>\n",
              "      <td>231.0_87.0</td>\n",
              "      <td>3.432373</td>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>949</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590538</th>\n",
              "      <td>3577538</td>\n",
              "      <td>0</td>\n",
              "      <td>15811088</td>\n",
              "      <td>117.00</td>\n",
              "      <td>W</td>\n",
              "      <td>7826</td>\n",
              "      <td>481.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>224.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0_224.0</td>\n",
              "      <td>387.0_87.0</td>\n",
              "      <td>4.762174</td>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590539</th>\n",
              "      <td>3577539</td>\n",
              "      <td>0</td>\n",
              "      <td>15811131</td>\n",
              "      <td>279.95</td>\n",
              "      <td>W</td>\n",
              "      <td>15066</td>\n",
              "      <td>170.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0_102.0</td>\n",
              "      <td>299.0_87.0</td>\n",
              "      <td>5.634611</td>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>949</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 230 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebf2b61d-c3cb-4e01-857f-b9d90dca6314')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ebf2b61d-c3cb-4e01-857f-b9d90dca6314 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ebf2b61d-c3cb-4e01-857f-b9d90dca6314');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-744c6f04-ea88-416a-9615-f57d899217d2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-744c6f04-ea88-416a-9615-f57d899217d2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-744c6f04-ea88-416a-9615-f57d899217d2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2i2DUZiZ-suE"
      },
      "source": [
        "Can't concatenate total data . so performing test and train seperately"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcML4ZwYjhB3",
        "outputId": "082eddb7-c9fd-44f8-b9ee-0e1f9128c320",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(train_df.shape)\n",
        "print(test_df.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(590540, 230)\n",
            "(506691, 229)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vt47B4kW91N2"
      },
      "source": [
        "def drop_missing_values(df):\n",
        "\n",
        "    missing_values_count = (df.isnull().sum() / df.shape[0]) * 100\n",
        "\n",
        "    columns_to_remove = []\n",
        "    for i in range(len(missing_values_count)):\n",
        "      if missing_values_count[i] > 70 :\n",
        "        columns_to_remove.append(df.columns.values[int(i)])\n",
        "\n",
        "\n",
        "    print(len(columns_to_remove))\n",
        "    return columns_to_remove"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJuuBybLJxPP",
        "outputId": "789b4e00-ef9f-4a86-8377-10dba70c215e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Train_data\n",
        "columns_to_remove = drop_missing_values(train_df)\n",
        "train_df.drop(columns_to_remove, axis=1, inplace=True)\n",
        "print(\"Train data shape {}\".format(train_df.shape))\n",
        "#Test_data\n",
        "columns_to_remove = drop_missing_values(test_df)\n",
        "test_df.drop(columns_to_remove, axis=1, inplace=True)\n",
        "print(\"Test data shape {}\".format(test_df.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "105\n",
            "Train data shape (590540, 125)\n",
            "105\n",
            "Test data shape (506691, 124)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ylFNDtCiIIRZ"
      },
      "source": [
        "#  frequency encoding\n",
        "\n",
        "def frequency_encode(df):\n",
        "    columns_to_encode = []\n",
        "    for f in df.columns:\n",
        "      if df[f].dtype == 'object':\n",
        "        columns_to_encode.append(f)\n",
        "\n",
        "    print(columns_to_encode)\n",
        "    for col in columns_to_encode:\n",
        "      fq_encode = df[col].value_counts(dropna=False) / len(df)\n",
        "      df[col] = df[col].map(fq_encode)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAKncqns27d1"
      },
      "source": [
        "# label encoding\n",
        "\n",
        "def label_encode(df):\n",
        "    columns_to_encode = []\n",
        "    for f in df.columns:\n",
        "      if df[f].dtype == 'object':\n",
        "        columns_to_encode.append(f)\n",
        "\n",
        "    print(columns_to_encode)\n",
        "\n",
        "    for col in columns_to_encode:\n",
        "       lbl = preprocessing.LabelEncoder()\n",
        "       lbl.fit(list(df[col].values) )\n",
        "       df[col] = lbl.transform(list(df[col].values))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGA6ZHy2Ai33",
        "outputId": "46cb32c6-9ba1-47ac-a3a9-1f5a8d4fdfe1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Train Data\n",
        "frequency_encode(train_df)\n",
        "#Test Data\n",
        "frequency_encode(test_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ProductCD', 'card4', 'card6', 'P_emaildomain', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'bank_type', 'full_addr']\n",
            "['ProductCD', 'card4', 'card6', 'P_emaildomain', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'bank_type', 'full_addr']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oe245qYtIcOM",
        "outputId": "24f57a38-7ae2-4f18-d7c3-b5dd2d6c4123",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "print(train_df.shape)\n",
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(590540, 125)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID_x  isFraud  TransactionDT  TransactionAmt  ProductCD  card1  \\\n",
              "0          2987000        0          86400            68.5   0.744522  13926   \n",
              "1          2987001        0          86401            29.0   0.744522   2755   \n",
              "2          2987002        0          86469            59.0   0.744522   4663   \n",
              "3          2987003        0          86499            50.0   0.744522  18132   \n",
              "4          2987004        0          86506            50.0   0.055922   4497   \n",
              "\n",
              "   card2  card3     card4  card5  ...   V310  V314  V320  bank_type  \\\n",
              "0    NaN  150.0  0.011263  142.0  ...    0.0   0.0   0.0   0.000469   \n",
              "1  404.0  150.0  0.320414  102.0  ...    0.0   0.0   0.0   0.043907   \n",
              "2  490.0  150.0  0.651551  166.0  ...    0.0   0.0   0.0   0.095658   \n",
              "3  567.0  150.0  0.320414  117.0  ...  354.0   0.0   0.0   0.043924   \n",
              "4  514.0  150.0  0.320414  102.0  ...    0.0   0.0   0.0   0.043907   \n",
              "\n",
              "   full_addr  TransactionAmt_Log  Transaction_day_of_week  Transaction_hour  \\\n",
              "0   0.039078            4.226834                      0.0               0.0   \n",
              "1   0.072388            3.367296                      0.0               0.0   \n",
              "2   0.044508            4.077537                      0.0               0.0   \n",
              "3   0.016046            3.912023                      0.0               0.0   \n",
              "4   0.006064            3.912023                      0.0               0.0   \n",
              "\n",
              "   TransactionAmt_decimal  lastest_browser  \n",
              "0                     500              0.0  \n",
              "1                       0              0.0  \n",
              "2                       0              0.0  \n",
              "3                       0              0.0  \n",
              "4                       0              0.0  \n",
              "\n",
              "[5 rows x 125 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-940cd787-5cd0-46aa-915f-c81954ce51ed\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID_x</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>...</th>\n",
              "      <th>V310</th>\n",
              "      <th>V314</th>\n",
              "      <th>V320</th>\n",
              "      <th>bank_type</th>\n",
              "      <th>full_addr</th>\n",
              "      <th>TransactionAmt_Log</th>\n",
              "      <th>Transaction_day_of_week</th>\n",
              "      <th>Transaction_hour</th>\n",
              "      <th>TransactionAmt_decimal</th>\n",
              "      <th>lastest_browser</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987000</td>\n",
              "      <td>0</td>\n",
              "      <td>86400</td>\n",
              "      <td>68.5</td>\n",
              "      <td>0.744522</td>\n",
              "      <td>13926</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.011263</td>\n",
              "      <td>142.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000469</td>\n",
              "      <td>0.039078</td>\n",
              "      <td>4.226834</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>500</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987001</td>\n",
              "      <td>0</td>\n",
              "      <td>86401</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.744522</td>\n",
              "      <td>2755</td>\n",
              "      <td>404.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.320414</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.043907</td>\n",
              "      <td>0.072388</td>\n",
              "      <td>3.367296</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987002</td>\n",
              "      <td>0</td>\n",
              "      <td>86469</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0.744522</td>\n",
              "      <td>4663</td>\n",
              "      <td>490.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.651551</td>\n",
              "      <td>166.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.095658</td>\n",
              "      <td>0.044508</td>\n",
              "      <td>4.077537</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987003</td>\n",
              "      <td>0</td>\n",
              "      <td>86499</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.744522</td>\n",
              "      <td>18132</td>\n",
              "      <td>567.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.320414</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>354.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.043924</td>\n",
              "      <td>0.016046</td>\n",
              "      <td>3.912023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987004</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.055922</td>\n",
              "      <td>4497</td>\n",
              "      <td>514.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.320414</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.043907</td>\n",
              "      <td>0.006064</td>\n",
              "      <td>3.912023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 125 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-940cd787-5cd0-46aa-915f-c81954ce51ed')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-940cd787-5cd0-46aa-915f-c81954ce51ed button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-940cd787-5cd0-46aa-915f-c81954ce51ed');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-056b1444-d894-4b41-8dd0-b0203ee110b7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-056b1444-d894-4b41-8dd0-b0203ee110b7')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-056b1444-d894-4b41-8dd0-b0203ee110b7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9azbUrVBc9Q",
        "outputId": "3628da0d-c80e-44bd-8521-8fd5ae474864",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "print(test_df.shape)\n",
        "test_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(506691, 124)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID_x  TransactionDT  TransactionAmt  ProductCD  card1  card2  \\\n",
              "0          3663549       18403224           31.95    0.71244  10409  111.0   \n",
              "1          3663550       18403263           49.00    0.71244   4272  111.0   \n",
              "2          3663551       18403310          171.00    0.71244   4476  574.0   \n",
              "3          3663552       18403310          284.95    0.71244  10989  360.0   \n",
              "4          3663553       18403317           67.95    0.71244  18018  452.0   \n",
              "\n",
              "   card3     card4  card5     card6  ...         V310        V314   V320  \\\n",
              "0  150.0  0.660920  226.0  0.759873  ...    47.950001    0.000000    0.0   \n",
              "1  150.0  0.660920  226.0  0.759873  ...   280.000000    0.000000    0.0   \n",
              "2  150.0  0.660920  226.0  0.759873  ...  1058.000000    0.000000  263.0   \n",
              "3  150.0  0.660920  166.0  0.759873  ...     0.000000  282.540009    0.0   \n",
              "4  150.0  0.312161  117.0  0.759873  ...   183.850006  183.850006    0.0   \n",
              "\n",
              "   bank_type  full_addr  TransactionAmt_Log  Transaction_day_of_week  \\\n",
              "0   0.485173   0.003570            3.464172                      2.0   \n",
              "1   0.485173   0.076368            3.891820                      2.0   \n",
              "2   0.485173   0.014261            5.141664                      2.0   \n",
              "3   0.088421   0.010460            5.652314                      2.0   \n",
              "4   0.041682   0.064556            4.218772                      2.0   \n",
              "\n",
              "   Transaction_hour  TransactionAmt_decimal  lastest_browser  \n",
              "0               0.0                     949              1.0  \n",
              "1               0.0                       0              1.0  \n",
              "2               0.0                       0              0.0  \n",
              "3               0.0                     949              1.0  \n",
              "4               0.0                     950              1.0  \n",
              "\n",
              "[5 rows x 124 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5bcfa66e-1afa-4b9c-a60a-fc87bcc7de85\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID_x</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>card6</th>\n",
              "      <th>...</th>\n",
              "      <th>V310</th>\n",
              "      <th>V314</th>\n",
              "      <th>V320</th>\n",
              "      <th>bank_type</th>\n",
              "      <th>full_addr</th>\n",
              "      <th>TransactionAmt_Log</th>\n",
              "      <th>Transaction_day_of_week</th>\n",
              "      <th>Transaction_hour</th>\n",
              "      <th>TransactionAmt_decimal</th>\n",
              "      <th>lastest_browser</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3663549</td>\n",
              "      <td>18403224</td>\n",
              "      <td>31.95</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>10409</td>\n",
              "      <td>111.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.660920</td>\n",
              "      <td>226.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>47.950001</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.485173</td>\n",
              "      <td>0.003570</td>\n",
              "      <td>3.464172</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>949</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3663550</td>\n",
              "      <td>18403263</td>\n",
              "      <td>49.00</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>4272</td>\n",
              "      <td>111.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.660920</td>\n",
              "      <td>226.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.485173</td>\n",
              "      <td>0.076368</td>\n",
              "      <td>3.891820</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3663551</td>\n",
              "      <td>18403310</td>\n",
              "      <td>171.00</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>4476</td>\n",
              "      <td>574.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.660920</td>\n",
              "      <td>226.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>1058.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>263.0</td>\n",
              "      <td>0.485173</td>\n",
              "      <td>0.014261</td>\n",
              "      <td>5.141664</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3663552</td>\n",
              "      <td>18403310</td>\n",
              "      <td>284.95</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>10989</td>\n",
              "      <td>360.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.660920</td>\n",
              "      <td>166.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>282.540009</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.088421</td>\n",
              "      <td>0.010460</td>\n",
              "      <td>5.652314</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>949</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3663553</td>\n",
              "      <td>18403317</td>\n",
              "      <td>67.95</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>18018</td>\n",
              "      <td>452.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.312161</td>\n",
              "      <td>117.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>183.850006</td>\n",
              "      <td>183.850006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.041682</td>\n",
              "      <td>0.064556</td>\n",
              "      <td>4.218772</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>950</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 124 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5bcfa66e-1afa-4b9c-a60a-fc87bcc7de85')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5bcfa66e-1afa-4b9c-a60a-fc87bcc7de85 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5bcfa66e-1afa-4b9c-a60a-fc87bcc7de85');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-012aca9a-21bb-498a-b46a-68fb72ec1bb4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-012aca9a-21bb-498a-b46a-68fb72ec1bb4')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-012aca9a-21bb-498a-b46a-68fb72ec1bb4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LcrzwzZ0ABd3"
      },
      "source": [
        "#Smooth data\n",
        "# Removal of uncommon data with nans\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WpaMIcFf0c0"
      },
      "source": [
        "# columns_name = full_df.columns\n",
        "# result = [i for i in columns_name if i.startswith('V')]\n",
        "\n",
        "# full_df.drop(result, axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGELZPVABxIT"
      },
      "source": [
        "#Fill Nans\n",
        "#Train_data\n",
        "train_df = train_df.fillna(-999)\n",
        "#Test_data\n",
        "test_df = test_df.fillna(-999)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EyJIme9lCJqO",
        "outputId": "3fa6ac32-7864-4df0-d44b-05fef16a9804",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "print(train_df.shape)\n",
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(590540, 125)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID_x  isFraud  TransactionDT  TransactionAmt  ProductCD  card1  \\\n",
              "0          2987000        0          86400            68.5   0.744522  13926   \n",
              "1          2987001        0          86401            29.0   0.744522   2755   \n",
              "2          2987002        0          86469            59.0   0.744522   4663   \n",
              "3          2987003        0          86499            50.0   0.744522  18132   \n",
              "4          2987004        0          86506            50.0   0.055922   4497   \n",
              "\n",
              "   card2  card3     card4  card5  ...   V310  V314  V320  bank_type  \\\n",
              "0 -999.0  150.0  0.011263  142.0  ...    0.0   0.0   0.0   0.000469   \n",
              "1  404.0  150.0  0.320414  102.0  ...    0.0   0.0   0.0   0.043907   \n",
              "2  490.0  150.0  0.651551  166.0  ...    0.0   0.0   0.0   0.095658   \n",
              "3  567.0  150.0  0.320414  117.0  ...  354.0   0.0   0.0   0.043924   \n",
              "4  514.0  150.0  0.320414  102.0  ...    0.0   0.0   0.0   0.043907   \n",
              "\n",
              "   full_addr  TransactionAmt_Log  Transaction_day_of_week  Transaction_hour  \\\n",
              "0   0.039078            4.226834                      0.0               0.0   \n",
              "1   0.072388            3.367296                      0.0               0.0   \n",
              "2   0.044508            4.077537                      0.0               0.0   \n",
              "3   0.016046            3.912023                      0.0               0.0   \n",
              "4   0.006064            3.912023                      0.0               0.0   \n",
              "\n",
              "   TransactionAmt_decimal  lastest_browser  \n",
              "0                     500              0.0  \n",
              "1                       0              0.0  \n",
              "2                       0              0.0  \n",
              "3                       0              0.0  \n",
              "4                       0              0.0  \n",
              "\n",
              "[5 rows x 125 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d8122326-2ed8-438b-a117-6dd639ab2658\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID_x</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>...</th>\n",
              "      <th>V310</th>\n",
              "      <th>V314</th>\n",
              "      <th>V320</th>\n",
              "      <th>bank_type</th>\n",
              "      <th>full_addr</th>\n",
              "      <th>TransactionAmt_Log</th>\n",
              "      <th>Transaction_day_of_week</th>\n",
              "      <th>Transaction_hour</th>\n",
              "      <th>TransactionAmt_decimal</th>\n",
              "      <th>lastest_browser</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987000</td>\n",
              "      <td>0</td>\n",
              "      <td>86400</td>\n",
              "      <td>68.5</td>\n",
              "      <td>0.744522</td>\n",
              "      <td>13926</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.011263</td>\n",
              "      <td>142.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000469</td>\n",
              "      <td>0.039078</td>\n",
              "      <td>4.226834</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>500</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987001</td>\n",
              "      <td>0</td>\n",
              "      <td>86401</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.744522</td>\n",
              "      <td>2755</td>\n",
              "      <td>404.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.320414</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.043907</td>\n",
              "      <td>0.072388</td>\n",
              "      <td>3.367296</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987002</td>\n",
              "      <td>0</td>\n",
              "      <td>86469</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0.744522</td>\n",
              "      <td>4663</td>\n",
              "      <td>490.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.651551</td>\n",
              "      <td>166.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.095658</td>\n",
              "      <td>0.044508</td>\n",
              "      <td>4.077537</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987003</td>\n",
              "      <td>0</td>\n",
              "      <td>86499</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.744522</td>\n",
              "      <td>18132</td>\n",
              "      <td>567.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.320414</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>354.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.043924</td>\n",
              "      <td>0.016046</td>\n",
              "      <td>3.912023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987004</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.055922</td>\n",
              "      <td>4497</td>\n",
              "      <td>514.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.320414</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.043907</td>\n",
              "      <td>0.006064</td>\n",
              "      <td>3.912023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 125 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8122326-2ed8-438b-a117-6dd639ab2658')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d8122326-2ed8-438b-a117-6dd639ab2658 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d8122326-2ed8-438b-a117-6dd639ab2658');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6ae01537-7d44-46d4-ac59-58f8d0abd765\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6ae01537-7d44-46d4-ac59-58f8d0abd765')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6ae01537-7d44-46d4-ac59-58f8d0abd765 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbY_OG4VCRs1",
        "outputId": "a969d648-eb60-4a32-f30e-7c3e2cff059f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "print(test_df.shape)\n",
        "test_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(506691, 124)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID_x  TransactionDT  TransactionAmt  ProductCD  card1  card2  \\\n",
              "0          3663549       18403224           31.95    0.71244  10409  111.0   \n",
              "1          3663550       18403263           49.00    0.71244   4272  111.0   \n",
              "2          3663551       18403310          171.00    0.71244   4476  574.0   \n",
              "3          3663552       18403310          284.95    0.71244  10989  360.0   \n",
              "4          3663553       18403317           67.95    0.71244  18018  452.0   \n",
              "\n",
              "   card3     card4  card5     card6  ...         V310        V314   V320  \\\n",
              "0  150.0  0.660920  226.0  0.759873  ...    47.950001    0.000000    0.0   \n",
              "1  150.0  0.660920  226.0  0.759873  ...   280.000000    0.000000    0.0   \n",
              "2  150.0  0.660920  226.0  0.759873  ...  1058.000000    0.000000  263.0   \n",
              "3  150.0  0.660920  166.0  0.759873  ...     0.000000  282.540009    0.0   \n",
              "4  150.0  0.312161  117.0  0.759873  ...   183.850006  183.850006    0.0   \n",
              "\n",
              "   bank_type  full_addr  TransactionAmt_Log  Transaction_day_of_week  \\\n",
              "0   0.485173   0.003570            3.464172                      2.0   \n",
              "1   0.485173   0.076368            3.891820                      2.0   \n",
              "2   0.485173   0.014261            5.141664                      2.0   \n",
              "3   0.088421   0.010460            5.652314                      2.0   \n",
              "4   0.041682   0.064556            4.218772                      2.0   \n",
              "\n",
              "   Transaction_hour  TransactionAmt_decimal  lastest_browser  \n",
              "0               0.0                     949              1.0  \n",
              "1               0.0                       0              1.0  \n",
              "2               0.0                       0              0.0  \n",
              "3               0.0                     949              1.0  \n",
              "4               0.0                     950              1.0  \n",
              "\n",
              "[5 rows x 124 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-602e6598-7530-4b2d-9011-d8ef9887407f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID_x</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>card6</th>\n",
              "      <th>...</th>\n",
              "      <th>V310</th>\n",
              "      <th>V314</th>\n",
              "      <th>V320</th>\n",
              "      <th>bank_type</th>\n",
              "      <th>full_addr</th>\n",
              "      <th>TransactionAmt_Log</th>\n",
              "      <th>Transaction_day_of_week</th>\n",
              "      <th>Transaction_hour</th>\n",
              "      <th>TransactionAmt_decimal</th>\n",
              "      <th>lastest_browser</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3663549</td>\n",
              "      <td>18403224</td>\n",
              "      <td>31.95</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>10409</td>\n",
              "      <td>111.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.660920</td>\n",
              "      <td>226.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>47.950001</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.485173</td>\n",
              "      <td>0.003570</td>\n",
              "      <td>3.464172</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>949</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3663550</td>\n",
              "      <td>18403263</td>\n",
              "      <td>49.00</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>4272</td>\n",
              "      <td>111.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.660920</td>\n",
              "      <td>226.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.485173</td>\n",
              "      <td>0.076368</td>\n",
              "      <td>3.891820</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3663551</td>\n",
              "      <td>18403310</td>\n",
              "      <td>171.00</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>4476</td>\n",
              "      <td>574.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.660920</td>\n",
              "      <td>226.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>1058.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>263.0</td>\n",
              "      <td>0.485173</td>\n",
              "      <td>0.014261</td>\n",
              "      <td>5.141664</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3663552</td>\n",
              "      <td>18403310</td>\n",
              "      <td>284.95</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>10989</td>\n",
              "      <td>360.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.660920</td>\n",
              "      <td>166.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>282.540009</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.088421</td>\n",
              "      <td>0.010460</td>\n",
              "      <td>5.652314</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>949</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3663553</td>\n",
              "      <td>18403317</td>\n",
              "      <td>67.95</td>\n",
              "      <td>0.71244</td>\n",
              "      <td>18018</td>\n",
              "      <td>452.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.312161</td>\n",
              "      <td>117.0</td>\n",
              "      <td>0.759873</td>\n",
              "      <td>...</td>\n",
              "      <td>183.850006</td>\n",
              "      <td>183.850006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.041682</td>\n",
              "      <td>0.064556</td>\n",
              "      <td>4.218772</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>950</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 124 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-602e6598-7530-4b2d-9011-d8ef9887407f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-602e6598-7530-4b2d-9011-d8ef9887407f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-602e6598-7530-4b2d-9011-d8ef9887407f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-790d51bf-b6a4-48b9-ba15-8db066ccbdfc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-790d51bf-b6a4-48b9-ba15-8db066ccbdfc')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-790d51bf-b6a4-48b9-ba15-8db066ccbdfc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwpV2tN5ErpO"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FyTItWsZmIzR",
        "outputId": "486245c1-c1fe-4116-cb04-b7d4bd7ef51c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['TransactionID_x', 'isFraud', 'TransactionDT', 'TransactionAmt',\n",
              "       'ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5',\n",
              "       ...\n",
              "       'V310', 'V314', 'V320', 'bank_type', 'full_addr', 'TransactionAmt_Log',\n",
              "       'Transaction_day_of_week', 'Transaction_hour', 'TransactionAmt_decimal',\n",
              "       'lastest_browser'],\n",
              "      dtype='object', length=125)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WeM9NExDM1e"
      },
      "source": [
        "Let's Reduce Memory and Pickle it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QY4iwOCJAFl",
        "outputId": "fa5ef1f4-c637-4840-aaaf-976384b0535a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(590540, 125)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcFpNEQoI_78",
        "outputId": "429949bc-6a43-439d-ee07-a377e49823fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506691, 124)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvlsd4PtnGt3"
      },
      "source": [
        "train_df.to_pickle('train_df.pkl')\n",
        "test_df.to_pickle('test_df.pkl')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vgfTBsUnKk-"
      },
      "source": [
        "# train_df = full_df[:TRAIN_SHAPE[0]]\n",
        "# test_df = full_df[TRAIN_SHAPE[0]:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkkGBVLInKV8"
      },
      "source": [
        "# print(train_df.shape)\n",
        "# print(test_df.shape)\n",
        "# print(train_df.shape[0] + test_df.shape[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1XQMTIFqKE0"
      },
      "source": [
        "# print(target.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFSSipO2rxPP"
      },
      "source": [
        "# train_df['isFraud'] = target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6K1rTAOdr5_4"
      },
      "source": [
        "# no_fraud, yes_fraud = train_df['isFraud'].value_counts()\n",
        "# total_transactions = len(train_df)\n",
        "# print(\" No Fraud transactions is {}%\".format(no_fraud * 100/total_transactions))\n",
        "# print(\"Fraud transactions is {}%\".format(yes_fraud * 100 /total_transactions))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDCfE-6htErE"
      },
      "source": [
        "# train_df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zj0Sw7eztV0w"
      },
      "source": [
        "# test_df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10il-lf-tq5f"
      },
      "source": [
        "# reduce_mem_usage(test_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18RVlSj6trcL"
      },
      "source": [
        "# reduce_mem_usage(train_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOjdQJXCvCxg"
      },
      "source": [
        "# train_df.to_pickle('train_df.pkl')\n",
        "# test_df.to_pickle('test_df.pkl')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHGYjBFtwIa6"
      },
      "source": [
        "# train_df.columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__pky7F1vCn-"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7M2ceraBJHz5"
      },
      "source": [
        "# 2. Drop Irrelevant V column features\n",
        "\n",
        "# import gc\n",
        "# nans_df = full_df.isna()\n",
        "# nans_groups={}\n",
        "# i_cols = ['V'+str(i) for i in range(1,340)]\n",
        "# for col in full_df.columns:\n",
        "#     cur_group = nans_df[col].sum()\n",
        "#     try:\n",
        "#         nans_groups[cur_group].append(col)\n",
        "#     except:\n",
        "#         nans_groups[cur_group]=[col]\n",
        "# del nans_df; x=gc.collect()\n",
        "\n",
        "\n",
        "# for k,v in nans_groups.items():\n",
        "#     print('####### NAN count =',k)\n",
        "#     print(v)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVcwujg5Jsyk"
      },
      "source": [
        "# Vs = nans_groups[144233]\n",
        "# Vtitle = 'V1 - V11, D11'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DdWBKqnALktI"
      },
      "source": [
        "# train_df['V2'].isnull().sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZDsgqAVKipA"
      },
      "source": [
        "# Vs = ['D11', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WoQNGtbMKLmw"
      },
      "source": [
        "# def make_corr(Vs,Vtitle=''):\n",
        "#     cols = ['TransactionDT'] + Vs\n",
        "#     plt.figure(figsize=(15,15))\n",
        "#     sns.heatmap(train_df[cols].corr(), cmap='RdBu_r', annot=True, center=0.0)\n",
        "#     if Vtitle!='': plt.title(Vtitle,fontsize=14)\n",
        "#     else: plt.title(Vs[0]+' - '+Vs[-1],fontsize=14)\n",
        "#     plt.show()\n",
        "# make_corr(Vs,Vtitle='D11-V11')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}