Regression__Activity_ Automatidata project lab Mohammed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtNBZFHO3M7n"
   },
   "source": [
    "# **Automatidata project**\n",
    "**Course 5 - Regression Analysis: Simplify complex data relationships**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kaOj1equPMAb"
   },
   "source": [
    "The data consulting firm Automatidata has recently hired you 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",
    "\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. You’ve impressed your Automatidata colleagues with your hard work and attention to detail. The data team believes that you are ready to build the regression model and update the client New York City TLC about your progress.\n",
    "\n",
    "A notebook was structured and prepared to help you in this project. Please complete the following questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgSbVJvomcVa"
   },
   "source": [
    "# Course 5 End-of-course project: Build a multiple linear regression model\n",
    "\n",
    "In this activity, you will build a multiple linear regression model. As you've learned, multiple linear regression helps you estimate the linear relationship between one continuous dependent variable and two or more independent variables. For data science professionals, this is a useful skill because it allows you to consider more than one variable against the variable you're measuring against. This opens the door for much more thorough and flexible analysis to be completed. \n",
    "\n",
    "Completing this activity will help you practice planning out and buidling a multiple linear regression model based on a specific business need. The structure of this activity is designed to emulate the proposals you will likely be assigned in your career as a data professional. Completing this activity will help prepare you for those career moments.\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": [
    "Throughout these project notebooks, you'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."
   ]
  },
  {
   "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\n",
    "Import the packages that you've learned are needed for building linear regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "ccfeg6X6eOVZ"
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "# 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": {
    "id": "TyR3sBUYJBO8"
   },
   "outputs": [],
   "source": [
    "# Load dataset into dataframe \n",
    "df0=pd.read_csv(\"2017_Yellow_Taxi_Trip_Data.csv\") # index_col parameter specified to avoid \"Unnamed: 0\" column when reading in data from 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 your code response:\n",
    "\n",
    "* What are some purposes of EDA before constructing a multiple linear regression model?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDA help us to have a fair idea of the data, clean it, and preprocess it for model development"
   ]
  },
  {
   "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": {
    "id": "T4Ag-sZhWg6K"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22699, 18)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start with `.shape` and `.info()`\n",
    "df0.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<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": [
    "df0.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SWLHv_h_3Hcf"
   },
   "source": [
    "Check for missing data and duplicates using `.isna()` and `.drop_duplicates()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "3QZZIxxi3OV3"
   },
   "outputs": [
    {
     "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing data and duplicates using .isna() and .drop_duplicates()\n",
    "df0.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df0.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WXWAlPTY9iLK"
   },
   "source": [
    "Use `.describe()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "2O3b9H9B9nwk"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .describe()\n",
    "df0.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9FRKZtHm3EM7"
   },
   "source": [
    "Check for missing data and duplicates using `.isna()` and `.drop_duplicates()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "ll2pxoClXgmx"
   },
   "outputs": [
    {
     "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing data and duplicates using .isna() and .drop_duplicates()\n",
    "df0.isna().sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nolKe_yg3XO2"
   },
   "source": [
    "Use `.describe()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "huWmzNp2Xj8o"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22699, 18)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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>03/25/2017 8:55:43 AM</td>\n",
       "      <td>03/25/2017 9:09:47 AM</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>04/11/2017 2:53:28 PM</td>\n",
       "      <td>04/11/2017 3:19:58 PM</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>12/15/2017 7:26:56 AM</td>\n",
       "      <td>12/15/2017 7:34:08 AM</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>05/07/2017 1:17:59 PM</td>\n",
       "      <td>05/07/2017 1:48:14 PM</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>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30841670</td>\n",
       "      <td>2</td>\n",
       "      <td>04/15/2017 11:32:20 PM</td>\n",
       "      <td>04/15/2017 11:49:03 PM</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>2</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23345809</td>\n",
       "      <td>2</td>\n",
       "      <td>03/25/2017 8:34:11 PM</td>\n",
       "      <td>03/25/2017 8:42:11 PM</td>\n",
       "      <td>6</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>161</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37660487</td>\n",
       "      <td>2</td>\n",
       "      <td>05/03/2017 7:04:09 PM</td>\n",
       "      <td>05/03/2017 8:03:47 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>12.83</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>241</td>\n",
       "      <td>1</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>59.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>69059411</td>\n",
       "      <td>2</td>\n",
       "      <td>08/15/2017 5:41:06 PM</td>\n",
       "      <td>08/15/2017 6:03:05 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>237</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8433159</td>\n",
       "      <td>2</td>\n",
       "      <td>02/04/2017 4:17:07 PM</td>\n",
       "      <td>02/04/2017 4:29:14 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>234</td>\n",
       "      <td>249</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>95294817</td>\n",
       "      <td>1</td>\n",
       "      <td>11/10/2017 3:20:29 PM</td>\n",
       "      <td>11/10/2017 3:40:55 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID    tpep_pickup_datetime   tpep_dropoff_datetime  \\\n",
       "0    24870114         2   03/25/2017 8:55:43 AM   03/25/2017 9:09:47 AM   \n",
       "1    35634249         1   04/11/2017 2:53:28 PM   04/11/2017 3:19:58 PM   \n",
       "2   106203690         1   12/15/2017 7:26:56 AM   12/15/2017 7:34:08 AM   \n",
       "3    38942136         2   05/07/2017 1:17:59 PM   05/07/2017 1:48:14 PM   \n",
       "4    30841670         2  04/15/2017 11:32:20 PM  04/15/2017 11:49:03 PM   \n",
       "5    23345809         2   03/25/2017 8:34:11 PM   03/25/2017 8:42:11 PM   \n",
       "6    37660487         2   05/03/2017 7:04:09 PM   05/03/2017 8:03:47 PM   \n",
       "7    69059411         2   08/15/2017 5:41:06 PM   08/15/2017 6:03:05 PM   \n",
       "8     8433159         2   02/04/2017 4:17:07 PM   02/04/2017 4:29:14 PM   \n",
       "9    95294817         1   11/10/2017 3:20:29 PM   11/10/2017 3:40:55 PM   \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",
       "5                6           2.30           1                  N   \n",
       "6                1          12.83           1                  N   \n",
       "7                1           2.98           1                  N   \n",
       "8                1           1.20           1                  N   \n",
       "9                1           1.60           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",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "4             4           112             2         16.5    0.5      0.5   \n",
       "5           161           236             1          9.0    0.5      0.5   \n",
       "6            79           241             1         47.5    1.0      0.5   \n",
       "7           237           114             1         16.0    1.0      0.5   \n",
       "8           234           249             2          9.0    0.0      0.5   \n",
       "9           239           237             1         13.0    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  \n",
       "3        6.39           0.0                    0.3         27.69  \n",
       "4        0.00           0.0                    0.3         17.80  \n",
       "5        2.06           0.0                    0.3         12.36  \n",
       "6        9.86           0.0                    0.3         59.16  \n",
       "7        1.78           0.0                    0.3         19.58  \n",
       "8        0.00           0.0                    0.3          9.80  \n",
       "9        2.75           0.0                    0.3         16.55  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iXhaBfP_WOSR"
   },
   "source": [
    "### Task 2b. Convert pickup & dropoff columns to datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "TbHu-SSInJCX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'03/25/2017 8:55:43 AM'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the format of the data\n",
    "df0['tpep_pickup_datetime'][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "h5L6OdYPqV0N"
   },
   "outputs": [],
   "source": [
    "# Convert datetime columns to datetime\n",
    "df0['tpep_pickup_datetime'] = pd.to_datetime (df0['tpep_pickup_datetime'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "df0['tpep_dropoff_datetime'] = pd.to_datetime (df0['tpep_dropoff_datetime'], format='%m/%d/%Y %I:%M:%S %p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
       "    <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>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</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>2</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23345809</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 20:34:11</td>\n",
       "      <td>2017-03-25 20:42:11</td>\n",
       "      <td>6</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>161</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37660487</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-03 19:04:09</td>\n",
       "      <td>2017-05-03 20:03:47</td>\n",
       "      <td>1</td>\n",
       "      <td>12.83</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>241</td>\n",
       "      <td>1</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>59.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>69059411</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-15 17:41:06</td>\n",
       "      <td>2017-08-15 18:03:05</td>\n",
       "      <td>1</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>237</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8433159</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-04 16:17:07</td>\n",
       "      <td>2017-02-04 16:29:14</td>\n",
       "      <td>1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>234</td>\n",
       "      <td>249</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>95294817</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-10 15:20:29</td>\n",
       "      <td>2017-11-10 15:40:55</td>\n",
       "      <td>1</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.55</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",
       "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",
       "5    23345809         2  2017-03-25 20:34:11   2017-03-25 20:42:11   \n",
       "6    37660487         2  2017-05-03 19:04:09   2017-05-03 20:03:47   \n",
       "7    69059411         2  2017-08-15 17:41:06   2017-08-15 18:03:05   \n",
       "8     8433159         2  2017-02-04 16:17:07   2017-02-04 16:29:14   \n",
       "9    95294817         1  2017-11-10 15:20:29   2017-11-10 15:40:55   \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",
       "5                6           2.30           1                  N   \n",
       "6                1          12.83           1                  N   \n",
       "7                1           2.98           1                  N   \n",
       "8                1           1.20           1                  N   \n",
       "9                1           1.60           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",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "4             4           112             2         16.5    0.5      0.5   \n",
       "5           161           236             1          9.0    0.5      0.5   \n",
       "6            79           241             1         47.5    1.0      0.5   \n",
       "7           237           114             1         16.0    1.0      0.5   \n",
       "8           234           249             2          9.0    0.0      0.5   \n",
       "9           239           237             1         13.0    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  \n",
       "3        6.39           0.0                    0.3         27.69  \n",
       "4        0.00           0.0                    0.3         17.80  \n",
       "5        2.06           0.0                    0.3         12.36  \n",
       "6        9.86           0.0                    0.3         59.16  \n",
       "7        1.78           0.0                    0.3         19.58  \n",
       "8        0.00           0.0                    0.3          9.80  \n",
       "9        2.75           0.0                    0.3         16.55  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.head (10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KlF7ZNSyW0yV"
   },
   "source": [
    "### Task 2c. Create duration column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w1v_Y1uunbsx"
   },
   "source": [
    "Create a new column called `duration` that represents the total number of minutes that each taxi ride took."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "suC4LJFPMPCo"
   },
   "outputs": [],
   "source": [
    "# Create `duration` column\n",
    "df0['duration'] = (df0['tpep_dropoff_datetime'] - df0['tpep_pickup_datetime'])/ np.timedelta64(1,'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7dcytBKhiGAr"
   },
   "source": [
    "### Outliers\n",
    "\n",
    "Call `df.info()` to inspect the columns and decide which ones to check for outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "W5bCdL5SSfg1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 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: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "df0.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gS7VR2S0izZE"
   },
   "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`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vtj4iAJMk9Vc"
   },
   "source": [
    "### Task 2d. Box plots\n",
    "\n",
    "Plot a box plot for each feature: `trip_distance`, `fare_amount`, `duration`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "KCEzE-gwL5gq"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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=df0['trip_distance'])\n",
    "sns.boxplot(ax=axes[1], x=df0['fare_amount'])\n",
    "sns.boxplot(ax=axes[2], x=df0['duration'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pqcGiHLa4TvP"
   },
   "source": [
    "**Questions:** \n",
    "1. Which variable(s) contains outliers? \n",
    "\n",
    "2. Are the values in the `trip_distance` column unbelievable?\n",
    "\n",
    "3. What about the lower end? Do distances, fares, and durations of 0 (or negative values) make sense?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FetTHatPoR6n"
   },
   "source": [
    "1. The 3 variables all contain outliers. However, the outliers are more pronounced in the fare amount, and duration.\n",
    "2. A bout 30 miles distance maximum makes sense for cars. Therefore, there is no need to alter them.\n",
    "3. Makes sense"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2e. Imputations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `trip_distance` outliers\n",
    "\n",
    "You 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": 17,
   "metadata": {},
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are trip distances of 0 bad data or very short trips rounded down?\n",
    "sorted (set(df0['trip_distance']))[:10]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df0['trip_distance']==0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `fare_amount` outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0['fare_amount'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What do you notice about the values in the `fare_amount` column?\n",
    "\n",
    "The min value is negative which is questionable. The maximum value also, is questionable.\n",
    "\n",
    "Impute values less than $0 with `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Impute values less than $0 with 0\n",
    "\n",
    "df0.loc[df0['fare_amount'] < 0, 'fare_amount'] = 0\n",
    "df0['fare_amount'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now impute the maximum value as `Q3 + (6 * IQR)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
    "        df0.loc[df0[col] < 0, col] = 0\n",
    "\n",
    "        # Calculate upper threshold\n",
    "        q1 = df0[col].quantile(0.25)\n",
    "        q3 = df0[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",
    "        df0.loc[df0[col] > upper_threshold, col] = upper_threshold\n",
    "        print(df0[col].describe())\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call .describe() for duration outliers\n",
    "df0['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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute a 0 for any negative values\n",
    "df0.loc[df0['duration'] < 0, 'duration'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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 you cannot train a model that uses this feature. However, you can use the statistics of trips you *do* know to generalize about ones you 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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        100 231\n",
       "1         186 43\n",
       "2        262 236\n",
       "3         188 97\n",
       "4          4 112\n",
       "          ...   \n",
       "22694     48 186\n",
       "22695    132 164\n",
       "22696    107 234\n",
       "22697     68 144\n",
       "22698    239 236\n",
       "Name: pickup_dropoff, Length: 22699, dtype: string"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create `pickup_dropoff` column\n",
    "df0['pickup_dropoff'] = df0['PULocationID'].astype('string') + ' ' + df0['DOLocationID'].astype('string')\n",
    "df0['pickup_dropoff']"
   ]
  },
  {
   "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": 27,
   "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",
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       "\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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 1</th>\n",
       "      <td>2.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 148</th>\n",
       "      <td>15.700000</td>\n",
       "    </tr>\n",
       "    <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>97 65</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97 66</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97 80</th>\n",
       "      <td>3.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97 90</th>\n",
       "      <td>4.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97 97</th>\n",
       "      <td>1.006667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4172 rows × 1 columns</p>\n",
       "</div>"
      ],
      "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\n",
       "...                       ...\n",
       "97 65                0.500000\n",
       "97 66                1.400000\n",
       "97 80                3.840000\n",
       "97 90                4.420000\n",
       "97 97                1.006667\n",
       "\n",
       "[4172 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df0.groupby('pickup_dropoff').mean(numeric_only = True)[['trip_distance']]\n",
    "grouped"
   ]
  },
  {
   "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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.521667</td>\n",
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       "    <tr>\n",
       "      <th>4909</th>\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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Create a mean_distance column that is a copy of the pickup_dropoff helper column\n",
    "df0['mean_distance'] = df0['pickup_dropoff']\n",
    "\n",
    "# 2. Map `grouped_dict` to the `mean_distance` column\n",
    "df0['mean_distance'] = df0['mean_distance'].map(grouped_dict)\n",
    "\n",
    "# Confirm that it worked\n",
    "df0[(df0['PULocationID']==100) & (df0['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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mean_duration</th>\n",
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       "       mean_duration\n",
       "0          22.847222\n",
       "4909       22.847222\n",
       "16636      22.847222\n",
       "18134      22.847222\n",
       "19761      22.847222\n",
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df0.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",
    "df0['mean_duration'] = df0['pickup_dropoff']\n",
    "df0['mean_duration'] = df0['mean_duration'].map(grouped_dict)\n",
    "\n",
    "# Confirm that it worked\n",
    "df0[(df0['PULocationID']==100) & (df0['DOLocationID']==231)][['mean_duration']]\n",
    "# Confirm that it worked\n",
    "### YOUR CODE HERE ###"
   ]
  },
  {
   "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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 'day' col\n",
    "df0['day'] = df0['tpep_pickup_datetime'].dt.day_name().str.lower()\n",
    "\n",
    "# Create 'month' col\n",
    "df0['month'] = df0['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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    " #Create 'rush_hour' col\n",
    "df0['rush_hour'] = df0['tpep_pickup_datetime'].dt.hour\n",
    "\n",
    "# If day is Saturday or Sunday, impute 0 in `rush_hour` column\n",
    "df0.loc[df0['day'].isin(['saturday', 'sunday']), 'rush_hour'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>VendorID</th>\n",
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       "      <th>passenger_count</th>\n",
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       "      <th>DOLocationID</th>\n",
       "      <th>...</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
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       "      <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",
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       "      <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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "      <td>14.066667</td>\n",
       "      <td>100 231</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>saturday</td>\n",
       "      <td>mar</td>\n",
       "      <td>0</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>...</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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "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\n",
    "# Apply the `rush_hourizer()` function to the new column\n",
    "df0['rush_hour'] = df0.apply(rush_hourizer, axis=1)\n",
    "df0.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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot to visualize the relationship between variables of interest\n",
    "sns.set(style='whitegrid')\n",
    "f = plt.figure()\n",
    "f.set_figwidth(5)\n",
    "f.set_figheight(5)\n",
    "sns.regplot(x=df0['mean_duration'], y=df0['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",
    "You 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": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0[df0['fare_amount'] > 50]['fare_amount'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine the first 30 of these trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set pandas to display all columns\n",
    "### YOUR CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What do you notice about the first 30 trips?\n",
    "\n",
    "==> ENTER YOUR RESPONSE HERE"
   ]
  },
  {
   "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>11</th>\n",
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       "      <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",
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       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>132</td>\n",
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       "      <td>14.58</td>\n",
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       "      <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",
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       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>0.5</td>\n",
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       "      <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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df0[df0['fare_amount']==52].head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 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  string        \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(3), string(1)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df0.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 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: 1.7 MB\n"
     ]
    }
   ],
   "source": [
    "df2 = df0.copy()\n",
    "\n",
    "df2 = df0.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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pairplot to visualize pairwise relationships between variables in the data\n",
    "sns.pairplot(df2[['fare_amount', 'mean_duration', 'mean_distance']],\n",
    "             plot_kws={'alpha':0.4, 'size':5},\n",
    "             );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 49,
   "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation matrix to help determine most correlated variables\n",
    "df2.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize a correlation heatmap of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 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": [
    "**Question:** Which variable(s) are correlated with the target variable of `fare_amount`? \n",
    "Mean distance and mean duration correlates very well with the fare amount( 91% & 86%) respectively\n",
    "\n",
    "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 your PACE Strategy Document to reflect on the Construct stage.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P_QYzJfVUrIc"
   },
   "source": [
    "### Task 8a. Split data into outcome variable and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AzcDgLRET4d7"
   },
   "outputs": [],
   "source": [
    "### YOUR CODE HERE ###\n",
    "\n"
   ]
  },
  {
   "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": 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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>1</td>\n",
       "      <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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X = df2.drop(columns='fare_amount')\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()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o3ArC_5xa7Oi"
   },
   "source": [
    "### Task 8b. Pre-process data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PdfTaopCcbTj"
   },
   "source": [
    "Dummy encode categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "4Y3T2poF28fP"
   },
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>1</td>\n",
       "      <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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert VendorID to string\n",
    "X['VendorID'] = X['VendorID'].astype('string')\n",
    "\n",
    "# Get dummies\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iDYyjWssbnBG"
   },
   "source": [
    "### Normalize the data\n",
    "\n",
    "Use `StandardScaler()` and `fit_transform()` to standardize the X variables. Assign the results to a variable called `X_scaled`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X scaled: [[ 0.8931955   3.39065627  0.17093801  0.83195364 -0.64959666]\n",
      " [-1.11957573 -0.4997803   0.05495383  0.99296921 -0.64959666]\n",
      " [-1.11957573 -0.4997803  -0.57092814 -0.7152838   1.53941679]\n",
      " ...\n",
      " [ 0.8931955  -0.4997803  -0.62633441 -0.77886169 -0.64959666]\n",
      " [ 0.8931955  -0.4997803  -0.23485053  0.21719198 -0.64959666]\n",
      " [-1.11957573 -0.4997803  -0.40359028 -0.50145366 -0.64959666]]\n"
     ]
    }
   ],
   "source": [
    "# Standardize the X variables\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "print('X scaled:', X_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jo706RzDN2ql"
   },
   "source": [
    "### Split data into training and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 79,
   "metadata": {
    "id": "A2BNUvacwaZY"
   },
   "outputs": [],
   "source": [
    "# Create training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size = 0.2, random_state = 42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wk0rjKeO3JLv"
   },
   "source": [
    "Instantiate your model and fit it to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "SClNm5hWotj6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit your model to the training data\n",
    "lr =LinearRegression().fit(X_train, y_train)\n",
    "lr"
   ]
  },
  {
   "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": 81,
   "metadata": {
    "id": "33rE1x9e3U6t"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination: 0.8488017574563115\n",
      "R^2: 0.8488017574563115\n",
      "MAE: 2.181924595628648\n",
      "MSE: 17.088312217818014\n",
      "RMSE: 4.13380118266687\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model performance on the training data\n",
    "r_sq = lr.score(X_train, y_train)\n",
    "print('Coefficient of determination:', r_sq)\n",
    "y_pred_train = lr.predict (X_train)\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "P5nXSpRCVXq6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination: 0.8304785329674021\n",
      "R^2: 0.8304785329674023\n",
      "MAE: 2.124781414213245\n",
      "MSE: 17.52179121269477\n",
      "RMSE: 4.185903870455552\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model performance on the testing data\n",
    "r_sq_test = lr.score(X_test, y_test)\n",
    "print('Coefficient of determination:', r_sq_test)\n",
    "y_pred_test = lr.predict(X_test)\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": "L3MCKUhPJLi5"
   },
   "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": 83,
   "metadata": {
    "id": "cSl5gbXfBPBN"
   },
   "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>9199</th>\n",
       "      <td>12.5</td>\n",
       "      <td>9.582803</td>\n",
       "      <td>2.917197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4955</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8.608098</td>\n",
       "      <td>-2.608098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16833</th>\n",
       "      <td>12.0</td>\n",
       "      <td>9.179057</td>\n",
       "      <td>2.820943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13244</th>\n",
       "      <td>20.5</td>\n",
       "      <td>20.628286</td>\n",
       "      <td>-0.128286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>14.0</td>\n",
       "      <td>15.324450</td>\n",
       "      <td>-1.324450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual  predicted  residual\n",
       "9199     12.5   9.582803  2.917197\n",
       "4955      6.0   8.608098 -2.608098\n",
       "16833    12.0   9.179057  2.820943\n",
       "13244    20.5  20.628286 -0.128286\n",
       "1063     14.0  15.324450 -1.324450"
      ]
     },
     "execution_count": 83,
     "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": 84,
   "metadata": {
    "id": "IBFU_dicBjwQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V7IPJtOe29vg9pZgKyocU2Vqp+4gQQlyQYiooH5TmghBCZEIOK5qlGNugOwVCCFEIKbK1SnankJ6eDn9/fwQEBAAA5s2bh1tuuQXV1dVYsGABzp8/j/DwcBQUFGDw4MFSFYsQQhRjzLBobN9/gvW4WCTtPnrrrbdw7bXX2hzLz89HTk4OMjMzsXXrVuTl5WHjxo1SFosQQhTh4DED5/HZIp3Dq2MKDQ0NqKqqwnvvvQcAyMjIwLJly9DY2IjIyEhvFo0QIiFPTrP006jQZWRYj/eU1Kkvet2Ywrx58zBt2jQsXrwYzc3NqKurQ0xMDDSa7sUmGo0G0dHRqKurk7JYhBAv8nQKiUk3XCnoOF/eSH3RN1gr6Lg7JLtT2LRpE2JjY9HR0YGXX34ZS5cuxUMPPSTKe1dWVrr9Wr1eL0oZvEnpdVB6+QGqQ0/8TVfHOs3yb7ojCAF7dwkbrvLv/M8pjuMncMMQ95PniVVua67+Bl2dXZzHxfr7SRYUYmNjAQD+/v7IycnB7NmzsXDhQtTX18NoNEKj0cBoNMJgMFiey1dSUpJlAFsIvV6PlJQUwa+TE6XXQenlB6gOPdW8mX1xWXOrkXeZnJXftJk9KJgY9KjOYpTbGp+/wWWOulzuZASds729nfNiWpKg0NraCqPRiJCQEDAMg+3btyMxMRH9+vVDYmIiioqKkJmZiaKiIiQmJtJ4AiE+hCuFRN9gLWYu3yW4v96+n99TvJH6om+wFhdbO1mPi0WSoNDQ0ICnnnoKRqMRJpMJCQkJyM/PBwAsXrwYCxYswNq1axEaGoqCggIpikQIkQm2FBIatQqX2jotDeDZpjas/KgcgPN0DmxpIKQstzdSX4hNkqAQHx8PnU7H+lhCQgK2bNkiRTEIITLElsSu+VI72jttZwwZTQwKdRVOg4KQTXbUPZx8xFZuT88+usRyl+DsuDsozQUhxOv4JrFj6zqxJmRqpqrnM1IlT30hRZcVpbkghPQaQhpHo8mDBfEQdzcfEoLuFAjxYeZB2bNNbYja0eC1fYfthXAMqIa4GFBl6+fvTaTosqKgQIiPkiI3v5CyWDd0t4yMw64DJ21WIvtpVJiVNdzp+3hqkx1fQkGBEB/lLDe/dVDwdCqHPfoarPq43BIAzja1YdeBk5h0w5U4eMwg+Lye2GRHLqQI5BQUCPFRfPLoSNEIFeoqHHITdRkZ7D1Si83LpopyDjFIneeIDd9A3hM00EyIj+KTm1+Knb64ZhS5mmnEF1fiOyEJ8byR54hNr0uIRwiRDz4zWaRohDxtbnayoONspAiOfPSqTXYIIfJg3Q3SN1gLf60aF1s7EcXSJSLFvHh3ZxrxJcaMHbkERylWUVNQIMSH2I8RXGztRIBWgz+MjcDDd41zeL4UjdCsrOFY+VE5jKbfxhU0atczjYTo6SIzb+Q5YkNTUgkhouLqBik50oyH73J8vhSNkDfSRQglpzxHnl5FTUGBEB/C1d1xoZV7sZc7jZDQmTpSp4sQSgmBSywUFAjxIVzdIGHBGpZnu8edaaxymO7pitwDl1goKBAikMPq22GBUMoeO1zdIBNHhop2DqFz6eW0sppQUCBEELYG7MsDlzFkSI0iGjCubhB3t49kI3SmDlcQKdRVyP7uoTeioECIAGwNWKeREXVFqZkYXSpc72H/Pnq9eEFB6EwdrmBxsdV2kx26e5AGBQVCBJBqvroYXSpidcuwBRYANmsdgO6NXvpHBCGuXzBrUBgzLJr1/bm2mLTnLC+T3LK8KhmtaCZEAClWlALirKAV4z3Y0jus/Kgcqz4utxwzX9GbHz/yUwPrex081vO7Eba8TOYA5K3UE70NBQVCBGBLDaHVqESfry7GHYkY78EWWIwmxiGBXU/KI2QrSanzMvki6j4iRAC2gdpbhgWK3mUhxgpad9/DurtIeNPvvDxcx/nsedAb8zJx8eYUXQoKhDjBZ6BWr9eLfl4xVtC68x724xBicXZePruleSsvkzd4e4ouBQVCOHjzxzkhJR4/VDdgZ+lJmEwM1GoVJqYOFHRed1bhsnXJ9BRbg85VzrNNbVCrVTCZGJevczdwyn2hnBR7JjhDQYEQDlL8OLkaqD36GhT/GhAAwGRiUFx6EolD+gkODGJkA3VG66dGZ5eJ9bGoiCC8mzsJe/Q1mLl8F2dD7M5qYftg4iqIAN6/CufD291iFBQI4eDpH6ezBqpQV2GTNRToHuAt1FV4tPHiOz3UGldAALo/K082xOZgotfrkcJjWbm3r8L58Ha3mOSzj1avXo2hQ4fi+PHjAIDq6mpkZ2dj8uTJyM7Oxi+//CJ1kQhh5enpp84aKE/vRiaV/hFBspol5O2rcD74bH7kSZLeKXz//fc4fPgw4uLiLMfy8/ORk5ODzMxMbN26FXl5edi4caOUxSJeJtc+Xk+nS5ZDA2W/+EvMoGP+rN7YfIj1cW80xN6+CufD2xlZJbtT6OjowNKlS5Gfnw+Vqntv1IaGBlRVVSEjIwMAkJGRgaqqKjQ2NkpVLOJlR6tbZLH3LZsJKfGYc/dIREUEQYXu/vE5d48U7cfp7E6Ea9cxsXYjA9gXf4lFrVZZPiupFvzx4e2rcL4mpMTj3dxJ+GJFJt7NnSTpRZLTO4Xx48dbGnBn9uzZ4/I5q1atwvTp0xEf/1vl6urqEBMTA42m+4+k0WgQHR2Nuro6REZGunxPonwlR5pl3cfryXTJru5EVn1cbrNIzE8jzm5k1ncHnhCg1dgET7ltUAPYznSy7sqSw3fO25wGhddff93y3xUVFdDpdJgxYwbi4uJQW1uLDz74AFlZWS5PUl5ejoqKCsybN6/nJWZRWVnp9ms9McdcakquA9fmLmeb2hRVL3fKGgLgjtRQlBxpxoVWI8KCu1NYmzOWTr8hnPUxIcnrjla32LzHNXEBOFLdhk4XK5LDgjW40GqEChC0gI2tnM7qKWYiPr5/gxAAtwwLxJcHLls+h7NNbXjr43JUV1djxJA+opVJKDl851UMw/D6m2dkZGDDhg2IiYmxHDtz5gweeeQRFBUVOX1tYWEhNm7cCH9/f8vr+vXrh4ULFyIvLw+lpaXQaDQwGo1IS0vDrl27eN0ptLe3o7KyEklJSQgICOBTDRt8ZyzImdLrcP+iItbAYJ7KqARS/A3cGXdxdyGa/We/7tPDNusltBoV2jsdZxzx/Zt5YgxJ6N9g5vJdrHdK3vzeSflbdtZ28h5TMBgMCA4OtjkWHByM+vp6l6+dNWsWvv32W+zevRu7d+/GgAEDsGHDBkydOhWJiYmWoFJUVITExETqOvIhE0eGKqKP15vYktLxGXdxZyGa/We/R1+DkrJTNusljCYGGrXK6euE1GXF5kPIWbRd0nEkOQzyyxXvoJCeno7Zs2dj3759+Pnnn/Htt9/iySefRHp6eo8KsHjxYnzwwQeYPHkyPvjgAyxZsqRH70eUZcSQPh4dzO0N3J3SKbSBY/vs2c7dZWQQHOjn1t+MK1BdbO2UdIKBnAa/5Yb3lNQlS5bg7bffRn5+PgwGA6Kjo3H77bdjzpw5gk+6e/duy38nJCRgy5Ytgt+D9B6+svctX/bdK1wDws4a/T36GqjUKjAm/iMCbN0mzjKbbl42lfd7u3o/QNoJBp4c/JbrFGu+eAeFgIAAzJs3z2ODxYQQ9lXOXFRqFfboHbcBNb+HSUBACAv+rQvPulHjCizuXlG7yogqVfeNp9YCKCGNhiuCFq/t27cP27ZtQ2NjI9avX4+KigpcunQJY8eO9VT5CPEpQsYBTCYGKz8qBwCXXT5A99qB29OuREnZKYcr5IkjQwE4NmpsAYHPFbX94PTtaVdi9l2jXGZElbL7xhN3qEpIo+EK7zGFf/zjH1i8eDEGDx6MgwcPAgACAwOxatUqjxWOEF8j9ErZnA+Jz3swJgaz7xrFOoZjnobpLKDwHT9Y9+lhbN9/wmZwevv+E1j36WHLgkC2RXi9YYJBbxjA5n2n8Pe//x3vv/8+Bg4ciHfeeQcAcNVVV6G6utpjhSPE13B1r0Q56XaxTk3hbCzBurvJvlE3rxdwFlC+WJHJqw47S09yHp991yjL+ZXe985GCWk0XOEdFFpaWhAbGwsAllXOXV1d0GrFW3ZPiK/gahCdDYCu4MghZP2ezsYSTCYGqz527G6yJkaj5uz81tzpvrH/3MYMi8bBYwacbWpDiK57evyl1k5eQcYTQUlOq7fdxTsojBkzBoWFhZg9e7bl2MaNG5GWluaRghEiNWeNhJgNCJ/BSLZzOQsKmc9/wbmozFqXkcGKzYewcccPeGBKos1GPth8ivU1fhoVLrd3YfpzW3nV3bxJDttxc/2Fbqhjfp3957Z9/wnL49Z3TPafKVswsR5bEWtA2NvJ7MTAOyjk5ubi8ccfx5YtW9DS0oLJkyejb9++WL9+vSfLR4gknDXUAJw24kerW7BmB/cGMtbn4Mo5ZD0Y6c4VtMnEoF3AbKOzTW1448NDcJXPICRYi9bLXZYGl0/jeXvalTaNtfVx+8/ZHDz4vK/QxXjWazmcBRP754u1xwMbJXSZ8Q4K0dHR+Oyzz1BRUYHTp08jNjYWI0aMgFot+ZYMhIjO1QIxZ499eeC8TQ6dFZsPYcXmQzZXv3v0NQ4J7uyd/XVDGq67E7G5CghqtQqBAX4O6bRdNZ6z7xoFAKyzj2Yu38XZsLt6X3cGa881tQkKJp4cEFbKdFXeQWH27NlYt24dRowYgREjRliOz5kzB6tXr/ZI4QiRirNZI1xt59lfGxyu5HLmH/0P1Q3Y8Z8TLhthAJbn7z1S69AdIjWTiXF7Ns3su0ZZgoOQ1zl73NUaB67XCGnoPTkgrJTpqrwv80tLS1mPHzhwQLTCEOItztIeqNXs6ePVapXLBqe904jt+/kFBOvny2GHNbVaJXo6CFevc/Y4214IzpgHePmW1dMDwkqZruryTsG8DqGzs9NhTUJNTY3NLmqEKJU7s37MA6TeuIqXwu1pVyJxSD9RZ9M4W7zm6n3ZBnFtZh/9uvaBbfYRWx0mpg7EwWMGyfr3ue50+gZrLVlbo3Y0eH2cwWVQOHPmDACAYRjLf5vFxsbiqaee8kzJCJGAdb9932At/LVqh0aFa3DYPGbw5oeHIGCMVxFGXt3PpvvH+jMCgDesZjAJacDYNrnhO/vI/Hr758yG87TTzmYEzWZ9hWewBUQ/jUrwQL6nuQwKr776KgAgOTkZ99xzj8cLRIhU7Af+LrZ2IkCrwbM5o21+kGOGRbPOVhkzLBo/VDf0uoAAAMdOnLdZ6GYeLBdjoNQbCRDlkHSRLThdbu8SPJDvabzHFPz9/XHs2DGbY8eOHYNOpxO9UIRIgW9K6oPH2HcHO3jMwLl6l68ArRpRv/Z5c41deAPb5+BuCm/yG/u9ly9xjB15c5yBd1BYtWqVZUWz2YABAyj3EVEsvgN/XM8729QmKBMpl8vtXQC4VwK7yzq/EVuuIVf4fg5yGyhVEjnu68A7KFy6dAl9+/a1ORYSEoLm5mbRC0WIFPj+ID35A23vNHlkplFIsJbXFakzfD8HJeX1kRu2GVXeTovBOygkJCSguLjY5thXX32FhIQE0QtFiBTGDItmPR7XLxgzl+/C9Oe2YubyXRgzLFrQVEhv06hVmJU13OaY0IabrWGSYwOmdOasseYuRDnsPMh78dq8efMwa9Ys7NixA/Hx8Th58iT279+PwsJCT5aPEI/hGis48lOD5b/PNrWhpOwUhg0KR8X/Gi0rdMXu6hHT0/cmOzQqrvYxsMfWMPWGvD5yZB4EdzaDypqnU2XwDgqpqakoKipCUVER6urqMGLECLz00ksO4wyEKAXfvvD2TqNNoJBzQAgJ1mLjjh/wxuZDNg0G11RQNlERQZyNjBxm8fgyKVJlCNp5LS4uDrNmzRLlxIR4mqsrKnfSJnjT1LGDsOvASaf5ky61dnLOeedayGWNuoPkTYpUGU6DwqJFi7Bs2TIAwPPPP2/ZR8Hea6+9JkphCBELnyuqB6YkYuVH5TDK+MrfLCRYi4PHDOgyOu++sj9q32A4Sw7HdwEZ8R4pZoA5DQoDBw60/PegQYNEOykhnsb3isr+Ose87aScAoX9qleh3VfWDYazxuPd3FGcCNYAACAASURBVEnuFVABlJCymg8pdnZzGhQee+wxy3/PmTNHtJMSIgZnP3Q+V1Qbd/zg0BVjMjEICdYiMMDPa11L5jUFF1s7oVarnHYX8WHdYHA1KmHBypldJZRSUlbzIcXObk6Dwv79+3m9ydixY0UpDPFN7lzFufqh87mi4gocF1s7ERggaLhNNAFajWU6qZDZQkD3HQXD2N7l2DcYXI3KxJGhIpRenpSSspoPKWaAOf3mv/TSSzb/Nhi6p/CFh4fj/PnzAICYmBiUlJS4PNETTzyBU6dOQa1WIzg4GIsWLUJiYiKqq6uxYMECnD9/HuHh4SgoKMDgwYPdrA5RmqPVLdhWJvwqztUPnc8VlbOBZm/dJTjb2MdegFaNTiNjmSY76YburKbOGgyuRiUE7NNzuSipO6a3rcT29Awwp0Fh9+7dlv9ev349zp8/j7lz5yIoKAhtbW146623EB4ezutEBQUFCAkJAQB8/fXXePHFF/H5558jPz8fOTk5yMzMxNatW5GXl4eNGzf2oEpESUqONPO6irNvhLgabfMPnc8VldC5+65o/dTo7HK+RzIffAKSRq2C0cRYxhdMJgYlZaeQOKSfy7EBtkZFr+cfFJTWHSNFP3xvwntF8/vvv4/nnnsOQUHdH2RQUBCeffZZvPfee7xebw4IQHfKDJVKhYaGBlRVVSEjIwMAkJGRgaqqKjQ2NgqpA1GwC63sDbL1VZy5ETr76y5ozhpN6x+6ffIxtsVY1qtJe0qMgAB0D3Y7S44XFRGE4EA/h7EGqZLTKS0xHq3EFoZ3UAgODsbRo0dtjlVUVFiCBB8vvfQSJkyYgDfffBMFBQWoq6tDTEwMNJruP5hGo0F0dDTq6up4vydRNq4BTuvGne8eu+780M2BQ05MVncAbJzlMjLv8+xJSuuOsQ7+5gSB3k4lIWcqhuG3UaBOp8OSJUuQnp6OAQMG4MyZM/jmm2+Ql5eHrKwsQSfV6XTYtm0b5s6di/nz52Pbtm2Wx6ZOnYrXX38d119/vcv3aW9vR2VlpaBzE3k5Wt1is/E9AGg1Kky7IRwjhvQBACzefIrz9WHBGlxoNSIsuHuw1PwaLkUHGqH/udVme8ywYA2aW42cezFLzRwo2e6igrQq+GvVnHdYgOPnJ7Y3dXWs5w8L1uCZLMpwoCRJSUkICAiwOcZ7ikVWVhaSkpJQXFwMg8GAIUOGYPbs2bj66qsFFyQrKwt5eXkYMGAA6uvrYTQaodFoYDQaYTAYBKfOYKsYH3xzjciZ8uugx5AhQ5z2/as/Os165axWq/DBsgzeZ1r36WGU/dTqcNxZAyu1AK0Gj2SNBOA4+0ijVqHTBLS5KG+nkcHeY5fx8F3jeJ9XyPfoEdSwDuI/kjUSKV66+lb+70DaOji7oBY07+7qq6/GVVddhXPnziE6mj3DJJuWlhY0NzdbGvvdu3cjLCwM/fr1Q2JiIoqKipCZmYmioiIkJiYiMjJSSLGIwrmaTcHVlWJ9nM9smJ5uiOMpURFBnOV2tUsXF0925VBivN6Nd1Bobm7GkiVLUFxcDD8/Pxw+fBglJSU4evQonnnmGaevbWtrw9y5c9HW1ga1Wo2wsDCsX78eKpUKixcvxoIFC7B27VqEhoaioKCgx5UivUsUx+wR8wAx39kwckxkFxURxDmmYR8spz+3lff7enpmDSXG6714B4X8/HyEhoZi9+7duOOOOwB079tcUFDgMij0798fn3zyCetjCQkJ2LJli4AiE1/jas0B12yYQl2FzdWs3AgdGOebwI9m1pCe4B0U9u/fj71790Kr1VoS40VGRqKhocHFKwnpGVfdFc5WJltnDJUTtVoleAYM52rk1IE4eMxAXTlEFLyDQkhICJqammzGEmpraxEVFeWRghFizVl3hdJSYAMAY2IEN9zOguNsTxSS+CTeQeHuu+/Gn//8Zzz99NMwmUwoLy/HG2+8gXvvvdeT5SM+ztkAsvkxpQUEwP0+f+rLd4+S0nJ4G++g8Oijj8Lf3x9Lly5FV1cXXnzxRWRnZ+PBBx/0ZPmID3M2gAwAqz4uZ80gGhURhIstHbjc4d2pplERQRgzLBolZadcZrWkRstzlJaWw9t4BQWj0YgXX3wRy5Ytw0MPPeThIhHS/UN+86NyhxlD5nQKl9u7WANCSLAW7+ZOQs6i7R4NCioVAMZxUxsz61lFrpLUUaPlWb0pS6oUeAUFjUaDffv2ce68RoiYzI0k1xTSc7/mQGJjHljmSgMhlr5BWqdrBs42tWHm8l0OeySzoUbLs5SWlsPbeOc+evDBB/H222+jo6PDk+UhxGWuIz798Z6egspnEZn5it9VLiJqtDyL67sgx2nKcsA7KHzwwQfYsGEDUlJSMH78eEyYMMHy/4SIyVljGKDVYMww16vp+TxHCnyyh1Kj5VmUJVUY3gPNr7/+uifLQYiFsymmwwaFo6SMO0EeAEwTsPJXCuauJCH7OlCjJR5KyyEM76AwatQorFu3Dtu2bYPBYEB0dDSmTp2K2bNphjQRB58ppkd+UuZiSXOd2AaRqdHyPJrKyx/voLB48WJUV1fjpZdewhVXXIHTp0+jsLAQ9fX1ePXVVz1ZRuID7Gfg9GZsg8jUaBG54B0USkpK8NVXXyE0tHuD76uvvhojR47EpEny2qCEKBPfjXSkEmV1tc43YLGlnHC1bSghcsM7KPTv3x9tbW2WoAB05+SmNBdEDHJrJNm6eZx1bYUEazEra7hDyomZy3fR/sBEUXgHhczMTDzyyCOYMWMGYmJicObMGWzatAmZmZnYv3+/5Xljx471SEFJ7ybH/EVsmVZDgtnXJwQG+LF2/7gziEyrm4k38Q4KH330EQBg/fr1DsfNj6lUKpSUlIhYPNJbWQ8qR+1oYE0HoVGrwMC7+yDwzbTKdacjdBCZVjcTb+MdFHbv3u3JchAFcXUly+dx+4avpOwUawpoAFix+ZC0FXSDSq3CHn0Na8MtZBCZVjcTbxO0HSchrq5k+VzpcjV8B48ZHHYhc7UaWAxqtQq3p13pcKcihMnEiHJFT6ubibdRUCCCuLqSdfX4Hn0NZzeM9fE9+hoU6ip470ncE4yJwey7RjkkruPaEzlAq0Z7p8nhuH093RkX4BpboYFpIhUKCsQp+8bNWYPONdMG6L7S3aOvwaqPy52eb+byXRgzLBrb95/ocdn5YtC9CjoqwnG/BrapqGwBwcxcT3fHBWh1M/E2CgqEFduVuqvZQc4e7x8RhEJdBWu6a/v3kDIg2J/bHLSsxwGE3LH0jwjq0bgArW4m3kZBgTjwxOpirq4YuekyMijUVdikoCjUVfB6rfmK/g2OgXG+4wK0upl4E+8sqcR38FldHBURBNWv/8+HEgKCmX1Z+ZRdrVZhzt0jMSElnrKeEkWjOwUfxjUYyveK9tmc0QDAukOaL9FqVPhzdrLl6p7GBYiSUVDwUc4GQ/msLj7b1IaVH5VDpfLu4jJPCAnWOvyb624hKiIItwwLdEhuB9C4AFEmSYJCU1MTXnjhBZw8eRL+/v4YNGgQli5disjISFRXV2PBggU4f/48wsPDUVBQgMGDB0tRLJ/mbDCU7+wfYy8LBkD3KupZWcNtjs3KGo43PjwExqq6KhXw7H2jMSElHnq93uF9aFyAKJUkYwoqlQqPPPIIiouL8eWXXyI+Ph5//etfAQD5+fnIyclBcXExcnJykJeXJ0WRfJ6zRVJ7j9RKXBp5UKtVePreZNbGXG23P7n9vwnpLSQJCuHh4UhLS7P8e9SoUaitrUVDQwOqqqqQkZEBAMjIyEBVVRUaGxulKJZP4xr07Oukq6Q3C9Bq8AxHQNi44weHuyKjiXG5zSYhSiT57COTyYQPP/wQ6enpqKurQ0xMDDSa7v1TNRoNoqOjUVdXJ3WxfA7bvrV+GhVaL3d5qUSe5WyWVEiw1jJziA2lniC+RPKB5mXLliE4OBj3338/qqqqRHnPyspKt1/L1h+sNO7UIQTAHamhKDnSjAutRoQFa9DRaUJbZ+8bJ/D3U+HJKf3wpq4OF1odp9qqYUIIDNDrDayvD9SqWD+XQK3K8tn76vdITpRefkAedZA0KBQUFODEiRNYv3491Go1YmNjUV9fD6PRCI1GA6PRCIPBgNjYWEHvm5SUhICAAMHl0ev1SElJEfw6OelJHVJSgIfv+u3f02W24b1YOroYrNnRwBoQAKC51ej0M/TT1QOdjl1qflo/pKSk+Pz3SA6UXn5A2jq0t7dzXkxL1n305ptvorKyEmvWrIG/vz8AoF+/fkhMTERRUREAoKioCImJiYiMjJSqWMRKX7upmL2JqxQczlziGGPhOk6Ikklyp/Djjz9i/fr1GDx4MO69914AwMCBA7FmzRosXrwYCxYswNq1axEaGoqCggIpikQUztnaASH4LCoTK3Mp7ahGlECSoHDNNdfgv//9L+tjCQkJ2LJlixTFIC4oadbRLSPjLKmu3d3G0z4rKhcxVijTjmpEKWhFs49Z9+lh7Cw9CZOJgUoF+Pt17w2gVitr3v32/Sewff8J3rmX7EVFBDls6MNFjBXKtKMaUQoKCj5k3aeHbVYqM8xvewMoNVWFO3cJAVoNxgyLxszlu3g38j1doUzTWolSUJZUH7FHX+O1fQq8LUCrtsnqOjF1IErKTuFsUxsY/NaV48mtPylzKlEKulPoxXLXfYsjPzV4uxheZ79T2t4jtZJ35VDmVKIUFBR6CfuZLYFaNWoMLd4ulmSinOypDPzWzeSsu8mTXTmUOZUoBQWFXmCPvgZvbD4E86iAu7NxlMpPo8IDUxJ575DGpW+wVtA4g1CUOZUoAQWFXmD1lsNQ5jCxOBgG+KG6oUdTajVqFdqs7jRoyijxVTTQrHBHq1sc+sx9jdHEuDWIbp6GGxURhOBAP3QZbUOreZyBEF9CQUHB9uhrsLW0ydvFkDWNWgU/DfsaDJOJsQz2cqWsoCmjxNdQUFCwQl0FjL59k+DS0/cmY252MuciN/PdAE0ZJaQbBQUFU1JaCm+IigiyDO6+mzsJXGu2zzW1se4vAQCX27s8un6BELmhgWYFsZ522pszmjqjUavAwPUKbLY1AM4S25kHkwt1FTbB9mJrJw0480QJ/3oHCgo8CP2yW+cXUqtVuD3tSlT8fM5m3UB8dB/cc9tQh4Ru1vmIrI+pVbDpKurtdwkhwVoEBvjhbFMb1GoVTCbG5VoEM65Ed64WkE1IicfGHT84vL+SchTZf1fHDIvGwWMGjzfUlPCv96Cg4ALbl33F5kNYsfkQoiKCYOwyovFih+X5fQI0aGn/rdExccyMqTG0YMXmQw7HrfMRWR8z+uCcU3NjbQ6cDRcuu7xDcJbojs8CMiXnKGL7rlp/98RoqLkukOSe8I/uYvijoOAC25fdjK0rwjogEPddbO3Eyo/KoVLBMlWUT9I+V423qwVkYu2d4A3OvqtmPWmond0NyDmY0l2MMDTQ7IIcvtS+ymhiHNYOuNLTxpttwFkpOYr4flfd/U47uxuQ8+wtZ+UmjigouCCHL3VvM3XsIDyXM9rtvRC4cDXee/Q1mLl8F6Y/txUzl+9yOptoQko85tw90iar6py7RyriipLvd9Xd77SzuwE5B1M538XIEXUfucA2OEl6ZvZdo0SZ5mkejLbuJwZgk79ozLBolJSdEtR1oNQcRXy+qz1pqPnM3pJjv72SuwS9gYKCC9Zfdl9LNOcp9n287gjQajAra7hNo+NqoNVMTgOgYmJrmMWcfcRn9pYcP1NKWy4MBQUerL/s9rMY7GcfxUf3wfCE/g5TUk+fvUR7G/yqUFchKCCYp6RaT01la9z4DLSa9dauA7aGebaI7w3I827AGaWW21soKAjk6mrIfLVqniljMjEoKTuFOXePxPLZN4tylax0QtdYmHMUuerbF9LQU9eBe+R6N+CKUsvtDRQURMY106FQV0FdUD3Ap8uHq+/YHnUdEMKNZh+JjOtq9WJrJwWEHjrb1OZ0gJorf5G9OXePBADeM5II8SUUFERG3RI9ExURhBAneZ1WbznC2YBbTyd19v7m9znb1AYGv81IosBAiERBoaCgAOnp6Rg6dCiOHz9uOV5dXY3s7GxMnjwZ2dnZ+OWXX6QojkfxvVoljswpKmZlDef8DF0tOjJnRH0uZzTnvHlazEQIN0mCwsSJE7Fp0yZcccUVNsfz8/ORk5OD4uJi5OTkIC8vT4rieNSElHgMGxTu7WIojv3URnMXDxs+A8rOFqHRYiZCuEky0JyamupwrKGhAVVVVXjvvfcAABkZGVi2bBkaGxsRGRkpRbE8Yt2nh2nqqR0/jQpBAX6cs47UapXDzCJzkrWeLDrimnFCi5kI4ea1MYW6ujrExMRAo+m+xddoNIiOjkZdXZ23iiSKHf8RvldwbxYVEYS52cnYvGwqa5eOVqPCM/cmszbeY4ZFs74n13G+5JySgRBv6xVTUisrK91+rV6v79G5j1a3oORIMy60GhGkVYHxwRTXznS0d6C6uhohMCAEwB2poZbPKyxYg4kjQxECA/R6g83rjla3YMd/2Pef3nfkFG4Y4v46DyHl4Kun3yM5UHodlF5+QB518FpQiI2NRX19PYxGIzQaDYxGIwwGA2JjYwW/V1JSEgICAgS/Tq/XIyUlRfDrzPboa7Ct7LeFaG2dFBHsXWg1YltZM4YMGYIJKfFISQEevuu3x9n+BubPlSvANrcae/R3A+BQjp7o6fdIDpReB1flV8J+ClL+Ddrb2zkvpr3WfdSvXz8kJiaiqKgIAFBUVITExERZjidwZdkUklbBlwmd2ePqc6W+fyKEOYsATUHmR5I7heXLl2PXrl04d+4cHn74YYSHh2Pbtm1YvHgxFixYgLVr1yI0NBQFBQVSFEeQPfoarPyoHMZf01ZY77xG+LMf2DVfuZ1takPUjgabKzdXs4Aut3dhj75Gdld6RJ7kviucUJ6+65EkKOTm5iI3N9fheEJCArZs2SJFEdxWqKuwBATCzj5hHddzzFzthOUqXcXF1k7aOYvw1pumILNdpK78qByAeL8FWtHsgtDkbb7IOvmfq+cArnfC4rMAkBabEb7kvCucUGwXqUYTg0JdhWjn8MmgYB4jWLz5FOW9kYh16gmuK7SzTW2YuXwXANgsPOOixCs9Ir3eNAWZ6yJVzItXnwsK1oNOAA06ScH+B+jsCs26K+nd3En4YkUmZy4jJV7pEelNSInHxNSBli5MtVqFiakDqeuRg88FBcp743khwVqnexy76h6y/3v0pis9ImzPbLHOV1J2ymGPEyVeCHIli3SWRFKoXrF4TQiuAUxKay0Otm0y7fHZ4tTcNWSeadHeaXS585r18+U8H92XuZpk4Am9afbRrKzhWPVxObqMv40r+GlUmJU1XLRz+NydgvUsGD7HnaVh9nV+GhWmjh3k9K6AizmbaVgw+x1D/4ggh64+8w5szgKC/Xz0FZsPYRrtmSAb3rhT700XghNS4jHphittusIm3XCl8qakygnXDBmu43H9ghX55fE066v1nuwBPHFkKLaVNbNuqu5qFzv7uwFni96kuCIlrnljeqhKBdbV8SpnsxhkiqsrLHFIP9G+1z4XFKI45sBz3RH4UsZTFQCVk7UGAPBczmhRG9URQ/pgyJAhrI38GxwLBC+2dlpmW1g39q4aFqV2GfQm3shQy5UuRYl5yqToCvO5oDBmWDS273fMZNrTzJtKFBKsRWCAn01jDABvflTOGhiiIoI80qAKTXFtz/yj4PN8msbqXQ9MSbQZUwBo0oAQUnSF+VxQOHiMPQvmwWOGHnWDyFVURBDGDItGSdkphx+iswFhOfxw2RoQLuea2vBszmiXz6dprN5lPcmAJgMIx5U1gGtM1B0+FxR605J3LioV8Ox9tt08iUP68f4hyuWHy1aOy+1drAt1+lvdxXDNaqIrUnngujMkrgkdE3WHzwUFn9h1i3EcTBX6Q5TLD9e+HPZTGgHHrTzNz6fpqaS3ETom6g6fCwq9oU8zQKvBxNSB2Fl6kvUKoVcFODtC7mLkEtiId4UEa1nvLsVc8CUVKdovnwsK9l0MzhZCedPIq/th+eybAbBf8QLA3iO1Dl92pQU4d1BjT4SQYsGXVKTo2vW5oAD81qjIdbcprZ8aR39qwMzluyx/cFddKED3lY+r1cSE+JoJKfH4obrBcmftiQVfUvL0RZFPBgVnG7xIzTp1g/0sIa4FV1yLtAID/DAhJZ760gmxskdfg10HTtos+Np14KSoC76ElKWnv01P/759Ls2F1FlSVSogQMv9MVunbjh4zMArBYCzGVS09SAhtgp1FTZdRwDQZRR3DwI+xPhtSvH79rmgIGbuFeucP1PHDmLN5PnsfaPx6V+m4bmc0ZwzBMzn5ztd1tmmIZQFlhBbUuxBwIcYv00pft8+130k5jqFd3Mn2fzb2VoAcz/g9Oe2gm1Gsfk1fKbLOpuBwJUaojetwyBEicRoe6RYZ+VzQcGT6xT4DAA5Oz/f6WbOZlBxLdzqzdNUCXFGLlNSxWh7pFhn5XPdR0I3bOkTwJ7amet4T84/ISXeZhtKZ6mozamnF+cMxLu5kyzPoQ1pCLE1K2s4NHZpIDRq6aekivHblOL37XN3CkLXKXz0SgbufbEILe2/Xb33CdDgo1cyenx+Z91M7pJLigpC5EIuvwkxykHrFDxE6DoFdwOAq/N7Ci3uIsSWXH4TYpTD03Xxue4jQggh3GQRFKqrq5GdnY3JkycjOzsbv/zyi7eLRAghPkkWQSE/Px85OTkoLi5GTk4O8vLyvF0kQgjxSV4PCg0NDaiqqkJGRne/fUZGBqqqqtDY2OjlkhFCiO/x+kBzXV0dYmJioNF0T7PSaDSIjo5GXV0dIiMjnb6W+XWT1Y6ODrfP397e7vZr5ULpdVB6+QGqgxwovfyAdHUwt5kMy0bVXg8KPdHZ2b0g5fjx426/R2VlpVjF8Rql10Hp5QeoDnKg9PID0tehs7MTgYGBNse8HhRiY2NRX18Po9EIjUYDo9EIg8GA2NhYl6/t06cPrr32Wmi1WqhU4u1RSgghvRnDMOjs7ESfPn0cHvN6UOjXrx8SExNRVFSEzMxMFBUVITEx0WXXEQCo1WqEhIRIUEpCCOld7O8QzFQMW6eSxH7++WcsWLAAzc3NCA0NRUFBAa666ipvF4sQQnyOLIICIYQQefD6lFRCCCHyQUGBEEKIBQUFQgghFhQUCCGEWFBQIIQQYuGTQUGJWVkLCgqQnp6OoUOH2qzgVkpdmpqa8Oijj2Ly5MmYNm0a5syZY8lvpZQ6AMATTzyB6dOnIysrCzk5Ofjhh+4N05VUBwBYvXq1zXdJSeVPT0/H7bffjszMTGRmZmLv3r0AlFWH9vZ25OfnY9KkSZg2bRoWLVoEQCZ1YHzQjBkzGJ1OxzAMw+h0OmbGjBleLpFrBw8eZGpra5lbb72V+e9//2s5rpS6NDU1Mf/5z38s//7LX/7CLFy4kGEY5dSBYRimubnZ8t9fffUVk5WVxTCMsupQWVnJ/OlPf2ImTJhg+S4pqfz2vwEzJdVh2bJlzMsvv8yYTCaGYRjm7NmzDMPIow4+FxTOnTvHpKSkMF1dXQzDMExXVxeTkpLCNDQ0eLlk/Fj/IJRcl507dzIPPvigouvw+eefM3feeaei6tDe3s7cc889zMmTJy3fJSWVn2HYg4KS6nDp0iUmJSWFuXTpks1xudTB62kupNaTrKxyo9S6mEwmfPjhh0hPT1dkHV566SXs27cPDMPgb3/7m6LqsGrVKkyfPh3x8b9t56ik8pvNmzcPDMMgJSUFzz77rKLqUFNTg/DwcKxevRqlpaXo06cP5s6di8DAQFnUwSfHFIh3LVu2DMHBwbj//vu9XRS3vPzyy9izZw+eeeYZvPbaa94uDm/l5eWoqKhATk6Ot4vSI5s2bcIXX3yBzz77DAzDYOnSpd4ukiBdXV2oqanBddddh3/+85+YN28ennrqKbS2tnq7aAB8MChYZ2UFICgrq9wosS4FBQU4ceIEVq5cCbVarcg6mGVlZaG0tBQDBgxQRB0OHjyI//3vf5g4cSLS09Nx5swZ/OlPf8LJkycVUX4zc7n8/f2Rk5ODQ4cOKep7FBcXBz8/P8vGYiNHjkRERAQCAwNlUQefCwrWWVkBCMrKKjdKq8ubb76JyspKrFmzBv7+/gCUVYeWlhbU1dVZ/r17926EhYUppg6zZs3Ct99+i927d2P37t0YMGAANmzYgKlTpyqi/ADQ2tqKixcvAuhO/7x9+3YkJiYq5m8AAJGRkUhLS8O+ffsAdM84amhowODBg2VRB59MiKfErKzLly/Hrl27cO7cOURERCA8PBzbtm1TTF1+/PFHZGRkYPDgwZaUvQMHDsSaNWsUU4dz587hiSeeQFtbG9RqNcLCwjB//nxcf/31iqmDtfT0dKxfvx7XXnutYspfU1ODp556CkajESaTCQkJCcjNzUV0dLRi6gB01+PFF1/E+fPn4efnh6effhrjx4+XRR18MigQQghh53PdR4QQQrhRUCCEEGJBQYEQQogFBQVCCCEWFBQIIYRYUFAgRKaGDh2KEydOeLsYxMdQUCBEJOnp6fjuu++8XQxCeoSCAiGEEAsKCoSwKCwsxG233Ybk5GRMnToVX331leWxTz75BFOmTLE89v333+P5559HbW0tHn/8cSQnJ+Odd95BaWkpxo0bZ/O+1ncTR48eRXZ2NlJTU3HzzTdj6dKl6OjokLSehNjzudTZhPARHx+PTZs2ISoqCjt37sTzzz+PXbt2Qa/X4+2338aaNWswfPhwnDx5En5+fnj99deh1+uxfPly3HjjjQCA0tJSp+dQq9VYuHAhkpKScObMGTz66KPYvHkzHnroIQlqSAg7ulMghMWUKVMQExMDtVqNqVOnYtCgQTh69Cg+/fRTPPLIIxgxYgRUKhUGDRqEGPyn/QAAAcFJREFUK664wq1zJCUlYdSoUfDz88PAgQORnZ2NgwcPilwTQoShOwVCWOh0Orz33ns4ffo0gO7snE1NTairq8OVV14pyjmqq6vxl7/8BZWVlWhra4PRaMT1118vynsT4i66UyDEzunTp5Gbm4tFixahtLQUZWVluOaaawB05/I/efIkr/cJCgrC5cuXLf82Go1obGy0/Hvx4sW46qqrUFxcjEOHDuGZZ54B5ack3kZBgRA7bW1tUKlUljz2n332GX788UcAwF133YV3330XlZWVYBgGJ06csNxN9O/fHzU1NZb3GTJkCNrb27Fnzx50dnZi3bp1NgPJLS0t6NOnD/r06YOff/4ZH374oYS1JIQdBQVC7Fx99dWYOXMm7r33Xtx44404fvw4Ro8eDaB7rOHxxx/Hc889h9GjR+PJJ5/EhQsXAHRvYrNu3TqkpqZiw4YNCAkJQX5+PnJzczFu3DgEBQVhwIABlvPMnz8fRUVFGD16NBYtWoSpU6d6pb6EWKP9FAghhFjQnQIhhBALCgqEEEIsKCgQQgixoKBACCHEgoICIYQQCwoKhBBCLCgoEEIIsaCgQAghxIKCAiGEEIv/B3tZ80iQjq3RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot to visualize `predicted` over `actual`\n",
    "plt.scatter (results['actual'], results['predicted'])\n",
    "plt.title ('Actual Vrs Predicted')\n",
    "plt.xlabel('actual')\n",
    "plt.ylabel('predicted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IbO71S_R9IcY"
   },
   "source": [
    "Visualize the distribution of the `residuals` using a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "3a0UYoEr9Nx6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'residual')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of the `residuals`\n",
    "sns.histplot(data = results['residual'])\n",
    "plt.title (\"residual\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.054483565873129504"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate residual mean\n",
    "residual_mean = results['residual'].mean()\n",
    "residual_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCnELck-9h5M"
   },
   "source": [
    "Create a scatterplot of `residuals` over `predicted`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "7Kmr2U8A95fY"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd0f7720610>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot of `residuals` over `predicted`\n",
    "plt.scatter (results['predicted'], results['residual'])"
   ]
  },
  {
   "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. Which feature had the greatest effect on trip fare?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>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>-0.031485</td>\n",
       "      <td>0.014927</td>\n",
       "      <td>7.134742</td>\n",
       "      <td>2.803586</td>\n",
       "      <td>0.136533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID  passenger_count  mean_distance  mean_duration  rush_hour\n",
       "0 -0.031485         0.014927       7.134742       2.803586   0.136533"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_\n",
    "coefficients = pd.DataFrame(lr.coef_, columns=X.columns)\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficients reveal that `mean_distance` was the feature with the greatest weight in the model's final prediction. For every mile traveled, the fare amount increases by a mean of \\\\$7. Note, however, that because some highly correlated features were not removed, the confidence interval of this assessment is wider."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P6AlDDyhdzmG"
   },
   "source": [
    "### Task 9d. Conclusion\n",
    "\n",
    "1. What are the key takeaways from this notebook?\n",
    "\n",
    "\n",
    "\n",
    "2. What results can be presented from this notebook?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.We can use regression models and machine learning to make very dependable predictions\n",
    "2. to get the best grounds before fitting the model, exploratory data analysis can help us get the best set of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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