04 Regression Assumptions After Modeling.ipynb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtNBZFHO3M7n"
   },
   "source": [
    "# **Taxi Cab Fares Prediction Project**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kaOj1equPMAb"
   },
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "The data consulting firm Automatidata has recently hired me as the newest member of their data analytics team. Their newest client, the NYC Taxi and Limousine Commission (New York City TLC), wants the Automatidata team to build a multiple linear regression model to predict taxi fares using existing data that was collected over the course of a year. The team is getting closer to completing the project, having completed an initial plan of action, initial Python coding work, EDA, and A/B testing.\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "The Automatidata team has reviewed the results of the A/B testing. Now it’s time to work on predicting the taxi fare amounts. I’ve impressed your Automatidata colleagues with my hard work and attention to detail. The data team believes that I am ready to build the regression model and update the client New York City TLC about my progress.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgSbVJvomcVa"
   },
   "source": [
    "# **Building a Multiple Linear Regression Model**\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "In this activity, we will build a multiple linear regression model. As we've learned, multiple linear regression helps us estimate the linear relationship between one continuous dependent variable and two or more independent variables. For data professionals, this is a useful skill because it allows us to consider more than one variable against the variable we're measuring against. This opens the door for much more thorough and flexible analysis to be completed.\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "Completing this activity will help us practice planning out and building a multiple linear regression model based on a specific business need.\n",
    "</p>\n",
    "\n",
    "<br/>\n",
    "\n",
    "**The purpose** of this project is to demostrate knowledge of EDA and a multiple linear regression model\n",
    "\n",
    "**The goal** is to build a multiple linear regression model and evaluate the model\n",
    "<br/>\n",
    "*This activity has three parts:*\n",
    "\n",
    "**Part 1:** EDA & Checking Model Assumptions\n",
    "* What are some purposes of EDA before constructing a multiple linear regression model?\n",
    "\n",
    "**Part 2:** Model Building and evaluation\n",
    "* What resources do you find yourself using as you complete this stage?\n",
    "\n",
    "**Part 3:** Interpreting Model Results\n",
    "\n",
    "* What key insights emerged from your model(s)?\n",
    "\n",
    "* What business recommendations do you propose based on the models built?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7KFOyc3JPSiN"
   },
   "source": [
    "# **Build a Multiple Linear Regression Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3UCHQclzQDUL"
   },
   "source": [
    "<img src=\"images/Pace.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "# **PACE stages**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "Throughout these project notebooks, we'll see references to the problem-solving framework PACE. The following notebook components are labeled with the respective PACE stage: Plan, Analyze, Construct, and Execute.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F5O5cx_qQJmX"
   },
   "source": [
    "<img src=\"images/Plan.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "\n",
    "## PACE: **Plan**\n",
    "\n",
    "Consider the questions in your PACE Strategy Document to reflect on the Plan stage.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D8qYlvkLQsf2"
   },
   "source": [
    "### **Task 1. Imports and loading**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "ccfeg6X6eOVZ"
   },
   "outputs": [],
   "source": [
    "# Packages for numerics + dataframes\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Packages for visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Packages for date conversions for calculating trip durations\n",
    "from datetime import datetime\n",
    "from datetime import date\n",
    "from datetime import timedelta\n",
    "\n",
    "# Packages for OLS, MLR, confusion matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn.metrics as metrics # For confusion matrix\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error,r2_score,mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dhSYPrzQ2lpH"
   },
   "source": [
    "**Note:** `Pandas` is used to load the NYC TLC dataset.  As shown in this cell, the dataset has been automatically loaded in for you. You do not need to download the .csv file, or provide more code, in order to access the dataset and proceed with this lab. Please continue with this activity by completing the following instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TyR3sBUYJBO8",
    "outputId": "5ce192d4-144e-44c9-9360-2bb6000dc743"
   },
   "outputs": [],
   "source": [
    "df0=pd.read_csv(r\"Datasets/2017_Yellow_Taxi_Trip_Data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OnrvCSfHUWPv"
   },
   "source": [
    "<img src=\"images/Analyze.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Analyze**\n",
    "\n",
    "In this stage, consider the following question where applicable to complete the code response:\n",
    "\n",
    "* What are some purposes of EDA before constructing a multiple linear regression model?\n",
    "\n",
    "**Response:**\n",
    "\n",
    "1.   Outliers and extreme data values can significantly impact linear regression equations. After visualizing data, make a plan for addressing outliers by dropping rows, substituting extreme data with average data, and/or removing data values greater than 3 standard deviations.\n",
    "\n",
    "2.   EDA activities also include identifying missing data to help the analyst make decisions on their exclusion or inclusion by substituting values with data set means, medians, and other similar methods.\n",
    "\n",
    "3.   It's important to check for things like multicollinearity between predictor variables, as well to understand their distributions, as this will help you decide what statistical inferences can be made from the model and which ones cannot.\n",
    "\n",
    "4.  Additionally, it can be useful to engineer new features by multiplying variables together or taking the difference from one variable to another. For example, in this dataset you can create a `duration` variable by subtracting `tpep_dropoff` from `tpep_pickup time`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rIcDG2e66wt9"
   },
   "source": [
    "### **Task 2a. Explore data with EDA**\n",
    "\n",
    "Analyze and discover data, looking for correlations, missing data, outliers, and duplicates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CLpoUCz1277k"
   },
   "source": [
    "Start with `.shape` and `.info()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T4Ag-sZhWg6K",
    "outputId": "15c6d684-14b1-4201-baa1-0770fa9ce18e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22699, 18)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 18 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Unnamed: 0             22699 non-null  int64  \n",
      " 1   VendorID               22699 non-null  int64  \n",
      " 2   tpep_pickup_datetime   22699 non-null  object \n",
      " 3   tpep_dropoff_datetime  22699 non-null  object \n",
      " 4   passenger_count        22699 non-null  int64  \n",
      " 5   trip_distance          22699 non-null  float64\n",
      " 6   RatecodeID             22699 non-null  int64  \n",
      " 7   store_and_fwd_flag     22699 non-null  object \n",
      " 8   PULocationID           22699 non-null  int64  \n",
      " 9   DOLocationID           22699 non-null  int64  \n",
      " 10  payment_type           22699 non-null  int64  \n",
      " 11  fare_amount            22699 non-null  float64\n",
      " 12  extra                  22699 non-null  float64\n",
      " 13  mta_tax                22699 non-null  float64\n",
      " 14  tip_amount             22699 non-null  float64\n",
      " 15  tolls_amount           22699 non-null  float64\n",
      " 16  improvement_surcharge  22699 non-null  float64\n",
      " 17  total_amount           22699 non-null  float64\n",
      "dtypes: float64(8), int64(7), object(3)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Start with `.shape` and `.info()`\n",
    "\n",
    "# Keep `df0` as the original dataframe and create a copy (df) where changes will go\n",
    "# Can revert `df` to `df0` if needed down the line\n",
    "df = df0.copy()\n",
    "\n",
    "# Display the dataset's shape\n",
    "print(df.shape)\n",
    "\n",
    "# Display basic info about the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for missing data and duplicates using `.isna()` and `.drop_duplicates()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444
    },
    "id": "3QZZIxxi3OV3",
    "outputId": "255e6481-0818-41a3-dc85-0fe7a9e0c5c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataframe: (22699, 18)\n",
      "Shape of dataframe with duplicates dropped: (22699, 18)\n",
      "Total count of missing values: 0\n",
      "Missing values per column:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Unnamed: 0               0\n",
       "VendorID                 0\n",
       "tpep_pickup_datetime     0\n",
       "tpep_dropoff_datetime    0\n",
       "passenger_count          0\n",
       "trip_distance            0\n",
       "RatecodeID               0\n",
       "store_and_fwd_flag       0\n",
       "PULocationID             0\n",
       "DOLocationID             0\n",
       "payment_type             0\n",
       "fare_amount              0\n",
       "extra                    0\n",
       "mta_tax                  0\n",
       "tip_amount               0\n",
       "tolls_amount             0\n",
       "improvement_surcharge    0\n",
       "total_amount             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing data and duplicates using .isna() and .drop_duplicates()\n",
    "\n",
    "# Check for duplicates\n",
    "print('Shape of dataframe:', df.shape)\n",
    "print('Shape of dataframe with duplicates dropped:', df.drop_duplicates().shape)\n",
    "\n",
    "# Check for missing values in dataframe\n",
    "print('Total count of missing values:', df.isna().sum().sum())\n",
    "\n",
    "# Display missing values per column in dataframe\n",
    "print('Missing values per column:')\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** There are no duplicates or missing values in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WXWAlPTY9iLK"
   },
   "source": [
    "Use `.describe()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "id": "2O3b9H9B9nwk",
    "outputId": "3fe65b1a-c169-4038-b283-7ac46ff5303a"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.269900e+04</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.675849e+07</td>\n",
       "      <td>1.556236</td>\n",
       "      <td>1.642319</td>\n",
       "      <td>2.913313</td>\n",
       "      <td>1.043394</td>\n",
       "      <td>162.412353</td>\n",
       "      <td>161.527997</td>\n",
       "      <td>1.336887</td>\n",
       "      <td>13.026629</td>\n",
       "      <td>0.333275</td>\n",
       "      <td>0.497445</td>\n",
       "      <td>1.835781</td>\n",
       "      <td>0.312542</td>\n",
       "      <td>0.299551</td>\n",
       "      <td>16.310502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.274493e+07</td>\n",
       "      <td>0.496838</td>\n",
       "      <td>1.285231</td>\n",
       "      <td>3.653171</td>\n",
       "      <td>0.708391</td>\n",
       "      <td>66.633373</td>\n",
       "      <td>70.139691</td>\n",
       "      <td>0.496211</td>\n",
       "      <td>13.243791</td>\n",
       "      <td>0.463097</td>\n",
       "      <td>0.039465</td>\n",
       "      <td>2.800626</td>\n",
       "      <td>1.399212</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>16.097295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.212700e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-120.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>-120.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.852056e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>8.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.673150e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.610000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>11.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.537452e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.060000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.134863e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>33.960000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>999.990000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1200.290000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0      VendorID  passenger_count  trip_distance  \\\n",
       "count  2.269900e+04  22699.000000     22699.000000   22699.000000   \n",
       "mean   5.675849e+07      1.556236         1.642319       2.913313   \n",
       "std    3.274493e+07      0.496838         1.285231       3.653171   \n",
       "min    1.212700e+04      1.000000         0.000000       0.000000   \n",
       "25%    2.852056e+07      1.000000         1.000000       0.990000   \n",
       "50%    5.673150e+07      2.000000         1.000000       1.610000   \n",
       "75%    8.537452e+07      2.000000         2.000000       3.060000   \n",
       "max    1.134863e+08      2.000000         6.000000      33.960000   \n",
       "\n",
       "         RatecodeID  PULocationID  DOLocationID  payment_type   fare_amount  \\\n",
       "count  22699.000000  22699.000000  22699.000000  22699.000000  22699.000000   \n",
       "mean       1.043394    162.412353    161.527997      1.336887     13.026629   \n",
       "std        0.708391     66.633373     70.139691      0.496211     13.243791   \n",
       "min        1.000000      1.000000      1.000000      1.000000   -120.000000   \n",
       "25%        1.000000    114.000000    112.000000      1.000000      6.500000   \n",
       "50%        1.000000    162.000000    162.000000      1.000000      9.500000   \n",
       "75%        1.000000    233.000000    233.000000      2.000000     14.500000   \n",
       "max       99.000000    265.000000    265.000000      4.000000    999.990000   \n",
       "\n",
       "              extra       mta_tax    tip_amount  tolls_amount  \\\n",
       "count  22699.000000  22699.000000  22699.000000  22699.000000   \n",
       "mean       0.333275      0.497445      1.835781      0.312542   \n",
       "std        0.463097      0.039465      2.800626      1.399212   \n",
       "min       -1.000000     -0.500000      0.000000      0.000000   \n",
       "25%        0.000000      0.500000      0.000000      0.000000   \n",
       "50%        0.000000      0.500000      1.350000      0.000000   \n",
       "75%        0.500000      0.500000      2.450000      0.000000   \n",
       "max        4.500000      0.500000    200.000000     19.100000   \n",
       "\n",
       "       improvement_surcharge  total_amount  \n",
       "count           22699.000000  22699.000000  \n",
       "mean                0.299551     16.310502  \n",
       "std                 0.015673     16.097295  \n",
       "min                -0.300000   -120.300000  \n",
       "25%                 0.300000      8.750000  \n",
       "50%                 0.300000     11.800000  \n",
       "75%                 0.300000     17.800000  \n",
       "max                 0.300000   1200.290000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display descriptive stats about the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9FRKZtHm3EM7"
   },
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** Some things stand out from this table of summary statistics. For instance, there are clearly some outliers in several variables, like `tip_amount` (\\$200) and `total_amount` (\\$1,200). Also, a number of the variables, such as `mta_tax`, seem to be almost constant throughout the data, which would imply that they would not be expected to be very predictive.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Task 2b. Convert Pickup & Dropoff Columns to Datetime**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "btROUPKa3gYh"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'03/25/2017 9:09:47 AM'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the format of the data\n",
    "df['tpep_dropoff_datetime'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "id": "suC4LJFPMPCo",
    "outputId": "65de0b5c-3aba-4aa8-ab9e-3e4bc04ee01a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data type of tpep_pickup_datetime: object\n",
      "Data type of tpep_dropoff_datetime: object\n",
      "Data type of tpep_pickup_datetime: datetime64[ns]\n",
      "Data type of tpep_dropoff_datetime: datetime64[ns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 08:55:43</td>\n",
       "      <td>2017-03-25 09:09:47</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-11 14:53:28</td>\n",
       "      <td>2017-04-11 15:19:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-15 07:26:56</td>\n",
       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \n",
       "0        2.76           0.0                    0.3         16.56  \n",
       "1        4.00           0.0                    0.3         20.80  \n",
       "2        1.45           0.0                    0.3          8.75  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert datetime columns to datetime\n",
    "\n",
    "# Display data types of `tpep_pickup_datetime`, `tpep_dropoff_datetime`\n",
    "print('Data type of tpep_pickup_datetime:', df['tpep_pickup_datetime'].dtype)\n",
    "print('Data type of tpep_dropoff_datetime:', df['tpep_dropoff_datetime'].dtype)\n",
    "\n",
    "# Convert `tpep_pickup_datetime` to datetime format\n",
    "df['tpep_pickup_datetime'] = pd.to_datetime(df['tpep_pickup_datetime'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "\n",
    "# Convert `tpep_dropoff_datetime` to datetime format\n",
    "df['tpep_dropoff_datetime'] = pd.to_datetime(df['tpep_dropoff_datetime'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "\n",
    "# Display data types of `tpep_pickup_datetime`, `tpep_dropoff_datetime`\n",
    "print('Data type of tpep_pickup_datetime:', df['tpep_pickup_datetime'].dtype)\n",
    "print('Data type of tpep_dropoff_datetime:', df['tpep_dropoff_datetime'].dtype)\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7dcytBKhiGAr"
   },
   "source": [
    "### **Task 2c. Create Duration Column**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "id": "W5bCdL5SSfg1",
    "outputId": "f8a754e3-8234-414f-92dd-721ea6d97dc0"
   },
   "source": [
    "Create a new column called `duration` that represents the total number of minutes that each taxi ride took."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "gS7VR2S0izZE"
   },
   "outputs": [],
   "source": [
    "# Create `duration` column\n",
    "df['duration'] = (df['tpep_dropoff_datetime'] - df['tpep_pickup_datetime'])/np.timedelta64(1,'m')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YhRKv1xzCniK"
   },
   "source": [
    "### Outliers\n",
    "\n",
    "Call `df.info()` to inspect the columns and decide which ones to check for outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EAGOvwrxDRDr",
    "outputId": "43372781-7a87-4dbc-fa9a-36aedded54b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 19 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   Unnamed: 0             22699 non-null  int64         \n",
      " 1   VendorID               22699 non-null  int64         \n",
      " 2   tpep_pickup_datetime   22699 non-null  datetime64[ns]\n",
      " 3   tpep_dropoff_datetime  22699 non-null  datetime64[ns]\n",
      " 4   passenger_count        22699 non-null  int64         \n",
      " 5   trip_distance          22699 non-null  float64       \n",
      " 6   RatecodeID             22699 non-null  int64         \n",
      " 7   store_and_fwd_flag     22699 non-null  object        \n",
      " 8   PULocationID           22699 non-null  int64         \n",
      " 9   DOLocationID           22699 non-null  int64         \n",
      " 10  payment_type           22699 non-null  int64         \n",
      " 11  fare_amount            22699 non-null  float64       \n",
      " 12  extra                  22699 non-null  float64       \n",
      " 13  mta_tax                22699 non-null  float64       \n",
      " 14  tip_amount             22699 non-null  float64       \n",
      " 15  tolls_amount           22699 non-null  float64       \n",
      " 16  improvement_surcharge  22699 non-null  float64       \n",
      " 17  total_amount           22699 non-null  float64       \n",
      " 18  duration               22699 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(9), int64(7), object(1)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-LSCP19IjnZ3",
    "outputId": "ef2158fa-e60b-4b0b-cd24-9627d257289f"
   },
   "source": [
    "Keeping in mind that many of the features will not be used to fit your model, the most important columns to check for outliers are likely to be:\n",
    "* `trip_distance`\n",
    "* `fare_amount`\n",
    "* `duration`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DKRGdmP7DC8f"
   },
   "source": [
    "### **Task 2d. Box plots**\n",
    "\n",
    "Plot a box plot for each feature: `trip_distance`, `fare_amount`, `duration`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nG-Ngt0FCE9M",
    "outputId": "8d0055f5-a490-4ab0-fe0b-9f566ea60340"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(15, 2))\n",
    "fig.suptitle('Boxplots for outlier detection')\n",
    "sns.boxplot(ax=axes[0], x=df['trip_distance'])\n",
    "sns.boxplot(ax=axes[1], x=df['fare_amount'])\n",
    "sns.boxplot(ax=axes[2], x=df['duration'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Response:** \n",
    "1. All three variables contain outliers. Some are extreme, but others not so much.\n",
    "\n",
    "2. It's 30 miles from the southern tip of Staten Island to the northern end of Manhattan and that's in a straight line. With this knowledge and the distribution of the values in this column, it's reasonable to leave these values alone and not alter them. However, the values for `fare_amount` and `duration` definitely seem to have problematic outliers on the higher end.\n",
    "\n",
    "3. Probably not for the latter two, but for `trip_distance` it might be okay."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vtj4iAJMk9Vc"
   },
   "source": [
    "### **Task 2e. Imputations**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PfWU7BVP3f7E"
   },
   "source": [
    "#### `trip_distance` outliers\n",
    "\n",
    "We know from the summary statistics that there are trip distances of 0. Are these reflective of erroneous data, or are they very short trips that get rounded down?\n",
    "\n",
    "To check, sort the column values, eliminate duplicates, and inspect the least 10 values. Are they rounded values or precise values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 632
    },
    "id": "KCEzE-gwL5gq",
    "outputId": "36c88d36-18d6-4556-c762-2b007e48bbd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are trip distances of 0 bad data or very short trips rounded down?\n",
    "sorted(set(df['trip_distance']))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pqcGiHLa4TvP"
   },
   "source": [
    "The distances are captured with a high degree of precision. However, it might be possible for trips to have distances of zero if a passenger summoned a taxi and then changed their mind. Besides, are there enough zero values in the data to pose a problem?\n",
    "\n",
    "Calculate the count of rides where the `trip_distance` is zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['trip_distance']==0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** 148 out of ~23,000 rides is relatively insignificant. You could impute it with a value of 0.01, but it's unlikely to have much of an effect on the model. Therefore, the `trip_distance` column will remain untouched with regard to outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `fare_amount` outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22699.000000\n",
       "mean        13.026629\n",
       "std         13.243791\n",
       "min       -120.000000\n",
       "25%          6.500000\n",
       "50%          9.500000\n",
       "75%         14.500000\n",
       "max        999.990000\n",
       "Name: fare_amount, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fare_amount'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Response:**\n",
    "\n",
    "The range of values in the `fare_amount` column is large and the extremes don't make much sense.\n",
    "\n",
    "* **Low values:** Negative values are problematic. Values of zero could be legitimate if the taxi logged a trip that was immediately canceled.\n",
    "\n",
    "* **High values:** The maximum fare amount in this dataset is nearly \\\\$1,000, which seems very unlikely. High values for this feature can be capped based on intuition and statistics. The interquartile range (IQR) is \\\\$8. The standard formula of `Q3 + (1.5 * IQR)` yields \\$26.50. That doesn't seem appropriate for the maximum fare cap. In this case, we'll use a factor of `6`, which results in a cap of $62.50.\n",
    "\n",
    "Impute values less than $0 with `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute values less than $0 with 0\n",
    "df.loc[df['fare_amount'] < 0, 'fare_amount'] = 0\n",
    "df['fare_amount'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now impute the maximum value as `Q3 + (6 * IQR)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_imputer(column_list, iqr_factor):\n",
    "    '''\n",
    "    Impute upper-limit values in specified columns based on their interquartile range.\n",
    "\n",
    "    Arguments:\n",
    "        column_list: A list of columns to iterate over\n",
    "        iqr_factor: A number representing x in the formula:\n",
    "                    Q3 + (x * IQR). Used to determine maximum threshold,\n",
    "                    beyond which a point is considered an outlier.\n",
    "\n",
    "    The IQR is computed for each column in column_list and values exceeding\n",
    "    the upper threshold for each column are imputed with the upper threshold value.\n",
    "    '''\n",
    "    for col in column_list:\n",
    "        # Reassign minimum to zero\n",
    "        df.loc[df[col] < 0, col] = 0\n",
    "\n",
    "        # Calculate upper threshold\n",
    "        q1 = df[col].quantile(0.25)\n",
    "        q3 = df[col].quantile(0.75)\n",
    "        iqr = q3 - q1\n",
    "        upper_threshold = q3 + (iqr_factor * iqr)\n",
    "        print(col)\n",
    "        print('q3:', q3)\n",
    "        print('upper_threshold:', upper_threshold)\n",
    "\n",
    "        # Reassign values > threshold to threshold\n",
    "        df.loc[df[col] > upper_threshold, col] = upper_threshold\n",
    "        print(df[col].describe())\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fare_amount\n",
      "q3: 14.5\n",
      "upper_threshold: 62.5\n",
      "count    22699.000000\n",
      "mean        12.897913\n",
      "std         10.541137\n",
      "min          0.000000\n",
      "25%          6.500000\n",
      "50%          9.500000\n",
      "75%         14.500000\n",
      "max         62.500000\n",
      "Name: fare_amount, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "outlier_imputer(['fare_amount'], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `duration` outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22699.000000\n",
       "mean        17.013777\n",
       "std         61.996482\n",
       "min        -16.983333\n",
       "25%          6.650000\n",
       "50%         11.183333\n",
       "75%         18.383333\n",
       "max       1439.550000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['duration'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `duration` column has problematic values at both the lower and upper extremities.\n",
    "\n",
    "* **Low values:** There should be no values that represent negative time. Impute all negative durations with `0`.\n",
    "\n",
    "* **High values:** Impute high values the same way you imputed the high-end outliers for fares: `Q3 + (6 * IQR)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute a 0 for any negative values\n",
    "df.loc[df['duration'] < 0, 'duration'] = 0\n",
    "df['duration'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration\n",
      "q3: 18.383333333333333\n",
      "upper_threshold: 88.78333333333333\n",
      "count    22699.000000\n",
      "mean        14.460555\n",
      "std         11.947043\n",
      "min          0.000000\n",
      "25%          6.650000\n",
      "50%         11.183333\n",
      "75%         18.383333\n",
      "max         88.783333\n",
      "Name: duration, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Impute the high outliers\n",
    "outlier_imputer(['duration'], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Task 3a. Feature engineering**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `mean_distance` column\n",
    "\n",
    "When deployed, the model will not know the duration of a trip until after the trip occurs, so we cannot train a model that uses this feature. However, we can use the statistics of trips we do know to generalize about ones we do not know.\n",
    "\n",
    "In this step, create a column called `mean_distance` that captures the mean distance for each group of trips that share pickup and dropoff points.\n",
    "\n",
    "For example, if your data were:\n",
    "\n",
    "|Trip|Start|End|Distance|\n",
    "|--: |:---:|:-:| :--|\n",
    "| 1  | A   | B | 1  |\n",
    "| 2  | C   | D | 2  |\n",
    "| 3  | A   | B |1.5 |\n",
    "| 4  | D   | C | 3  |\n",
    "\n",
    "The results should be:\n",
    "```\n",
    "A -> B: 1.25 miles\n",
    "C -> D: 2 miles\n",
    "D -> C: 3 miles\n",
    "```\n",
    "\n",
    "Notice that C -> D is not the same as D -> C. All trips that share a unique pair of start and end points get grouped and averaged.\n",
    "\n",
    "Then, a new column `mean_distance` will be added where the value at each row is the average for all trips with those pickup and dropoff locations:\n",
    "\n",
    "|Trip|Start|End|Distance|mean_distance|\n",
    "|--: |:---:|:-:|  :--   |:--   |\n",
    "| 1  | A   | B | 1      | 1.25 |\n",
    "| 2  | C   | D | 2      | 2    |\n",
    "| 3  | A   | B |1.5     | 1.25 |\n",
    "| 4  | D   | C | 3      | 3    |\n",
    "\n",
    "\n",
    "Begin by creating a helper column called `pickup_dropoff`, which contains the unique combination of pickup and dropoff location IDs for each row.\n",
    "\n",
    "One way to do this is to convert the pickup and dropoff location IDs to strings and join them, separated by a space. The space is to ensure that, for example, a trip with pickup/dropoff points of 12 & 151 gets encoded differently than a trip with points 121 & 51.\n",
    "\n",
    "So, the new column would look like this:\n",
    "\n",
    "|Trip|Start|End|pickup_dropoff|\n",
    "|--: |:---:|:-:|  :--         |\n",
    "| 1  | A   | B | 'A B'        |\n",
    "| 2  | C   | D | 'C D'        |\n",
    "| 3  | A   | B | 'A B'        |\n",
    "| 4  | D   | C | 'D C'        |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100 231\n",
       "1     186 43\n",
       "Name: pickup_dropoff, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create `pickup_dropoff` column\n",
    "df['pickup_dropoff'] = df['PULocationID'].astype(str) + ' ' + df['DOLocationID'].astype(str)\n",
    "df['pickup_dropoff'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, use a `groupby()` statement to group each row by the new `pickup_dropoff` column, compute the mean, and capture the values only in the `trip_distance` column. Assign the results to a variable named `grouped`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>trip_distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_dropoff</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <th>1 1</th>\n",
       "      <td>2.433333</td>\n",
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       "      <th>10 148</th>\n",
       "      <td>15.700000</td>\n",
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       "    <tr>\n",
       "      <th>100 1</th>\n",
       "      <td>16.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100 100</th>\n",
       "      <td>0.253333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100 107</th>\n",
       "      <td>1.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                trip_distance\n",
       "pickup_dropoff               \n",
       "1 1                  2.433333\n",
       "10 148              15.700000\n",
       "100 1               16.890000\n",
       "100 100              0.253333\n",
       "100 107              1.180000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('pickup_dropoff').mean(numeric_only=True)[['trip_distance']]\n",
    "grouped[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`grouped` is an object of the `DataFrame` class.\n",
    "\n",
    "1. Convert it to a dictionary using the [`to_dict()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_dict.html) method. Assign the results to a variable called `grouped_dict`. This will result in a dictionary with a key of `trip_distance` whose values are another dictionary. The inner dictionary's keys are pickup/dropoff points and its values are mean distances. This is the information you want.\n",
    "\n",
    "```\n",
    "Example:\n",
    "grouped_dict = {'trip_distance': {'A B': 1.25, 'C D': 2, 'D C': 3}\n",
    "```\n",
    "\n",
    "2. Reassign the `grouped_dict` dictionary so it contains only the inner dictionary. In other words, get rid of `trip_distance` as a key, so:\n",
    "\n",
    "```\n",
    "Example:\n",
    "grouped_dict = {'A B': 1.25, 'C D': 2, 'D C': 3}\n",
    " ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Convert `grouped` to a dictionary\n",
    "grouped_dict = grouped.to_dict()\n",
    "\n",
    "# 2. Reassign to only contain the inner dictionary\n",
    "grouped_dict = grouped_dict['trip_distance']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Create a `mean_distance` column that is a copy of the `pickup_dropoff` helper column.\n",
    "\n",
    "2. Use the [`map()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html#pandas-series-map) method on the `mean_distance` series. Pass `grouped_dict` as its argument. Reassign the result back to the `mean_distance` series.\n",
    "</br></br>\n",
    "When you pass a dictionary to the `Series.map()` method, it will replace the data in the series where that data matches the dictionary's keys. The values that get imputed are the values of the dictionary.\n",
    "\n",
    "```\n",
    "Example:\n",
    "df['mean_distance']\n",
    "```\n",
    "\n",
    "|mean_distance |\n",
    "|  :-:         |\n",
    "| 'A B'        |\n",
    "| 'C D'        |\n",
    "| 'A B'        |\n",
    "| 'D C'        |\n",
    "| 'E F'        |\n",
    "\n",
    "```\n",
    "grouped_dict = {'A B': 1.25, 'C D': 2, 'D C': 3}\n",
    "df['mean_distance`] = df['mean_distance'].map(grouped_dict)\n",
    "df['mean_distance']\n",
    "```\n",
    "\n",
    "|mean_distance |\n",
    "|  :-:         |\n",
    "| 1.25         |\n",
    "| 2            |\n",
    "| 1.25         |\n",
    "| 3            |\n",
    "| NaN          |\n",
    "\n",
    "When used this way, the `map()` `Series` method is very similar to `replace()`, however, note that `map()` will impute `NaN` for any values in the series that do not have a corresponding key in the mapping dictionary, so be careful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>3.521667</td>\n",
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       "      <th>16636</th>\n",
       "      <td>3.521667</td>\n",
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       "    <tr>\n",
       "      <th>18134</th>\n",
       "      <td>3.521667</td>\n",
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       "      <th>19761</th>\n",
       "      <td>3.521667</td>\n",
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       "    <tr>\n",
       "      <th>20581</th>\n",
       "      <td>3.521667</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       mean_distance\n",
       "0           3.521667\n",
       "4909        3.521667\n",
       "16636       3.521667\n",
       "18134       3.521667\n",
       "19761       3.521667\n",
       "20581       3.521667"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Create a mean_distance column that is a copy of the pickup_dropoff helper column\n",
    "df['mean_distance'] = df['pickup_dropoff']\n",
    "\n",
    "# 2. Map `grouped_dict` to the `mean_distance` column\n",
    "df['mean_distance'] = df['mean_distance'].map(grouped_dict)\n",
    "\n",
    "# Confirm that it worked\n",
    "df[(df['PULocationID']==100) & (df['DOLocationID']==231)][['mean_distance']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `mean_duration` column\n",
    "\n",
    "Repeat the process used to create the `mean_distance` column to create a `mean_duration` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_duration</th>\n",
       "    </tr>\n",
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       "      <td>22.847222</td>\n",
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      "text/plain": [
       "       mean_duration\n",
       "0          22.847222\n",
       "4909       22.847222\n",
       "16636      22.847222\n",
       "18134      22.847222\n",
       "19761      22.847222\n",
       "20581      22.847222"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('pickup_dropoff').mean(numeric_only=True)[['duration']]\n",
    "grouped\n",
    "\n",
    "# Create a dictionary where keys are unique pickup_dropoffs and values are\n",
    "# mean trip duration for all trips with those pickup_dropoff combos\n",
    "grouped_dict = grouped.to_dict()\n",
    "grouped_dict = grouped_dict['duration']\n",
    "\n",
    "df['mean_duration'] = df['pickup_dropoff']\n",
    "df['mean_duration'] = df['mean_duration'].map(grouped_dict)\n",
    "\n",
    "# Confirm that it worked\n",
    "df[(df['PULocationID']==100) & (df['DOLocationID']==231)][['mean_duration']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `day` and `month` columns\n",
    "\n",
    "Create two new columns, `day` (name of day) and `month` (name of month) by extracting the relevant information from the `tpep_pickup_datetime` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 'day' col\n",
    "df['day'] = df['tpep_pickup_datetime'].dt.day_name().str.lower()\n",
    "\n",
    "# Create 'month' col\n",
    "df['month'] = df['tpep_pickup_datetime'].dt.strftime('%b').str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `rush_hour` column\n",
    "\n",
    "Define rush hour as:\n",
    "* Any weekday (not Saturday or Sunday) AND\n",
    "* Either from 06:00&ndash;10:00 or from 16:00&ndash;20:00\n",
    "\n",
    "Create a binary `rush_hour` column that contains a 1 if the ride was during rush hour and a 0 if it was not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 'rush_hour' col\n",
    "df['rush_hour'] = df['tpep_pickup_datetime'].dt.hour\n",
    "\n",
    "# If day is Saturday or Sunday, impute 0 in `rush_hour` column\n",
    "df.loc[df['day'].isin(['saturday', 'sunday']), 'rush_hour'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rush_hourizer(hour):\n",
    "    if 6 <= hour['rush_hour'] < 10:\n",
    "        val = 1\n",
    "    elif 16 <= hour['rush_hour'] < 20:\n",
    "        val = 1\n",
    "    else:\n",
    "        val = 0\n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-11 14:53:28</td>\n",
       "      <td>2017-04-11 15:19:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>186 43</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>apr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-15 07:26:56</td>\n",
       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>262 236</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>friday</td>\n",
       "      <td>dec</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-07 13:17:59</td>\n",
       "      <td>2017-05-07 13:48:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>188 97</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>sunday</td>\n",
       "      <td>may</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30841670</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-15 23:32:20</td>\n",
       "      <td>2017-04-15 23:49:03</td>\n",
       "      <td>1</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "      <td>16.716667</td>\n",
       "      <td>4 112</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>apr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "4    30841670         2  2017-04-15 23:32:20   2017-04-15 23:49:03   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "4                1           4.37           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  ...  tolls_amount  improvement_surcharge  \\\n",
       "0           100           231  ...           0.0                    0.3   \n",
       "1           186            43  ...           0.0                    0.3   \n",
       "2           262           236  ...           0.0                    0.3   \n",
       "3           188            97  ...           0.0                    0.3   \n",
       "4             4           112  ...           0.0                    0.3   \n",
       "\n",
       "   total_amount   duration  pickup_dropoff  mean_distance  mean_duration  \\\n",
       "0         16.56  14.066667         100 231       3.521667      22.847222   \n",
       "1         20.80  26.500000          186 43       3.108889      24.470370   \n",
       "2          8.75   7.200000         262 236       0.881429       7.250000   \n",
       "3         27.69  30.250000          188 97       3.700000      30.250000   \n",
       "4         17.80  16.716667           4 112       4.435000      14.616667   \n",
       "\n",
       "        day  month rush_hour  \n",
       "0  saturday    mar         0  \n",
       "1   tuesday    apr         0  \n",
       "2    friday    dec         1  \n",
       "3    sunday    may         0  \n",
       "4  saturday    apr         0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the `rush_hourizer()` function to the new column\n",
    "df.loc[(df.day != 'saturday') & (df.day != 'sunday'), 'rush_hour'] = df.apply(rush_hourizer, axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4. Scatter plot\n",
    "\n",
    "Create a scatterplot to visualize the relationship between `mean_duration` and `fare_amount`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of duration and trip_distance, with a line of best fit\n",
    "sns.set(style='whitegrid')\n",
    "f = plt.figure()\n",
    "f.set_figwidth(5)\n",
    "f.set_figheight(5)\n",
    "sns.regplot(x=df['mean_duration'], y=df['fare_amount'],\n",
    "            scatter_kws={'alpha':0.5, 's':5},\n",
    "            line_kws={'color':'red'})\n",
    "plt.ylim(0, 70)\n",
    "plt.xlim(0, 70)\n",
    "plt.title('Mean duration x fare amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `mean_duration` variable correlates with the target variable. But what are the horizontal lines around fare amounts of 52 dollars and 63 dollars? What are the values and how many are there?\n",
    "\n",
    "We know what one of the lines represents. 62 dollars and 50 cents is the maximum that was imputed for outliers, so all former outliers will now have fare amounts of $62.50. What is the other line?\n",
    "\n",
    "Check the value of the rides in the second horizontal line in the scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.0    514\n",
       "62.5     84\n",
       "59.0      9\n",
       "50.5      9\n",
       "57.5      8\n",
       "Name: fare_amount, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['fare_amount'] > 50]['fare_amount'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** There are 514 trips whose fares were \\$52.\n",
    "\n",
    "Examine the first 30 of these trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>pickup_dropoff</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18600059</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-05 19:15:30</td>\n",
       "      <td>2017-03-05 19:52:18</td>\n",
       "      <td>2</td>\n",
       "      <td>18.90</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.58</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>72.92</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>236 132</td>\n",
       "      <td>19.211667</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>sunday</td>\n",
       "      <td>mar</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>47959795</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-03 14:24:57</td>\n",
       "      <td>2017-06-03 15:31:48</td>\n",
       "      <td>1</td>\n",
       "      <td>18.00</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>66.850000</td>\n",
       "      <td>132 163</td>\n",
       "      <td>19.229000</td>\n",
       "      <td>52.941667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>jun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>95729204</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-11 20:16:16</td>\n",
       "      <td>2017-11-11 20:17:14</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>132 132</td>\n",
       "      <td>2.255862</td>\n",
       "      <td>3.021839</td>\n",
       "      <td>saturday</td>\n",
       "      <td>nov</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>103404868</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-12-06 23:37:08</td>\n",
       "      <td>2017-12-07 00:06:19</td>\n",
       "      <td>1</td>\n",
       "      <td>18.93</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>29.183333</td>\n",
       "      <td>132 79</td>\n",
       "      <td>19.431667</td>\n",
       "      <td>47.275000</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>80479432</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-24 23:45:45</td>\n",
       "      <td>2017-09-25 00:15:14</td>\n",
       "      <td>1</td>\n",
       "      <td>17.99</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.64</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>73.20</td>\n",
       "      <td>29.483333</td>\n",
       "      <td>132 234</td>\n",
       "      <td>17.654000</td>\n",
       "      <td>49.833333</td>\n",
       "      <td>sunday</td>\n",
       "      <td>sep</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>16226157</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-02-28 18:30:05</td>\n",
       "      <td>2017-02-28 19:09:55</td>\n",
       "      <td>1</td>\n",
       "      <td>18.40</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>62.84</td>\n",
       "      <td>39.833333</td>\n",
       "      <td>132 48</td>\n",
       "      <td>18.761905</td>\n",
       "      <td>58.246032</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>55253442</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-05 12:51:58</td>\n",
       "      <td>2017-06-05 13:07:35</td>\n",
       "      <td>1</td>\n",
       "      <td>4.73</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>228</td>\n",
       "      <td>88</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>15.616667</td>\n",
       "      <td>228 88</td>\n",
       "      <td>4.730000</td>\n",
       "      <td>15.616667</td>\n",
       "      <td>monday</td>\n",
       "      <td>jun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>65900029</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-03 22:47:14</td>\n",
       "      <td>2017-08-03 23:32:41</td>\n",
       "      <td>2</td>\n",
       "      <td>18.21</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>45.450000</td>\n",
       "      <td>132 48</td>\n",
       "      <td>18.761905</td>\n",
       "      <td>58.246032</td>\n",
       "      <td>thursday</td>\n",
       "      <td>aug</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>80904240</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-26 13:48:26</td>\n",
       "      <td>2017-09-26 14:31:17</td>\n",
       "      <td>1</td>\n",
       "      <td>17.27</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>42.850000</td>\n",
       "      <td>186 132</td>\n",
       "      <td>17.096000</td>\n",
       "      <td>42.920000</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>sep</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>33706214</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-23 21:34:48</td>\n",
       "      <td>2017-04-23 22:46:23</td>\n",
       "      <td>6</td>\n",
       "      <td>18.34</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>57.80</td>\n",
       "      <td>71.583333</td>\n",
       "      <td>132 148</td>\n",
       "      <td>17.994286</td>\n",
       "      <td>46.340476</td>\n",
       "      <td>sunday</td>\n",
       "      <td>apr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>99259872</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-22 21:31:32</td>\n",
       "      <td>2017-11-22 22:00:25</td>\n",
       "      <td>1</td>\n",
       "      <td>18.65</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>63.36</td>\n",
       "      <td>28.883333</td>\n",
       "      <td>132 144</td>\n",
       "      <td>18.537500</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>nov</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>61050418</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-07-18 13:29:06</td>\n",
       "      <td>2017-07-18 13:29:19</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>230</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>230 161</td>\n",
       "      <td>0.685484</td>\n",
       "      <td>7.965591</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>jul</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>54444647</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-26 13:39:12</td>\n",
       "      <td>2017-06-26 14:34:54</td>\n",
       "      <td>1</td>\n",
       "      <td>17.76</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>211</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>55.700000</td>\n",
       "      <td>211 132</td>\n",
       "      <td>16.580000</td>\n",
       "      <td>61.691667</td>\n",
       "      <td>monday</td>\n",
       "      <td>jun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>94424289</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-07 22:15:00</td>\n",
       "      <td>2017-11-07 22:45:32</td>\n",
       "      <td>2</td>\n",
       "      <td>16.97</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>30.533333</td>\n",
       "      <td>132 170</td>\n",
       "      <td>17.203000</td>\n",
       "      <td>37.113333</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>nov</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>103094220</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-06 05:19:50</td>\n",
       "      <td>2017-12-06 05:53:52</td>\n",
       "      <td>1</td>\n",
       "      <td>20.80</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.85</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>64.41</td>\n",
       "      <td>34.033333</td>\n",
       "      <td>132 239</td>\n",
       "      <td>20.901250</td>\n",
       "      <td>44.862500</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>66115834</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-08-04 17:53:34</td>\n",
       "      <td>2017-08-04 18:50:56</td>\n",
       "      <td>1</td>\n",
       "      <td>21.60</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>12.60</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>75.66</td>\n",
       "      <td>57.366667</td>\n",
       "      <td>264 264</td>\n",
       "      <td>3.191516</td>\n",
       "      <td>15.618773</td>\n",
       "      <td>friday</td>\n",
       "      <td>aug</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>55934137</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-09 09:31:25</td>\n",
       "      <td>2017-06-09 10:24:10</td>\n",
       "      <td>2</td>\n",
       "      <td>18.81</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>163</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>66.00</td>\n",
       "      <td>52.750000</td>\n",
       "      <td>163 132</td>\n",
       "      <td>17.275833</td>\n",
       "      <td>52.338889</td>\n",
       "      <td>friday</td>\n",
       "      <td>jun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>13731926</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-21 06:11:03</td>\n",
       "      <td>2017-02-21 06:59:39</td>\n",
       "      <td>5</td>\n",
       "      <td>16.94</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>60.34</td>\n",
       "      <td>48.600000</td>\n",
       "      <td>132 170</td>\n",
       "      <td>17.203000</td>\n",
       "      <td>37.113333</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>52277743</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-20 08:15:18</td>\n",
       "      <td>2017-06-20 10:24:37</td>\n",
       "      <td>1</td>\n",
       "      <td>17.77</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>88.783333</td>\n",
       "      <td>132 246</td>\n",
       "      <td>18.515000</td>\n",
       "      <td>66.316667</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>jun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>2684305</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-01-10 22:29:47</td>\n",
       "      <td>2017-01-10 23:06:46</td>\n",
       "      <td>1</td>\n",
       "      <td>18.57</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>66.00</td>\n",
       "      <td>36.983333</td>\n",
       "      <td>132 48</td>\n",
       "      <td>18.761905</td>\n",
       "      <td>58.246032</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>jan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>90860814</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-27 21:50:00</td>\n",
       "      <td>2017-10-27 22:35:04</td>\n",
       "      <td>1</td>\n",
       "      <td>22.43</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>45.066667</td>\n",
       "      <td>132 163</td>\n",
       "      <td>19.229000</td>\n",
       "      <td>52.941667</td>\n",
       "      <td>friday</td>\n",
       "      <td>oct</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>106575186</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-16 06:39:59</td>\n",
       "      <td>2017-12-16 07:07:59</td>\n",
       "      <td>2</td>\n",
       "      <td>17.80</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>75</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>64.56</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>75 132</td>\n",
       "      <td>18.442500</td>\n",
       "      <td>36.204167</td>\n",
       "      <td>saturday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>110495611</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-12-30 05:25:29</td>\n",
       "      <td>2017-12-30 06:01:29</td>\n",
       "      <td>6</td>\n",
       "      <td>18.23</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>68</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>68 132</td>\n",
       "      <td>18.785000</td>\n",
       "      <td>58.041667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>87017503</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-15 22:39:12</td>\n",
       "      <td>2017-10-15 23:14:22</td>\n",
       "      <td>1</td>\n",
       "      <td>21.80</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>261</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>35.166667</td>\n",
       "      <td>132 261</td>\n",
       "      <td>22.115000</td>\n",
       "      <td>51.493750</td>\n",
       "      <td>sunday</td>\n",
       "      <td>oct</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>12762608</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-17 20:39:42</td>\n",
       "      <td>2017-02-17 21:13:29</td>\n",
       "      <td>1</td>\n",
       "      <td>19.57</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.67</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.01</td>\n",
       "      <td>33.783333</td>\n",
       "      <td>132 140</td>\n",
       "      <td>19.293333</td>\n",
       "      <td>36.791667</td>\n",
       "      <td>friday</td>\n",
       "      <td>feb</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>71264442</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-08-23 18:23:26</td>\n",
       "      <td>2017-08-23 19:18:29</td>\n",
       "      <td>1</td>\n",
       "      <td>16.70</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>42.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>99.59</td>\n",
       "      <td>55.050000</td>\n",
       "      <td>132 230</td>\n",
       "      <td>18.571200</td>\n",
       "      <td>59.598000</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>aug</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>11006300</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-07 17:20:19</td>\n",
       "      <td>2017-02-07 17:34:41</td>\n",
       "      <td>1</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>170</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>62.84</td>\n",
       "      <td>14.366667</td>\n",
       "      <td>170 48</td>\n",
       "      <td>1.265789</td>\n",
       "      <td>14.135965</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>68882036</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-14 23:01:15</td>\n",
       "      <td>2017-08-14 23:03:35</td>\n",
       "      <td>5</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>265</td>\n",
       "      <td>265</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>265 265</td>\n",
       "      <td>0.753077</td>\n",
       "      <td>3.411538</td>\n",
       "      <td>monday</td>\n",
       "      <td>aug</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>74720333</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-09-06 10:46:17</td>\n",
       "      <td>2017-09-06 11:44:41</td>\n",
       "      <td>1</td>\n",
       "      <td>19.10</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>15.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>68.60</td>\n",
       "      <td>58.400000</td>\n",
       "      <td>239 132</td>\n",
       "      <td>19.795000</td>\n",
       "      <td>50.562500</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>sep</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>51937907</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-19 06:23:13</td>\n",
       "      <td>2017-06-19 07:03:53</td>\n",
       "      <td>6</td>\n",
       "      <td>19.77</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>238</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17.57</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>76.13</td>\n",
       "      <td>40.666667</td>\n",
       "      <td>238 132</td>\n",
       "      <td>19.470000</td>\n",
       "      <td>53.861111</td>\n",
       "      <td>monday</td>\n",
       "      <td>jun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "11      18600059         2  2017-03-05 19:15:30   2017-03-05 19:52:18   \n",
       "110     47959795         1  2017-06-03 14:24:57   2017-06-03 15:31:48   \n",
       "161     95729204         2  2017-11-11 20:16:16   2017-11-11 20:17:14   \n",
       "247    103404868         2  2017-12-06 23:37:08   2017-12-07 00:06:19   \n",
       "379     80479432         2  2017-09-24 23:45:45   2017-09-25 00:15:14   \n",
       "388     16226157         1  2017-02-28 18:30:05   2017-02-28 19:09:55   \n",
       "406     55253442         2  2017-06-05 12:51:58   2017-06-05 13:07:35   \n",
       "449     65900029         2  2017-08-03 22:47:14   2017-08-03 23:32:41   \n",
       "468     80904240         2  2017-09-26 13:48:26   2017-09-26 14:31:17   \n",
       "520     33706214         2  2017-04-23 21:34:48   2017-04-23 22:46:23   \n",
       "569     99259872         2  2017-11-22 21:31:32   2017-11-22 22:00:25   \n",
       "572     61050418         2  2017-07-18 13:29:06   2017-07-18 13:29:19   \n",
       "586     54444647         2  2017-06-26 13:39:12   2017-06-26 14:34:54   \n",
       "692     94424289         2  2017-11-07 22:15:00   2017-11-07 22:45:32   \n",
       "717    103094220         1  2017-12-06 05:19:50   2017-12-06 05:53:52   \n",
       "719     66115834         1  2017-08-04 17:53:34   2017-08-04 18:50:56   \n",
       "782     55934137         2  2017-06-09 09:31:25   2017-06-09 10:24:10   \n",
       "816     13731926         2  2017-02-21 06:11:03   2017-02-21 06:59:39   \n",
       "818     52277743         2  2017-06-20 08:15:18   2017-06-20 10:24:37   \n",
       "835      2684305         2  2017-01-10 22:29:47   2017-01-10 23:06:46   \n",
       "840     90860814         2  2017-10-27 21:50:00   2017-10-27 22:35:04   \n",
       "861    106575186         1  2017-12-16 06:39:59   2017-12-16 07:07:59   \n",
       "881    110495611         2  2017-12-30 05:25:29   2017-12-30 06:01:29   \n",
       "958     87017503         1  2017-10-15 22:39:12   2017-10-15 23:14:22   \n",
       "970     12762608         2  2017-02-17 20:39:42   2017-02-17 21:13:29   \n",
       "984     71264442         1  2017-08-23 18:23:26   2017-08-23 19:18:29   \n",
       "1082    11006300         2  2017-02-07 17:20:19   2017-02-07 17:34:41   \n",
       "1097    68882036         2  2017-08-14 23:01:15   2017-08-14 23:03:35   \n",
       "1110    74720333         1  2017-09-06 10:46:17   2017-09-06 11:44:41   \n",
       "1179    51937907         2  2017-06-19 06:23:13   2017-06-19 07:03:53   \n",
       "\n",
       "      passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "11                  2          18.90           2                  N   \n",
       "110                 1          18.00           2                  N   \n",
       "161                 1           0.23           2                  N   \n",
       "247                 1          18.93           2                  N   \n",
       "379                 1          17.99           2                  N   \n",
       "388                 1          18.40           2                  N   \n",
       "406                 1           4.73           2                  N   \n",
       "449                 2          18.21           2                  N   \n",
       "468                 1          17.27           2                  N   \n",
       "520                 6          18.34           2                  N   \n",
       "569                 1          18.65           2                  N   \n",
       "572                 1           0.00           2                  N   \n",
       "586                 1          17.76           2                  N   \n",
       "692                 2          16.97           2                  N   \n",
       "717                 1          20.80           2                  N   \n",
       "719                 1          21.60           2                  N   \n",
       "782                 2          18.81           2                  N   \n",
       "816                 5          16.94           2                  N   \n",
       "818                 1          17.77           2                  N   \n",
       "835                 1          18.57           2                  N   \n",
       "840                 1          22.43           2                  N   \n",
       "861                 2          17.80           2                  N   \n",
       "881                 6          18.23           2                  N   \n",
       "958                 1          21.80           2                  N   \n",
       "970                 1          19.57           2                  N   \n",
       "984                 1          16.70           2                  N   \n",
       "1082                1           1.09           2                  N   \n",
       "1097                5           2.12           2                  N   \n",
       "1110                1          19.10           2                  N   \n",
       "1179                6          19.77           2                  N   \n",
       "\n",
       "      PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "11             236           132             1         52.0    0.0      0.5   \n",
       "110            132           163             1         52.0    0.0      0.5   \n",
       "161            132           132             2         52.0    0.0      0.5   \n",
       "247            132            79             2         52.0    0.0      0.5   \n",
       "379            132           234             1         52.0    0.0      0.5   \n",
       "388            132            48             2         52.0    4.5      0.5   \n",
       "406            228            88             2         52.0    0.0      0.5   \n",
       "449            132            48             2         52.0    0.0      0.5   \n",
       "468            186           132             2         52.0    0.0      0.5   \n",
       "520            132           148             1         52.0    0.0      0.5   \n",
       "569            132           144             1         52.0    0.0      0.5   \n",
       "572            230           161             1         52.0    0.0      0.5   \n",
       "586            211           132             1         52.0    0.0      0.5   \n",
       "692            132           170             1         52.0    0.0      0.5   \n",
       "717            132           239             1         52.0    0.0      0.5   \n",
       "719            264           264             1         52.0    4.5      0.5   \n",
       "782            163           132             1         52.0    0.0      0.5   \n",
       "816            132           170             1         52.0    0.0      0.5   \n",
       "818            132           246             1         52.0    0.0      0.5   \n",
       "835            132            48             1         52.0    0.0      0.5   \n",
       "840            132           163             2         52.0    0.0      0.5   \n",
       "861             75           132             1         52.0    0.0      0.5   \n",
       "881             68           132             2         52.0    0.0      0.5   \n",
       "958            132           261             2         52.0    0.0      0.5   \n",
       "970            132           140             1         52.0    0.0      0.5   \n",
       "984            132           230             1         52.0    4.5      0.5   \n",
       "1082           170            48             2         52.0    4.5      0.5   \n",
       "1097           265           265             2         52.0    0.0      0.5   \n",
       "1110           239           132             1         52.0    0.0      0.5   \n",
       "1179           238           132             1         52.0    0.0      0.5   \n",
       "\n",
       "      tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "11         14.58          5.54                    0.3         72.92   \n",
       "110         0.00          0.00                    0.3         52.80   \n",
       "161         0.00          0.00                    0.3         52.80   \n",
       "247         0.00          0.00                    0.3         52.80   \n",
       "379        14.64          5.76                    0.3         73.20   \n",
       "388         0.00          5.54                    0.3         62.84   \n",
       "406         0.00          5.76                    0.3         58.56   \n",
       "449         0.00          5.76                    0.3         58.56   \n",
       "468         0.00          5.76                    0.3         58.56   \n",
       "520         5.00          0.00                    0.3         57.80   \n",
       "569        10.56          0.00                    0.3         63.36   \n",
       "572        11.71          5.76                    0.3         70.27   \n",
       "586        11.71          5.76                    0.3         70.27   \n",
       "692        11.71          5.76                    0.3         70.27   \n",
       "717         5.85          5.76                    0.3         64.41   \n",
       "719        12.60          5.76                    0.3         75.66   \n",
       "782        13.20          0.00                    0.3         66.00   \n",
       "816         2.00          5.54                    0.3         60.34   \n",
       "818        11.71          5.76                    0.3         70.27   \n",
       "835        13.20          0.00                    0.3         66.00   \n",
       "840         0.00          5.76                    0.3         58.56   \n",
       "861         6.00          5.76                    0.3         64.56   \n",
       "881         0.00          0.00                    0.3         52.80   \n",
       "958         0.00          0.00                    0.3         52.80   \n",
       "970        11.67          5.54                    0.3         70.01   \n",
       "984        42.29          0.00                    0.3         99.59   \n",
       "1082        0.00          5.54                    0.3         62.84   \n",
       "1097        0.00          0.00                    0.3         52.80   \n",
       "1110       15.80          0.00                    0.3         68.60   \n",
       "1179       17.57          5.76                    0.3         76.13   \n",
       "\n",
       "       duration pickup_dropoff  mean_distance  mean_duration        day month  \\\n",
       "11    36.800000        236 132      19.211667      40.500000     sunday   mar   \n",
       "110   66.850000        132 163      19.229000      52.941667   saturday   jun   \n",
       "161    0.966667        132 132       2.255862       3.021839   saturday   nov   \n",
       "247   29.183333         132 79      19.431667      47.275000  wednesday   dec   \n",
       "379   29.483333        132 234      17.654000      49.833333     sunday   sep   \n",
       "388   39.833333         132 48      18.761905      58.246032    tuesday   feb   \n",
       "406   15.616667         228 88       4.730000      15.616667     monday   jun   \n",
       "449   45.450000         132 48      18.761905      58.246032   thursday   aug   \n",
       "468   42.850000        186 132      17.096000      42.920000    tuesday   sep   \n",
       "520   71.583333        132 148      17.994286      46.340476     sunday   apr   \n",
       "569   28.883333        132 144      18.537500      37.000000  wednesday   nov   \n",
       "572    0.216667        230 161       0.685484       7.965591    tuesday   jul   \n",
       "586   55.700000        211 132      16.580000      61.691667     monday   jun   \n",
       "692   30.533333        132 170      17.203000      37.113333    tuesday   nov   \n",
       "717   34.033333        132 239      20.901250      44.862500  wednesday   dec   \n",
       "719   57.366667        264 264       3.191516      15.618773     friday   aug   \n",
       "782   52.750000        163 132      17.275833      52.338889     friday   jun   \n",
       "816   48.600000        132 170      17.203000      37.113333    tuesday   feb   \n",
       "818   88.783333        132 246      18.515000      66.316667    tuesday   jun   \n",
       "835   36.983333         132 48      18.761905      58.246032    tuesday   jan   \n",
       "840   45.066667        132 163      19.229000      52.941667     friday   oct   \n",
       "861   28.000000         75 132      18.442500      36.204167   saturday   dec   \n",
       "881   36.000000         68 132      18.785000      58.041667   saturday   dec   \n",
       "958   35.166667        132 261      22.115000      51.493750     sunday   oct   \n",
       "970   33.783333        132 140      19.293333      36.791667     friday   feb   \n",
       "984   55.050000        132 230      18.571200      59.598000  wednesday   aug   \n",
       "1082  14.366667         170 48       1.265789      14.135965    tuesday   feb   \n",
       "1097   2.333333        265 265       0.753077       3.411538     monday   aug   \n",
       "1110  58.400000        239 132      19.795000      50.562500  wednesday   sep   \n",
       "1179  40.666667        238 132      19.470000      53.861111     monday   jun   \n",
       "\n",
       "      rush_hour  \n",
       "11            0  \n",
       "110           0  \n",
       "161           0  \n",
       "247           0  \n",
       "379           0  \n",
       "388           1  \n",
       "406           0  \n",
       "449           0  \n",
       "468           0  \n",
       "520           0  \n",
       "569           0  \n",
       "572           0  \n",
       "586           0  \n",
       "692           0  \n",
       "717           0  \n",
       "719           1  \n",
       "782           1  \n",
       "816           1  \n",
       "818           1  \n",
       "835           0  \n",
       "840           0  \n",
       "861           0  \n",
       "881           0  \n",
       "958           0  \n",
       "970           0  \n",
       "984           1  \n",
       "1082          1  \n",
       "1097          0  \n",
       "1110          0  \n",
       "1179          1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set pandas to display all columns\n",
    "pd.set_option('display.max_columns', None)\n",
    "df[df['fare_amount']==52].head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Response:** \n",
    "\n",
    "It seems that almost all of the trips in the first 30 rows where the fare amount was \\$52 either begin or end at location 132, and all of them have a `RatecodeID` of 2.\n",
    "\n",
    "There is no readily apparent reason why PULocation 132 should have so many fares of 52 dollars. They seem to occur on all different days, at different times, with both vendors, in all months. However, there are many toll amounts of $5.76 and $5.54. This would seem to indicate that location 132 is in an area that frequently requires tolls to get to and from. It's likely this is an airport.\n",
    "\n",
    "\n",
    "The data dictionary says that `RatecodeID` of 2 indicates trips for JFK, which is John F. Kennedy International Airport. A quick Google search for \"new york city taxi flat rate $52\" indicates that in 2017 (the year that this data was collected) there was indeed a flat fare for taxi trips between JFK airport (in Queens) and Manhattan.\n",
    "\n",
    "Because `RatecodeID` is known from the data dictionary, the values for this rate code can be imputed back into the data after the model makes its predictions. This way you know that those data points will always be correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Task 5. Isolate Modeling Variables**\n",
    "\n",
    "Drop features that are redundant, irrelevant, or that will not be available in a deployed environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 25 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   Unnamed: 0             22699 non-null  int64         \n",
      " 1   VendorID               22699 non-null  int64         \n",
      " 2   tpep_pickup_datetime   22699 non-null  datetime64[ns]\n",
      " 3   tpep_dropoff_datetime  22699 non-null  datetime64[ns]\n",
      " 4   passenger_count        22699 non-null  int64         \n",
      " 5   trip_distance          22699 non-null  float64       \n",
      " 6   RatecodeID             22699 non-null  int64         \n",
      " 7   store_and_fwd_flag     22699 non-null  object        \n",
      " 8   PULocationID           22699 non-null  int64         \n",
      " 9   DOLocationID           22699 non-null  int64         \n",
      " 10  payment_type           22699 non-null  int64         \n",
      " 11  fare_amount            22699 non-null  float64       \n",
      " 12  extra                  22699 non-null  float64       \n",
      " 13  mta_tax                22699 non-null  float64       \n",
      " 14  tip_amount             22699 non-null  float64       \n",
      " 15  tolls_amount           22699 non-null  float64       \n",
      " 16  improvement_surcharge  22699 non-null  float64       \n",
      " 17  total_amount           22699 non-null  float64       \n",
      " 18  duration               22699 non-null  float64       \n",
      " 19  pickup_dropoff         22699 non-null  object        \n",
      " 20  mean_distance          22699 non-null  float64       \n",
      " 21  mean_duration          22699 non-null  float64       \n",
      " 22  day                    22699 non-null  object        \n",
      " 23  month                  22699 non-null  object        \n",
      " 24  rush_hour              22699 non-null  int64         \n",
      "dtypes: datetime64[ns](2), float64(11), int64(8), object(4)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   VendorID         22699 non-null  int64  \n",
      " 1   passenger_count  22699 non-null  int64  \n",
      " 2   fare_amount      22699 non-null  float64\n",
      " 3   mean_distance    22699 non-null  float64\n",
      " 4   mean_duration    22699 non-null  float64\n",
      " 5   rush_hour        22699 non-null  int64  \n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "\n",
    "df2 = df2.drop(['Unnamed: 0', 'tpep_dropoff_datetime', 'tpep_pickup_datetime',\n",
    "               'trip_distance', 'RatecodeID', 'store_and_fwd_flag', 'PULocationID', 'DOLocationID',\n",
    "               'payment_type', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge',\n",
    "               'total_amount', 'tpep_dropoff_datetime', 'tpep_pickup_datetime', 'duration',\n",
    "               'pickup_dropoff', 'day', 'month'\n",
    "               ], axis=1)\n",
    "\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 6. Pair plot\n",
    "\n",
    "Create a pairplot to visualize pairwise relationships between `fare_amount`, `mean_duration`, and `mean_distance`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pairplot to visualize pairwise relationships between variables in the data\n",
    "\n",
    "sns.pairplot(df2[['fare_amount', 'mean_duration', 'mean_distance']],\n",
    "             plot_kws={'alpha':0.4, 'size':5},\n",
    "             );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These variables all show linear correlation with each other. Investigate this further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 7. Identify correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, code a correlation matrix to help determine most correlated variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VendorID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266463</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>0.001876</td>\n",
       "      <td>-0.002874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>0.266463</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014942</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>-0.022035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.014942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910185</td>\n",
       "      <td>0.859105</td>\n",
       "      <td>-0.020075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_distance</th>\n",
       "      <td>0.004741</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.910185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.874864</td>\n",
       "      <td>-0.039725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_duration</th>\n",
       "      <td>0.001876</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>0.859105</td>\n",
       "      <td>0.874864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.021583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rush_hour</th>\n",
       "      <td>-0.002874</td>\n",
       "      <td>-0.022035</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.039725</td>\n",
       "      <td>-0.021583</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 VendorID  passenger_count  fare_amount  mean_distance  \\\n",
       "VendorID         1.000000         0.266463     0.001045       0.004741   \n",
       "passenger_count  0.266463         1.000000     0.014942       0.013428   \n",
       "fare_amount      0.001045         0.014942     1.000000       0.910185   \n",
       "mean_distance    0.004741         0.013428     0.910185       1.000000   \n",
       "mean_duration    0.001876         0.015852     0.859105       0.874864   \n",
       "rush_hour       -0.002874        -0.022035    -0.020075      -0.039725   \n",
       "\n",
       "                 mean_duration  rush_hour  \n",
       "VendorID              0.001876  -0.002874  \n",
       "passenger_count       0.015852  -0.022035  \n",
       "fare_amount           0.859105  -0.020075  \n",
       "mean_distance         0.874864  -0.039725  \n",
       "mean_duration         1.000000  -0.021583  \n",
       "rush_hour            -0.021583   1.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create correlation matrix containing pairwise correlation of columns, using pearson correlation coefficient\n",
    "df2.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize a correlation heatmap of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rUF/MOfsdH58Sc+6/Hmr9VmhoKHfu3CFLlixkzZoVPT091q5dy+fPn8mRIwfbt2/X2pP06tWrJKt3XC1duhSImgcY86ilr718+fK75T08PFAqlVr/nmLm+X27/IcQSU0moAuRREaMGIGOjg43btz44QNkZ82ahZ+fH7q6uqpHywBUrlwZgF27dmkdAgsLC2PTpk1A4vTsxLC3t1cFGhs2bNCa59q1a7Ru3ZpatWpx8+bN7+4vpjckd+7cWidi79q1SzVn6ts5Sd8LQr9lZ2enCkbWrl2rNc/169e5ffs2gOoOvd9JxYoV0dPT49mzZ5w/f15rnjVr1tC2bVvq16+vmqT9/PlzIOraaQukzp8/z4sXLwCIjIxU2xZzjhMyXy2uYt4Lsc0D3L59u+r/tc1Pe/v2LSdPntRIf/36NceOHQPQWPBTiKQmwZQQSaRQoUJ07doVgLlz5zJw4ECN3gY/Pz8GDRqkuhW+Z8+eFChQQLW9ZcuWZMqUibdv39K1a1d8fX1V2969e0ffvn15/PgxJiYm9O7dO1HrH/MInGXLlrF8+XLCwsJU265evaraXqhQoR8uTBrTY7R//37VyvAQNYF9w4YNTJw4UZX29WRs+G/IMSZYiGu9t2zZwrx589Tq7e7uTp8+fYCo4DO2hVKTk5WVleputQEDBqju6ISoiePbtm1TLe/QunVr1fywmCDy/Pnzaks+REREsG/fPrUFML89xzHDnTHBVlKKeS9s2bJFracsKCiI+fPns2zZsljrGWPkyJFqbfTz86N79+6EhIRQokQJypUrl0S1F0I7GeYTIgn179+fNGnSMGPGDPbt28e+ffvIkCEDmTNnJiAgAG9vbyBq0nPfvn3p3LmzWnlzc3OWLFlCly5duHHjBtWqVSNnzpyqFdDDw8NJkyYNM2fOjNPK4D+jdu3aeHl5MX/+fGbOnMnSpUuxtbXl/fv3qsDGzs6ORYsW/XBfPXv25MKFC7x584a6detia2uLgYEB3t7ehISEkDZtWuzs7Hj48KHGME/evHk5efIke/fu5dGjRxQrVowxY8bEeqyaNWvi4+ODq6srCxcuZO3atdjZ2anVu0SJEsyYMeOner1+pREjRvDq1StOnjxJ9+7dyZgxI5kyZeL58+eqZRSqV69Ov379VGU6duzIvn37+PDhA61bt1at3u7n58enT58wNjamcOHC3LhxQ+Mc58mTB4jqtatRowY5c+ZUBWyJrX///vTo0YOnT5/i4uKiCgK9vb358uUL2bNnR0dHBx8fH61DfmZmZhgaGtK6dWvs7OwwMjLi8ePHREZGkjt37t/6uor/X9IzJUQS69ChAwcOHODvv/+mQIECfPnyhfv37/P27Vvy5MlDx44dOXDggEYgFSNv3rzs27ePHj16kCtXLnx9ffHy8sLOzo5u3bqxZ8+eRB3i+1rPnj3ZsmULdevWxdTUlIcPH/Lhwwfy5s1L37592bFjR5wm++bPn5/du3dTr149smbNio+PDz4+PlhbW9OtWzf27dunekbcqVOn1IabOnfuTNOmTUmTJg1eXl48evToh8fr2rUrW7dupU6dOqp6f/78mdKlSzNt2jTWrl2baPPLkoKhoSGLFy/G1dWV8uXLEx4ezoMHD4iMjKRkyZKqZ9N9PWSaNWtW9uzZQ8uWLbG1tcXf3x9PT0/Sp09P27Zt2bNnjyr4cnd3V5vI3aBBAzp16kSGDBlUa4zFZ/mEuKhcuTLbt2+nSpUqZMiQgWfPnuHv74+DgwMDBw5k9+7d1K1bF0DrcJ6xsTHbtm2jadOmBAQE4OXlRc6cORk8eDCbNm0ic+bMSVJvIb5HR/krBsmFEEKIBIh50HGmTJk4c+ZMcldHCDXSMyWEEEIIkQASTAkhhBBCJIAEU0IIIYT4v7Ro0SLV80tj8+HDBwYOHEjx4sUpXrw4o0eP/u7isNpIMCWEEEKI/ztr1qyJ01ME+vTpg6+vryr/+fPnGTdu3E8dSyagCyGEEOL/xqtXrxg5ciTXrl0jc+bMpE+fnvXr12vNe+PGDVq0aMGBAwdUT0w4d+4cnTp14vTp07E+pPxb0jMlhBBCiP8b9+7dw8LCgj179uDk5PTdvFevXiVDhgxqj54qUaIEOjo6XLt2Lc7HlEU7hRBCCPFbcXFx+e7248ePx7rN2dkZZ2fnOB3n1atXZMmSRS3NwMCANGnS4O/vH6d9gARTIh666ZgndxWSxcIdE5K7CslCr3Lz5K6CEEkvpa6aniZuw1jxkaDvCufiiVeR7wgNDVU9WP5rhoaGfPnyJc77kWBKCCGEEL+V7/U8JSYjIyO153fG+PLli+q5oHEhc6aEEEIIkeh0E/D6VTJnzszr16/V0sLCwvj48WOcJ5+DBFNCCCGESAK6Ojrxfv0qxYsX5+XLl6qHzkPUsysBihQpEuf9SDAlhBBCiET3O/ZMRUZG8ubNGz5//gyAk5MTRYoUoX///ty+fZtLly4xZswYGjRoID1TQgghhEheujrxfyUVf39/ypUrx4EDBwDQ0dFhwYIFZMuWjb/++ot+/fpRoUIFxo4d+1P7lUU7xU+Tu/lSFrmbT6QIcjdfohugZxHvsrMjPyViTZKe9EwJIYQQQiSALI0ghBBCiET3KyeSJzcJpoQQQgiR6FLS0JcEU0IIIYRIdEk5kfx3I8GUEEIIIRKd9EwJIYQQQiSATgqaM5WSAkchhBBCiEQnPVNCCCGESHQpqbdGgikhhBBCJDqZgC6EEEIIkQDSMyWEEEIIkQCyaKcQQgghRAJIz1QK0rZtWwICAti9e7fW7f/88w/nzp3j+PHjiX6b57Bhw3j+/Dnr16+P9z78/PxwcXFh3bp1lCxZEnd3d9q1a6farqOjQ+rUqbGzs6Nly5Y0bdo0MaqebCyzWTH6zkWWNGjF49Pnkrs6P+3sIx/mHXXH4/UHLE2MaF4yH50rFon1vRUWEcmaczfZff0RLz8GkcnClDqFctGpYhEMUunx/EMAVadviPV4DYrmZnIT56RqTqzOXHRnztKVeHh6kdYyDS0a1qNLu9bf/RvaffAIy9ZtxPfFC7JkykSn1i1oWr+OWp7b9x4wfcFi7j18jHHq1NSrUZX+3TphYGCgdZ+9ho7C1MSEqf8MT9T2we/TxmNnztFzyEjWLZxDyaKFARg2fgr/HjgUaz1O/LsFqyyZ49Hq36fdsV3bV6/fMH3BYs5eukxERCSF8uelX9dOFMyXJ17t/VpQcAjT5y/i+OlzBIeEULhgfkb270POHLbfLff67VumzlnI+ctXCQ8Pp1zJ4owc0IdMGTOo8kRERDBv2Sp2HzzMh0+fyJMrF0P69KCoUwFVnrDwcFZv3MK/Bw7x8tVrMmXMQN3qVenyV2sM9PVV+XbuO8iqjZvx9ntOhnRpaVCrBt07tkM/VYr/yk8yKf7MNmnShCFDhvDkyRNy5cqlti0sLIxDhw7Rrl27P269jG3btpElSxYUCgUBAQGcOHGCcePG8eLFC/r27Zvc1YuXtNbZ6XP4X4zTpEnuqsTLDW9/eq4/QM0COelTtSTXvf2Ze8QdhVJJt8rFtJaZuu8cu68/optzMfJny8j9F29YdOwKLz4GMrGxMxnMTNjUvZFGObeLdzl45ymNiyX8C+RnXb99lx6DR1CzSmX6df2ba7fu4LpkBQqFku4d2motc/D4SYaOn0y75k0oX6oEx06fZdSUGRgaGlKvRlUAfPye06HPQAoXyMecSWPx8PTGdekKAoOCmDhiiNr+IiMjmew6n6Onz9KwVo3/yzYCfPj0iTFTZ2mk9+jYjhaN6qmlfQoIpO+IMZQoUogsmTL+se3+3rUNDAqiZddehH7+TN8uf2ObPRtHT5+lTY8+rF84F6f8eePV7hgDR4/j9v0HDO7VHVMTYxasWMNfPfuxf/M60liYay0TERFB535DCAkNZezQAURERDBr4VI69hnIrg2rVAHOpNnz+Hf/IQb16krWzJlZ7baVTn0HsXPdCuysswMw2XU+u/YfokfHdhTIm4d7Dx+zYMVqXrx8yeRRwwBYu3kbk13nU925EoN7d+fDx0/MX76KR089WDh9UoLa/7NkAnoKUr16dSZMmMDevXsZMGCA2rbjx48TGBhI48aNk6l28Zc2bVoyZIj61ZMpUyZy5cqFgYEBM2bMoH79+tja2iZvBX+Cjo4Opf5qReOZv/aDILEtOn6VPFnSM615FQDKO1oTEalgxakbtC9XCCN99T/HjyGf2XL5HgNqlObvClE9DqVzZgNg1sGLDKhemrSmqXGyVu9huOv3moN3ntKvWkmK2mb5BS1Tt3DlGnI75GTG2FEAVChdkoiICJat30iHls0wMjLUKDNn6UqqO1dkRL9eAJQvVYJPAYHMX7Fa9YW7YsMmTIyNWTRjMgb6+lQsUwojI0MmzJpL9w7tVD0tD594MGHWHO4+eISRoeax/h/aGGPcdFdSpdLTOJZ1Niuss1mppfUcOhILczNmjhuFrm78BmCSu90/urY79h7guf9LNi1bSJGC+QEoW7I4Hz59YsrchWxevjBe7Qa4cecup85fZNnsaVQsWxqAYoWccGnYHLcd/9Kj419ayx06foqHT56yb9NacuWwAyBPrlzUafUXB46eoH7Navi/esXWXXsZMaAPrZs0BKBcyeJUb9qaFevdmDRyKB8/BbB5524G9exGp7YtAShdvCgAMxYsZlDPbliYm7Fw5RrKlijGvCnjVXXIn9uR2i3bcd79CmVLFo/3OfhZKWmYLyW1VSsjIyPq1KnDvn37UCqVatt2795N2bJlyZIlCzt27KBmzZoULFiQmjVrsnbtWhQKBRA11Obo6MjBgwdp2rQpBQoUwMXFhe3bt6v2pVQqWbRoERUqVKBQoUKMHDmSL1++qB3P39+fQYMGUbZsWQoVKsTff//No0ePVNuHDRtGr1696NixI0WKFGHp0qU/1dZmzZqhr6/PgQMHfvY0JSurgvlptdiVS2vdWNO2S3JXJ17CIiK5/Ow5VfLlUEuvlj8HIWHhXPN8oVEm6HMYzUvkwzmPrVq6bfo0APi+D9Aoo1QqmbD7DPYZLPmrnFOi1T+uwsLCcL9+k2qVKqilV3euSEhIKFdv3dYo4/fCHy8fX61lfPye4+njC8C5S1eoVK602nBGDedKKBQKzrlfVqUNHT8ZhULJlhWLSWdpmZjNA36PNgIcOHqCC1euMrhXtx/W+eS5Cxw7fY7h/XphbmYW57Z+7Xdo94+urYeXNxbmZqpAKkaJwoW4cecunwICf77h0c5duoxx6tRqwUhayzQUL+zEmQuXYi/nfhk7G2tVIAWQM4ct9rY2qnIXr1wnIjKSapX/O08GBgZUKlua09F5AoODaNGoPs4Vyqrt39Y66geW7/MXvH3/gU8BgVQur54nZw5bLNNYcPLchXi2Pn500Yn360+T4oMpiBrqe/78OdeuXVOlvXv3jrNnz9K0aVO2bNnCtGnT6NmzJ/v376dfv34sX76cmTNnqu1n6tSpdOvWjV27dlG6dGlGjx6Nr2/Uh8WyZctYsWIFQ4YMYefOnZiamqoFNUFBQbRs2ZJXr16xePFiNm/ejLGxMW3atOHFi/++aI8ePUqZMmXYsWMH9eqpd+P/iImJCdmyZePhw4fxOU3J5r2PH6NzFmL7wBGEhYQkd3Xixff9J8IjFdimt1BLt47+t9fbTxplsqU1558GFbHLoP6lcezuM1Lp6WKbwUKjzP5bT7jj95rhdcuhF8/eh4Twff6C8PBwbLNnV0u3yRb1ge8V/eX5NQ8vbwAtZaxUZT5//sLzly+xy55NLU9ayzSYmpjg5eOnSpv2zwg2LVtA7lz2CW+QFr9DG9++e8+4mXMY0b83GdKl+259FQoF0+cvpkThQtRwrhS3RmrxO7T7R9c2bZo0BAWHaARNPn7PgajgLr48vLzJZpWFVN/MO7LOZoXnV3XUVi4m4NEs5xudxwtj49Qa19ImezbevH1HcEgI2bNmZeyQAeSwsVbLc/TUGfRTpcLWOjvmZqak0tPjub96Oz8FBBIQGISff/zbHx+6OvF//WkkmALy589P7ty52bt3rypt7969mJub4+zszKJFi+jatSt16tQhe/bsVK9enf79+7Nhwwa13qUOHTrg4uKCvb09Q4cORaFQcOvWLZRKJevXr6ddu3bUqVOHHDlyMHz4cPLk+W8+y549e/jw4QNz586lYMGC5M6dm5kzZ2JkZMTGjRtV+SwsLOjUqRN2dnZkyfLzQzhmZmYEBsb/11lyCPnwgY/PNXtu/iSBoWEAmBipT6Y1iZ5cG/QlLE77OXLXgz03H9GqVH4sUhtpbF999iZFbDJTIoeVltJJLyAoCABTE2O1dBPj1AAEBQdrlAmMtYyxqsx/+zXRKG9ibKy236QKomL8Dm0cPXUmhfPnpUHN6j+s74lzF3jm7RPrnKa4+h3a/aNrW7dGVXR1dek7YgxPnnkSEBjInkNH2bn/IAChn0O/W/57AgODMDXWUkcTY4K1tD1GQGCQ9rZ9VS4wMAgzE1PNPKrzpP1H5OETp9h98AitmjTEwtyM1EZG1KzqzIZt/7J9z34+BQTyzNuHAaPHkSqVHqGhn+PU1sSim4DXn+ZPrHOSaNKkCYcOHSI8PByAXbt20aBBAwIDA3n58iVz586lcOHCqtf48eP58uULfn7//SKxt//vD90suis9PDycDx8+8ObNGwoUKKB2zEKFCqn+//Hjx9ja2pI2bVpVmqGhIQULFlQb6rOxsUlQO4OCglR1E7+OInoIWSeW7uu4rMdy+I4HgzcfpbhdVgbUKK2x/bqXPw9evKVD9Pyq5KBQRLczlvZom6sTW5mYUXddXV2U0UPqaNmvEiU6v7AXLrnb+O/+Q1y7dZtxwwbFqb4bt+0kj0NOypTQfpNDXCV3u+Mip50tS2ZOwcfvOXVatad41Tqs2byNfl07AZDaKHWc9qNQKIiIiFB7KZRK7W1X8t06KhUKrX/3SuV/bYvat5ay0SdKV0tXzaHjpxj4zwSKFy7EoJ5dVenjhg6kXo2qjJo8nRJVa9OoXScKF8hHgTy5Sa3lB5hIHCl+AnqMunXrMn36dM6cOUP27Nl58OABs2bNUs2LGj58OGXKlNEolyVLFl6/fg2g9Rber+dhfTsn6+vuYmUsf6iRkZFq+YyM4v/HEBwcjJeXF3Xq1PlxZpGozFJHTZYN/qYHKjgs6t9mRtpv/46x5uxNZh68SPEcWVnQthYGWiYdH7nrgXlqQyo4WmvZw69hbhb16/rbXorgkKgeAW2/0GMrExIaoipjFp1HWw9ASEgoZlr2m1SSs42vXr9h8pz5DO3Tg3SWaaK+5KM/oxQKBZGRkejp/ffe+PDpE+7XbzKwR8LnGv4p17ZcyeIc/3czfv4vAcieNQs79kVNqbAwj9sPyYUr17BgxRq1tOrOlXj3XnMoMzgk5Lt1NDMz09prFxISiplpVDkzU1OtvU8hoVHn9tteq9VuW6KGbosUipq0/9V3j4mxMZNHDWPkgD68ePkKqyyZMU6dmh17D1DSKmvsjU4Cf+JwXXxJz1S0NGnSULVqVQ4dOsTevXspUqQI9vb2pEuXjnTp0uHj44ONjY3qde/ePebMmROnfadNm5YsWbKozckCuHv3rur/HRwc8PT05N27d6q0L1++cPfuXXLmzJkobdy6dSsKhYJatWolyv5E3FmnNUdPVwfvd+pzo3yi50rZZ9Q+UVqpVDJxz1mmH7hAtfw5WNq+DiaG+lrznnrojUteO/T1NAOtX8XaKit6enp4R89RieEd3YOb085Wo0zMbd+aZZ6ryhinTk2mDBk08rz/8JGg4GCt+00qydnG85evEhAYxMhJ08lXzoV85Vxo3zvqLuT2vQdQtXErtbJnL14mMjIyQXOlYvwJ1/bFy1ds37uf8IgIsmfNQvasUVMh7j18TBpz8zivrdWsQT22r1mm9rKzyY7fC39V8BrDx+859t+po51NdtWcrW/L5Yy+qzqHjTVBwcG8//BRLY+3rx9WWTKr7pJUKpVMmDmHqXMXUt25Istdp6uGAmOcPHeBa7fuYGJsTK4cdhinTs279x/wf/WavI4OcWp/YpEJ6ClUkyZNOHXqFIcOHaJJkyZAVPd0p06dWL9+PevXr8fHx4djx44xbtw4DAwMYl1Q7ludO3dm48aNbNu2DU9PT+bMmcPt2//d/VK3bl3Mzc3p168ft2/f5uHDhwwePJiQkBCaN2/+0215//49b9684fXr1zx58oTly5cze/ZsunXrhrV18vVcpFSG+qkoZpuVY3efqfVQHrn7DHMjQwpkz6S1nOvhS7hdvMNfZZ2Y1bKa1h4piFpGwefdJ4rYxG8hxsRiaGhIsUIFOXrqjFo7D584jbmZKQXzaq57ZZM9G9mtsnL4xGm19MMnTmNrnV31BVi2ZDFOnb9IWNh/vXuHTpxCT0+PUsWKJFGLNCVnGyuXL8P21UvVXuOGDgSihncWz5ystv/b9+6TOWOGeC/Q+bu0O67effjAyEnTcb92Q5X25t079h85jkvFcnFeLzBThvQUyJNb7VWuZHGCQ0I4e+m/uwvff/jIlRu3KPed5QbKlSyOh5c3T595qdKePvPCw8tbdWdgzBDsoROnVHnCwsI4df6i2r5nL1rGhm07ad+yGa4Tx2r9/tm8czfT5y1SS1u7eRt6urpULqc5upKUUtIEdBnm+0rp0qUxMzPj3bt31KxZU5XesWNHDA0NWb9+PdOmTSNdunQ0atSI/v37x3nfrVu3RqFQsHjxYt6+fUv58uVp0qQJnp6eAJibm7NhwwamTZtG+/btAShatCibNm0i+zd3wsRFzErnOjo6WFpakjNnTqZNmya9Usmoq3NR/l65h/5uR2hULDc3vV+y6uwNBtQojZF+KoI+h+Hx+j3Z01qQ1jQ1D168ZeWZG+S3ykCNgvbc9n2ltj/7jGkxjR4efPLynSotuXXv0I4OvQfQd+QYGtepxY0791i5cTODenbFyMiQoOBgnnp6YW1lRVrLNAD06NCO4ROnksbCHOfyZTlx9jwHj5/EdeIY1X47tWnJ/qPH6dR/CB1aNsPLx5fZS1bQvEHdeC9C+Se20dJC/S7OkOhhNjvr7DjmVJ+g/cjjWaL22v3u1zZ/bkeKFCzA2OmzGdKrO3qp9JizZAV6enr0+rt9gtpevHAhShQpzOAxExjcqztpLMyZv3w1ZqamtGhUX5Xv6TMvwsLDVL1Atao4s2TNBjr3H8zAHlFzm2YtWoqDfQ5quFQCwCpLZhrWrsGUOQv48uULttbZWe22hYDAIP5uE7Wm1IPHT1i+3o38eXJT06Uyt+7eV6tfTjtbTE1NaNusMX/3HcSk2fNwrlCWS1evs3TtBrq0a032Xz3M90uPlrx0lN9O5BHiB7rpaF/p91dwqFiOAacOMLtSrV/+OJmFOyYkeB/H7j1jwbHLeL75SCZzU1qWzk+H8oUAuPzsOe2X72ZSE2caFs3N/KOXWXziaqz7WtO5vuquvYO3nzJw0xH29W9JjliGDONLr/LP94wePXWGectX4+njS6YM6WnduCEdW0ftx/3aDdr17MeUUcNoVOe/Hy2b/93Dqo2b8X/9huxZs9Dlr9Yad6tdvXmL6fOX8ODJUywtLKhfsxp9u3TUuF09hnOD5pQoUihJHifzu7Qx5lhfP04mRq0W7XDMaa8WuPy/tDu2a/v23XumzF3AOferoFRSsmhhBnTvonV5AjVx6LX6FBDI1DkLOHbmLAqFkiIF8zO8f2+15Qradu/Dc/+XnNi1VZXm/+oVk2bP4/zlq+inSkXZEsUZ3r8XGdOnV+UJCwtj5sKl7Dt8jJDQUPLldmBonx4UzBe1avvcpStZtGptrHVbt2iu6vrvO3yMxavX4ffCn6xZMtGqcUPaNotl8ek02nvFE8NSs/Q/zhSLroFvE7EmSU+CKfHTkjOYSk6JEUz9ieITTAnxx/nDHhmWaCSYShQyzCeEEEKIRPcnTiSPLwmmhBBCCJHo/sSJ5PElwZQQQgghEl0KiqUkmBJCCCFE4pOeKSGEEEKIBEhJc6ZS0jIQQgghhBCJTnqmhBBCCJHoZJhPCCGEECIBUtLQlwRTQgghhEh0KahjSoIpIYQQQiQ+3RS0qrwEU0IIIYRIdCknlEpZQ5pCCCGEEIlOeqaEEEIIkehSUs+UBFNCCCGESHQSTAkhhBBCJICOTEAXQgghhIi/lBNKSTAlhBBCiCSQku5wS0ltFUIIIYRIdNIzJYQQQohEl4KmTEkwJYQQQojEp5OCZk1JMCV+2sIdE5K7CsmiZ+PRyV2FZLHkbZPkroIQSc/AKLlr8H8n5YRSEkwJIYQQIgmkpGBKJqALIYQQItHp6sT/lRAKhYJ58+ZRvnx5nJyc6NixI97e3rHmf/PmDQMGDKBkyZKULFmSvn378vLly59ra8KqLIQQQgjx+1i0aBGbN29m4sSJbNmyBR0dHTp37kxYWJjW/P3798ff35/Vq1ezevVqXr58SY8ePX7qmBJMCSGEECLR6STgv/gKCwtj1apV9O7dm4oVK5I7d25cXV159eoVR48e1cgfEBDAlStX6Ny5M3nz5iVv3rx06dKFe/fu8eHDhzgfV4IpIYQQQiQ6nQS84uvhw4cEBwdTqlQpVZq5uTl58+blypUrGvkNDQ0xNjZm165dBAUFERQUxO7du7G1tcXCwiLOx5UJ6EIIIYRIdMmxzlTMXKcsWbKopWfMmBF/f3+N/IaGhkyaNInx48dTrFgxdHR0yJAhAxs2bEBXN+79TRJMCSGEECLRJSSWcnFx+e7248ePa00PDQ0FwMDAQC3d0NCQT58+aeRXKpU8evSIwoUL06lTJyIjI3F1daVnz55s2rQJU1PTONVXgikhhBBCJDrdZFgcwcgoar2wsLAw1f8DfPnyhdSpU2vk379/P25ubpw8eVIVOC1ZsoTKlSuzY8cO/vrrrzgdV4IpIYQQQvxWYut5+pGY4b3Xr19jbW2tSn/9+jW5c+fWyH/t2jXs7OzUeqAsLCyws7PDy8srzseVCehCCCGESHTJMQE9d+7cmJqa4u7urkoLCAjg/v37FCtWTCN/lixZ8Pb25suXL6q00NBQ/Pz8sLGxifNxJZgSQgghRKLT0Yn/K74MDAxo06YNM2fO5Pjx4zx8+JD+/fuTOXNmqlatSmRkJG/evOHz588ANGjQAIB+/frx8OFDVX4DAwMaNWoU5+NKMCWEEEKIRJccPVMAffr0oUmTJowaNYqWLVuip6fHypUrMTAwwN/fn3LlynHgwAEg6i4/Nzc3lEolf/31Fx06dEBfX59NmzZhbm4e97YqlUplAustUpjInXOTuwrJIuU+6PhBcldBiKSXUh90bJYuyXZ9KmO2eJet9NovEWuS9GQCuhBCCCESXUKfsfcnkWE+IYQQQogEkJ4pIYQQQiS6FNQxJcGUEEIIIRKfBFPi/1Z4eDgbN26kffv2yVqPs498mHfUHY/XH7A0MaJ5yXx0rlgEnVjuiQ2LiGTNuZvsvv6Ilx+DyGRhSp1CuehUsQgGqfR4/iGAqtM3xHq8BkVzM7mJc1I1J8lZZrNi9J2LLGnQisenzyV3dX7ozKXLzFm2Cg9Pb9KmsaBFw3p0adsy1usLsPvQUZatd8P3uT9ZMmekU6vmNK1XW2veoOBg6rXtRK+//6JR7Rpq2/qPnsCB4yc1ysweN4raVZP2PZCc7b5y4xauy1bx4MlTTFKnpkblivTr2hFTE5NEbaM2ydnugMAgZi9ZwdHTZwkJDcUhRw76de1I6WJFErWNsQkKDmb63IUcP32G4OAQChcswMhB/ciZw+675V6/fcvU2fM4736F8IhwypUswchB/cmUMYMqz7v373FdtJTzly7zMSAQO+vsdGrXmlrVqqjyhIWHs3rDJv7dd4CXr16TKWNG6tasRpf2bTHQ10+ydseFTgoKpySYSmH27dvHlClTkjWYuuHtT8/1B6hZICd9qpbkurc/c4+4o1Aq6VZZc1E1gKn7zrH7+iO6ORcjf7aM3H/xhkXHrvDiYyATGzuTwcyETd011wRxu3iXg3ee0rhYnqRuVpJJa52dPof/xThNmuSuSpxcv3OXHkNGUdOlEv26dOTarTu4Ll2JQqGge/s2WsscPHGKoROm0q5ZI8qXLMGxs+cYNXUWhoaG1KteRS3vx4AAug8ZxfOXr7Tu68GTp9SrXoXWjRuopdtkt0qU9sUmOdt9/9ET/u4/lDLFizB/0lhev33HrMXLeebtw6q5M5KkvTGSs92RkZF0HjCMF69eMbhnV9JZWrJu2w66DBrOthWLyJ3TPkna/LWBI8dw+959BvfpiamJCQuWr+Kv7r3Zv9WNNBbab62PiIigc5+BhISEMHb4YCIiIpg1fzEde/Vjl9ta9FOlIiw8nL979ScgMJA+3TqTKUMGjpw8Rf8R/xAWHk6D2jUBmDxrDrv2HaRHp/YUyJuXew8fsWDZSl74v2TyPyOSvP3fkxwPOk4uEkylML/DShiLjl8lT5b0TGse9aFZ3tGaiEgFK07doH25Qhjpq78tP4Z8ZsvlewyoUZq/KxQGoHTOqFtuZx28yIDqpUlrmhon68xq5e76vebgnaf0q1aSorbqTxD/E+jo6FDqr1Y0njkpuavyUxauXEfuXPbMGBP1QV6hVAkiIiJZtmETHVo2xcjQUKPMnGWrqF65AiP69gSgfKnifAoIZP6KNWpfrsfOnGeS63xCQj9rPXbo5894+z2na7tWFMqfNwlaF7vkbPfqzdtIa5mGeZPHqfVGDJ80nWfePuSwsdZaLjEkZ7v3HjnOnYcP2bl6qSpwKlHYiXrtOnH+8tUkD6Zu3L7DqXMXWDZnJhXLlQGgWGEnXOo1wW3bDnp06qC13KFjJ3j4+An7tmwgl30OAPI45KJO8zYcOHKM+rVqcOrseR48fsK2tSsomC/qvVymZHFe+L9kxdoNNKhdk4+fAti8YxeDevegU7vWAJQuEfWDdMa8hQzq3Z20lpZJeg6+JyXd4fZTbXV0dGTTpk20bNmSggULUrduXbXn5yiVSlasWEHNmjXJnz8/RYsWpWvXrvj6+qrynD59mkaNGuHk5ETp0qUZNmyY2pOcV65cSZUqVcifPz/Ozs4sXLhQLQA4efIkjRo1omDBglStWpU5c+YQFhamVsetW7fSoUMHChYsSPny5Vm6dKlaO/bu3UvNmjUpUKAATZo0Ye3atTg6Oqq2BwYGMnr0aEqVKkXRokVp164dd+7cUW2fP38+LVq0YMCAARQpUoRx48bF6fxFREQwf/58nJ2dcXJyolGjRpw5c0a13cPDg27dulGyZEmKFi1Knz59ePHihWp727ZtGTZsmNo+hw0bRtu2bQHw8/PD0dGRgwcP0rRpUwoUKICLiwvbt28HYOfOnQwfPlx1nr5ebv9XCYuI5PKz51TJl0MtvVr+HISEhXPN84VGmaDPYTQvkQ/nPLZq6bbp0wDg+z5Ao4xSqWTC7jPYZ7Dkr3JOiVb/X8mqYH5aLXbl0lo31rTtktzViZOwsDDcb9yiWsXyaunVK1cgJCSUqzdva5Tx83+Jl4+f1jI+z1/g6RP1+REQGETvEWMoUaQQK1ynaT3+o6fPUCgU5MmVM5FaFDfJ3e6B3TuzZMYktUBKP/r/w8LDE9S270nudh8+eYbihZzUgiZDQwMOb1nH362aJ7R5P3TuojvGqVNTtlQJVVpaS0uKFynEmQsXYy936TJ2NtaqQAogZw477O1sOXM+qpypiQnNG9anQF71XnVba2t8nj8HIDAoiBaNG+Bcodw3ebID4Ptc8/NUJI2fDhynT59OnTp12LVrFxUrVqRXr15cv34dgLVr17J06VIGDx7M4cOHWbRoEZ6enkydOhWA9+/f06tXLxo3bsyBAwdYsGABV65cYfr06QCcOHGCJUuWMG7cOI4cOcKgQYNYvHgxe/bsAeDMmTP07duXpk2bsm/fPsaMGcPBgwcZPHiwRh0bNGjA7t27ady4MbNnz+bq1atAVDA2dOhQmjRpwp49e2jcuDGzZs1SlVUqlXTu3BkvLy+WLl3K1q1bKVSoEC1btuT+/fuqfDdu3CBdunTs3r07zk+Vnjx5Mhs3bmTQoEHs3buXihUr0qNHD54+fcrz589p3rw5BgYGrF27ltWrV/Pu3TvatGlDUFDQT12jqVOn0q1bN3bt2kXp0qUZPXo0vr6+1KpVixEjon49njt3jsKFC//UfhOD7/tPhEcqsE1voZZuHf1vr7efNMpkS2vOPw0qYpdB/RfWsbvPSKWni20GC40y+2894Y7fa4bXLYee7p/5++i9jx+jcxZi+8ARhIWEJHd14sT3hT/h4eHYWqsv1meTLWqIzctXcyE+Dy9vAM0yVupljIwM2b9xFdNGD8MyjeY1h6ghPoBNu/ZQtk5j8leoRqvufbl1L2kXHk3udmfOmEEVUASHhHLhyjVcl6ygmFOBJO2dSe52P3zylFw57FizZTvOjVuRt3wVGrbvwuUbtxLWsDjy8PImm1VWUqVS7023zp4NT2/fWEqBh6cXttaavYXW2axUwWSZksUZP3Ko2ryz8IgITp07rwrCsltlZeywweSwVX+G3NGTp9FPlUoVVCWX5FoBPTn89DBf48aNad06qjtx0KBBXLlyhQ0bNlCkSBGsra2ZOnUqzs5RkzytrKyoWbMm+/fvB+DVq1eEhYWRNWtWrKyssLKyYsmSJURGRgLg4+ODoaEh2bJlI2vWrGTNmpWMGTOSNWtWAJYsWUKTJk1o2bIlANbW1owbN46//voLPz8/smWL+uNs2LAh9evXB6Ket+Pm5sa1a9coVqwYK1eupEaNGvz9998A2NnZ4e3tzerVqwG4dOkSN27c4OLFi6RNmxaAAQMGcP36ddatW6cKDCFqyXozM7M4nbegoCC2bt3KqFGjqFWrFgB9+/ZFoVAQHBzMv//+i7GxMTNnzsTAwACAefPm4ezszJ49e2jVqlWcr1GHDh1wcXEBYOjQoWzbto1bt25Rp04dVX0zZMjwvV0kmcDQqF5EEyMDtXST6DYHfQnTKKPNkbse7Ln5iLZlCmKRWnPl4tVnb1LEJjMlciTtPJmkFPLhAyEfPiR3NX5KQGBU4P/tpGcTY2MAgoI1g8LA2MqYqJcx0Nf/4XDVgyceAHz5Esbs8aP5+CmAZes30a7XALYsX5BkgUVytzuGUqmkZM0GhIeHk8bCnKG9u/9EK35ecrf7/cdPHDp5GgszU4b07EpqI0OWrd/E3/2HsHXZQvI4JG0PZWBgoNYJ/ibGxgQHB8daLiAwCBtrzdXBTUyMCfaKvdw013l4+/qxcOaUWPMcPn6S3QcO0a5FMyx+4nEoSeF7NyD8v/npYKpEiRJq/3ZycuLChQsAODs7c+vWLebNm4e3tzceHh48efKETJkyAZAnTx7q1KlDt27dyJIlC2XKlKFSpUqq4KtevXrs2LGDatWq4ejoSNmyZalataoqmLp//z63b9/m33//VR0/ZgjQw8NDFUzZ26t/YJqamhIe3dV97949qlWrpra9WLFiqmDq3r17AKpgJEZYWJjaU6XTpUsX50AKwNPTk/DwcAoVKqSW3r9/fwAWLFhA/vz5VYFUzDHs7Ox49OhRnI8D6u2PqWN4Enb1/wxF9PWK7S4P3Tj88R2+48GQLUcpbpeVATVKa2y/7uXPgxdvmd+2ZsIqK36a6vrGch21Xd//3hPqYv624/KeiNG+eWNqOldUu5OrdLEiVGveliVrNzJnwj9x3tfPSO52x4iIjGTJ9IlERkaybutOWvfoy/JZUylVNGl6oZO73eER4QQGBrF9xSIyR98FV9SpIFWbtmH5hk3MHp94j4BSKBQoFIpv0pTa265UovOdHnGlUqH1M1Cp1H4ulUol0+cuYP2W7XRp35YqlSpq3e+hYycYNHocxYsUZlASB9JxkXJCqXgEU992ZyoUCnSj3zTLly9n/vz5NGrUiBIlStC2bVuOHz+u6pkCmDVrFj179uTMmTNcuHBBNe9o3bp1pE2blt27d3Pjxg3Onz/PuXPnWLVqFb1796ZXr14oFAo6depEw4YNNer1dU/L1wFJjJg/1FSpUmn8QXzbHlNTU3bu3Kmx7ev9Ghn93HOc9H9wi6pSqf2PMjIyUq3stxPItQVJ32t/cjNLHTUZNfibHqjg6HlvZkaadf/amrM3mXnwIsVzZGVB21oYpNLTyHPkrgfmqQ2p4Jh0k26FduamUb/Sg775VR4cPUxpaqr5K97c1DS6jHovRkhIaKxlYpPDxlqjN8PczJQiBfLzMLrXKikkd7tj6KdKRbmSxQEoXbwodVp3ZOk6tyQLppK73SbGxtjbWKsCKQBTE2MKF8in6qVMLAuXr2LB8lVqadVdKvPOR3M4Lzg0FLPvtMPMzFTjnAGEhIRgFn1+Ynz58oVhYydy4OhxOrVrzcBe2oOk1Rs2MX3eQkoULcyiWdO0fg/8aikpmPrpySRfT8QGuHnzJvny5QNg8eLF9OrVi7Fjx9K8eXMKFSqEl5eX6ov85s2bTJ48mRw5ctC+fXuWLVvG5MmTcXd35927d+zevZtNmzapJl9v3bqVpk2bqp7unCtXLp49e4aNjY3q9erVK6ZPn/7dLtWv5c6dm1u31MfTv/63g4MDQUFBhIWFqR1n+fLlapPtf5aNjQ36+voa569JkyasWLECBwcHbt++rTaZ/u3bt3h7e6t6mvT19QkMDFQr7+Pj81P1SO5uV+u05ujp6uD9Tn1ulE/0XCn7jNrvPFEqlUzcc5bpBy5QLX8Olravg4mh9gD11ENvXPLaoa+nGWiJpGVtZYWeni7efs/V0mP+ndPOVqOMnU3UvA7v59+Uif53zm/mg3zP/qMnOH/5qkb6ly9fYp13kxiSu93Hz57nyjfzhAz09XHMmQP/V6/jvJ+fldzttslmpXWCfUREBEaGiRtMNGtUn+3rVqq97Gys8XvxQuMHuo+vH/Z2sa8zZWdjjc835wzAx+85OXPYqv4dGBTEX937cPDYCYb1683gPj01yiiVSiZMn83UOfOp7lKZ5XNnqYZZxa/z08HU2rVr2bt3L56enkybNo2HDx+qJmBnyZKF8+fP8/TpU549e4arqytHjhxRBQimpqa4ubkxY8YMvL29efToEfv378fW1hZLS0u+fPnCtGnT2LVrF35+fly9epXLly+rJkp37tyZI0eOMH/+fDw9Pbl48SLDhw8nICAgznOAOnfuzOHDh1m9ejXe3t78+++/rF+/XrW9fPny5MmTh379+nHx4kW8vb2ZNm0aO3bs0Bg+/BmpU6emTZs2zJ07l+PHj+Pj44OrqytPnz6lcuXKtGzZkqCgIAYNGsTDhw+5ffs2ffv2xdLSktq1oxayK1KkCBcuXODEiRP4+voyb948Hj9+/FP1MI7+I7t79y6fP2u/3TgpGeqnophtVo7dfabWW3bk7jPMjQwpkD2T1nKuhy/hdvEOf5V1YlbLalp7pCBqGQWfd58oYpNZ63aRtAwNDSjmVJCjp86pXd/DJ89gbmZKwby5NcrYZLMie9YsHD55Ri398Mkz2FpnwypL3K+l287djJ0xR+0L9tWbN1y/c5cShZPurs7kbvcqt62MmTGHiIhIVVpgUBA37t4nd66km4Ce3O2uWLokD554qCa1A3z49Inrd+5S1KlgPFoUu0wZMlAgbx61V7lSJQgODuHsxf/ujH7/4QNXrt+kXKkSse6rXMmSeHh68fSZpyrt6TNPPDy9KFsyqlxERATd+g/m7v0HuE6ZQIc2LbXua/bCJWzYup32rZrjOnn8b9EjFUNHRyferz/NTw/zNW/enNWrV/PkyRNy587NypUryZ076g9m+vTpjB8/nsaNG2NiYoKTkxPjxo1j7Nix+Pn5kTNnTubPn8+CBQtwc3NDV1eXUqVKsXz5cnR1dWnWrBmfPn1i0aJF+Pv7Y2FhQfXq1Rk0aBAANWrUwNXVlaVLl7J06VIsLCyoXLmyxt1831OhQgXGjRvH0qVLmTVrFvnz56dFixZs2BC1eraenh6rVq1ixowZ9O/fn9DQUOzt7Zk/fz6lS2vOz/kZAwYMIFWqVIwdO5aAgAAcHR1ZtmyZKkhbv349M2fOVN3VV7ZsWWbMmIF59CTC9u3b4+vry+DBg9HR0aFWrVq0b99edTdlXJQqVQonJydatGjBjBkzqFnz188r6upclL9X7qG/2xEaFcvNTe+XrDp7gwE1SmOkn4qgz2F4vH5P9rQWpDVNzYMXb1l55gb5rTJQo6A9t33VF++zz5gW0+jhwScv36nSRPLo3r4NHfoOpu+ocTSuU5Mbd+6x0m0Lg3p0xsjQkKDgYJ56emNtlZW0lmkA6NGhLcMnTSeNuTnO5ctw4uwFDh4/hetPznHq2bEdf/cfSu/hY2jdpAGfAgJZsHIt5mZmSX6r/O/Q7j4jx9KyYT2CQoJZvn4ToaGf6f13+8Rv7FeSs93tmjVm5/5DdBk0gv5dOmJsnJpFq9ejo6NDp9ZJvzRC8SKFKVG0CINHj2Vwn56ksbBg/rKVmJmZ0uKrRWOfPvMkLCyMvLmjluCpVc2FJavX0rnPANWw3awFi3HIaU+NKlFziDdu28HVG7do3qgBWTJl5Oadu2rHLlQgPw8ePWb52g3kz5ubmlVduHX3nlqenHZ28RouTiy6f15MFG86yp+YTOPo6MiUKVNo1Ehzpek/xeXLl0mfPj05cvy3vseSJUvYvn07x44dS8aa/Tkid85N8D6O3XvGgmOX8XzzkUzmprQsnZ8O5QsBcPnZc9ov382kJs40LJqb+Ucvs/iE5tBNjDWd66vu2jt4+ykDNx1hX/+W5IhlyDC+ejZOvMmsP8uhYjkGnDrA7Eq1fvnjZJa8/fllBY6ePsu8FWvx9PElU4b0tG5Un46tmgHgfv0m7XoNYMrIIWqPBtm8ay+r3Lbi//o12bNmpUvbljSoWU3r/v38X+LSuJXGPgDOX77KwtXrefT0Gbq6OpQrUZzBPbuQNbP2Xs/ElJztvnj1OgtWruXBEw90dXUoWaQQA7p1wv4nhs3iKznb/fL1G2YsWsbZi5cJjwinSMECDOvdjVw/eJyLBoOfmwcb41NAAFNd53Hs1FkUCgVFnAoyfEAfteUK2nbpyXN/f07s/W8urv/LV0yaNYfz7lfQT6VH2ZIlGD6wLxnTpwegdefuXP3OEg+Prl5g7pLlLFqxOtY865YsoOSPHqtjli6OLf15N7PbxrtsIV+vRKvHr5DigilXV1f27NnDlClTsLa25sGDB4waNYpWrVrRu3fv5K7eHyExgqk/UXIGU8kpPsGUEH+ceAZTf7wkDKZuWdvGu6yTj1ei1eNXSHGPk+nZsyfBwcEMGTKE9+/fkyVLFtq3b0+nTp3ivc/x48erLdegzdy5c6lQoUK8jyGEEEL8Sf7AqU/x9lM9U0K79+/fa9xl962MGTOSOnXqX1SjpCU9UymL9EyJFEF6phLdbRvbeJct6O2VaPX4FVJcz1RSSJs2rWq1dCGEEEIk/1I8v5IEU0IIIYRIdCkolpJgSgghhBCJT3qmhBBCCCESIAXFUhJMCSGEECLxxedh3X+qn36cjBBCCCGE+I/0TAkhhBAi0aWgjikJpoQQQgiR+GQCuhBCCCFEAuikoIlEEkwJIYQQItFJz5QQQgghRAKkoFhK7uYTQgghhEgI6ZkSQgghRKKTYT4hhBBCiARIQbGUBFNCCCGESHwpaQV0CaaEEEIIkehSUCwlwZQQQgghEl9KmjMld/MJIYQQQiSAjlKpVCZ3JcQf5sPL5K5B8lBEJncNkkW39HmSuwrJIp+xQXJXIVlkNNBL7ioki+Y+95O7CsnDLF2S7dqvUO54l81282Ei1iTpyTCfEEIIIRJdChrlk2BKCCGEEIlPRzflRFMSTAkhhBAi0UnPlBBCCCFEAqSkdabkbj4hhBBCiASQnikhhBBCJLoU1DElwZQQQgghEl9KWrRTgikhhBBCJLoUFEtJMCWEEEKIxJeSeqZkAroQQgghEp2OTvxfCaFQKJg3bx7ly5fHycmJjh074u3tHWv+8PBwZs2aRfny5SlUqBBt2rThwYMHP3VMCaaEEEII8X9j0aJFbN68mYkTJ7JlyxZ0dHTo3LkzYWFhWvOPHTuW7du3M2HCBHbs2EGaNGno3LkzgYGBcT6mBFNCCCGESHQ6OjrxfsVXWFgYq1atonfv3lSsWJHcuXPj6urKq1evOHr0qEZ+X19ftm/fzpQpU6hUqRL29vZMnjwZAwMD7t69G+fjSjAlhBBCiESnoxv/V3w9fPiQ4OBgSpUqpUozNzcnb968XLlyRSP/uXPnMDc3p0KFCmr5T5w4QenSpeN8XAmmhBBCCJHokqNn6uXLlwBkyZJFLT1jxoz4+/tr5Pfy8iJ79uwcOXKERo0aUbZsWTp37oyHh8dPHVfu5hNCCCFE4kvAg45dXFy+u/348eNa00NDQwEwMDBQSzc0NOTTp08a+YOCgvDx8WHRokUMGTIEc3NzFi9eTKtWrThw4ADp0qWLU32lZ0oIIYQQiS8ZbuczMjIC0Jhs/uXLF1KnTq2RX19fn8DAQFxdXSlXrhwFCxbE1dUVgH///TfOx5WeKSGEEEL8VmLrefqRmOG9169fY21trUp//fo1uXPn1sifOXNmUqVKhb29vSrNyMiI7Nmz4+fnF+fjSs+U+CXOXHSnUfsuOFWsRuUGzVi6dgNKpfK7ZXYfPELtln9RsGJVqjdrw7bd+zTy3L73gDbd+1C4cg3K1mrItHmLYr39FaDX0FEMGz8lwe2JqzOXLtOoYzecKtekcsMWLF3n9uN2HzpK7dYdKFipBtVbtGPbnv2x5g0KDsa5UUt27j+ksa3/6Ak4lnHWeO0/eiLB7fpVLLNZMfuDDw4VyyV3VeLFuqozzc4eo9sbH/56cIOig/p+N7+ugQGlx42i/aNbdH/rS4sLJ3Bo3iTW/Ppmpvx1/zq527RI7KonSGaXylQ9cZjGzz2pc/sqefr3+W5+HT09cvfrTa2rF2ns50m1M8fJ3rC+Rj6zXDkp57aORt5PaeDxgLLrV2NiY5NUzYiXoOBg/pk8nbLV61ConDMdevTl6TPPH5Z7/fYtA0b8Q0mXmhSpWIU+Q0bw6vUbtTzv3r9n1MQpVK7TkMIVqtCoTQcOHDmWVE1JsOSYM5U7d25MTU1xd3dXpQUEBHD//n2KFSumkb9YsWJERERw584dVdrnz5/x9fXF5ifeW39Uz9S9e/cYOnQoXl5euLi4MHfu3OSu0h/nyZMnPH/+nEqVKv2yY16/fZceg0dQs0pl+nX9m2u37uC6ZAUKhZLuHdpqLXPw+EmGjp9Mu+ZNKF+qBMdOn2XUlBkYGhpSr0ZVAHz8ntOhz0AKF8jHnElj8fD0xnXpCgKDgpg4Yoja/iIjI5nsOp+jp8/SsFaNJG8zwPU7d+kxZBQ1XSrRr0vHqHYvXYlCoaB7+zZayxw8cYqhE6bSrlkjypcswbGz5xg1dVZUu6tXUcv7MSCA7kNG8fzlK637evDkKfWqV6F14wZq6TbZrRKlfUktrXV2+hz+F+M0aZK7KvGSuWRx6mzdwJMdu7g0fjJZS5ek9JiR6OjocnWGq9YyNdYux7ZmNW7MXYjfqTOkdypI5XkzSZ0uLbcWLVPLa2iZhjpbN2BuY611X8klXYlilHNbh++/u7kzaSrpS5WkwKjhoKvLg1lztJbJN2wwefr34f70Wbx1v0K2erUps2oZ5yMj8dsT9SMqtVVWXA7tJfCpBxe7dEfPyIgCI4dRcecWDpetROTnz7+wlbEbOHIMt+/dZ3CfnpiamLBg+Sr+6t6b/VvdSGNhrrVMREQEnfsMJCQkhLHDBxMREcGs+Yvp2Ksfu9zWop8qFWHh4fzdqz8BgYH06daZTBkycOTkKfqP+Iew8HAa1K75i1saBwmYMxVfBgYGtGnThpkzZ5I2bVqsrKyYMWMGmTNnpmrVqkRGRvL+/XvMzMwwMjKiWLFilClThqFDhzJ+/HjSpEnDvHnz0NPTo359zYA+Nn9UMLVo0SJ0dHTYt28fpqamyV2dP1LXrl1p2LDhLw2mFq5cQ26HnMwYOwqACqVLEhERwbL1G+nQshlGRoYaZeYsXUl154qM6NcLgPKlSvApIJD5K1argqkVGzZhYmzMohmTMdDXp2KZUhgZGTJh1ly6d2iHVZbMADx84sGEWXO4++ARRoaax0oqC1euI3cue2aMGQFAhVIliIiIZNmGTXRo2VRrXeYsW0X1yhUY0bcnAOVLFY9u9xq1YOrYmfNMcp1PSKj2L5DQz5/x9ntO13atKJQ/bxK0Luno6OhQ6q9WNJ45KbmrkiAlRgzm7e27HO3UAwCfoyfQ1den6MC+3Ji/WOPLP71TAezr1ebimIlcnTkHAN+TZ4gIDqbMxDE82LiZsE8BANjVrkmFmZPRNzH5pW2Ki3xDBvHxzj3cu0X97b48fhJd/VTk6dubxwuXaA16crRuic/2ndybPguAV6fPYFmgADk7dVQFU/mHDSEiKIhTDZoSGT3JONjbh/Ju67As7MTbi+4a+/3Vbty+w6lzF1g2ZyYVy5UBoFhhJ1zqNcFt2w56dOqgtdyhYyd4+PgJ+7ZsIJd9DgDyOOSiTvM2HDhyjPq1anDq7HkePH7CtrUrKJgv6m+6TMnivPB/yYq1G37PYCqZHifTp08fIiIiGDVqFJ8/f6Z48eKsXLkSAwMD/Pz8cHFxYcqUKTRq1AiA+fPnM3PmTHr16sXnz58pUqQI69atI23atHE+5h81zBcQEEDevHmxtbUlffr0yV0dEQdhYWG4X79JtUoV1NKrO1ckJCSUq7dua5Txe+GPl4+v1jI+fs/x9PEF4NylK1QqVxoDfX1VnhrOlVAoFJxzv6xKGzp+MgqFki0rFpPO0jIxmxersLAw3G/colrF8uptqFwhqt03tbTb/yVePn5ay/g8f6Fqd0BgEL1HjKFEkUKscJ2m9fiPnj5DoVCQJ1fORGrRr2NVMD+tFrtyaa0ba9p2Se7qxIuugQHZypfFY4/60PTTf/diYGZK1rKlNMqkdXQAwPPgYbX05+cuYGBqSrYKUUOdBhbm1Nq0hudnz7OnfrMkakH86BoYkLFcGfz2qQ9N++3eh76ZKelLl9ReztCA8G9Wm/7y/j2Gaf/7e81WtxbP1rupAimADzdvsSfv7xFIAZy76I5x6tSULVVClZbW0pLiRQpx5sLF2MtduoydjbUqkALImcMOeztbzpyPKmdqYkLzhvUpkDePWllba2t8nj9P5JYkDh1dnXi/EkJPT4/Bgwdz8eJFbty4wbJly8iWLRsA2bJl49GjR6pACsDU1JSxY8dy6dIlbt68yapVq8iZ8+c+O/+YYMrZ2ZnLly+za9cuHB0dOXv2LLNmzaJKlSrkz5+fkiVLMmDAAD58+ACAn58fjo6OLFq0iLJly+Ls7ExAQACBgYGMHj2aUqVKUbRoUdq1a6c2VhoXT548oUePHpQsWZL8+fNTtWpV1q5dq9o+f/582rdvz7p16yhXrhyFChViwIABvHnzhiFDhlC4cGEqVqyodqdAZGQka9asoXr16hQoUIDq1auzdetW1XZ3d3ccHR3VJsTFtDFmbHjYsGEMHjyYadOmUbp0aZycnOjRowdv3rxRncPnz5+zYMEC2rbVPryW2HyfvyA8PBzb7NnV0m2i39he0QHC1zy8op6hpFnGSlXm8+cvPH/5Ervs2dTypLVMg6mJCV4+/52naf+MYNOyBeTOZc+v4vvCP6rd1ur1U7XBV3Nio6rd35axUi9jZGTI/o2rmDZ6GJZpLLQe/8GTpwBs2rWHsnUak79CNVp178utez/3vKnk8N7Hj9E5C7F94AjCQkKSuzrxYmFng56hIR+fqK9V8+nZMwAsc2q+F0PfvgXAzFp92M7Czg4Ac9uo+RsRIaFsLFqWY116EfruXaLXPSFMbKPaHfhUvd2B0XOGzLS0G+DRoqXYNm9GZpfKpDIzxaZpYzK7VMZry7ao/VpbY2BhQbCvL0VmTKGBxwOa+HtTbtN6jLP9PsPWHl7eZLPKSqpU6oM+1tmz4emt+VmnKufpha215nCtdTYr1Y+oMiWLM37kULX5ROEREZw6d14tCPutJNfD+ZLBHxNMbd++ncKFC1OzZk3OnTvHsWPH2LdvH5MmTeLw4cNMmzaN8+fPs3jxYrVye/bsYe3atcydOxczMzM6d+6Ml5cXS5cuZevWrRQqVIiWLVty//79ONUjNDSUDh06YGxsjJubG/v376dmzZpMnjxZ7cGIV69e5erVq6xdu5Y5c+Zw+PBh6tSpQ548edixYwcVKlTgn3/+UQV/U6dOZdGiRfTq1Yu9e/fSrl07xo8fz/r163/qPB08eJCPHz+yYcMGFixYwLVr11S3eW7fvp3MmTPTsWNH5s+f/1P7ja+AoCAATE2M1dJNjKNuUQ0KDtYoExhrGWNVmf/2qznMYWJsrLbfXxlExQgI1F6//9qgGSQExlbGRL2Mgb4+OX4wT+ZB9Jf4ly9hzB4/mlnjRvHlSxjteg3g4dOfW4zuVwv58IGPz18kdzUSxNAiKsgN+6a3JSz6GhuYmWmUeX72Ap+eeVJh5mSyVSqPvpkpWcuUosyEf1BERqIf/d5RhIfzMTpY/t0YRLc7IrqdMSKi/171tbQb4MmyFbx1v0zF7Ztp7ONBqWWL8Nq0lUfzFwFgmD5qrR+nMaNJnSULFzt140rfAVgWyEflPTvRMzbWut9fLTAwMNbPpGAtn3UxAgKDMDXVbIOJyffLTXOdh7evH907/hW/CotE88fMmUqbNi36+voYGRmRIUMGihQpQu3atSlRIqo71crKinLlyvHo0SO1cq1atVJ118V0+V28eFE1FjpgwACuX7/OunXrmDp16g/rERoaSrt27WjVqpVq3lavXr1YunQpjx49Ik+eqC5YhULBxIkTMTc3x97enjx58qCvr0+HDlFj5u3bt2fr1q14e3ujr6/Ppk2bGDZsGHXr1gXA1tYWX19flixZQps22icra2Nqasr48ePR19fH3t6e+vXrc/r0adU51NPTw9jYmDS/aFKvQhF151psd2fo6mrG87GVibkJTldXF6VCQXQmjfJKlOho2e+vpFD+oN1a0lVlvkmPuftPW5nYtG/emJrOFSldrIgqrXSxIlRr3pYlazcyZ8I/cd6X+Hkx77/Y7txUKhUaaYrwcHbXb4bL4nk03B/Vax3k/5Izg4ZTY90Kwv+AXrqY4ZlY263QbLeugQEuB/ZglDEjV/sPIuDxU9KXLkneAX2JCA7mxvBR6BpEDeV/fvOG8207qD4Mgp55UeXoAWybNcZjzc/98EwohUKB4pv2KBRK7X/zyu9/JimVCnQ0/vKjmqltf0qlkulzF7B+y3a6tG9LlUoVf74Bv0BCh+v+JH9MMPWt+vXrc/HiRWbPno2XlxceHh48e/ZM49bHr29tvHfvHqC5smpYWBhfvnyJ03HTpk2rWhn14cOHeHt7q3qkvv7DSpcuHebm/925kTp1arXl7Q2jJx9/+fKFZ8+eER4eTtGiRdWOVaxYMVavXs27n+jKt7GxQf+rOURmZmaEh4fHuXxiMzeLCji/7YEKDoma96DtV1xsZUJCQ1RlzKLzaPvVFhISilkyT8w1N406vma7o9tgqqXdpjHtVv/SDIk5V1rKxCaHjbVG75W5mSlFCuTn4ZPfu2fq/8GX6JWWv+2BMoh+3375pP1p9J+eebKzel1SZ0iPUVpLPj59hlk2K3T19Pgc3Yv9O4uZIK9vpn6DUKro93Z4QIBGmWz16pAmfz5ONWjKq9NnAHhz4SLhnz5RdMZUnq3fqOrZ8j924r9fVcC7q9cI+/iRNAXyJ0l7vmfh8lUsWL5KLa26S2XeaZm6EBwaitl3/n7NzEy19tKHhIRg9s3NVl++fGHY2IkcOHqcTu1aM7BX93i24Bf4A4fr4uuPDabGjh3LgQMHaNCgAZUqVaJ79+6sXLmSV6/UbxOPWQ0VooIdU1NTdu7cqbG/b5eej83bt29p1qwZlpaWuLi4ULp0aQoUKEDFiuq/DL4OaGJo64WB/37FffsLJCY4+3r8/etffBEREfFux69ibZUVPT09vP3UJ0h6R8/9ymlnq1HGzjp7dJ7n5I2elBvz75gyxqlTkylDBo39vv/wkaDgYK37/ZWsrazQ09PV0u7/2vAtO5vodj9/Tl7HXP+ViZ5cmtM27mue7D96gjQW5pQtof7j4suXL7HOsxKJ59MzLxQREVh8M5fFIkfUv98/fKRRRs/IiJwN6uJ/0Z0Abx9C30TNocpQ2AmAN1puWvjdBHlGtds0h51auln0vwMePtYoYxI97/HtVzeNALyJnnht7uiA/5GjKCIj0dPy+aaTSp/IWO5qTUrNGtWnUvmyamnHTp3h3CV3FAqF2ue9j68f9nZ23+5Cxc7GmgePnmik+/g9p2C+/yacBwYF0bnPQG7eucuwfr3p0KZlIrQkCaWgnqk/Zs7U1z58+MCmTZsYO3YsI0aMoFGjRuTJk4dnz559d0FEBwcHgoKCCAsLw8bGRvVavnx5nFdb3bt3Lx8/fmTz5s306NGDqlWrqp7386PFGGOTI0cOUqVKxdWrV9XSr169SoYMGbCwsFAFZ0FB/81F8Pb2jtfxfiVDQ0OKFSrI0VNn1M7P4ROnMTczpeA3d6YA2GTPRnarrBw+cVot/fCJ09haZ1cteVC2ZDFOnb+otkjnoROn0NPTo9RXw1vJwdDQgGJOBTl66px6u0+eiW635kq8NtmsyJ41C4dPnlFLP3zyDLbW2VTtjgu3nbsZO2MOYV/1Sr5684brd+5SIvrLWSSdyC9feH7+Ivb1aqul52xYl88fPvLq6nWNMoqwMCrOmkq+ju1UaTq6ujh168zHp8949wfcPKD48oU3Fy6RrY56u7PVr0PYx4+8v35Do0zA46gg4ts7/dKXLA5ELX8QERzC24uXsKpbG92vAqqMFcqjb2rCm4uXErspP5QpQwYK5M2j9ipXqgTBwSGc/eruwvcfPnDl+k3KfXWH37fKlSyJh6eX2uKeT5954uHpRdmSUeUiIiLo1n8wd+8/wHXKhN8/kCJ5Fu1MLn9kz5SZmRlmZmYcP36cfPny8fnzZzZs2MC9e/dwcor9i6J8+fLkyZOHfv36MWrUKLJmzcrmzZvZsWMHq1atirXc1zJnzkxoaCgHDx6kWLFiPHv2jClTolbU/t7K2z9qT7NmzZg3bx4WFhYULFiQc+fO4ebmxoABA9DR0cHBwQETExMWL17MwIEDef36Na6urj/9pjMxMcHLy4u3b9/+suUlundoR4feA+g7cgyN69Tixp17rNy4mUE9u2JkZEhQcDBPPb2wtrIirWUaAHp0aMfwiVNJY2GOc/mynDh7noPHT+I6cYxqv53atGT/0eN06j+EDi2b4eXjy+wlK2jeoC5ZMmX8JW37nu7t29Ch72D6jhpH4zo1o9rttoVBPTpjZBjTbm+srbJ+1e62DJ80nTTm5jiXL8OJsxc4ePwUrj85x6lnx3b83X8ovYePoXWTBnwKCGTByrWYm5nxd6vmSdBa8a2r02bTYN8OaqxfyYP1bmQuWZwi/XpxfvR4Ij9/Rt/MlLS5Hfnk6cXnt+9QKhTcWb4Kp55dCX7hz/tHjynYtRNZSpdgf7O2asNbv7P7M12ptGsbZVYv59nGTaQvUZzcvXtya+wEIj9/JpWZKRaOjgR5evHl3TteHDzMuyvXKLV0EXenTifw8VPSFitC3oH9eX7wsCoAuz1+MpX37qTCVjceLliEUYYMOI0dzbsr13jxzXISyaV4kcKUKFqEwaPHMrhPT9JYWDB/2UrMzExp8dXiuU+feRIWFkbe3I4A1KrmwpLVa+ncZ4Bq2G7WgsU45LSnRhVnADZu28HVG7do3qgBWTJl5Oadu2rHLpQMQ50/lIJ6pv7IYCpVqlTMnTuXqVOnUrduXSwsLFRLIyxZsoSQWCZq6unpsWrVKmbMmEH//v0JDQ3F3t6e+fPnU7p06Tgdu0aNGty7d49p06YRFBSElZUVTZs25fjx49y+fZuWLeP3a2HkyJFYWloya9Ys3r59i42NDf/88w/NmkWtI2NqasrMmTOZNWsWtWvXxs7OjuHDh9OpU6efOk7btm2ZNm0aT548Yc+ePfGq688qXawI86eMZ97y1fQcOopMGdIzpFd3OraO+lK/9/Ax7Xr2Y8qoYTSqE7XwXKM6NQkLD2fVxs3s2HeQ7FmzMG3MCGpFf7AA2NvasGruTKbPX0KfEWOwtLCgfYum9O3S8Ze060dKFyvC/MljmbdiLT2H/RPV7p5d6dgq6pree/SEdr0GMGXkEBrVjlqVvVHtGlHtdtvKjv0HyZ41K9NGD6OWS6WfOnaZ4kVZMXsqC1evp//oCejq6lCuRHEG9+yimpMmkpbf6bMcaNWekqOGUnvzOoJe+HN+5FhuzIu6Qy1jIScaHdrN0a69eLhhMwDuE6ehVCgo0r83hpZpeHv7LnsatcD3+KlkbMnPeX32HOfbdST/8CGU27CGUP+X3PpnHI8WLgHAsmBBnPf9i3uPPnht2oJSoeBU42YUHDWCfIMGYGCZhiAvb+7PcuVxdBmAd1eucrJeIwqMGkHZtSuJDA3l+f5D3Bw9VuvE9uSyYMZkprrOY/rchSgUCoo4FWTOlAlYfDWHdtzUmTz39+fE3qgpJwYGBqxeOJdJs+YwevJ09FPpUbZkCYYP7Kua5nHkxCkAtuzcxZaduzSO++jqhSRvm4idjjK+Y1Mi5frwMrlrkDwUkcldg2TRLb3mUGxKkM/495p/+KtkNNBL7ioki+Y+cVse5/+OWbok23Vww7I/zhQLk3/PJ2JNkt4f2TMlhBBCiN+bzh85Kzt+JJj6Srdu3dSeNK3N9u3bsbf/9YtACiGEEH+UP3AieXxJMPWVcePG8fkHTx7/eq0oIYQQQmgni3amUJkyZUruKgghhBD/H1JQz1QKGtEUQgghhEh80jMlhBBCiMQnw3xCCCGEEPH3J65kHl8STAkhhBAi8UnPlBBCCCFEAkjPlBBCCCFE/KWkYT65m08IIYQQIgGkZ0oIIYQQiU/mTAkhhBBCxF9KGuaTYEoIIYQQiU96poQQQgghEkB6poQQQggh4i8lPehY7uYTQgghhEgA6ZkSQgghROKTYT4hhBBCiARIQcN8EkwJIYQQItHJ0ghCCBEtn7FBclchWdwLCUvuKiQLi1RGyV2F5KEnX4eJLgX1TMkEdCGEEEKIBJBQXAghhBCJT4b5hBBCCCESQIIpIYQQQogEkGBKCCGEECIBdFPOtGwJpoQQQgiR+FJQz1TKCRuFEEIIIZKA9EwJIYQQIvGloJ4pCaaEEEIIkfgkmBJCCCGESACZgC6EEEIIkQDSMyWEEEIIkQApKJhKOX1wQgghhBBJQHqmhBBCCJH4UlDPlARTQgghhEh8MgFdCCGEECIBpGdKCCGEECIBUlAwlXL64MQvc+aiO43ad8GpYjUqN2jG0rUbUCqV3y2z++ARarf8i4IVq1K9WRu27d6nkef2vQe06d6HwpVrULZWQ6bNW0RYWFis+zx25hyOpSrifu2GKm3Y+Ck4lqoY6+u5/8v4N1yLM5cu06hjN5wq16RywxYsXef243Nx6Ci1W3egYKUaVG/Rjm179seaNyg4GOdGLdm5/5DGtis3btGqe18KV6lNubpNmDh7PkHBwQluU3xYV3Wm2dljdHvjw18PblB0UN/v5tc1MKD0uFG0f3SL7m99aXHhBA7Nm8SaX9/MlL/uXyd3mxaJXfVkYZnNitkffHCoWC65qxIvWV0qU+vUEVr5e9HozjXyD+jz3fw6enrk79+bBtcv0fKFJ3XOnsC2UX3VdhPr7LT79DrWV5mFc5O6SXEWFBzMPxOnULZKDQqVrkCHbr146vHsh+Vev3nLgGGjKFm5KkXKVabPoGG8ev061vz+L19RtHxl5i9ZlpjVT1w6OvF//WGkZ+onubu7065dO44fP062bNlo27YtVlZWTJ069Ydlw8PD2bhxI+3bt0/6iiaT67fv0mPwCGpWqUy/rn9z7dYdXJesQKFQ0r1DW61lDh4/ydDxk2nXvAnlS5Xg2OmzjJoyA0NDQ+rVqAqAj99zOvQZSOEC+ZgzaSwent64Ll1BYFAQE0cM0djnh0+fGDN1lkZ6j47taNGonlrap4BA+o4YQ4kihciSKWMinIUo1+/cpceQUdR0qUS/Lh2jzsXSlSgUCrq3b6O1zMETpxg6YSrtmjWifMkSHDt7jlFTZ0Wdi+pV1PJ+DAig+5BRPH/5SmM/9x894e/+QylTvAjzJ43l9dt3zFq8nGfePqyaOyPR2hgXmUsWp87WDTzZsYtL4yeTtXRJSo8ZiY6OLldnuGotU2PtcmxrVuPG3IX4nTpDeqeCVJ43k9Tp0nJrkfqXh6FlGups3YC5jfWvaE6SS2udnT6H/8U4TZrkrkq8ZChRnMqb1+O1czc3J0whY+mSFB49Ah1dXe7MnKO1jNPwweQf0Jfb02bxxv0y1vXqUGH1chSRkfjs3kfoy1cccKmpUc6xc0dsG9Xn6Xq3JG5V3A0cPorbd+8zuF9vTE1MWLB0OX917cH+HVtIY2GhtUxERASde/UlJDSUsSOGEhERwax5C+nYvTe7Nm9EX1/9q1qpVDJi7ASCgpLnx9HvTqFQsGDBArZt20ZAQABFixZlzJgx2NjY/LDs3r17GTRokOo7Pq4kmEqg+fPno6enF6e8+/btY8qUKf/XwdTClWvI7ZCTGWNHAVChdEkiIiJYtn4jHVo2w8jIUKPMnKUrqe5ckRH9egFQvlQJPgUEMn/FalUwtWLDJkyMjVk0YzIG+vpULFMKIyNDJsyaS/cO7bDKklltn+Omu5IqleZ1sc5mhXU2K7W0nkNHYmFuxsxxo9BNxAmTC1euI3cue2aMGQFAhVIliIiIZNmGTXRo2RQjQy3nYtkqqleuwIi+PQEoX6p49LlYoxZMHTtznkmu8wkJ/az12Ks3byOtZRrmTR6Hgb6+Kn34pOk88/Yhxy8MPEqMGMzb23c52qkHAD5HT6Crr0/RgX25MX8xkZ/V25DeqQD29WpzccxErkZ/+fqePENEcDBlJo7hwcbNhH0KAMCudk0qzJyMvonJL2tPUtHR0aHUX61oPHNSclclQZyGDeLDnbuc7xr1Hn5x/CS6+vrk79eH+wuWaFxvgJxtWuG5bSe3p80EwP/UGdIWzE/uTh3x2b0PRVgYb69eUyuTrrATto3qc2P8ZF5fck/6hsXBjVu3OXX2PMvmuVKxfFkAihUuhEudBrht3U6Pzn9rLXfo6HEePn7Cvu2byGVvD0AeBwfqNG3JgSNHqV9bPZB027aDZ15eSdqWxKCTTBPQFy1axObNm5kyZQqZMmVixowZdO7cmX379mFgYBBruefPnzNu3Lh4HVOG+RIoTZo0mJmZxSnvj4Z3/nRhYWG4X79JtUoV1NKrO1ckJCSUq7dua5Txe+GPl4+v1jI+fs/x9PEF4NylK1QqV1otMKjhXAmFQsE598tqZQ8cPcGFK1cZ3KvbD+t88twFjp0+x/B+vTCP43WMi7CwMNxv3KJaxfJq6dUrV4g6Fze1nAv/l3j5+Gkt4/P8hepcBAQG0Tu6J22F6zStxx/YvTNLZkxSO1/60f8fFh6eoLb9DF0DA7KVL4vHHvVh26f/7sXAzJSsZUtplEnr6ACA58HDaunPz13AwNSUbBWihr4MLMyptWkNz8+eZ0/9ZknUgl/HqmB+Wi125dJaN9a07ZLc1YkXXQMDMpUrg89e9aFp79170TczJWMZzesNoGdoQHhgoFral3fvMUybNtZjlZw1jU+PnvBg4ZKEVzyRnLt4CePUqSlbuqQqLW1aS4oXLcKZcxe+W87O1kYVSAHktM+BvZ0tZ86dV8vr6/ecmXMXMGH0yMRvQGJLhmG+sLAwVq1aRe/evalYsSK5c+fG1dWVV69ecfTo0VjLKRQKBg8eTL58+eJ13F8STDk6OrJv3z7atWtHwYIFqVq1KidOnODEiRNUr16dQoUK0alTJ96/f68q4+HhQefOnSlcuDDlypVj4MCBvHnzRrU9ICCAMWPGULFiRfLly0fZsmUZM2YMn6N/9bi7u+Po6Mjp06epU6cO+fPnp3bt2pw8efKn6n716lWaNm1KwYIFadCgAY8ePVLb3rZtW4YNGwZAZGQkM2bMoGLFiuTPn58aNWqwadMmAHbu3Mnw4cNV58Pd3R2lUsmKFSuoWbMm+fPnp2jRonTt2hVfX1+1c7d161Y6dOhAwYIFKV++PEuXLlWrw/nz52nRogVOTk5UqFCBWbNmERkZCUS9sWbMmEH58uUpXLgwzZo149y5cz91DuLK9/kLwsPDsc2eXS3dJrqr1MvHV6OMh5c3gJYyVqoynz9/4fnLl9hlV+9yTWuZBlMTE7x8/FRpb9+9Z9zMOYzo35sM6dJ9t74KhYLp8xdTonAhajhXilsj48j3hX/UubBWr7OqXb5+GmVU5+LbMlbqZYyMDNm/cRXTRg/DMo32YYPMGTOQO2fUB3NwSCgXrlzDdckKijkVUKX/ChZ2NugZGvLxiYda+qdnUXNILLXUJfTtWwDMrNV7zyzs7AAwt43qqo8ICWVj0bIc69KL0HfvEr3uv9p7Hz9G5yzE9oEjCAsJSe7qxIuZbdT1DniqPkco8JknAOb2ObSWu79wCfYtmpHVpTL6ZqbYNW1M1irOPNuyTWt+uyaNSF+0CFeGjUSpUCRuIxLAw9OLbNmsSJVKfdDHOns2PH18vlvO1lqzt9g6e3Y8vf8rp1AoGPbPOGpWdaFC2dKJV/GkkgzB1MOHDwkODqZUqf8Cd3Nzc/LmzcuVK1diLbdkyRLCw8Pp2rVrvI77y4b5Jk6cyLhx45g4cSJTpkxh4MCB5MyZkxkzZhASEkKfPn1Yvnw5Q4cO5dWrV7Rq1YratWszbNgwQkNDmT9/Pi1atGDv3r0YGxszdOhQXr58ybx580iXLh03b95k+PDh5MiRg7/++kt13BkzZjBy5EjSpUvH7NmzGTRoEGfOnMEkDsMCvr6+dOzYkQYNGjB16lSePn3KP//8E2t+Nzc3Dh06hKurK5kyZeLkyZOMHTuWXLlyUatWLQIDA5k8eTLnzp3DwsKCtWvXsnTpUqZNm4ajoyN+fn6MHj2aqVOnsnDhQtV+p0+fzujRo/nnn3/YvXs3s2fPpmjRohQrVoxbt27RqVMn/vrrLyZNmoS/vz+DBg1CV1eX/v37M3z4cJ48ecKMGTPInDkzJ0+epFu3bixYsIBKlSol6Jp+KyAoCABTE2O1dBPj1ABaJz8HxlrGWFXmv/1qXjMTY2O1/Y6eOpPC+fPSoGZ1tYnn2pw4d4Fn3j6MHvj9ydDxERCovc7/tUvzyzIwtjIm6mUM9PXjPEynVCopWbMB4eHhpLEwZ2jv7j/RioQzjJ4jEvZNr0NYdFsNtPQGPj97gU/PPKkwczIRoSG8unaDDAXyU2bCPygiI9GPPoeK8HA+PnmaxC34dUI+fCDkw4fkrkaC6Edf7297mcKjr7e+ufbe34dLV5CxdCmq7NyiSnuyfiP35i3Umj9vnx68vujOq+/09iSHwMBA7Z9TJsYEf2d+U0BgIDbW2TXSTUyMCf7q823txk34Pn/BkrmzE6fCSS0BQZGLi8t3tx8/flxr+suXUTcRZcmSRS09Y8aM+Pv7ay1z+/ZtVq1axfbt23n1SnMOalz8smCqYcOGVK9eHYAWLVpw4sQJ+vfvT8GCBQEoW7Ysjx8/BmDTpk1kzJhRLXCZM2cOpUqV4tChQzRq1IiyZctSrFgxcufODUC2bNnYsGGDRs9Rv379KF26tOr/69evz+PHjylcuPAP67x161bSp0/PmDFj0NPTw97eHn9/f6ZMmaI1v4+PD8bGxmTPnp0MGTLQpk0bcuTIgZ2dHUZGRqrhwAwZMgBgbW3N1KlTcXZ2BsDKyoqaNWuyf796F3nDhg2pX7++qg1ubm5cu3aNYsWKsW7dOgoWLKjqHbO3t2fChAm8fv0ab29v9u3bx/bt2ylQoAAAHTp04OHDh6xcuTLRgymFImoYUyeWPyBt85FiKxMzIqqrq/vfL08t+1WiVI3L/7v/ENdu3WbvxjVxqu/GbTvJ45CTMiWKxSn/z1Aof3AutKSrynyTHjM8rK3Mj0RERrJk+kQiIyNZt3UnrXv0ZfmsqZQq+uP3f2KIuTaxDXErlZq9CorwcHbXb4bL4nk03P8vAEH+LzkzaDg11q0g/A/ttUkJfnS90dKLpGtgQPWDe0mdKSMX+w0i4PETMpYuSYGB/YgICubKsFFq+TOULEE6p4KcbNku0ev/MxQKBYpv2qNQKLXHD0rld+cPKRVKrZ8Vyq/KPfPyZs6iJcybMQ0zM9ME1f2XSYY5U6GhoQAac6MMDQ359OmTRv6QkBAGDRrEoEGDsLW1/f2DKbvoLnoAIyMjALJ/NbRjaGious39/v37eHh4aAQ8X758wcMjarigVatWnDhxgt27d+Pj48Pjx4/x9fXF1tZWrUyOHP91K5uaRr0Bw+M4Z+Tx48fkzZtXbYJ5kSJFYs3funVrjh07RoUKFcifPz9ly5alZs2apItluMnZ2Zlbt24xb948vL298fDw4MmTJ2TKlEktn729+lCIqampqg2PHj2iTJkyaturVo2atH3w4EEA2rVT/9AJDw/H3Nz8e02PF/PoP/Bve6CCQ6Le3Np+scVWJiQ0RFUm5oMjWEvPVkhIKGYmJrx6/YbJc+YztE8P0lmmISIiQvVBp1AoiIyMVLuOHz59wv36TQb2SJq5KeamJlrbFRwdCJiaajkXpjHnQj1YCIk5f1rK/Ih+qlSUK1kcgNLFi1KndUeWrnP7ZcHUl+gPr297oAyir+mXT4EaZQA+PfNkZ/W6pM6QHqO0lnx8+gyzbFbo6unx+Q/vvfl/FhZ9vfW/ud760dc7LEDzetvUq0PaAvk4Wr8J/qfOAPDq/EXCPgZQctZUnqzbyMf7D/7LX78OXz58wO/IsaRqRpwsXLaCBUtXqKVVr+LMO+/3GnmDQ0IxM409ADIzM9V6Z15IdLnIyEiG/TOOGlVcKFuqBBEREao8CoWSiIgIjaHFP11sPU8/EhNfhIWFqf4fouKH1KlTa+SfOHEitra2tGiRsGVVftnZ13ahY/vVrlAoKFWqFGPGjNHYZmZmhlKppFu3bjx69Ii6detSvXp1BgwYwOjRozXya5u5/zMTwb/N+703rK2tLUeOHOHy5cucP3+e48ePs2TJEqZMmULDhg018i9fvpz58+fTqFEjSpQoQdu2bTl+/LhGz9T32pAqVapYz2NMno0bN2oMaybmXWsxrK2yoqenh7ffc7V0b7+ouT457Ww1ythFd217+z0nb/TE45h/x5QxTp2aTBkyaOz3/YePBAUHk9POlvOXrxIQGMTISdMZOWm6Wr72vQdglTkzJ3b9N4Rw9uJlIiMjE32uVAxrKyv09HS1nIv/2vUtO5voc/H8OXkdc/1X5nl0Gdsf39Yb4/jZ85ibmlK8sJMqzUBfH8ecOXjyzCvO+0moT8+8UEREYPHNXBmL6B857x8+0iijZ2REzgZ18b/oToC3D6FvouZQZYhuyxstk/fF7yHQM+p6m+ewU0s3i/73Jy3X2yR6juDrS+o3krw6HzWElya3g1owla1GNXz3HUT5VUCRHJo1akil8urrgB07dZpzF91RKBRqn7E+vn7Yf3NOvmZna8MDLefGx9eXgvnz4f/qFbfu3OXWnbvs2ndALc+i5StZtHwlx/fvIlvWrAlsVSJLhvWiYob3Xr9+jfVX89Bev36tGsn62o4dOzAwMFB13sTMN65Tpw716tVj/PjxcTrub3k3X65cufDw8CBLlizY2NhgY2ODhYUFkydP5vHjx9y/f5/Tp08zb948Bg0aRL169bC2tsbHxydR75jLkycPd+7cUVsY8s6dO7HmX7duHUeOHKFs2bIMGTKEvXv3Urp0aQ4ciHrzfxv0LF68mF69ejF27FiaN29OoUKF8PLy+qk22Nvba9RpzZo1NGzYkFy5or6QX79+rTqPNjY27Ny5kx07dsT5GHFlaGhIsUIFOXrqjFobDp84jbmZKQXz5tEoY5M9G9mtsnL4xGm19MMnTmNrnV215EHZksU4df6i2rU4dOIUenp6lCpWhMrly7B99VK117ihAwEYN3Qgi2dOVtv/7Xv3yZwxg8aSConF0NCAYk4FOXrqnPq5OHkm+lxo/lHbZLMie9YsHD55Ri398Mkz2Fpn+6m6rnLbypgZc4iIiFSlBQYFcePufXLn+nUT0CO/fOH5+YvY16utlp6zYV0+f/jIq6vXNcoowsKoOGsq+Tr+16Oqo6uLU7fOfHz6jHf3HmiUEb8HxZcvvLpwEeu66tfbpn5dvnz8yFst8xg/PY6a9/btnX4ZSpUAINDrvwnYBpZpMLfPwetv7uBNDpkyZqBAvrxqr3KlShIcHMzZC5dU+d6//8CVa9cpV6pkrPsqV6okHp5eaot7PvV4hoenF2VLlSRjhgxs37BG4wXQrFEDtm9YQ8bo6SO/lWSYgJ47d25MTU1xd/9vuYyAgADu379PsWKaUzqOHDnCvn372LVrF7t27WLixIkALFu2jL594z6f9rfsF2zVqhVbtmxhwIAB9OzZEx0dHWbMmMH9+/fJlSuXqkvz4MGDpE2blo8fP7JkyRLevHnz3RWxf1bLli3ZuHEjI0aMoHv37vj4+LBgwYJY8797946FCxdiZGRE7ty58fDw4P79+6oJ8cbRE2fv3r1Lzpw5yZIlC+fPn8fZ2RldXV12797NkSNHSJ8+fZzr2KlTJxo3bsycOXOoX78+Pj4+LF26lNatW5MrVy4qV67MmDFj+Oeff3BwcODIkSMsXbqUSZOSZi2b7h3a0aH3APqOHEPjOrW4ceceKzduZlDPrhgZGRIUHMxTTy+sraxIa5kGgB4d2jF84lTSWJjjXL4sJ86e5+Dxk7hO/K9nslObluw/epxO/YfQoWUzvHx8mb1kBc0b1FUttGn5zYJ4McNjdtbZcfzmrrFHHs+09g4lpu7t29Ch72D6jhpH4zo1o86F2xYG9eiMkWHMufDG2irrV+eiLcMnTSeNuTnO5ctw4uwFDh4/heuE2G980KZnx3b83X8ofUaOpWXDegSFBLN8/SZCQz/T++/2id/Y77g6bTYN9u2gxvqVPFjvRuaSxSnSrxfnR48n8vNn9M1MSZvbkU+eXnx++w6lQsGd5atw6tmV4Bf+vH/0mIJdO5GldAn2N2v734Q68Vu6M8OVqru3U2HtCp6udyNjyeLk69OT62MmqK63haMjgZ5efHn3Dr8Dh3hz5Srlli3k1pQZfHr8hAzFilBg0AB8Dxzi3fX/AjDL6B9kHx8+Tq7mfVfxokUoUawog0f+w+C+vUiTxoL5S5ZjZmZGi6aNVPmeejwjLDycvLkdAahVvSpLVq2hc69+DOwTtT7XrHkLcchpT42qLqRKlYoC+fJqPWbGDOlj3ZbskqFnysDAgDZt2jBz5kzSpk2LlZWV6gasqlWrEhkZyfv37zEzM8PIyEhjIc+YCexZs2aNdYqONr9lz1T27NnZsGEDoaGhtGrVijZt2qCjo8PatWtJly4dmTJlYurUqZw4cYJatWrRt29fMmXKRPv27blz506i9U5lypSJtWvX8vLlSxo2bMjUqVPp3j32u6F69epFkyZNmDBhAtWrV2fMmDG0atVKdatlqVKlcHJyokWLFpw8eZLp06fz+fNnGjduTJs2bXj8+DHjxo3j3bt3+Plp3jqvTZ48eVi0aBFnzpyhbt26jB07lrZt29KjR9QCia6urqq61KpVix07djBhwgQaN26c8BOkReliRZg/ZTye3r70HDqKvYePMqRXdzq1aQnAvYePad6pB6fOX1SVaVSnJuOGDuTC5av0HDqKy9dvMm3MCGpVcVblsbe1YdXcmXz+/IU+I8awetM22rdoysj+veNVz3fvPyTqulLalC5WhPmTx+Lp40fPYf+w98hxhvTsSqfWUWPz9x49oXmXXpz66ldso9o1GDekPxeuXKPnsNFcvnGLaaOHUcul0k8du0zxoqyaM51PAQH0HTWO0VNnkSVTRrYuX4C97a9dKdzv9FkOtGqPpUNOam9eh2PzJpwfOZYbc6J+mGQs5ESzU4exjV6gFcB94jRuzl9Mkf69qb1lPanTp2NPoxZ4HY59nRjxe3h55hyn2nbEImdOKrutxa5ZE66NHqe6My+tU0FqHT9ItuhFaJUKBccaNsN7524KDh5Ale2byNGiGXdmzuZ0O/VFLo0yRvW+hH38+Evb9DMWzJqGS6UKTJ8zn2FjxpMpY0bWLF2IxVfzVMdNmU6vAf89ucHAwIDVixeQL29uRk+cwvipMyhUsAArFs77s+dC6erG/5UAffr0oUmTJowaNYqWLVuip6fHypUrMTAwwN/fn3LlyqlGjBKLjvL/fSVJkfg+JO7z6/4Yisgf5/k/NN/a6ceZ/g/dC0m8Xu4/SRlzox9n+j/Uzv9JclcheRhrX6suMUTO6BnvsnqDtS+L8bv6LXumhBBCCCH+FH9w/2HCFCtWTDVrXxtLS0tOnDjxC2skhBBC/B9JhjlTySXFBlM7d+787tyqpFg6QAghhEgxUtD3aIoNpqy1PAdJCCGEEIlEeqaEEEIIIRJAgikhhBBCiARIQcFUyhnQFEIIIYRIAtIzJYQQQojEJxPQhRBCCCESIAUN80kwJYQQQojEJ8GUEEIIIUQC6MgwnxBCCCFE/OmmnJ6plBM2CiGEEEIkAemZEkIIIUTik2E+IYQQQogEkAnoQgghhBAJIOtMCSGEEEIkgPRMCSGEEEIkQAqaM5VyWiqEEEIIkQSkZ0oIIYQQiU+G+YQQQgghEkAmoAshRJSMBnrJXYVkYZHKKLmrkCwuBHxO7ioki3aREcldhf8/0jMlhBBCCJEAKWgCugRTQgghhEh88mw+IYQQQggRF9IzJYQQQojEJ8N8QgghhBAJIBPQhRBCCCESQHqmhBBCCCESIAVNQJdgSgghhBCJLwUN86WcPjghhBBCiCQgPVNCCCGESHwyZ0oIIYQQIgFkzpQQQgghRAJIz5QQQgghRAKkoAnoEkwJIYQQIvGloJ6plNNSIYQQQogkID1TQgghhEh8MgFdCCGEECIBZJhP/Ii7uzuOjo74+fkl6XGUSiX//vsv7969A2Dnzp04Ojom6TET6sxFdxq174JTxWpUbtCMpWs3oFQqv1tm98Ej1G75FwUrVqV6szZs271PI8/tew9o070PhSvXoGythkybt4iwsLBY99lr6CiGjZ+ikf7q9RsG/jOeEtXqUMS5Jh37DOT2vQc/39A4OHPpMo06dsOpck0qN2zB0nVuPz4Xh45Su3UHClaqQfUW7di2Z3+seYOCg3Fu1JKd+w9pbAsIDGLsjDmUrdOYwi61aN65FxevXk9wm+Ijs0tlqp44TOPnntS5fZU8/ft8N7+Onh65+/Wm1tWLNPbzpNqZ42RvWF8jn1munJRzW0cj76c08HhA2fWrMbGxSapm/LSsLpWpdeoIrfy9aHTnGvkH/Ljd+fv3psH1S7R84UmdsyewbfRfu02ss9Pu0+tYX2UWzk3qJiUpy2xWzP7gg0PFcsldlTgLCg7mn8nTKVu9DoXKOdOhR1+ePvP8YbnXb98yYMQ/lHSpSZGKVegzZASvXr9Ry/Pu/XtGTZxC5ToNKVyhCo3adODAkWNqecLCw1m6eh01GregUDlnqjdqwYLlqwgLD0/UdsaLjk78X38Y6Zn6zV25coVhw4Zx/PhxAGrVqkX58uWTuVaxu377Lj0Gj6Bmlcr06/o3127dwXXJChQKJd07tNVa5uDxkwwdP5l2zZtQvlQJjp0+y6gpMzA0NKRejaoA+Pg9p0OfgRQukI85k8bi4emN69IVBAYFMXHEELX9RUZGMtl1PkdPn6VhrRpq2wKDgmjZtRehnz/Tt8vf2GbPxtHTZ2nTow/rF87FKX/exDsXd+7SY8goarpUol+XjlHnYulKFAoF3du30X4uTpxi6ISptGvWiPIlS3Ds7DlGTZ0VdS6qV1HL+zEggO5DRvH85SuN/URGRtJ5wDBevHrF4J5dSWdpybptO+gyaDjbViwid077RGvnj6QrUYxybuvw/Xc3dyZNJX2pkhQYNRx0dXkwa47WMvmGDSZP/z7cnz6Lt+5XyFavNmVWLeN8ZCR+e6IC7dRWWXE5tJfApx5c7NIdPSMjCowcRsWdWzhcthKRnz//sjZqk6FEcSpvXo/Xzt3cnDCFjKVLUnj0CHR0dbkzc47WMk7DB5N/QF9uT5vFG/fLWNerQ4XVy1FERuKzex+hL19xwKWmRjnHzh2xbVSfp+vdkrhVSSetdXb6HP4X4zRpkrsqP2XgyDHcvnefwX16YmpiwoLlq/ire2/2b3UjjYW51jIRERF07jOQkJAQxg4fTEREBLPmL6Zjr37scluLfqpUhIWH83ev/gQEBtKnW2cyZcjAkZOn6D/iH8LCw2lQO+p9MHnWHHbtO0iPTu0pkDcv9x4+YsGylbzwf8nkf0b8ylOhSTfl9NdIMPWb+7YXw8jICCMjo2SqzY8tXLmG3A45mTF2FAAVSpckIiKCZes30qFlM4yMDDXKzFm6kurOFRnRrxcA5UuV4FNAIPNXrFYFUys2bMLE2JhFMyZjoK9PxTKlMDIyZMKsuXTv0A6rLJkBePjEgwmz5nD3wSOMDDWPtWPvAZ77v2TTsoUUKZgfgLIli/Ph0yemzF3I5uULE/FcrCN3LntmjIn6QKtQqgQREZEs27CJDi2baq3fnGWrqF65AiP69ow+F8Wjz8UatWDq2JnzTHKdT0io9oBh75Hj3Hn4kJ2rl6oCpxKFnajXrhPnL1/9pcFUviGD+HjnHu7doq7vy+Mn0dVPRZ6+vXm8cInWoCdH65b4bN/JvemzAHh1+gyWBQqQs1NHVTCVf9gQIoKCONWgKZGhoQAEe/tQ3m0dloWdeHvR/Re1UDunYYP4cOcu57tGXcsXx0+iq69P/n59uL9Ae7tztmmF57ad3J42EwD/U2dIWzA/uTt1xGf3PhRhYby9ek2tTLrCTtg2qs+N8ZN5fSl52xwfOjo6lPqrFY1nTkruqvy0G7fvcOrcBZbNmUnFcmUAKFbYCZd6TXDbtoMenTpoLXfo2AkePn7Cvi0byGWfA4A8Drmo07wNB44co36tGpw6e54Hj5+wbe0KCuaL+pFX5n/t3XlYjOsbB/DvaENNKVvRRlG2VFpQR5R9zZIlsmT7cWyRfc2eEKGSY4nsRJZwHBE6tizJoSJpt5W00Tbz+6MM00wpNb1j5v5cV9fFM+87cz/PbPc822tpjpTUt/jLPwD2/fog43Mmjp0+C9eZ0zFp7GgAQCcLMwCAh9cuuM6cBjVVVVE3Q9l+wx6mX1WjaaOBgQEuXLiAsWPHwsjICD169EBISAhCQkLQq1cvGBsbY9KkSUhPT+edExsbi8mTJ8PExATW1taYN28ePnz43hWamZmJlStXwsbGBm3atIGVlRVWrlyJryUfVN+G40JDQ9G/f3+0bdsW/fr1w/Xr1ysVe3h4OBwcHGBkZAR7e3tER0fz3e7k5IRFixbxlS1atAhOTsW9MUlJSTAwMIC3tzesrKxga2uLzMxMvHz5EtOnT4elpSXatm2LHj16wN/fnxf72LFjAQB2dnYIDAwUGObLyMiAm5sbbGxsYGRkhFGjRiE8PJx3+44dO+Dk5IQ9e/agS5cuaNeuHcaOHYvXr19Xqv4VkZ+fj3uPnqBn1y585b1sbZCb+wXhEU8FzklKScWbhESh5yQkJSMuIREAcPvuA3S17gR5OTneMb1tu4LD4eD2vfu8soWr14PD4eL4Xz6oL+RDJPZNPFSU2bxE6hsLE2M8jnyGz5lZla+4EPn5+bj3OAI9bfh7EXt161LcFk+EtEXqW7xJSBJ6TkJyCq8tMrOyMXPJSliYGuMvT3ehj3/l+k2YG7fnS5oUFORx5fhBTHQcUdXqVVgteXk0su6MpAv8Q5VJQRcgx1ZCg06Wws9TkEdBFv9zkZeeDgW178+p5oC+eH3oCC+RAoBPTyJwrjXziVQteXk0tu6MhPP89Y4POg85thIade4o9DwZYfVOS4eCmlqZj2W5xR2fo1/ixS7fqgfOgKZGbeHo44m7/kdwwGkK0+FUyu0791C3Th1YdbTglampqsLc1Bg3/71T9nl376OZjjYvkQIA/ebNoNdMFzfDis9TUlTEiMGD0K51K75zdbW1kZCcDKC4p33kUHvYdrEudYwWACAxOaVqFSQVVuN9cGvXrsXo0aNx4cIF6OvrY968efDx8YGHhwd8fX3x9OlT7NmzBwDw7t07ODo6QktLC6dOnYKvry+ys7MxcuRI5ObmAgAWLlyIp0+fwsvLC1euXMHixYsRGBiI48eP8z2uh4cHli5disDAQGhpacHV1RU5OTkVijkxMRHOzs5o1aoVzpw5g2nTpmHXrl/rwTh37hz8/f2xfft2yMnJYcKECahbty6OHDmCixcvok+fPli/fj1evHgBExMT7NixAwBw8uRJ9O3bl+++ioqK4OzsjPDwcLi7u+PMmTMwNDTE+PHjERkZyTvu8ePHePDgAfz8/HDgwAGkpKTAzc3tl+IvT2JyCgoKCqCrpcVXrqOpCQB4U5IM/Cj2TTwACDmnKe+cr1/zkPz2LZppafIdo6ZaD0qKiniT8H3emvuKJTjqtxOGLYT3vKjVq4fsnFyBpCkhqfjDKSkl9af1rIjElNTittDmj5lXr0TBuXa8tih9TlP+c2rXVsDFw/vgvnwRVOupCH38qJev0KJ5Mxw4fgq2Qx3R+o/uGDx+Cu4/jqhaxSpJUVcHMgoKyHoVy1eeVTKnhF1GD1m0927ojhgOdbtukGUrQcdhKNTtuuHN8ZPF96utDXkVFeQkJsLUYwPsY19gWGo8rI8eQt2SNmYSu6Tema/4f7R8q7fyD1+iP3q+yxd6I4ejiV03yLGV0MxhKJp0t8XrknqX1mzYEDToYIoHi5aCy+FUbyVqSHpCEpbrG+PUvCXIL/lc/13EvomHZtMmkJXlH+TR1tJEXLzg5x3vvLg30NXWFijX1mzK+9HU2dIcq5cuBOuH3p2CwkLcuB3GS8K0mjbBqkXz0VyXf57g1euhkJOV5SVVjGHV+vW/30yND/MNHjwYvXr1AgCMHDkSISEhcHFxgZGREQDAysoKMTExAICjR4+iUaNGWLFiBe/8bdu2oWPHjrh8+TKGDBkCKysrmJmZwdDQEACgqamJgIAAgZ6jOXPmoFOnTrx/Dxo0CDExMTAxMflpzCdOnECDBg2wcuVKyMjIQE9PD6mpqdiwQXBy8884OjpCX18fAJCeno6xY8fC0dERSkpKAIAZM2Zg9+7diI6ORqtWraCiUvxlqaamJjC8d/v2bfz33384f/48WrZsCQBYsWIFIiIisHfvXmzbtg1A8fj8pk2bUK9kLoKTkxM8PDwqHfvPZGZnAwCUFOvylSvWrQOgeKJmaVllnlOXd873+1UUOF+xbl2++y0rifpmQO8e2HvkOGYvWYmlc2eiccMGuBF2F4EXLwEAvnz9Uu75FZWZJTzm7/US/NLIKuscRf5z5OXk0FxH8IP4R+kZn3H5eihU2EpY8OdU1KmtAL9DRzHRZQFO+O1Cq5b6v1CrypMvef0WltTtm8KS51SOzRZ63ku/v9CwoyVsTh3jlb0+dATRO7wBAAoN6gMA2q9cjrRHj3Fn0v9Qu2EDGK1Yim7nAnHZuhuKGPxiliupd+lepoKSdpBTFl7vqN1/oVGnjuge+P3H4MtDh/Gfl/Afb61nTcf7O/fw7va/1RE2I3I/fULup09Mh/FLsrKyyvxcKu/HemZWNnRK/WgCit/rOW/KPs/d0wvxiUnYtbns754r164jKPgyxo4cDhVl4XO2aowUDfPVeDLVrFkz3r+/JQdaP/RKKCgo8FZoPX/+HLGxsQIJT15eHmJji3/pOjo6IiQkBEFBQUhISEBMTAwSExOhq6vLd07z5t9/CX5LXAoquNohJiYGrVu3hoyMDK/M1NS0QueWpvPDSiM1NTU4OjoiODgYUVFRiI+Px4sXxavKOBX4lRkTEwM2m81LpIDi+QdmZma4desWr6xBgwa8RAoA2Gx2heteGRwOlxeDMLWETEYs65xvU8Vq1ar1/Re3kPvlggtWJSY56jfThe/mDVixYTP6O44HALQxNMCcqZOwevM21Kldp8L3VR4O9ydtIaScd06p8m/z5oSdU5aCwgJkZWXj1F/eUG/UEADQob0RejiMwZ6Ao9i6enmF76sqWCX7zJS1glFYb0oteXnYBZ9D7UaNEO7iisyYV2jQyRKt585GYU4OHi9ehlryxcO9Xz98QJjTBN4LJvv1G3S/Ggzd4UMRe+CQiGr1c99ek2Wu3Cyj3r0unUedxo1wZ44rMmNeolEnS7SbNweF2Tl4sGgZ3/ENLS1Qv70Rro8aW+3xE0EcDkfgc5nD4Qp/j3PL/1zicjlgCbzTi1/Gwu6Py+Vi0/adOHT8FKaMd0L3rjZC7/fyPyFwXe4Gc1MTuM6c9pMa1QCagC7CB5QVfMiyvnA4HA46duyIlStXCtzGZrPB5XLxv//9D9HR0RgwYAB69eqFuXPnYvlywS8KeXl5gbKfLVEv71hh9Sh9jLCE5cfepY8fP2L48OFQVVWFnZ0dOnXqhHbt2sHGRvgbRdjjCWs7DofDF5+wuouCMrs4SS3dA5WTW9zbI+wXXFnn5H7J5Z3DLjlG2C+93NwvYAu53/JYW5rj2pljSEp9CwDQaqKB0xeCAQAqZfQYVJayUnFMgm1RUi8lIW2h9K0t+HtUcr+1n5BzyqJYty70dLR5iRRQ3Ptn0q4NXryMLefM6pX/ORMAIFfyHH4j++0HTWamwDmaA/ujXts2uGHvgHehNwEAH/69g4LPn9HBYyNeHzrM69lK/Sfke+YNIC38IfIzMlCvXVuB+61J+Z8/AxDsefvWDvlC5ubpDOwPtXZtcHXQMKTeKK73u7A7yM/IhOWWjXh58DAynn/fwkNnUH/kffqEpFJL5Ylo7NqzDzv37OMr62XXDWlCpi/kfPkCdjnvVzZbSWhPfW5uLthK/O+VvLw8LFq1FsFXr2HS2NGYN0N4krQ/4Cg2ee2CRQcTeG9xr7HP/XJRz5R4aNGiBYKDg6GhocF7YWRkZGDhwoWYMGEC2Gw2QkNDceLECbRv3x5AcQKTkJDA19tVVa1atUJgYCDy8/N5cfw4JwkA5OTkkFWqSz8hIaHclXfnz59HRkYGrly5ArmSidXfhie5P+nZAIon9GdmZiImJoavd+rhw4e8ocSapN20CWRkZBBfMv/om/iSvbj0m+kKnNOsZEw/PikZrQ1a/nBOMu+cunXqoHHDhgL3m/4pA9k5OULvtywpb9/h3wfhGNi7J7SaaPDK/4uKQT1lZd6qwKrSbtoUMjK1hLTF93qV1kynpC2Sk9HaoMX3c0omm+qXmhdRHh3NpkL3mSksLERthZr7kM2OewNOYSGUmjfjK2eX/D8zKkbgHMWSuXEff1hYAAAfSibmKhu0ROrfV8EpKoKMkC8MlqwcispY5VhTskrqrVxGvT9HRQuco1gy7PP+Ln+934UVD+HVM2zJl0xp9u6JxAuXwC0srNbYiXDDhwxC1z+s+Mr+uXETt+/eA4fD4et5T0hMgl6zZqXvgqeZjjZeRL8UKE9ISoZRm+8TzrOyszF51jw8iXyGRXNmYsKYUQLncLlcrPXwRMCJU+jTww6b3JaLRyLFIA6Hg507d+LkyZPIzMxEhw4dsHLlSr6RoR+9fPkSHh4eiIiIQK1atWBubo5FixahSZMmFX5Mse6Dc3R0RFZWFubOnYsXL14gKioK8+bNw9OnT9GiRQs0aNAAsrKyuHTpEhITExEZGYk5c+bgw4cP5W7mWFmjRo3Cly9fsGTJEsTGxuL69evYuXMn3zGmpqb4999/ERISgsTERHh5efHmfpVFXV0dX758waVLl5CSkoLbt29j7ty5AMCLv27JHJuoqCiBnhkrKysYGBhg3rx5uHfvHmJjY+Hm5oaYmBiMGzeuuqpfYQoKCjAzNsLVGzf5eumuhIRCma0Eo1KrUgBAR0sTWk2b4EpIKF/5lZBQ6Gpr8ZIbK0sz3Ai7w/e8Xg65ARkZGXQ0q/iQa9qnT1i6bhPuPXzMK/uQloaLf1+DnY11uclrZSgoyMOsvRGu3rjN3xbXb5a0haHAOTqaTaHVRANXrt/kK79y/SZ0tTUrlejZdLLEi5exvEntAPDp82c8inyGDu2NfqFGv4aTl4cP/96FZv9+fOWag/ojPyMD6Y8eC5yTGVP8JVN6pV8DS3MAxdsfFObk4uOdu2g6oB9q/fDF0ajLH5BTUsSHO3eruyqVwsnLw7t/70B7AH+9dQYNQF5GBj4+FKz355hXACCw0q9hyUqxrDcJvDJ51XpQ1muO96USTiI6jRs2RLvWrfj+rDtaICcnF7d+WD2a/ukTHjx6AusfVviVZm1pidi4N3ybe756HYfYuDewsiw+r7CwEP9zmY9nz1/Ac8MaoYkUAGzd5YuAE6cw3nEEPNevFq9EiqEJ6N7e3jh27BjWrl2L48ePg8ViYfLkyULzgk+fPmHChAlQVFREQEAA9uzZg0+fPmHSpEnIy8ur8GOKdc+UlpYWAgICsGXLFjg6OkJGRgbGxsbw9/dH/frFE1A3btyIHTt24PDhw2jYsCG6du2K8ePH49q1a5UaxitP48aN4e/vj/Xr12Pw4MHQ0NDAtGnT+FbEjR8/HomJiZg/fz5YLBb69u2L8ePH49Gjsnec7t27N/777z+4u7sjOzsbTZs2hYODA65du4anT59i1KhRaNmyJWxsbDBnzhzMnTuXb+6TrKws9u/fD3d3d8ycORP5+flo06YNDhw4AGNj42qpe2VNmzAWE2bOxeylKzG0f188jvwPew8fg+ufU1G7tgKyc3LwKu4NtJs2hZpqcV2mTxiLxWs3op6KMmz/sELIrTBcunYdnmu/D+9OGjMKF69ewySXBZgwajjeJCRiq+9fGGE/ABqNG1U4vraGBjA1aodVm7ZiwYxpkJGVwTbfvyAjI4MZE8dXb1uMH4MJs+dj9jI3DO3fp7gtjhyH6/TJqK3wrS3iod20yQ9t4YTF6zahnrIybP/ojJBb/+LStRvwXLOi/AcrZezwoQi8eBlTXJfAZYoz6tatA+/9h8BisTBpdM1tjQAAzzd7ouvZk+i8fw9eHz6KBhbmMJz5JyJWrUHR16+QZStBxcAA2XFvkJeWhpRLV5D24CE67vbGs42bkBXzCmpmpmg9zwXJl67wErCnq9ej2/lAdDlxBFE7vVG7YUO0X7UcaQ8eIuXSlRqtozCRHp7oEXQKXfz/wqtDR9DI0hxtZv2JRyuL6y1XUu+sknonBV/GhwfhsPbbhYgNHvgc8xINzUzRznUuEoMvI+2HxFO15IdJhpCePVJzzE1NYNHBFPOXr8L8WX+inooKdvjtBZuthJFD7XnHvXodh/z8fLQ2LN7Wpm9PO/ju98fkWXN5w3Zbdvqgpb4eene3BQAcPnka4Y8jMGKIPTQaN8KTyGd8j23cri1eRMdgj38A2rY2RJ8edoh49h/fMfrNmlVqekC1Y2CYLz8/H/v27cP8+fN5U2Y8PT3xxx9/4OrVq+jXj/8Hzj///IMvX75g48aNUCjZ+8/DwwM2NjZ49OgRb+Haz7C41ZVxEOnx6W25N1+9cRNee/YjLiERjRs2wOihg+Fc8gV+7+FjjP1zDjYsW4Qh/b/v5HzszDnsO3wMqe8/QKuJBqaMGw37Pr347jf8SQQ27fDFi5evoKqigkF9emL2FGeh89cAwNZ+BCxMjbFxxWK+8o9p6diwfSdu3wsHuFxYdjDB3GlTBLYkEMApKv92YW0Regtef/l/b4shg+DsOLy4LR49wdgZc7Fh6QIM6fd9p/ZjZ89j35ETSH3/HlpNmmCK0yjY9+kp9P6TUt/CbqijwH0AwNv3H+Dh7Ydbd+6joLAApkbtsGjm/9CiednDD8Ic1/+1xRY/atqvD9ouXgC2vh6+pL7Fq7/2IbpkX6SGVp1he+EM7k2fhTdHi1exybKVYLRsCTQH9IO8aj1kv4nHm+MnEbPLF5wfhi/rW5ih3bIlqN/BBEVfviD54mU8Wb5K6FysysrjVP2jUat/XxgvXgDlFnrITX2L6D378HynDwCgsXVn9Lp4FmHTZiL2SHG95dhKMFm+BNoD+0NBtR6y3sTj9bETeL6Tv946gwfC5sBfOGvWGZkvX1U5zh/9m8ncEGlLG2vMvRGMrV37Iib0do0+tm/mzy8BI8znzExs9PTCPzdugcPhwLS9ERbPncW3XYHTlD+RnJqKkPOBvLLUt++wbss2hN17ADlZGVhZWmDxvNlo1KABAGD05GkIL2crk+jwf7Hddw+8/9pf5jEHfXfC8mc99+z6Faxp5RXdOPbzg8og03XkL5339OlTODg44PLly3wL3kaNGgUDAwOsWrWK7/ikpCQkJSWhY8fvPcJv376FjY0Ntm/fjt69+T9Xy0LJFKm8nyRTEusXkilJUB3J1O+oOpKp3xGTyRSTfjWZ+u2JMpm6efznB5Whp5tfubd/u8RaaX///TdmzpyJiIgIvjnLs2fPxtevX7F79+6fPrabmxtOnz6N69ev80bBfkash/lqgpmZGYqKyv6SVFVVRUhISA1GRAghhEgABjbf/FJyRYTSc8cUFBTwuWSVbXkOHjyII0eOYPHixRVOpABKphAYGFju3CpheyMRQgghRHTK6nn6mW+9Ufn5+Xw9U3l5eahTp+x9BLlcLrZv3w4fHx9MnToV48ePr9TjSn0ypS1kS39CCCGEVBEDE9A1NIq3vHn//j3f9/v79+95V0opraCgAIsXL8aFCxewYMECTJw4sdKPS90uhBBCCKl+DGyNYGhoCCUlJdy79327iszMTDx//hxmZmZCz1mwYAEuX76MLVu2/FIiBVDPFCGEEEJEoLr27asMeXl5jBkzBps3b4aamhqaNm0KDw8PqKuro0ePHigqKkJ6ejrYbDZq166NwMBABAcHY8GCBbCwsMCHDx949/XtmIqgnilCCCGEVD+GNu2cNWsWhg0bhmXLlmHUqFGQkZHB3r17IS8vj9TUVFhbWyM4uPgSYhcuXAAAbNq0CdbW1nx/346pUFVpawRSabQ1glShrRGkC22NIGVEuDUC5+75Xz63VscB1RiJ6FHPFCGEEEJIFdCcKUIIIYRUv1o1P2eKKZRMEUIIIaT6MbBpJ1MomSKEEEJI9WNgNR9TKJkihBBCSPWjnilCCCGEkCqQop4p6UkbCSGEEEJEgHqmCCGEEFL9aJiPEEIIIaQKaGsEQgghhJAqoJ4pQgghhJAqkKIJ6JRMEUIIIaT6SVHPlPTUlBBCCCFEBFhcLlc6L41Ofl3GO6YjYIYMdeRKFWl9vosKmY6AEf9TbsZ0CIzw5WaK7L45/9365XNrtfmjGiMRPSn9tCCEEEKISEnRMB8lU4QQQgipfrUomSKEEEII+WUsWs1HCCGEEFIFUjTMJz01JYQQQggRAeqZIoQQQkj1o2E+QgghhJAqkKJhPkqmCCGEEFL9qGeKEEIIIaQKaGsEQgghhJAqkKKeKelJGwkhhBBCRIB6pgghhBBS/WgCOiGEEEJIFUjRMB8lU4QQQggRAUqmCCGEEEJ+HfVMEUIIIYRUgRQlU9IzO4wQQgghRASoZ4qIRHZOLjbt8Ma10NvIyc2FiVFbLHWZBf3muuWe9/7jR2zctgth98NRUFAAa0tzLJ07C40bNeQdU1hYCC+/fQi6dAWfPn9GqxYtsGDWdHRo3453TH5BAfYfPo4zwZfx9t17NG7UEAN69cCUcaMhLyfHOy7wwiXsO3wM8UnJaFhfDfZ9e2Oa81jIyVbfWyM7Jwebtu/CtdCbyMnJhYlROyx1nQP95s1+3hZbvRB27wEKCgtgbWmBpa4ufG2Rlp4OT+/dCLt7HxmZWWimrYVJY0ejb8/u/G0RcBRnLgSXtEUjDOjTE1PGO/G1hagx3Q5Myc7JwSZPL1y7EVpc7/ZGWDp/LvT1mpd73vsPH7FxyzaE3buHgoJCWHe0xNIFc9G4USOhx6e+fYf+DiMxfvQozPzfFFFUpVxMP7/i8jr/FaqaTbE88g587R0RE3qb6XCqkfT0TLG4XC6X6SB+Zzt27MCZM2cQEhLC6H3UqIx3Pz1k6tyFePr8BebPmAYlxbrY+dcBpKV/wsVjB1FPRVnoOYWFhRg6fgpyv3zB3OlTUFhYiC27dkOxbl2cDdjHS3DcNm3FmYuX4TpjKpqoq2P/kRN49iIKgQf/QjNtLQDAqk1bcfbiZUx3Hot2rVvhv6gY7PxrP/r2sMX6ZYsAAP7HTmK95w70su2KoQP64lPGZ+zYsw+GLfSxa9M6wQBlfi3BmjrHFU//e475s/6EkqIidu7Zh7T0dFw8caT8thg7Ebm5uZg7Y1pxW+zwgaJiXZw94g85WVnkFxRg+LhJyMzKwsypk9C4YUP8ff0Gjp46A3e35bDv16e4LTZ64OyFS5g+aTzatW6N/6KisdNvL/r27I71K5b8Up1+x3aotF98vkubOssFT589x/w5M4vrvXtPcb1PH0c9FRWh5xQWFmLo6HHF74WZ04vr7bWr+L1w7DDk5Phj43K5cJ42E//eu48ZUydVLZkqKvyl05h+fqv6Ov+fcvlJn6ioaWth1pUzUDdsia1d+9Z4MuXLzRTZfXOTo3/5XFZTg2qMRPSoZ4pUu8eRz3Aj7A78trrDxqoTAMDMuD3sBo/AkdNnMN15nNDzLl+7gaiXr3DhqD9alPyabdWiBfo7jkPw1RAM6tMTqe/e4cTZ81gydxZGDxsMALC2NEcvh9H469ARrFu6EBmfM3EsMAiuf/4Pk5xGAQA6mXcAAHjs9IHrn/+DijIbu/YegJWFGbw2rObF0NbQAP1GjUXYvQewsjSvels8jcSN2//Cb9tm2Fh3Lm4Lk/awGzgMR06exvRJE4S3xT8hiIp5iQvHA9CipAejVcsW6D9iDIL//geD+vbGjVtheBHzEif9/4JRm9YAgM6W5khJfYu//ANg369PcVucPgvXmdMxaezo4rawMCtuC69dcJ05DWqqqlWu588w3Q5MeRzxFDduhcHPyxM2f1gBAMxMjGHX3x5HTpzC9MkThZ53+eq14nqfOooWenoAgFYtW6K/wygE/30Vg0rV6cjJ03j95o1I61Iepp9fcXmdVwaLxULHcY4YulnIDzdJIT0dUzRnilS/23fvo26dOnzJiJpqPZibtMfNf++Wfd69+2imo81LpABAv7ku9HR1eOfdefAIhUVF6NmtC+8YeXl5dLXqhNCSY7JysjFyyCDYdrHiu39dbU0AQGJyCj6mf8LnzCx0+4P/GP3mulCtp4Lrt//9xdqXqtOde8Vt0dGCV6amqgpzU2Pc/PdO2efdLWmLH4aC9Js3g14zXdwMKz5PSVERIwYPQrvWrfjO1dXWRkJyMgAgKzsbI4faw7aLdaljinvwEpNTqlbBCmK6HZhy+87d4np3suSVqampwryDKW6W8xq7fecumunq8BIpANDXa15c79thfMcmJiVj8/adWLN8afVXoIKYfn7F5XVeGU2N2sLRxxN3/Y/ggFPND8vWDFYV/n4vUpVMGRgYwNPTE926dYOVlRVev34NW1tb7Nixg+84JycnLFq0iPf/vXv3onv37mjbti1sbW2xa9culB4d3bNnD2xsbGBkZAQnJye8+YVfieXdR0ZGBtzc3Hi3jxo1CuHh4bzbd+zYAVtbW777CwwMhIHB965SW1tbrF+/Hn379oWlpSXu3i07samK2Dfx0GyqAdlS8460NZsiLiGp3PO+JTyC5yWWHPMGdevWQcP69fmO0dHSxIePacjJzYVWkyZYtWAumuto8x1z9cZNyMnKQldbC8psJcjKyCA5NZXvmM+ZWcjMykZSqfJfVdwWTQTbQksTcfGJZZ8X9wa62toC5T+2RWdLc6xeuhCsH1bMFBQW4sbtMN6Xk1bTJli1aD6a6+rw3c/V66G8tqgJTLcDU2Lj3kBTs6nweicklHue0HpraSEu/vt5HA4Hi1a4oU8PO3Qp6QVmAtPPr7i8zisjPSEJy/WNcWreEuTn5jIdjmiwWL/+95uRqmQKAI4fPw4vLy/s2rULzZv//IM2JCQEvr6+cHNzw99//w1XV1f4+Pjg3LlzvGOSk5Px8OFD7N69GwEBAfjw4QOWLq3cr8Ty7qOoqAjOzs4IDw+Hu7s7zpw5A0NDQ4wfPx6RkZGVepyjR49i2bJl+Ouvv2BqalqpcysqKysbSnUVBcoVFesiJyenzPMys7KhpFj+eVlZ2WArKgkeU7cugOKJ78JcCbmBoEt/w3HYYKgos1Gndm306WGLgJNncOrcRXzOzMLr+ATMXe4GWVkZfPnytUJ1/ZmsrCzhdapbgbZQqit43k/a0N3TC/GJSZhWxlAqAFy5dh1BwZfh6DAUKsrC57JUN3Fsh5pQZr0V6yInu7x6Z0FJ6efvIf/DR5GYnILF81yqJ+BfJI7PLxOv88rI/fQJGWLYY0Z+jdTNmRo0aBDatWv38wNLJCQkQEFBAZqammjSpAmaNGmCRo0aoUmTJrxjZGVl4eHhATabDQAYOXIkPD09KxVXefdx+/Zt/Pfffzh//jxatmwJAFixYgUiIiKwd+9ebNu2rcKPY2Njg86dO1cqtvJwOBxwOBz+Mi6X71ckDxdg1So7f+dyOGAJ6d7lcrm884rvW8i5JT2FtWoJ3nj52g24rlwDcxNjuP45lVfutnAe5OXksGz9Jixd5446tWtjktMofP36FXXq1C4zzrIIbQtOWW3BLb8tuGW1BYTeH5fLxabtO3Ho+ClMGe+E7l1thN7v5X9C4LrcDeamJnCdOe0nNfo1v0M7iELZ9RZy8M/qXUZ7/fheeP0mHtu8feHl4Q42W/AHhqj8Ds9vTbzOSQX8hj1Mv0rqkikdHZ2fH/SDgQMH4vTp0+jZsycMDAxgZWWFHj168CVTDRo04CVBAKCsrIyvXyvXs1HefcTExIDNZvMSKaD4g8bMzAy3bt2q1ONUtv4/s2vvAez86wBfWS/brkhLF+zaz8nNBVvIr9dv2Gw2soX8Gs3N/QJ2ya90tpKS0N6n3C9fim8v1Wu1/8hxbNrhAwtTY3h7rIe8vDzvNsW6dbF+2SIsnTsLKW/foamGOurWqYPT54Nh2bQJKmvXnn3YuWcfX1kvu25ISxDSFl++10kYNlupjLbIBVuJv455eXlYtGotgq9ew6SxozFvhvAvj/0BR7HJaxcsOpjAe4s7X1tUJ3FvB1HZ5fcXdu7+i6+sV3dbpMWnCxybk/tFIP4fsdlKyBbSc5Vbcl5RUREWrXBD7+52sOpogcLC7yvwOBwuCgsLBYbcqou4P7819TonFUHJlMSqXVuwx6H0/KeCggLev9XU1BAUFITHjx8jLCwMt2/fxr59+zBz5kzMmDEDACAjI1PluMq7D24ZPT0cDofvA7N0PX78gP1GWP2rYrj9QHS15u/p+if0Fm7fvQ8Oh4NaP/wqTUhKhl4z3TLvq5mOFl5EvxQoT0hKhlHJ5NPmOtrIzslB+qcMqKnW4x0Tn5iEphrqqF1bAUBxW6zdsh0BJwPRp3s3bFq5VOBD9frtf6HMZqND+3a8Se9p6Z+Q+u49Whu0RGUNHzIIXUtNaP/nxk3cvntPsC0Sk6DXrOyl2M10tMtuizbfJ+JmZWdj8qx5eBL5DIvmzMSEMaMEzuFyuVjr4YmAE6fQp4cdNrktF+kXjLi2g6gNHzIYXf/gnwD9z41Q3L5TRr3L2X+pma4OXkQJLitPSEyEUds2SH33DhGRzxAR+QxnLwTzHeO9Zy+89+zFtYtnodmk8j8KfkZcn9+afp2TCpCinimpmzNVmpycHLKysnj/53A4SEr6Pkk6KCgIR48eRYcOHTBr1iycOHECDg4OCA4OFnZ3ImFgYIDMzEzExMTwlT98+BD6+voAiuuRnZ3Nl1DFx8eLPLbGDRugXStDvj9rS3Pk5Obi1t37vOPSP2XgweMIWJez3YC1pTli38Tj1es3vLJXr98g9k08b2Vg55LlzpdDbvCOyc/Px42wO3z3vdXbDwEnAzF+1HB4rl0l9EP1WGAQNnl585X5HzsJmVq10M268kOhjRs2RLvWrfj+rDtaICcnF7fu3OMdl/7pEx48egLrH1Y+lWZtaYnYuDd49TqOV/bqdRxi497AyrL4vMLCQvzPZT6ePX8Bzw1rykwgtu7yRcCJUxjvOAKe61eL/AtGXNtB1Bo3aoh2bVrz/Vl3tEROTg5u/bCKNT39Ex48fATrjpZl3pd1x5J6x77mlb2KfV1c746WaNSwIU4FHBD4A4DhQ+xxKuAAGjVsWMa9V7GeYvr81vTrnFSE9Kzmk7qeqdJMTU0RHByMnj17okGDBti/fz9fcpWXlwd3d3coKirCzMwMb9++xf3792FuXvU9iCrKysoKBgYGmDdvHpYtW4YGDRogICAAMTExWLlyJa8enp6e8PPzQ79+/fD48WMEBgbWWIw/MjcxhoWpCeavXIP5M6ahnooyduzZD7aSEkYOGcQ77tXrN8gvyOf1AvXtbgvfAwGY7DIf86YXz23a4r0bLfWao7ddVwBAUw11DO7XGxu27UReXh50tbWw/8hxZGZlY2LJh+yLmJfYc+gI2rYyRB+7boh49pwvPv1mulBSUoTT8KGYONsV67Z6wbaLFe6GP8Ju/wBMGTsaWr8wzCe0LUxNYNHBFPOXr8L8WX+inooKdvjtBZuthJFD7X9oizjk5+ejtWHx6su+Pe3gu98fk2fN5Q1nbNnpg5b6eujdvXjV5uGTpxH+OAIjhthDo3EjPIl8xvfYxu3a4kV0DPb4B6Bta0P06WGHiGf/lWqLZkInOlc3ptuBKeYdTGFh1gHzl67A/NkzUK+eCnb47gGbzcZIhyG8417FvkZ+QcH3evfqAd99BzB5xhzMm/UnAGCL167ievewg6ysLNqV7LlUWqOGDcq8TVSYfn7F5XVOSpGinimpT6ZcXFzw+fNnTJ48GXXq1IGDgwP69u3L6+EZPnw4Pn/+DG9vb6SmpkJFRQW9evWCq6trjcUoKyuL/fv3w93dHTNnzkR+fj7atGmDAwcOwNjYGABgYWEBFxcXBAQEYNeuXTA3N8fChQuxcOHCGovzRzvd12Ljtp3YtMMbHA4XpkZtsW29G1SUv88Lc/PYiuTUtwg5ewJA8X5R+3dswbqtXli+0QNysrKwsjDHYpcZfMOZqxe5QpnNxp6DR5D75QvaGLbE/h1boKNVvK3C39dvgsvl4tmLKIyYJDiv4qD3dlh2MIF1RwtsWb0CPvsP4sTZ82ii0RjL5s2G0/Ch1dsWHuux0dMLm7bvAofDgWl7I2zbsIZvhZHbxs1ITk1FyPnA722xazvWbdmG5es3QU5WBlaWFlg8bzavLf4u6Z07HngWxwPPCjxudPi/+Pt6aHFbPI/CiAmCe9kc9N0JSzPRrOosjcl2YNLOLe7YuGUbNm3bAQ6XA9P27bFt0wb+em/YhOSUVIQEBwEoqbfPTqzz2ILlazcUvxc6WmKxq4vI5kJVFb3OiTSjy8mQyqvA5WQkUjVdXoT8JqT1+f7Fy8n87pi6nAzTRHo5mQ+/PtWE1bB6F0uJmpR+WhBCCCFEtGiYj1SDx48fw9nZudxjunfvDg8PjxqKiBBCCKkZQvcek1CUTIlQ69atcfbs2XKPqVtXcPdfQggh5LdHyRSpDgoKCtW+SSYhhBDye2AmmeJwONi5cydOnjyJzMxMdOjQAStXrizz+/jTp09Yu3Ytbt68CQDo3bs3Fi9eXKnODqnfZ4oQQgghksPb2xvHjh3D2rVrcfz4cbBYLEyePBn5+flCj581axYSExNx4MABeHl5ISwsDG5ubpV6TEqmCCGEEFL9WKxf//tF+fn5vKuU2NjYwNDQEJ6ennj37h2uXr0qcPzjx49x//59bNiwAW3atEGnTp2wevVqBAUF4d27iq9cp2SKEEIIIdWPgWQqKioKOTk56NixI69MWVkZrVu3xoMHDwSODw8PR8OGDaGnp8crs7CwAIvFwsOHDyv8uDRnihBCCCEiUPNzpt6+fQsA0NDQ4Ctv1KgRUlNTBY5/9+6dwLHy8vKoV6+e0OPLQskUIYQQQqpfFXqY7Ozsyr392rVrQsu/fPkCAALXZlRQUMDnz5+FHi/sOo4KCgrIy8uraLg0zEcIIYQQEWDgOse1a9cGAIHJ5nl5eahTp47Q44VNTM/Ly6vUaj7qmSKEEEKIWCmr5+lnvg3ZvX//Htra2rzy9+/fw9DQUOB4dXV1/PPPP3xl+fn5yMjIQOPGjSv8uNQzRQghhBARqPmuKUNDQygpKeHevXu8sszMTDx//hxmZmYCx5ubm+Pt27eIj/9+HcFv55qaVvzi2NQzRQghhJDqx8AO6PLy8hgzZgw2b94MNTU1NG3aFB4eHlBXV0ePHj1QVFSE9PR0sNls1K5dG+3bt4epqSlcXFywatUq5ObmYuXKlbC3t6eeKUIIIYQwjIGtEYDiTTiHDRuGZcuWYdSoUZCRkcHevXshLy+P1NRUWFtbIzg4uCREFnbu3AlNTU2MGzcOc+bMQZcuXbBq1arKVZXL5XKrFDWRPhkV38hMoshQR65Ukdbnu6iQ6QgY8T/lZkyHwAhfbqbo7jwr7dfPZdevvjhqgJR+WhBCCCFEpKToQsc0zEcIIYQQUgXUM0UIIYSQ6idFPVOUTBFCCCFEBCiZIoQQQgj5dVLUM0Wr+QghhBBCqoAmoBNCCCGEVAElU4QQQgghVUDJFCGEEEJIFVAyRQghhBBSBZRMEUIIIYRUASVThBBCCCFVQMkUIYQQQkgVUDJFCCGEEFIFlEwRQgghhFQBJVOEEEIIIVVAyRQhhBBCSBVQMkUIIYQQUgWUTBFCCCGEVAElU4QQQgghVUDJFCFi6MGDBygsLBQoz8vLw5UrVxiIiBBSXe7fv4+CggKmwyDViJIpIpYSEhLg6+uL2bNnY+LEiZg9ezb8/PyQmJjIdGg1YuzYscjMzBQof/XqFebPn89ARKJXVp3T0tJgb29f8wHVsNDQUDg5OcHa2hrJycnw8vLC2bNnmQ6LiMCsWbMQExPDdBikGskyHQAhpe3YsQO7d++GrKwsNDU1wWaz8f79e1y7dg1eXl743//+hxkzZjAdZrU7cOAA3N3dAQBcLhdWVlZCjzMyMqrJsEQqNDQUkZGRAIp743x9fVG3bl2+Y+Lj45GcnMxEeDUmLCwMM2bMQL9+/RAREQEOh4OioiIsWbIERUVFGDp0KNMhisSbN2/g5uaGhw8fCu2pefHiBQNRiV79+vWRlZXFdBikGlEyRcTKqVOn4OfnB1dXVwwfPpzvizU3NxfHjx/Htm3boK+vj969ezMYafUbM2YM6tWrBw6HgyVLlmDx4sVgs9m821ksFurWrYuOHTsyGGX1atq0KVavXg0ulwsACA4ORq1a3zvMv9V5wYIFTIVYI3bs2IF58+Zh/PjxvGFcFxcXKCsrY//+/RKbTK1cuRIpKSlwdXXle61LOmtra0ydOhU2NjbQ0dGBgoIC3+2S+GNR0rG43z7FCBEDQ4cORa9evTBlypQyj9mzZw9u3bqFgwcP1mBkNevMmTPo168f5OXlmQ6lxtja2uLUqVNQU1NjOpQaZ2JigqCgIGhra8PExATnzp2DlpYWEhMT0b9/f0RERDAdokgYGRnB398fJiYmTIdSo2xtbcu8jcVi4dq1azUYDakO1DNFxMrr169hZ2dX7jHdunXDgQMHaiYghgwePBjJycmIiIhAfn6+wO2SOIcoJCSE6RAYw2az8e7dO2hra/OVv3z5EioqKgxFJXqqqqpQVFRkOowaJ82vdUlFyRQRK1+/foWysnK5x6ioqODTp081FBEzTp8+jRUrVqCoqEjgNhaLJZHJVFpaGjw9PXnzZ0p3mkvyr/UBAwZg3bp1WLduHVgsFnJychAaGoo1a9agb9++TIcnMk5OTti6dSs8PDykapiPSB5KpohY4XK5fHNmhGGxWAJftJLGx8cHQ4YMwcKFC6GkpMR0ODVixYoVCA8Ph729vdR9sc6ZMwdv377lzY0aPHgwuFwuunbtChcXF4ajE53Q0FA8efIElpaWqF+/vsCwtqQm0IaGhmCxWGXeLqkT7yUZJVNE7Lx9+xZ5eXll3p6WllaD0TDj/fv3cHZ2lppECihe0bZr164yVzFKMjk5OWzZsgWzZ8/G8+fPweFwYGBgAD09PaZDEylLS0tYWloyHUaNW79+PV8yVVhYiDdv3uDMmTNYtGgRg5GRX0XJFBE7w4YNK/d2Lpdb7q86SWBoaIj4+Hg0a9aM6VBqTN26daGhocF0GIzgcDjYsWMHGjZsCEdHRwDAkCFD0KNHD0ybNo3h6ERHWletDRkyRGi5oaEhgoKCMHDgwBqOiFQVJVNErEjyCr3KcHZ2hpubGxITE9G8eXOB4Q9zc3OGIhMde3t77N27F6tXr4aMjAzT4dSobdu24eTJk1izZg2vbODAgfDz80OtWrUwdepUBqMTrf/++w979+5FdHQ0ZGVloa+vj3HjxknUfmoVZWpqiuXLlzMdBvkFtDUCIWLI0NCwzNtYLJZEzqlYsGABLl26BDabDW1tbYEEUpIT7a5du2L9+vXo3LkzX3loaCjc3NwkdvVXeHg4JkyYgJYtW8LMzAxFRUV49OgRYmJi4O/vjw4dOjAdYo3y8/PD4cOHERoaynQopJKoZ4qIlQcPHlT4WEnsnflGUifelkdGRgb9+/dnOgxGZGRkCB3i1NHRwcePHxmIqGZs3boVDg4OWLFiBV+5m5sbtm3bhkOHDjEUmWjZ2tryTVXgcrnIyclBZmamRC84kGTUM0XEyrdVLj97WUpq7wyRTiNHjoSpqanATu/bt2/HzZs3cfr0aYYiE6327dvjzJkzaN68OV95bGwshg0bhsePHzMUmWjt2LFDYN6nnJwcTE1NJfpHoiSjnikiVqSxR0aYsWPHlnu7JA55/axXUpK/ZGbOnInJkyfj0aNHMDY2BovFQmRkJJ48eYJdu3YxHZ7IqKqqIi0tTSCZSktLk+jd/2fOnMl0CKSaUTJFxErTpk15//bz88OgQYPQuHFjBiNixo/tAAAFBQVISEhATEwMxo8fz0xQIubk5CTQK8liscBisVCrVi08e/aMwehEy8rKCkePHsXBgwcRFhYGWVlZ6Onp4dSpU+XOn/vddevWDWvWrIGnpydvG4hXr15h3bp16NatG8PRiRZNvJcsNMxHxFaHDh0QGBgIHR0dpkMRG15eXkhLS4ObmxvToVS75ORkvv9/23tn27ZtWLBgATp16sRQZERUPn/+jAkTJuDFixdgs9lgsVjIzMxEy5YtsX//fom9TiNNvJc8lEwRsTVx4kRYW1tjwoQJTIciNhITEzF06FDcv3+f6VBqzIMHD7B27VoEBQUxHYpIPXz4sMxL6UjyfkwcDge3bt3Cy5cvweVy0bJlS1hbW0v09hiOjo4wNDQUOvH+1atXEjvxXpLRMB8RW3Xr1sWmTZvg6+sLXV1dKCgo8N0uifOGfubVq1cSfymd0ho3boy4uDimwxApPz8/bN26FSoqKgIX/mWxWBKdTNWqVQs2NjawsbFhOpQa899//2Ht2rUC5WPGjPnppsVEPFEyRcSWkpKSRF7QtyIWL14sUJaVlYWwsDD07t2bgYhELyUlhe//XC4XWVlZ8PHxkfih3oCAAEybNg2zZ89mOhSRa9WqFW7fvo369etL7TXqpHXivSSjZIqIrQ0bNjAdAmOSkpIEyuTl5TFx4kSJHfYsvfcOUJxQKSoqYsuWLQxFVTM+f/4sNT8c1q9fz7uQtbS+x6V54r2kojlTRKy9ffsWhw8f5q14adGiBUaMGIEmTZowHRqpZsLmgcnJyaFly5YCQ1+SxtnZGUOGDJG6TUvPnj2Lvn37CvTG5Obm4sSJExK7clVaJ95LMkqmiNiKiYnBmDFjULt2bRgZGaGoqAjPnj3Dly9fcPToUbRo0YLpEEUqNzcX586d40sk+/btCyUlJaZDE7n09HTIyspCWVmZ6VBqxMmTJ+Hh4YEhQ4YIvRajJPVapaen4+vXrwAAOzs7nDp1CqqqqnzHvHjxAi4uLnj69CkTIdYIaZx4L8komSJia9KkSahbty42b97M+3LJy8vD/PnzkZeXh927dzMcoeikpqZizJgxSEtLQ7NmzVBUVIT4+HjUr18fR44cgbq6OtMhisThw4fh4+ODtLQ0AECDBg0wceJEie2h+EaarsV49uxZLFq0iLenmLA5U1wuFzY2NhL9HieShZIpIrZMTExw/PhxtGzZkq88KioKY8aMQXh4OEORid7s2bORlpYGLy8vXpf/x48fMXv2bKirq0vkHKKTJ09i9erVGDNmDMzMzMDhcPDgwQMcO3YMK1asoFVOEuTBgwfgcDgYN24cduzYARUVFd5tLBYLdevWRcuWLSEnJ8dglKITHx+PZcuW4dmzZ7xeuh9JUvIsLWgCOhFbioqKyM/PFygXViZpwsLCBOZONGjQAAsXLsTkyZMZjEx09u7di8WLF8PR0ZFX1qNHD+jo6MDf319qk6mUlBSJmyP47dJABw8ehKmpKWRlpeuraNmyZbwfRz8mkuT3JV2vYPJb6dixIzZt2gQvLy/Uq1cPQPF8i82bN6Njx47MBidiMjIyqF27tkC5goKCxCaTKSkpsLa2Fij/448/4O7uzkBENScpKQnu7u6Ijo5GUVERgOKhrvz8fKSnp+P58+cMRygaFhYWiIqKQkxMDDgcDoDv9Y6IiMD69esZjlA0nj59isOHD6Nt27ZMh0KqCSVTRGy5urpi5MiR6NatG3R1dcFisRAXFwdlZWUEBAQwHZ5ImZqawtvbG5s2beINdRQUFMDHxwcmJiYMRycaTZo0wbNnz6Ctrc1X/vTpUzRo0IChqGrG2rVrERcXhz59+mDv3r1wdnZGXFwcrl69itWrVzMdnsgcPHiQlzD9eF1GFosFMzMzJkMTKVVVVYFNiMnvjZIpIrbU1dVx8eJFBAUF8Va8DBs2DAMGDODtUyOpviWSPXr0QNu2bcFisfD06VNkZ2dL7KUmRo4cCTc3N2RkZMDU1BQsFgvh4eHw8vKCk5MT0+GJVHh4OHx8fGBubo6bN2+ie/fuMDIygqenJ0JDQzF8+HCmQxSJgIAATJ06FX/++Se6deuGwMBAZGRkYN68ebCzs2M6PJEZM2YMtm7dis2bN0v8th/SgpIpItYUFRX55tBICz09PQQFBeHIkSOIiYkBl8tF//79MXLkSGhpaTEdnkiMHTsWycnJWL9+PYqKisDlciErK4vhw4dj+vTpTIcnUnl5edDU1AQANG/eHNHR0TAyMoK9vb1EJ5IpKSkYNmwY5OXlYWhoiMjISHTv3h2LFi3Cxo0bJWoVZ+lNaZOTk2FpaYmGDRuiVq1afMdeu3atpsMjVUTJFBErY8eOrfCxkn5tvi9fvqBnz55wdXUFAOzbtw+FhYUMRyU6tWrVwtKlSzF79my8fv0aQHFiIQ37amlpaSEmJgYaGhrQ1dXlrebicDjIyclhODrRUVRU5L2mdXV18erVK3Tv3h16enpITk5mOLrqNXjw4HIvnUN+b5RMEbHStGlT3r/z8vIQHByMVq1awdjYGLKysoiMjERkZCQcHBwYjFL0bt26hT///BPOzs4wMjICAFy6dAk7duzAnj17JHY+yefPnxEfH4+8vDwA/EvEv60Ak0RDhgzBggULsHHjRtjY2MDJyQlNmjRBWFgYDAwMmA5PZMzMzODr64sVK1bA0NAQJ06cwJQpUxAeHi5xw18zZ86s9DkLFy7EggULUL9+fRFERKoT7TNFxNayZcugpKSERYsW8ZVv27YNsbGx2LFjB0ORid7QoUNhY2ODWbNm8ZV7enri3r17OHbsGEORic7Zs2excuVK5Ofno/THkqRtXCnMgQMHoKuri65du2LPnj3w9fWFhoYGPDw80KpVK6bDE4mXL19iwoQJGD9+PEaNGoUBAwYgMzMTX758wcSJEzF37lymQ2SUqakpgoKCJHZoX5JQMkXElomJCc6cOQNdXV2+8jdv3sDe3h5PnjxhJK6aYGxsjPPnzwt8iCYmJmLgwIF4/PgxQ5GJTteuXdGlSxeMHz9e6EqnH3stJc2DBw9gbGwssEllXl4ebty4gV69ejEUmWhlZ2dDVlYWubm5UFNTQ1paGs6fPw91dXX07t2b6fAYZ2JignPnzlEy9Ruo9fNDCGGGsrKy0P11wsPDJb7bW01NTWjdX758KbHXq/v8+TOcnZ3RvHlzNG3aVOBPko0dOxZZWVkC5bGxsZg/fz4DEdWMQYMGITY2lrc5bf369TF+/HhKpMhvh+ZMEbE1YsQIrFixArGxsWjbti24XC4ePnyIw4cPS/QXDFA8WdXNzQ2ZmZkwMjICi8VCZGQktm3bhsGDBzMdnkj07NkToaGhAj2RkurAgQO8zUi5XC6srKyEHvdtzpwkysvLE7o5LSG/GxrmI2Jt9+7dCAgIwIcPHwAAGhoamDx5ssRvl1BUVIR169bh5MmTKCws5G0T4OTkhHnz5knk5Tc+fvyIfv36QV9fH1paWgIrnzZs2MBQZKJRWFiICxcugMPhYMmSJViyZAnf/mnfrlHXsWNHie2N9PHxwfnz5zF69Ghoa2sLJFaSvOigImiY7/dByRT5LXz69AlA8c7B0iQnJwdxcXGQlZWFrq6uwJfN7du3YW5uLhG7Kc+bNw9XrlxBq1athPZWSOpmpQBw5swZ9OvXD/Ly8kyHUqMMDQ3LvE0aFh38DCVTvw9KpohYS05ORkREhNDr0dnb29d8QGJGklb7mJiYYNu2bbCxsWE6FEY8evQIurq6UFNTw9mzZ3Hp0iWYmppiypQpErs/0c/2kpL0uXI/I0nvb0kneWMFRGKcPn0aK1as4F349UcsFouSKUBgC4HfmaKiosB1+aTFsWPH4Obmhn379qF+/fpYvHgxOnXqhP3796OgoAAzZsxgOkSRkPZk6Wck6f0t6ahnioit7t27o1OnTli4cKFU7IL9KyRpGODQoUMIDw/HunXrpO757tOnD8aMGYPRo0dj+/btuHbtGs6dO4ebN29i1apVCAkJYTpEkfjZFQ8k/SoHP/PkyRO0bt1a6oZ/f0fUM0XE1vv37+Hs7Cx1X6zSKiQkBOHh4ejYsSPq168vMMlekq9XlpSUBFtbWwBAWFgYunTpAgDQ19fHx48fmQxNpEr3TBUUFCAhIQExMTESdV2+0uLj47Fs2TI8e/YMX79+Fbj921wxY2PjGo6M/CpKpojYMjQ0RHx8PJo1a8Z0KKQGdOjQAR06dGA6DEbUr18f79+/h5ycHJ49ewYXFxcAQFRUFBo0aMBwdKJT1gpNLy8vpKWl1XA0NWfZsmX4+PEjZs+eDRUVFabDIdWAkikitpydneHm5obExEQ0b95coKtb2pdNSxpJnRdUEf369YOrqyvq1KkDdXV1WFhYIDg4GGvWrMGwYcOYDq/GDR48GEOHDoWbmxvToYjE06dPcfjwYbRt25bpUEg1oWSKiK05c+YAANatWydwGy2blkxRUVGIiYkBh8MBUDwBNz8/HxEREVi/fj3D0YnOvHnzoK6ujsTERIwePRoyMjJIS0vD8OHDf+kCub+7V69eSfTka1VVVYnYzoR8R8kUEVuSPEeGCDp48CAvYWKxWLwvUxaLBTMzMyZDE7latWrBycmJr6z0/yXR4sWLBcqysrIQFhYm0ZeUGTNmDLZu3YrNmzdDUVGR6XBINaBkioitb5NT8/PzkZSUBG1tbXC5XIGLwUqiNWvWYNy4cT/dKmDIkCESM0E/ICAAU6dOxZ9//olu3bohMDAQGRkZmDdvHuzs7JgOr9otXrwYS5cuhZKSktCk4keStvv7N0lJSQJl8vLymDhxIiZMmMBARKJja2vLt19YcnIyLC0t0bBhQ9SqxX+ZXPoh+fuhZIqILS6Xiy1btuDQoUMoKCjAlStX4OnpCQUFBaxevVqik6qzZ89W6Mtk+fLlNRBNzUhJScGwYcMgLy8PQ0NDREZGonv37li0aBE2btwocau7kpKSeMOZwpIKaSDJu9qXNnjwYIndfJVQMkXE2KFDhxAUFISVK1di9erVAIr3nnJzc0P9+vXh6urKcISi07VrVwQEBGDmzJlSMwygqKiIwsJCAICuri5evXqF7t27Q09P76c7Zf+OfkwkpCmpSElJqfCxTZo0EWEkNUsa575JE0qmiNg6fvw4VqxYgR49emDNmjUAgL59+0JeXh7r1q2T6GQqJSUFFy9ehL+/P+rXry8wWVUShwHMzMzg6+uLFStWwNDQECdOnMCUKVMQHh4ukQmltCYVpYe7yiPJi0zOnz8Pc3NzqKurw9vbG8HBwTA1NcXSpUtpcvpviJIpIraSkpLQqlUrgXIDAwOJ3sgQAKysrGBlZcV0GDVqzpw5mDBhAo4ePYpRo0bBx8cHFhYW+PLlCyZOnMh0eNVOWpOKH3c1j46Oxs6dOzF9+nSYmJhATk4OT58+xa5duzB9+nQGoxQtb29v+Pr64sCBA0hNTYWXlxccHBxw7949bN68GUuXLmU6RFJJlEwRsdW0aVM8ffoUmpqafOWhoaEScfmU8kjjnkstWrTAP//8g9zcXCgqKuLkyZM4f/481NXV+VZ2vX79Gtra2gI7pP9upDWpsLCw4P1748aNWLt2LXr06MEra9WqFRo1aoRNmzZh5MiRTIQocqdPn4a7uztMTU3h7u4OY2NjrFmzBuHh4XBxcaFk6jf0e38aEYk2ceJEuLm54d27d+Byubhz5w6OHTuGQ4cO/XT1kySIioqCv78/4uLisH37dvzzzz/Q19eHpaUl06GJTO3atVG7dm0AxbuCC5t0PmzYMAQFBf32CTUlFUBsbCz09fUFyrW1tZGamspARDXj/fv3MDExAQD8+++/vOddQ0MDmZmZTIZGflGtnx9CSM2xs7ODj48P3r17h6FDh2Lu3Lnw9/fH169fsWLFCpw9exYuLi4YNWoU06GK1LNnz+Dg4ICkpCQ8e/YM+fn5ePHiBZydnXH9+nWmw2OUJG7mKK1JhYGBAQ4ePMj3nBYWFmL37t1o164dg5GJlrq6OuLi4pCQkIDo6GjekH54eDjU1dUZjo78CuqZImLFysoKBw4cwM6dO2FlZQUHBwdcu3YNnz9/BpfLRf369ZkOsUZs3rwZzs7OcHFx4f2CXbt2LdhsNnbu3Ilu3boxHCGpTt+SihUrVvDmUUlDUrFgwQJMnDgRt27dQuvWrcHlchEZGYkvX77A39+f6fBEZuTIkZg9ezYUFBRgYGAAExMTHD58GB4eHrTq7zfF4krizzzyW8vPz8e1a9cQFBSE27dvQ0VFBfb29nBwcICuri7T4dUIMzMznDx5Es2aNYOJiQnOnTsHLS0tJCQkYNCgQXj8+DHTITLmx/aQFOHh4Zg4cSIaNmwoNKkwNDRkOkSRSUpKwvHjx/Hy5UsAxcObo0aNQqNGjRiOTLRCQkKQmJiIgQMHQlVVFefOnUNeXh4cHByYDo38AuqZImJHXl4effr0QZ8+fZCeno5z584hKCgI+/btg6mpKYYNG4Y+ffrw5tZIIjk5OWRnZwuUp6SkoE6dOgxERETJzMwMFy9e5EsqBg8eLBVJhaamJubNm1fuMXZ2djh48CDvqgiSwNbWlu//AwcOZCgSUh2oZ4r8Nl6+fInz58/j8uXL+PTpEx48eMB0SCKzfPlyJCYmwtPTE7a2tjh37hzy8/MxZ84ctGvXTqIv+vszktgzVVGSmFRUhKQ959J6+SBJRj1T5LdQVFSEpKQkvH37FhkZGVBWVmY6JJFauHAhJk2ahM6dO4PL5WLIkCHIzs6GoaEhFixYwHR4hCHp6em8S9CQ31fpywcVFhYiMTEROTk56Nu3L0NRkaqgZIqItUePHuH8+fO4dOkScnNz0aNHD2zfvh2dOnViOjSRUlJSwrFjx3Dnzh08f/4cHA4HLVu2xB9//CFwUVRCyO9F2OWDuFwuVq5cCVVVVQYiIlVFyRQRO7GxsTh37hwuXLiAlJQUGBgYYMaMGRgwYABUVFSYDq9GderUSeITx8qytLSU6PlyRDqxWCw4Oztj9OjRcHFxYTocUkmUTBGxYm9vj+joaCgpKaF///4YNmwY2rRpw3RYNc7Q0LDMS43IyclBXV0dgwYNwvTp0yXqSvTCNirV09NDx44decf4+voyGCEhovPx40fk5uYyHQb5BZRMEbGirKwMd3d39OrVS6ov9rl48WJs3boVjo6O6NChAwAgIiICAQEBGDlyJFRUVHDw4EHIy8tj8uTJDEdbPZ49ewZHR0e0b9+eb6PS9evX095aRKLs3LlToCwrKwsXL16UumtySgpKpohY+fF6ZdLs4sWLWLJkCUaMGMEr6969O5o3b44TJ07g6NGjaNGiBTZt2iQxydTmzZsxYcIE2qiUCJCk3lcACAwMFCiTk5PDH3/8gblz5zIQEakqSqYIEUNRUVF8Q1vfdOjQAStXrgQAtG7dWqIuNfLs2TNe3X40atQoHDt2jIGIxI+kJRUVJSMjw3QI1Wrv3r1o1qwZ02GQakTJFCFiSFNTE9evXxe40G9ISAjv2l0JCQlQU1NjIDrRoI1Kf07SkgoA+PLlC2JiYlBQUCBw3UVzc3MAkLg95ZycnODt7Q0jIyOmQyHVhJIpQsTQtGnTsGjRIkRGRsLExAQcDgcRERG4cuUK3NzcEBcXh8WLF6Nnz55Mh1ptunfvji1btsDT05NXFhsbi3Xr1qFr167MBVZDpDGpuHHjBubPn4/s7GyBOrNYLLx48YKhyERLXl4esrL09StJaAd0QsTU9evXsW/fPvz333+QlZWFgYEBpkyZgj/++AMPHjzA7du3MWPGDMjJyTEdarXIzs7GpEmTEBERAS6XCzabzduodP/+/ahXrx7TIYqMtCYV/fv3h5aWFmbPng02my1wu6Tu9L5t2zacOHECgwYNgo6OjsBWH/b29swERn4ZJVOEELGQnZ0NJSUlqdyoVFqTinbt2uHMmTPQ19dnOpQaVd6FqyU5eZZk1M9IiJh6+PAhHj58KHTYZ8aMGQxFJTqDBg2Cl5eXVG5UGh8fj23btkldUqGrq4v09HSmw6hxUVFRTIdAqhklU4SIIT8/P2zduhUqKipQVFTku43FYklkMpWXlye1O5tLa1Ixf/58rFmzBi4uLmjevDnk5eX5bm/SpAlDkRFSOTTMR4gY6tKlC4YOHYrZs2czHUqN8fHxwfnz5zF69Ghoa2sLJFbfJmFLops3b8LDw0Pqkoofh7t+3PaBy+XScBf5rVAyRYgYat++Pc6dOwcdHR2mQ6kx0jyPRFqTivv375d7u4WFRQ1FQkjV0DAfIWKoQ4cOiIyMlKpk6tq1a0yHwBhp3fmfkiUiKahnihAxdPLkSXh4eGDIkCFCh32kben0169fpXY+lSTLz8/H8ePHER0djaKiIr7yyMhI/P333wxGR0jFUTJFiBiSxiGvz58/w8fHh++LlcvloqCgAC9fvsTDhw8ZjlB0pDWpWLVqFQIDA9GmTRtERETAxMQE8fHxSEtLw/jx47Fw4UKmQySkQmiYjxAxJI1Lp1evXo2wsDBYW1sjODgY/fr1Q2xsLJ4/fy7xF39dv359uUmFpPrnn3+wceNG9O3bFz179sSaNWugpaUFFxcXFBQUMB0eIRUm2TvhEfKbS0lJwa1bt/D161ekpaUxHY5I3b59G+7u7ti8eTOaN2+OiRMnIjAwEA4ODnj16hXT4YnUt6Ti6NGj0NTUxJo1a3D9+nXY2dlJdFKRkZEBY2NjAEDLli3x/PlzyMnJYerUqbh+/TqzwRFSCZRMESKG8vPz4eLiAltbW0ydOhUfPnzAypUrMW7cOGRlZTEdnkjk5OSgZcuWAAA9PT1e79yYMWNw7949JkMTOWlNKho0aMD7kaCtrY2YmBgAgKqqKj5+/MhkaIRUCiVThIghHx8fREVFwd/fHwoKCgCAsWPHIjk5GR4eHgxHJxoaGhpITk4GULyJ5bdkqk6dOvj8+TOToYmctCYVNjY2WLlyJaKjo2Fqaorz588jMjIShw8fhrq6OtPhEVJhlEwRIoYuXryI5cuXw9LSkldmYWGBNWvWICQkhMHIRKd3795YsGABwsPD0bFjR5w5cwaXL1+Gl5eXxG8RIa1JhaurK9TV1REeHg47Ozu0aNECDg4OOHToEGbNmsV0eIRUGE1AJ0QMvXv3Dtra2gLlGhoayMzMZCAi0XBxccHy5cuhpqYGS0tLZGVlITU1FQMGDECfPn0wZ84csNlseHl5MR2qSLm6umLhwoUIDw+Ho6MjTpw4AQcHB8jKysLd3Z3p8ESGzWbD29ub938/Pz88f/4cDRo0QKNGjRiMjJDKoa0RCBFDQ4YMwciRIzF8+HCYmJjg3Llz0NLSws6dOxESEoLAwECmQ6wW7dq1Q3BwMLS0tNCqVSvcvn0b9evX592ekZEBNpsNGRkZBqNkhrQkFV+/fsXly5cRGxuLiRMnIiYmBvr6+lBTU2M6NEIqjHqmCBFDM2fOxJw5cxATE4OioiKcOXMGr1+/xt9//w1PT0+mw6s2Ojo6+PPPP9GmTRtwuVysW7eON0estA0bNtRwdDWrdFKRnZ0t0UN8APDx40eMHDkSHz9+RH5+PoYPH459+/YhMjIS/v7+0NfXZzpEQiqEeqYIEVM3b97E7t278fz5c3A4HLRo0QKTJ09Gr169mA6t2jx//hze3t7IzMzEgwcP0L59e8jJyQk99tChQzUcXc0pnVRcuXIF69atk/ikwtXVFdnZ2fD09ETnzp1x7tw5KCsrY+7cuZCRkYGfnx/TIRJSIZRMEULEgq2tLU6fPg1VVVWmQ6lx0ppUWFtbw8/PD61bt+Ybzo6KisLYsWN/eiFkQsQFreYjREydP38eb9++BQB4e3ujf//+WLFiBfLy8hiOTDRCQkKkMpECgLt372LWrFmoU6cOr0xFRQXz58/HkydPmAtMxHJycvjq/KPCwsIajoaQX0fJFCFiyNvbG0uXLkVKSgoeP34MLy8vmJiY4N69e9i8eTPT4ZFqJq1Jhbm5OQ4fPsxXVlBQgF27dsHU1JShqAipPEqmCBFDp0+fhru7O0xNTfH333/D2NgYa9aswbp163D58mWmwyPVTFqTioULF+LChQsYOHAgCgoKsGrVKvTs2RN37tyBq6sr0+ERUmG0mo8QMfT+/XuYmJgAAP7991/06NEDgOTtM0WKLVy4EKNHj8b9+/d5ScXr16+RlZWFgIAApsMTGT09PZw7dw5HjhyBhoYGOBwO+vTpA0dHR2hqajIdHiEVRskUIWJIXV0dcXFxyM/PR3R0NFatWgUACA8Pl/jl8tJImpOKRo0aYc6cOUyHQUiV0Go+QsTQ3r17sXv3bigoKEBNTQ1BQUE4fPgwPDw8MHPmTEycOJHpEAmpsoyMDPj5+eHly5dCF1YcPHiQgagIqTxKpggRUyEhIUhMTMTAgQOhqqqKc+fOIS8vDw4ODkyHRqqZtCYVkydPxtOnT2FlZSV0s1ZJ36iVSA5Kpgj5TaSnp9MlNiSUtCYVJiYm2L17NywsLJgOhZAqoTlThIihzMxMeHh4YMyYMdDX18fEiRNx79496Orqws/PD1paWkyHSKpReHi4VCYVjRs3hqKiItNhEFJltDUCIWJow4YNuHv3LmRlZRESEoKHDx9i06ZN0NHRwaZNm5gOj1QzaU0qFi5ciNWrV+Pu3btISkpCSkoK3x8hvwvqmSJEDIWGhmLXrl3Q09PDvn37YGVlhQEDBqBly5YYM2YM0+GRavYtqXBxcYGmpiZq1eL/ndukSROGIhO9ly9fYsKECXxlXC4XLBYLL168YCgqQiqHkilCxFBubi40NDQAFO8z9e3Lpk6dOigqKmIyNCIi0phUbNiwAR07dsSIESPK3AGekN8BJVOEiCE9PT3cuHEDGhoaSE1NRZcuXQAAJ06cgJ6eHsPRkeomrUnFu3fvsHfvXpoDSH57lEwRIoZmzZqFmTNnoqCgAP3794euri42bNiAw4cPY9euXUyHR6qZtCYVxsbGiI6Olrp6E8lDWyMQIqY+ffqEd+/ewdDQEAAQEREBJSUl6pmSQOPGjYOTkxO6d+/OdCg16uLFi1i3bh0GDx4MXV1dyMnJ8d1ub2/PTGCEVBIlU4T8ZlJSUiR6QrI0ktak4tsPBWEkea4YkTyUTBEihpKSkuDu7o7o6GjehHMul4v8/Hykp6fj+fPnDEdIqhMlFYT83mjOFCFiaO3atYiLi0OfPn2wd+9eODs7Iy4uDlevXsXq1auZDo9Us6ioKKZDIIRUASVThIih8PBw+Pj4wNzcHDdv3kT37t1hZGQET09PhIaGYvjw4UyHSAghpATtgE6IGMrLy4OmpiYAoHnz5oiOjgZQPHcmIiKCydAIIYSUQskUIWJIS0sLMTExAABdXV3enBkOh4OcnBwmQyOEEFIKDfMRIoaGDBmCBQsWYOPGjbCxsYGTkxOaNGmCsLAwGBgYMB0eIYSQH1AyRYgYmjRpEmRlZcFisWBkZIQZM2bAx8cHGhoa8PDwYDo8QgghP6CtEQghhBBCqoB6pggRU0+ePMGhQ4cQExMDGRkZtGnTBuPHj0eLFi2YDo0QQsgPaAI6IWIoJCQEjo6OSE5ORufOnWFubo6YmBgMGTIE4eHhTIdHCCHkBzTMR4gYGjBgALp27Yp58+bxlbu7u+PRo0c4fvw4Q5ERQggpjXqmCBFDCQkJGDp0qED5iBEjaLdsQggRM5RMESKG2rRpgzt37giUP3v2DHp6egxERAghpCw0AZ0QMTRw4EB4eHggLi4OFhYWkJWVRWRkJPz9/TFixAicPXuWd6y9vT1jcRJCCKE5U4SIJUNDwwodx2KxeLujE0IIYQYlU4QQQgghVUBzpgghhBBCqoCSKUIIIYSQKqBkihBCCCGkCiiZIoQQQgipAkqmCCGEEEKqgJIpQgghhJAqoGSKEEIIIaQKKJkihBBCCKmC/wNGWEQ4bwcHLgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create correlation heatmap\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(df2.corr(method='pearson'), annot=True, cmap='Reds')\n",
    "plt.title('Correlation heatmap',\n",
    "          fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Response:** `mean_duration` and `mean_distance` are both highly correlated with the target variable of `fare_amount` They're also both correlated with each other, with a Pearson correlation of 0.87.\n",
    "\n",
    "Recall that highly correlated predictor variables can be bad for linear regression models when you want to be able to draw statistical inferences about the data from the model. However, correlated predictor variables can still be used to create an accurate predictor if the prediction itself is more important than using the model as a tool to learn about your data.\n",
    "\n",
    "This model will predict `fare_amount`, which will be used as a predictor variable in machine learning models. Therefore, try modeling with both variables even though they are correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lgPul2DiY6T4"
   },
   "source": [
    "<img src=\"images/Construct.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Construct**\n",
    "\n",
    "After analysis and deriving variables with close relationships, it is time to begin constructing the model. Consider the questions in the PACE Strategy Document to reflect on the Construct stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "07UJJm41ajgf"
   },
   "source": [
    "### **Task 8a. Split Data Into Outcome Variable and Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 288
    },
    "id": "AzcDgLRET4d7",
    "outputId": "17e373f7-1716-4dfa-d76b-b63818f1bfa0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   VendorID         22699 non-null  int64  \n",
      " 1   passenger_count  22699 non-null  int64  \n",
      " 2   fare_amount      22699 non-null  float64\n",
      " 3   mean_distance    22699 non-null  float64\n",
      " 4   mean_duration    22699 non-null  float64\n",
      " 5   rush_hour        22699 non-null  int64  \n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your X and y variables. X represents the features and y represents the outcome (target) variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.881429</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
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       "      <td>4.435000</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID  passenger_count  mean_distance  mean_duration  rush_hour\n",
       "0         2                6       3.521667      22.847222          0\n",
       "1         1                1       3.108889      24.470370          0\n",
       "2         1                1       0.881429       7.250000          1\n",
       "3         2                1       3.700000      30.250000          0\n",
       "4         2                1       4.435000      14.616667          0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the target column from the features\n",
    "X = df2.drop(columns=['fare_amount'])\n",
    "\n",
    "# Set y variable\n",
    "y = df2[['fare_amount']]\n",
    "\n",
    "# Display first few rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o3ArC_5xa7Oi"
   },
   "source": [
    "### **Task 8b. Pre-process Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dummy encode categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "QvDpwcQm0f35",
    "outputId": "308b7689-9ff9-4347-9a2a-4050eb7432e8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>passenger_count</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "      <th>VendorID_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  mean_distance  mean_duration  rush_hour  VendorID_2\n",
       "0                6       3.521667      22.847222          0           1\n",
       "1                1       3.108889      24.470370          0           0\n",
       "2                1       0.881429       7.250000          1           0\n",
       "3                1       3.700000      30.250000          0           1\n",
       "4                1       4.435000      14.616667          0           1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert VendorID to string\n",
    "X['VendorID'] = X['VendorID'].astype(str)\n",
    "\n",
    "# Get dummies\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iDYyjWssbnBG"
   },
   "source": [
    "### Split Data into Training and Test Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jo706RzDN2ql"
   },
   "source": [
    "Create training and testing sets. The test set should contain 20% of the total samples. Set `random_state=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "A2BNUvacwaZY"
   },
   "outputs": [],
   "source": [
    "# Create training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jI4w3gXP5bhl"
   },
   "source": [
    "### Standardize the data\n",
    "\n",
    "Use `StandardScaler()`, `fit()`, and `transform()` to standardize the `X_train` variables. Assign the results to a variable called `X_train_scaled`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4Y3T2poF28fP",
    "outputId": "e22f38e7-42af-4e96-ec5b-e2fe64935b90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train scaled: [[-0.50301524  0.8694684   0.17616665 -0.64893329  0.89286563]\n",
      " [-0.50301524 -0.60011281 -0.69829589  1.54099045  0.89286563]\n",
      " [ 0.27331093 -0.47829156 -0.57301906 -0.64893329 -1.11998936]\n",
      " ...\n",
      " [-0.50301524 -0.45121122 -0.6788917  -0.64893329 -1.11998936]\n",
      " [-0.50301524 -0.58944763 -0.85743597  1.54099045 -1.11998936]\n",
      " [ 1.82596329  0.83673851  1.13212101 -0.64893329  0.89286563]]\n"
     ]
    }
   ],
   "source": [
    "# Standardize the X variables\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "print('X_train scaled:', X_train_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wk0rjKeO3JLv"
   },
   "source": [
    "### Fit the model\n",
    "\n",
    "Instantiate your model and fit it to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SClNm5hWotj6",
    "outputId": "8df935cb-95ae-4fd8-8c88-50931f492141"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit your model to the training data\n",
    "lr=LinearRegression()\n",
    "lr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMj6QkK1cLmS"
   },
   "source": [
    "### **Task 8c. Evaluate Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AromLx7t5hjt"
   },
   "source": [
    "### Train data\n",
    "\n",
    "Evaluate your model performance by calculating the residual sum of squares and the explained variance score (R^2). Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "33rE1x9e3U6t",
    "outputId": "7cd2e70f-38c7-415a-b31b-927c2b59a3ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination: 0.8398434585044773\n",
      "R^2: 0.8398434585044773\n",
      "MAE: 2.186666416775414\n",
      "MSE: 17.88973296349268\n",
      "RMSE: 4.229625629236313\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model performance on the training data\n",
    "r_sq = lr.score(X_train_scaled, y_train)\n",
    "print('Coefficient of determination:', r_sq)\n",
    "y_pred_train = lr.predict(X_train_scaled)\n",
    "print('R^2:', r2_score(y_train, y_pred_train))\n",
    "print('MAE:', mean_absolute_error(y_train, y_pred_train))\n",
    "print('MSE:', mean_squared_error(y_train, y_pred_train))\n",
    "print('RMSE:',np.sqrt(mean_squared_error(y_train, y_pred_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test data\n",
    "\n",
    "Calculate the same metrics on the test data. Remember to scale the `X_test` data using the scaler that was fit to the training data. Do not refit the scaler to the testing data, just transform it. Call the results `X_test_scaled`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the X_test data\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sCyrnw_nyAcK",
    "outputId": "ef5b979a-6598-446b-def9-9463aa0e44dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination: 0.8682583641795454\n",
      "R^2: 0.8682583641795454\n",
      "MAE: 2.1336549840593864\n",
      "MSE: 14.326454156998945\n",
      "RMSE: 3.785030271609323\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model performance on the testing data\n",
    "r_sq_test = lr.score(X_test_scaled, y_test)\n",
    "print('Coefficient of determination:', r_sq_test)\n",
    "y_pred_test = lr.predict(X_test_scaled)\n",
    "print('R^2:', r2_score(y_test, y_pred_test))\n",
    "print('MAE:', mean_absolute_error(y_test,y_pred_test))\n",
    "print('MSE:', mean_squared_error(y_test, y_pred_test))\n",
    "print('RMSE:',np.sqrt(mean_squared_error(y_test, y_pred_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vB3DOXfIySe3"
   },
   "source": [
    "**Note:** The model performance is high on both training and test sets, suggesting that there is little bias in the model and that the model is not overfit. In fact, the test scores were even better than the training scores.\n",
    "\n",
    "For the test data, an R<sup>2</sup> of 0.868 means that 86.8% of the variance in the `fare_amount` variable is described by the model.\n",
    "\n",
    "The mean absolute error is informative here because, for the purposes of the model, an error of two is not more than twice as bad as an error of one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cp7ojoBldEYy"
   },
   "source": [
    "<img src=\"images/Execute.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Execute**\n",
    "\n",
    "Consider the questions in your PACE Strategy Document to reflect on the Execute stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F_l3bkxQdJ3a"
   },
   "source": [
    "### **Task 9a. Results**\n",
    "\n",
    "Use the code cell below to get `actual`,`predicted`, and `residual` for the testing set, and store them as columns in a `results` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "cSl5gbXfBPBN",
    "outputId": "ca3ca3db-4f77-402f-9e98-14986d4fbf2c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>actual</th>\n",
       "      <th>predicted</th>\n",
       "      <th>residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5818</th>\n",
       "      <td>14.0</td>\n",
       "      <td>12.356503</td>\n",
       "      <td>1.643497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18134</th>\n",
       "      <td>28.0</td>\n",
       "      <td>16.314595</td>\n",
       "      <td>11.685405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4655</th>\n",
       "      <td>5.5</td>\n",
       "      <td>6.726789</td>\n",
       "      <td>-1.226789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7378</th>\n",
       "      <td>15.5</td>\n",
       "      <td>16.227206</td>\n",
       "      <td>-0.727206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13914</th>\n",
       "      <td>9.5</td>\n",
       "      <td>10.536408</td>\n",
       "      <td>-1.036408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual  predicted   residual\n",
       "5818     14.0  12.356503   1.643497\n",
       "18134    28.0  16.314595  11.685405\n",
       "4655      5.5   6.726789  -1.226789\n",
       "7378     15.5  16.227206  -0.727206\n",
       "13914     9.5  10.536408  -1.036408"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a `results` dataframe\n",
    "results = pd.DataFrame(data={'actual': y_test['fare_amount'],\n",
    "                             'predicted': y_pred_test.ravel()})\n",
    "results['residual'] = results['actual'] - results['predicted']\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mwRmSDS3eyeH"
   },
   "source": [
    "### **Task 9b. Visualize model results**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y3vQ-mB51dfd"
   },
   "source": [
    "Create a scatterplot to visualize `actual` vs. `predicted`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 887
    },
    "id": "IBFU_dicBjwQ",
    "outputId": "7216a9e5-2f17-4352-a0a9-809132756acf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot to visualize `predicted` over `actual`\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "sns.set(style='whitegrid')\n",
    "sns.scatterplot(x='actual',\n",
    "                y='predicted',\n",
    "                data=results,\n",
    "                s=20,\n",
    "                alpha=0.5,\n",
    "                ax=ax\n",
    ")\n",
    "# Draw an x=y line to show what the results would be if the model were perfect\n",
    "plt.plot([0,60], [0,60], c='red', linewidth=2)\n",
    "plt.title('Actual vs. predicted');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IbO71S_R9IcY"
   },
   "source": [
    "Visualize the distribution of the `residuals` using a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "3a0UYoEr9Nx6",
    "outputId": "4a3b91b1-7f30-43d6-8f2f-24e12217a866"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of the `residuals`\n",
    "sns.histplot(results['residual'], bins=np.arange(-15,15.5,0.5))\n",
    "plt.title('Distribution of the residuals')\n",
    "plt.xlabel('residual value')\n",
    "plt.ylabel('count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.01544262152868049"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['residual'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exemplar note:** The distribution of the residuals is approximately normal and has a mean of -0.015. The residuals represent the variance in the outcome variable that is not explained by the model. A normal distribution around zero is good, as it demonstrates that the model's errors are evenly distributed and unbiased."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCnELck-9h5M"
   },
   "source": [
    "Create a scatterplot of `residuals` over `predicted`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "7Kmr2U8A95fY",
    "outputId": "da0e2714-e9be-4d19-8a5e-11d16a437ce2",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot of `residuals` over `predicted`\n",
    "\n",
    "sns.scatterplot(x='predicted', y='residual', data=results)\n",
    "plt.axhline(0, c='red')\n",
    "plt.title('Scatterplot of residuals over predicted values')\n",
    "plt.xlabel('predicted value')\n",
    "plt.ylabel('residual value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The model's residuals are evenly distributed above and below zero, with the exception of the sloping lines from the upper-left corner to the lower-right corner, which you know are the imputed maximum of $62.50 and the flat rate of $52 for JFK airport trips."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Task 9c. Coefficients**\n",
    "\n",
    "Use the `coef_` attribute to get the model's coefficients. The coefficients are output in the order of the features that were used to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>passenger_count</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "      <th>VendorID_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.030825</td>\n",
       "      <td>7.133867</td>\n",
       "      <td>2.812115</td>\n",
       "      <td>0.110233</td>\n",
       "      <td>-0.054373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  mean_distance  mean_duration  rush_hour  VendorID_2\n",
       "0         0.030825       7.133867       2.812115   0.110233   -0.054373"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get model coefficients\n",
    "coefficients = pd.DataFrame(lr.coef_, columns=X.columns)\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "The coefficients reveal that `mean_distance` was the feature with the greatest weight in the model's final prediction. Be careful here! A common misinterpretation is that for every mile traveled, the fare amount increases by a mean of $7.13. This is incorrect. Remember, the data used to train the model was standardized with `StandardScaler()`. As such, the units are no longer miles. In other words, you cannot say \"for every mile traveled...\", as stated above. The correct interpretation of this coefficient is: controlling for other variables, *for every +1 change in standard deviation*, the fare amount increases by a mean of $7.13. \n",
    "</p>\n",
    "\n",
    "Note also that because some highly correlated features were not removed, the confidence interval of this assessment is wider.\n",
    "\n",
    "So, translate this back to miles instead of standard deviation (i.e., unscale the data). \n",
    "\n",
    "1. Calculate the standard deviation of `mean_distance` in the `X_train` data.\n",
    "\n",
    "2. Divide the coefficient (7.133867) by the result to yield a more intuitive interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.574812975256415\n",
      "1.9955916713344426\n"
     ]
    }
   ],
   "source": [
    "# 1. Calculate SD of `mean_distance` in X_train data\n",
    "print(X_train['mean_distance'].std())\n",
    "\n",
    "# 2. Divide the model coefficient by the standard deviation\n",
    "print(7.133867 / X_train['mean_distance'].std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can make a more intuitive interpretation: for every 3.57 miles traveled, the fare increased by a mean of $7.13. Or, reduced: for every 1 mile traveled, the fare increased by a mean of $2.00."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P6AlDDyhdzmG"
   },
   "source": [
    "### **Task 9d. Conclusion**\n",
    "\n",
    "**Responses:**\n",
    "**What are the key takeaways from this notebook?**\n",
    "\n",
    "* Multiple linear regression is a powerful tool to estimate a dependent continous variable from several independent variables.\n",
    "* Exploratory data analysis is useful for selecting both numeric and categorical features for multiple linear regression.\n",
    "* Fitting multiple linear regression models may require trial and error to select variables that fit an accurate model while maintaining model assumptions (or not, depending on your use case).\n",
    "\n",
    "**What results can be presented from this notebook?**\n",
    "\n",
    "*  We can discuss meeting linear regression assumptions, and we can present the MAE and RMSE scores obtained from the model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More work must be done to prepare the predictions to be used as inputs into the model for the upcoming tasks. This work will be broken into the following steps:\n",
    "\n",
    "1. Get the model's predictions on the full dataset.\n",
    "\n",
    "2. Impute the constant fare rate of \\$52 for all trips with rate codes of `2`.\n",
    "\n",
    "3. Check the model's performance on the full dataset.\n",
    "\n",
    "4. Save the final predictions and `mean_duration` and `mean_distance` columns for downstream use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Predict on full dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled = scaler.transform(X)\n",
    "y_preds_full = lr.predict(X_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Impute ratecode 2 fare\n",
    "\n",
    "The data dictionary says that the `RatecodeID` column captures the following information:\n",
    "\n",
    "1 = standard rate  \n",
    "2 = JFK (airport)  \n",
    "3 = Newark (airport)  \n",
    "4 = Nassau or Westchester  \n",
    "5 = Negotiated fare  \n",
    "6 = Group ride  \n",
    "\n",
    "This means that some fares don't need to be predicted. They can simply be imputed based on their rate code. Specifically, all rate codes of `2` can be imputed with \\$52, as this is a flat rate for JFK airport.\n",
    "\n",
    "The other rate codes have some variation (not shown here, but feel free to check for yourself). They are not a fixed rate, so these fares will remain untouched.\n",
    "\n",
    "Impute `52` at all predictions where `RatecodeID` is `2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>y_preds_full</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     RatecodeID  y_preds_full\n",
       "11            2          52.0\n",
       "110           2          52.0\n",
       "161           2          52.0\n",
       "247           2          52.0\n",
       "379           2          52.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new df containing just the RatecodeID col from the whole dataset\n",
    "final_preds = df[['RatecodeID']].copy()\n",
    "\n",
    "# Add a column containing all the predictions\n",
    "final_preds['y_preds_full'] = y_preds_full\n",
    "\n",
    "# Impute a prediction of 52 at all rows where RatecodeID == 2\n",
    "final_preds.loc[final_preds['RatecodeID']==2, 'y_preds_full'] = 52\n",
    "\n",
    "# Check that it worked\n",
    "final_preds[final_preds['RatecodeID']==2].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check performance on full dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.8910853978683975\n",
      "MAE: 1.992506252269974\n",
      "MSE: 12.101575504689935\n",
      "RMSE: 3.4787318816905013\n"
     ]
    }
   ],
   "source": [
    "final_preds = final_preds['y_preds_full']\n",
    "print('R^2:', r2_score(y, final_preds))\n",
    "print('MAE:', mean_absolute_error(y, final_preds))\n",
    "print('MSE:', mean_squared_error(y, final_preds))\n",
    "print('RMSE:',np.sqrt(mean_squared_error(y, final_preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save final predictions with `mean_duration` and `mean_distance` columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>mean_duration</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>predicted_fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.847222</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>16.434245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.470370</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>16.052218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.250000</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.053706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.250000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>18.731650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.616667</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>15.845642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_duration  mean_distance  predicted_fare\n",
       "0      22.847222       3.521667       16.434245\n",
       "1      24.470370       3.108889       16.052218\n",
       "2       7.250000       0.881429        7.053706\n",
       "3      30.250000       3.700000       18.731650\n",
       "4      14.616667       4.435000       15.845642"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine means columns with predictions column\n",
    "nyc_preds_means = df[['mean_duration', 'mean_distance']].copy()\n",
    "nyc_preds_means['predicted_fare'] = final_preds\n",
    "\n",
    "nyc_preds_means.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save as a csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOTES\n",
    "\n",
    "This notebook was designed for teaching purposes. As such, there are some things to note that differ from best practice or from how tasks are typically performed.\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "1. When the `mean_distance` and `mean_duration` columns were computed, the means were calculated from the entire dataset. These same columns were then used to train a model that was used to predict on a test set. A test set is supposed to represent entirely new data that the model has not seen before, but in this case, some of its predictor variables were derived using data that *was* in the test set.</br></br>\n",
    "This is known as <u>dataleakage</u>. Data leakage is when information from your training data contaminates the test data. If your model has unexpectedly high scores, there is a good chance that there was some data leakage.\n",
    "<p>\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "To avoid data leakage in this modeling process, it would be best to compute the means using only the training set and then copy those into the test set, thus preventing values from the test set from being included in the computation of the means. This would have created some problems because it's very likely that some combinations of pickup-dropoff locations would only appear in the test data (not the train data). This means that there would be NaNs in the test data, and further steps would be required to address this.\n",
    "</p>\n",
    "\n",
    "In this case, the data leakage improved the R<sup>2</sup> score by ~0.03.\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "2. Imputing the fare amount for `RatecodeID 2` after training the model and then calculating model performance metrics on the post-imputed data is not best practice. It would be better to separate the rides that did *not* have rate codes of 2, train the model on that data specifically, and then add the `RatecodeID 2` data (and its imputed rates) *after*. This would prevent training the model on data that you don't need a model for, and would likely result in a better final model. However, the steps were combined for simplicity.\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "3. Models that predict values to be used in another downstream model are common in data science workflows. When models are deployed, the data cleaning, imputations, splits, predictions, etc. are done using modeling pipelines. Pandas was used here to granularize and explain the concepts of certain steps, but this process would be streamlined by machine learning engineers. The ideas are the same, but the implementation would differ. Once a modeling workflow has been validated, the entire process can be automated, often with no need for pandas and no need to examine outputs at each step. This entire process would be reduced to a page of code.\n",
    "</p>"
   ]
  }
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