Automatidata project lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtNBZFHO3M7n"
   },
   "source": [
    "# **Automatidata project**"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgSbVJvomcVa"
   },
   "source": [
    "# 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 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 building 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?\n",
    "\n",
    "**Exemplar responses: Find the answers to those questions later in the notebook.**"
   ]
  },
  {
   "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": {
    "id": "F5O5cx_qQJmX"
   },
   "source": [
    "<img src=\"images/Plan.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "\n",
    "## PACE: **Plan**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D8qYlvkLQsf2"
   },
   "source": [
    "### Task 1. Imports and loading\n",
    "Import the packages that are needed for building linear regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TyR3sBUYJBO8",
    "outputId": "5ce192d4-144e-44c9-9360-2bb6000dc743"
   },
   "outputs": [],
   "source": [
    "df0=pd.read_csv(\"2017_Yellow_Taxi_Trip_Data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OnrvCSfHUWPv"
   },
   "source": [
    "<img src=\"images/Analyze.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Analyze**\n",
    "\n",
    "In this stage, consider the following question where applicable to complete your code response:\n",
    "\n",
    "* What are some purposes of EDA before constructing a multiple linear regression model?\n",
    "\n",
    "1.   Outliers and extreme data values can significantly impact linear regression equations. After visualizing data, make a plan for addressing outliers by dropping rows, substituting extreme data with average data, and/or removing data values greater than 3 standard deviations.\n",
    "\n",
    "2.   EDA activities also include identifying missing data to help the analyst make decisions on their exclusion or inclusion by substituting values with data set means, medians, and other similar methods.\n",
    "\n",
    "3.   It's important to check for things like multicollinearity between predictor variables, as well to understand their distributions, as this will help you decide what statistical inferences can be made from the model and which ones cannot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rIcDG2e66wt9"
   },
   "source": [
    "### Task 2a. Explore data with EDA\n",
    "\n",
    "Analyze and discover data, looking for correlations, missing data, outliers, and duplicates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CLpoUCz1277k"
   },
   "source": [
    "Start with `.shape` and `.info()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T4Ag-sZhWg6K",
    "outputId": "15c6d684-14b1-4201-baa1-0770fa9ce18e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22699, 18)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 18 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Unnamed: 0             22699 non-null  int64  \n",
      " 1   VendorID               22699 non-null  int64  \n",
      " 2   tpep_pickup_datetime   22699 non-null  object \n",
      " 3   tpep_dropoff_datetime  22699 non-null  object \n",
      " 4   passenger_count        22699 non-null  int64  \n",
      " 5   trip_distance          22699 non-null  float64\n",
      " 6   RatecodeID             22699 non-null  int64  \n",
      " 7   store_and_fwd_flag     22699 non-null  object \n",
      " 8   PULocationID           22699 non-null  int64  \n",
      " 9   DOLocationID           22699 non-null  int64  \n",
      " 10  payment_type           22699 non-null  int64  \n",
      " 11  fare_amount            22699 non-null  float64\n",
      " 12  extra                  22699 non-null  float64\n",
      " 13  mta_tax                22699 non-null  float64\n",
      " 14  tip_amount             22699 non-null  float64\n",
      " 15  tolls_amount           22699 non-null  float64\n",
      " 16  improvement_surcharge  22699 non-null  float64\n",
      " 17  total_amount           22699 non-null  float64\n",
      "dtypes: float64(8), int64(7), object(3)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Start with `.shape` and `.info()`\n",
    "\n",
    "# Keep `df0` as the original dataframe and create a copy (df) where changes will go\n",
    "# Can revert `df` to `df0` if needed down the line\n",
    "df = df0.copy()\n",
    "\n",
    "# Display the dataset's shape\n",
    "print(df.shape)\n",
    "\n",
    "# Display basic info about the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for missing data and duplicates using `.isna()` and `.drop_duplicates()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444
    },
    "id": "3QZZIxxi3OV3",
    "outputId": "255e6481-0818-41a3-dc85-0fe7a9e0c5c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataframe: (22699, 18)\n",
      "Shape of dataframe with duplicates dropped: (22699, 18)\n",
      "Total count of missing values: 0\n",
      "Missing values per column:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Unnamed: 0               0\n",
       "VendorID                 0\n",
       "tpep_pickup_datetime     0\n",
       "tpep_dropoff_datetime    0\n",
       "passenger_count          0\n",
       "trip_distance            0\n",
       "RatecodeID               0\n",
       "store_and_fwd_flag       0\n",
       "PULocationID             0\n",
       "DOLocationID             0\n",
       "payment_type             0\n",
       "fare_amount              0\n",
       "extra                    0\n",
       "mta_tax                  0\n",
       "tip_amount               0\n",
       "tolls_amount             0\n",
       "improvement_surcharge    0\n",
       "total_amount             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing data and duplicates using .isna() and .drop_duplicates()\n",
    "### YOUR CODE HERE ###\n",
    "\n",
    "# Check for duplicates\n",
    "print('Shape of dataframe:', df.shape)\n",
    "print('Shape of dataframe with duplicates dropped:', df.drop_duplicates().shape)\n",
    "\n",
    "# Check for missing values in dataframe\n",
    "print('Total count of missing values:', df.isna().sum().sum())\n",
    "\n",
    "# Display missing values per column in dataframe\n",
    "print('Missing values per column:')\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicates or missing values in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WXWAlPTY9iLK"
   },
   "source": [
    "Use `.describe()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "id": "2O3b9H9B9nwk",
    "outputId": "3fe65b1a-c169-4038-b283-7ac46ff5303a"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.269900e+04</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.675849e+07</td>\n",
       "      <td>1.556236</td>\n",
       "      <td>1.642319</td>\n",
       "      <td>2.913313</td>\n",
       "      <td>1.043394</td>\n",
       "      <td>162.412353</td>\n",
       "      <td>161.527997</td>\n",
       "      <td>1.336887</td>\n",
       "      <td>13.026629</td>\n",
       "      <td>0.333275</td>\n",
       "      <td>0.497445</td>\n",
       "      <td>1.835781</td>\n",
       "      <td>0.312542</td>\n",
       "      <td>0.299551</td>\n",
       "      <td>16.310502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.274493e+07</td>\n",
       "      <td>0.496838</td>\n",
       "      <td>1.285231</td>\n",
       "      <td>3.653171</td>\n",
       "      <td>0.708391</td>\n",
       "      <td>66.633373</td>\n",
       "      <td>70.139691</td>\n",
       "      <td>0.496211</td>\n",
       "      <td>13.243791</td>\n",
       "      <td>0.463097</td>\n",
       "      <td>0.039465</td>\n",
       "      <td>2.800626</td>\n",
       "      <td>1.399212</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>16.097295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.212700e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-120.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>-120.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.852056e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>8.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.673150e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.610000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>11.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.537452e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.060000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.134863e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>33.960000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>999.990000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1200.290000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0      VendorID  passenger_count  trip_distance  \\\n",
       "count  2.269900e+04  22699.000000     22699.000000   22699.000000   \n",
       "mean   5.675849e+07      1.556236         1.642319       2.913313   \n",
       "std    3.274493e+07      0.496838         1.285231       3.653171   \n",
       "min    1.212700e+04      1.000000         0.000000       0.000000   \n",
       "25%    2.852056e+07      1.000000         1.000000       0.990000   \n",
       "50%    5.673150e+07      2.000000         1.000000       1.610000   \n",
       "75%    8.537452e+07      2.000000         2.000000       3.060000   \n",
       "max    1.134863e+08      2.000000         6.000000      33.960000   \n",
       "\n",
       "         RatecodeID  PULocationID  DOLocationID  payment_type   fare_amount  \\\n",
       "count  22699.000000  22699.000000  22699.000000  22699.000000  22699.000000   \n",
       "mean       1.043394    162.412353    161.527997      1.336887     13.026629   \n",
       "std        0.708391     66.633373     70.139691      0.496211     13.243791   \n",
       "min        1.000000      1.000000      1.000000      1.000000   -120.000000   \n",
       "25%        1.000000    114.000000    112.000000      1.000000      6.500000   \n",
       "50%        1.000000    162.000000    162.000000      1.000000      9.500000   \n",
       "75%        1.000000    233.000000    233.000000      2.000000     14.500000   \n",
       "max       99.000000    265.000000    265.000000      4.000000    999.990000   \n",
       "\n",
       "              extra       mta_tax    tip_amount  tolls_amount  \\\n",
       "count  22699.000000  22699.000000  22699.000000  22699.000000   \n",
       "mean       0.333275      0.497445      1.835781      0.312542   \n",
       "std        0.463097      0.039465      2.800626      1.399212   \n",
       "min       -1.000000     -0.500000      0.000000      0.000000   \n",
       "25%        0.000000      0.500000      0.000000      0.000000   \n",
       "50%        0.000000      0.500000      1.350000      0.000000   \n",
       "75%        0.500000      0.500000      2.450000      0.000000   \n",
       "max        4.500000      0.500000    200.000000     19.100000   \n",
       "\n",
       "       improvement_surcharge  total_amount  \n",
       "count           22699.000000  22699.000000  \n",
       "mean                0.299551     16.310502  \n",
       "std                 0.015673     16.097295  \n",
       "min                -0.300000   -120.300000  \n",
       "25%                 0.300000      8.750000  \n",
       "50%                 0.300000     11.800000  \n",
       "75%                 0.300000     17.800000  \n",
       "max                 0.300000   1200.290000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display descriptive stats about the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9FRKZtHm3EM7"
   },
   "source": [
    "There are clearly some outliers in several variables, like `tip_amount` (\\$200) and `total_amount` (\\$1,200). Also, a number of the variables, such as `mta_tax`, seem to be almost constant throughout the data, which would imply that they would not be expected to be very predictive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2b. Convert pickup & dropoff columns to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "btROUPKa3gYh"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'03/25/2017 9:09:47 AM'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the format of the data\n",
    "df['tpep_dropoff_datetime'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "id": "suC4LJFPMPCo",
    "outputId": "65de0b5c-3aba-4aa8-ab9e-3e4bc04ee01a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data type of tpep_pickup_datetime: object\n",
      "Data type of tpep_dropoff_datetime: object\n",
      "Data type of tpep_pickup_datetime: datetime64[ns]\n",
      "Data type of tpep_dropoff_datetime: datetime64[ns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 08:55:43</td>\n",
       "      <td>2017-03-25 09:09:47</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-11 14:53:28</td>\n",
       "      <td>2017-04-11 15:19:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-15 07:26:56</td>\n",
       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \n",
       "0        2.76           0.0                    0.3         16.56  \n",
       "1        4.00           0.0                    0.3         20.80  \n",
       "2        1.45           0.0                    0.3          8.75  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert datetime columns to datetime\n",
    "# Display data types of `tpep_pickup_datetime`, `tpep_dropoff_datetime`\n",
    "print('Data type of tpep_pickup_datetime:', df['tpep_pickup_datetime'].dtype)\n",
    "print('Data type of tpep_dropoff_datetime:', df['tpep_dropoff_datetime'].dtype)\n",
    "\n",
    "# Convert `tpep_pickup_datetime` to datetime format\n",
    "df['tpep_pickup_datetime'] = pd.to_datetime(df['tpep_pickup_datetime'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "\n",
    "# Convert `tpep_dropoff_datetime` to datetime format\n",
    "df['tpep_dropoff_datetime'] = pd.to_datetime(df['tpep_dropoff_datetime'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "\n",
    "# Display data types of `tpep_pickup_datetime`, `tpep_dropoff_datetime`\n",
    "print('Data type of tpep_pickup_datetime:', df['tpep_pickup_datetime'].dtype)\n",
    "print('Data type of tpep_dropoff_datetime:', df['tpep_dropoff_datetime'].dtype)\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7dcytBKhiGAr"
   },
   "source": [
    "### Task 2c. Create duration column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "id": "W5bCdL5SSfg1",
    "outputId": "f8a754e3-8234-414f-92dd-721ea6d97dc0"
   },
   "source": [
    "Create a new column called `duration` that represents the total number of minutes that each taxi ride took."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "gS7VR2S0izZE"
   },
   "outputs": [],
   "source": [
    "# Create `duration` column\n",
    "df['duration'] = (df['tpep_dropoff_datetime'] - df['tpep_pickup_datetime'])/np.timedelta64(1,'m')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YhRKv1xzCniK"
   },
   "source": [
    "### Outliers\n",
    "\n",
    "Call `df.info()` to inspect the columns and decide which ones to check for outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EAGOvwrxDRDr",
    "outputId": "43372781-7a87-4dbc-fa9a-36aedded54b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 19 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   Unnamed: 0             22699 non-null  int64         \n",
      " 1   VendorID               22699 non-null  int64         \n",
      " 2   tpep_pickup_datetime   22699 non-null  datetime64[ns]\n",
      " 3   tpep_dropoff_datetime  22699 non-null  datetime64[ns]\n",
      " 4   passenger_count        22699 non-null  int64         \n",
      " 5   trip_distance          22699 non-null  float64       \n",
      " 6   RatecodeID             22699 non-null  int64         \n",
      " 7   store_and_fwd_flag     22699 non-null  object        \n",
      " 8   PULocationID           22699 non-null  int64         \n",
      " 9   DOLocationID           22699 non-null  int64         \n",
      " 10  payment_type           22699 non-null  int64         \n",
      " 11  fare_amount            22699 non-null  float64       \n",
      " 12  extra                  22699 non-null  float64       \n",
      " 13  mta_tax                22699 non-null  float64       \n",
      " 14  tip_amount             22699 non-null  float64       \n",
      " 15  tolls_amount           22699 non-null  float64       \n",
      " 16  improvement_surcharge  22699 non-null  float64       \n",
      " 17  total_amount           22699 non-null  float64       \n",
      " 18  duration               22699 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(9), int64(7), object(1)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-LSCP19IjnZ3",
    "outputId": "ef2158fa-e60b-4b0b-cd24-9627d257289f"
   },
   "source": [
    "Keeping in mind that many of the features will not be used to fit your model, the most important columns to check for outliers are likely to be:\n",
    "* `trip_distance`\n",
    "* `fare_amount`\n",
    "* `duration`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DKRGdmP7DC8f"
   },
   "source": [
    "### Task 2d. Box plots\n",
    "\n",
    "Plot a box plot for each feature: `trip_distance`, `fare_amount`, `duration`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nG-Ngt0FCE9M",
    "outputId": "8d0055f5-a490-4ab0-fe0b-9f566ea60340"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XXjpkulqnUT+u2fBGZWfmkPGTTbt900htN5nU7owNX9bh22ir22yv2VnW42Szs623HemfxkvtmDDacaRT62kyr/Oq2lBDjaVtL7vssrbrqWqdjXonLDPPBs4GWLBgQUu3sebOnQvAGWecMWT44sWLuWpt+886P/aEJ9M/f8/tyhsrX8ihsYgIZs2a1TARmzdvXktlHHbYYXzzm9/cLhGbNm0ar371q4dMd+GFF7Jt27Yh45oNb1R2RAwZP9m02zeN1HadUN/vHXXUUWzatIn+/v7KH3+OCPbdd1/uvPPOIcu6evXqIYlXq9tsrxnv9ajxsbOttx3pn6A4JmzNAOhYP1I7JtQez292HOnUeprM63y0Y/FkXe5uG0vbHnbYYZx//vlt1VPVOtspfxOmyWHatGlMnz59yPeR1E87FtOnT2/6OOKSJUtaKmPhwoUN45w6dSrHHHPMkOmmTJmy3bhmw2vj6pdx+vTpQ8bv7EZqu8lk+vTpLFmyZLtlHb6NtrrN9pqdZT1ONq63kdW3T0Ts8PGqkdoxYbTjSKfW02Re51W1oYYaS9s2O+8aSVXrbKdMwp7znOd0OwR1wFFHHcURRxwx5Hvt6v7s2bMHh8+ePZuI4Mgjj2x49b/Rzjlv3jwiYsj0RxxxBAsWLBgcVptv3rx5Lb/uu6+vb0jMtXoOP/zwIa9E7evr4/DDD99uXLPh9ePq4/XVuI8bqe3G0+WXX96xsmrbdW2bbrad9vf3b7es/f39g9O1s832mm6tR+0Y19vI6ttnt912GzzRrOlEP1I7JrRyHOnEeprM67yqNtRQY2nbvr4+jjzyyCHDIqLp9EcffbSvqJeaiQjmz58/eEXvoIMO4sADDxy82j9r1iyWLl3KAQccwMyZMznllFM4+OCDB8fPnDmTffbZhxkzZjBz5kxOOukkZs6cyfz58+nv7+eggw5iyZIlHHzwwSxZsmRI+cBgHR/84AeZNWtW23cUFi5cyIEHHjiknkZXXRYuXNhwXLPhtXHD49XjRmq7XjJnzhzmz58/mOj39fVx4IEHsnTpUmbNmjW4TY+0nTZa1tq2O1HvgtVMlPWooVxvI6u1z5577tmxMufOncsBBxyw3TFhtONIp9bTZF7nVbWhhhpL2y5cuJD+/n522WUXAI4//nj233//7S7Cz5gxo9J1FiO9pnG4BQsW5KpVq0adrvYWw2a/Cdv8rCIjnXlj8crI2vdmZt54Ic/v4G/CmsUn7Ywi4qrMXNDtOHZEq33TeKrvV2q/CXvOc57DGWecYZ8jjcFk6Jug/f5p8eLFDAwM8PBviv/T6JBnH2Q/IvWYTvRP3gmTJEmSpAqZhEmSJElShUzCJEmSJKlCJmGSJEmSVCGTMEmSJEmqkEmYJEmSJFXIJEySJEmSKmQSJkmSJEkVMgmTJEmSpAqZhEmSJElShUzCJEmSJKlCJmGSJEmSVCGTMEmSJEmqkEmYJEmSJFXIJEySJEmSKjRtPArt7+8fj2I7ptfjkzTxjNSv2OdIalV/fz/r16/n4d88st1wSZPHuCRhixYtGo9iO6bX45M08YzUr9jnSGrVokWLGBgY4J77H9xuuKTJw8cRJUmSJKlCJmGSJEmSVCGTMEmSJEmqkEmYJEmSJFXIJEySJEmSKmQSJkmSJEkVMgmTJEmSpAqZhEmSJElShUzCJEmSJKlCJmGSJEmSVCGTMEmSJEmqkEmYJEmSJFXIJEySJEmSKmQSJkmSJEkVMgmTJEmSpAqZhEmSJElShUzCJEmSJKlCJmGSJEmSVCGTMEmSJEmq0LSqK5z66/uYeeOF5eeNAIPfR5oH9hzv0CRJknrDo9u6HYGkcVRpEtbf3z/k+/r1RQczd+5oCdae280rSb1qr7324oEHHrDfkjQm/f39rF+/fvCzpMknMrPliRcsWJCrVq0ax3AkVS0irsrMBd2OY0fYN0mTz2Tom8D+SZqMOtE/+ZswSZIkSaqQSZgkSZIkVcgkTJIkSZIqZBImSZIkSRUyCZMkSZKkCpmESZIkSVKFTMIkSZIkqUImYZIkSZJUIZMwSZIkSapQZGbrE0fcA9zW4uS7A/eOJagOMgZjMIbRY9g3M/foVjCd0GbfNN56YR2PxPh2jPHtmHbim/B9E4y5f+rl9dirsfVqXNC7sRlX+2qx7XD/1FYS1lbBEasyc8G4FG4MxmAMEzqGyazX29f4dozx7Zhej69X9HI79WpsvRoX9G5sxtW+Tsbm44iSJEmSVCGTMEmSJEmq0HgmYWePY9mtMoaCMRSModALMUxmvd6+xrdjjG/H9Hp8vaKX26lXY+vVuKB3YzOu9nUstnH7TZgkSZIkaXs+jihJkiRJFep4EhYRh0fETRExEBEf6HT5LcawLiKui4hrImJVhfV+NiLujojr64btFhGXRsTPy79Pqbj+pRGxvmyLayLiyPGqv6xv74j4TkSsiYifRcTicniV7dAshsraIiKeEBFXRsTqMoZTyuFVtkOzGCrdJnYm3e7/xrL/RcTfl/HeFBF/UFGcUyPipxHx370WX0TsGhHnRcSNZTu+qMfie2+5bq+PiC+W+3nX4mv3uNcsnoh4fhTH7YGIODMiotOxThTd7EfGcvysah+IBud2vbBvRsQz69rlmoj4VUS8pxtt1sv7Y5PYPl72tddGxNciYtdy+LyI2FzXdmeNV2xN4mp73Y0prszs2D9gKnALMB/YBVgNHNTJOlqMYx2wexfqfTnwPOD6umEfAz5Qfv4A8NGK618KvK/CNtgLeF75+UnAzcBBFbdDsxgqawsggNnl5+nAj4EXVtwOzWKodJvYWf71Qv/X7v5XjlsNzAD2K+OfWkGcxwPnAv9dfu+Z+IAVwDvLz7sAu/ZKfMBc4FZgZvn9S8Cx3YyPNo57I8UDXAm8qOy3LgKOGO/tsBf/dbsfGaEPaXjcqHIfoMG5Xa/sm8PW3y+BfbvRZr28PzaJ7feBaeXnj9bFNq9+umHldDS2JnG1ve7GElen74S9ABjIzLWZ+Qjwn8DRHa6jZ2Xmd4H7hg0+muKgTvn3Dyuuv1KZuSEzry4/PwSsoThxqLIdmsVQmSw8XH6dXv5Lqm2HZjFofHS9/xvD/nc08J+ZuSUzbwUGKJZj3ETE04GjgH+vG9wT8UXEkykOyJ8ByMxHMvOBXomvNA2YGRHTgCcCv+hmfG0e9xrGExF7AU/OzB9mcTazknHsG3tcV/uRMRw/u7EPDK+/V/ZNgFcBt2TmSP8597jF1sv7Y6PYMvOSzNxWfv0R8PSRyhiP2No8d+5om3U6CZsL3FH3/U4qPvktJXBJRFwVEX/Zhfrr7ZmZG6Do3ICndiGGd5e3ej8b4/j423ARMQ94LsUdmK60w7AYoMK2iOKRq2uAu4FLM7PydmgSA3Rpm5jkeqX/A1re/7oR8z8B7wceqxvWK/HNB+4BPhfF45L/HhGzeiW+zFwPfAK4HdgAPJiZl/RKfHXajWdu+bnqOHtRz/QjLR4/q4y30bldr237bwa+WPe9220GE2d/fDvFHaSa/cp++IqIeFk5rMrY2ll3Y4qr00lYo+cfu3Hl/SWZ+TzgCOBvI+LlXYihV/wr8AzgEIqD9mlVVBoRs4GvAO/JzF9VUWcLMVTaFpn5aGYeQnFl5wUR8ezxrK+NGLqyTewEeqX/a2f/qzTmiHgNcHdmXtXqLA2GjWebTqN4LOVfM/O5wCaKx3eaqbr9nkJxJXY/4GnArIj485FmaTCsm3fDm8XTa3F2U0+0RRvHzyrjbefcrvJ2jIhdgNcBXy4H9UKbjaRn9seIOAnYBnyhHLQB2Kfsh48Hzi2fVKgqtnbX3Zji6nQSdiewd933p1M8KlGpzPxF+fdu4GtUe2t8uLvK25S126h3V1l5Zt5Vnog/BnyaCtoiIqZTdN5fyMyvloMrbYdGMXSjLcp6HwAuBw6nS9tDfQzdaoedQE/0f23uf1XH/BLgdRGxjuIxq9+LiP/oofjuBO6su2N8HkVS1ivxHQbcmpn3ZOZW4KvAi3sovpp247mToY8hdWXf6RFd70faPH5WFm+Tc7te2vaPAK7OzLvKOLveZqWe3h8jYiHwGuAt5aN8lI/7bSw/X0Xx26sDqoptDOtuTHF1Ogn7CbB/ROxXXhF4M3B+h+sYUUTMiogn1T5T/Ojv+pHnGlfnAwvLzwuBb1RZeW3HK72ecW6L8m0wnwHWZObpdaMqa4dmMVTZFhGxRzz+lp+ZFCdPN1JtOzSMoeptYifSC/1fu/vf+cCbI2JGROwH7E/x4+JxkZl/n5lPz8x5FO3z7cz88x6K75fAHRHxzHLQq4AbeiU+iscQXxgRTyzX9asofrPTK/HVtBVP+YjUQxHxwnK5jqHiY2UP6Wo/MobjZyXb2Ajndr207f8pdY8idrvN6vTs/hgRhwMnAq/LzF/XDd8jIqaWn+eXsa2tKrZ2192Y48rOvxnmSIq36dwCnNTp8luofz7Fm0tWAz+rMgaKnW8DsJUiK34H0Ad8C/h5+Xe3iuv/PHAdcG258ew1zm3wUopbsNcC15T/jqy4HZrFUFlbAL8D/LSs63rg/5bDq2yHZjFUuk3sTP96oP9re/8DTirjvYkK30gHHMrjb0fsmfgoHj9ZVbbh14Gn9Fh8p1Bc0Lm+3JdndDO+JsedtuMBFpTLdAvwKSCq2hZ77V83+5ER+pCmx40q9gGanNv1yr5J8ZKcjcCcumGVt1kv749NYhug+I1VbVs7q5z2DeV6Xg1cDbx2vGJrElfb624scUU5oyRJkiSpAh3/z5olSZIkSc2ZhEmSJElShUzCJEmSJKlCJmGSJEmSVCGTMEmSJEmqkEmYJEmSdmoRsTQi3teBcnaNiL+p+/60iDhvR8vV5GMS1qOG78QNxv+gA3UcGxGfKj+/KyKOGWHaQyPixTtap6TJISKOi4g1EfGFbscyniLiPRHxxG7HIal3RMS0EUbvCgyev2XmLzLzjeMflSYak7DeNWQnrqn9D+KZ2dGEKDPPysyVI0xyKGASJqnmb4AjM/Mto004yglLr3sPxX/EKmmSiYiTIuKmiLgMeGY57PKIWFB+3j0i1pWfj42IL0fEN4FLImJ2RHwrIq6OiOsi4uiy2H8EnhER10TExyNiXkRcX5bxhIj4XDn9TyPilXVlfzUiLo6In0fExypuCnWBSVjvqt+JfxIR34mIcyn+B28i4uHy76ER8d2I+FpE3BARZ0VE0/UaEW+LiJsj4grgJXXDB2/Dl1e4b4iIayPiPyNiHvAu4L1lPC+LiNdGxI/LTuSyiNizrpzPlp3Y2og4rq6OY8oyV0fE58the0TEV8pl/ElEvARJPS0izgLmA+dHxIkR8YOyL/hBRNROZIafsMwq+4aflNMePUL58yLif8uTm6trd+HL/u6KiPhS2Y/9Y0S8JSKuLE9qnlFOt295cnRt+Xefcvg5EfHGunrq+9HLI+K8iLgxIr4QheOApwHfiYjvjFNzSuqCiHg+8GbgucAfAf+nhdleBCzMzN8DfgO8PjOfB7wSOC0iAvgAcEtmHpKZfzds/r8FyMyDgT8FVkTEE8pxhwBvAg4G3hQRe+/QAqrnTeSrk5PdB4BnZ+YhEXEocEH5/dYG074AOAi4DbiYojPZ7vnjiNgLOAV4PvAg8B3gp03q3i8zt0TErpn5QHnS9XBmfqIs6ynACzMzI+KdwPuBE8r5n0XRIT0JuCki/hU4ADgJeElm3hsRu5XTngF8MjO/V54o/Q9wYOvNJKlqmfmuiDicYj9/BDgtM7dFxGHAR4A3lJO+CPidzLwvIj4CfDsz3x4RuwJXRsRlmbmpQRV3A6/OzN9ExP7AF4EF5bjnUPQR9wFrgX/PzBdExGJgEcWdq08BKzNzRUS8HTgT+MNRFuu5wG8DvwC+T9FXnRkRxwOvzMx7220nST3tZcDXMvPXABFxfgvzXJqZ95WfA/hIRLwceAyYC+w5yvwvBZYDZOaNEXEbxfkRwLcy88EylhuAfYE72lgeTTAmYRPHlU0SsNq4tQAR8UWKnbzRj0B/F7g8M+8pp/0vHt/5610LfCEivg58vUmdTwf+q0zsdgHqY7sgM7cAWyLibopO6feA82onMnWd2GHAQcXFIwCeHBFPysyHmtQrqcLejIcAAANYSURBVLfMobiauz+QwPS6cfUnLL8PvC4e/+H7E4B9gDUNypwOfCoiDgEeZWg/9ZPM3AAQEbcAl5TDr6NICqFI/v6o/Px5oJVHe67MzDvLcq8B5gHfa2E+SRNXNhi2jcefFHvCsHH1F43eAuwBPD8zt0bx2OLw6YeLEcZtqfv8KJ6jT3o+jjhxNLpaXDO8E2nUqbQyruYo4J8p7phdFY1/z7Ec+FR5S/2vGNrxNOpIokndU4AXlbftD8nMuSZg0oTyYeA7mfls4LUM7Qvq+60A3lC3r++TmY0SMID3AndR3PVaQHGhp6a+f3ms7vtjND9pqfU9gydX5WNDzcr1BEia/L4LvD4iZkbEkyj6L4B1FOc/ACO9UGMOcHeZgL2S4s4VwEMUTwI1q/MtABFxAMWFqJvGvASa0EzCetdIO/FwL4iI/aL4LdibaH719sfAoRHRFxHTgT8ePkFZxt6Z+R2KRwx3BWY3iGcOsL78vLCFGL8F/ElE9JX11B5HvAR4d139h7RQlqTeUd8XHDvCdP8DLCqTHyLiuaOUuSEzHwPeCkxtM6YfUPzWA4oTnlqfuI7HT66OZuhdu2ba6YslTRCZeTXwX8A1wFeA/y1HfQL46yjeQr37CEV8AVgQEaso+pkby3I3At+PiOsj4uPD5vkXYGpEXFfWfWz55JB2Ql7p61GZuTEivh/FG3U2U1wVbuaHFC/yOJjiKsvXmpS5ISKWltNvAK5m+5ObqcB/RMQciivXnyx/E/ZN4Lzyx/SLgKXAlyNiPfAjYL9RludnEXEqcEVEPErxW7RjgeOAf46Iaym2x+9SvARE0sTwMYrHEY8Hvj3CdB8G/gm4tkzE1gGvaTLtvwBfiYg/pvjt6khPAjRyHPDZiPg74B7gbeXwTwPfiIgrKS4MtVLu2cBFEbEhM1856tSSJozMPBU4tcGo36n7vKSc9hzgnLp576V49LlRuX82bNCzy+G/ocHFqgZlN+sbNYlEZitPp6lXlS/teJ87rCRJkjQx+DiiJEmSJFXIO2GTVET8GJgxbPBbM/O6bsQjScNFxB8AHx02+NbMfH034pEkqSomYZIkSZJUIR9HlCRJkqQKmYRJkiRJUoVMwiRJkiSpQiZhkiRJklQhkzBJkiRJqtD/B7NwQ9LgwB/GAAAAAElFTkSuQmCC",
      "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=df['trip_distance'])\n",
    "sns.boxplot(ax=axes[1], x=df['fare_amount'])\n",
    "sns.boxplot(ax=axes[2], x=df['duration'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. All three variables contain outliers. Some are extreme, but others not so much.\n",
    "\n",
    "2. It's 30 miles from the southern tip of Staten Island to the northern end of Manhattan and that's in a straight line. With this knowledge and the distribution of the values in this column, it's reasonable to leave these values alone and not alter them. However, the values for `fare_amount` and `duration` definitely seem to have problematic outliers on the higher end.\n",
    "\n",
    "3. Probably not for the latter two, but for `trip_distance` it might be okay."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vtj4iAJMk9Vc"
   },
   "source": [
    "### Task 2e. Imputations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PfWU7BVP3f7E"
   },
   "source": [
    "#### `trip_distance` outliers\n",
    "To check, sort the column values, eliminate duplicates, and inspect the least 10 values. Are they rounded values or precise values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 632
    },
    "id": "KCEzE-gwL5gq",
    "outputId": "36c88d36-18d6-4556-c762-2b007e48bbd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are trip distances of 0 bad data or very short trips rounded down?\n",
    "sorted(set(df['trip_distance']))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pqcGiHLa4TvP"
   },
   "source": [
    "The distances are captured with a high degree of precision. However, it might be possible for trips to have distances of zero if a passenger summoned a taxi and then changed their mind. Besides, are there enough zero values in the data to pose a problem?\n",
    "\n",
    "Calculate the count of rides where the `trip_distance` is zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['trip_distance']==0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "148 out of ~23,000 rides is relatively insignificant. You could impute it with a value of 0.01, but it's unlikely to have much of an effect on the model. Therefore, the `trip_distance` column will remain untouched with regard to outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `fare_amount` outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22699.000000\n",
       "mean        13.026629\n",
       "std         13.243791\n",
       "min       -120.000000\n",
       "25%          6.500000\n",
       "50%          9.500000\n",
       "75%         14.500000\n",
       "max        999.990000\n",
       "Name: fare_amount, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fare_amount'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The range of values in the `fare_amount` column is large and the extremes don't make much sense.\n",
    "\n",
    "* **Low values:** Negative values are problematic. Values of zero could be legitimate if the taxi logged a trip that was immediately canceled.\n",
    "\n",
    "* **High values:** The maximum fare amount in this dataset is nearly \\\\$1,000, which seems very unlikely. High values for this feature can be capped based on intuition and statistics. The interquartile range (IQR) is \\\\$8. The standard formula of `Q3 + (1.5 * IQR)` yields \\$26.50. That doesn't seem appropriate for the maximum fare cap. In this case, we'll use a factor of `6`, which results in a cap of $62.50.\n",
    "\n",
    "Impute values less than $0 with `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute values less than $0 with 0\n",
    "df.loc[df['fare_amount'] < 0, 'fare_amount'] = 0\n",
    "df['fare_amount'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now impute the maximum value as `Q3 + (6 * IQR)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_imputer(column_list, iqr_factor):\n",
    "    '''\n",
    "    Impute upper-limit values in specified columns based on their interquartile range.\n",
    "\n",
    "    Arguments:\n",
    "        column_list: A list of columns to iterate over\n",
    "        iqr_factor: A number representing x in the formula:\n",
    "                    Q3 + (x * IQR). Used to determine maximum threshold,\n",
    "                    beyond which a point is considered an outlier.\n",
    "\n",
    "    The IQR is computed for each column in column_list and values exceeding\n",
    "    the upper threshold for each column are imputed with the upper threshold value.\n",
    "    '''\n",
    "    for col in column_list:\n",
    "        # Reassign minimum to zero\n",
    "        df.loc[df[col] < 0, col] = 0\n",
    "\n",
    "        # Calculate upper threshold\n",
    "        q1 = df[col].quantile(0.25)\n",
    "        q3 = df[col].quantile(0.75)\n",
    "        iqr = q3 - q1\n",
    "        upper_threshold = q3 + (iqr_factor * iqr)\n",
    "        print(col)\n",
    "        print('q3:', q3)\n",
    "        print('upper_threshold:', upper_threshold)\n",
    "\n",
    "        # Reassign values > threshold to threshold\n",
    "        df.loc[df[col] > upper_threshold, col] = upper_threshold\n",
    "        print(df[col].describe())\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fare_amount\n",
      "q3: 14.5\n",
      "upper_threshold: 62.5\n",
      "count    22699.000000\n",
      "mean        12.897913\n",
      "std         10.541137\n",
      "min          0.000000\n",
      "25%          6.500000\n",
      "50%          9.500000\n",
      "75%         14.500000\n",
      "max         62.500000\n",
      "Name: fare_amount, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "outlier_imputer(['fare_amount'], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `duration` outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22699.000000\n",
       "mean        17.013777\n",
       "std         61.996482\n",
       "min        -16.983333\n",
       "25%          6.650000\n",
       "50%         11.183333\n",
       "75%         18.383333\n",
       "max       1439.550000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['duration'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `duration` column has problematic values at both the lower and upper extremities.\n",
    "\n",
    "* **Low values:** There should be no values that represent negative time. Impute all negative durations with `0`.\n",
    "\n",
    "* **High values:** Impute high values the same way you imputed the high-end outliers for fares: `Q3 + (6 * IQR)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute a 0 for any negative values\n",
    "df.loc[df['duration'] < 0, 'duration'] = 0\n",
    "df['duration'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration\n",
      "q3: 18.383333333333333\n",
      "upper_threshold: 88.78333333333333\n",
      "count    22699.000000\n",
      "mean        14.460555\n",
      "std         11.947043\n",
      "min          0.000000\n",
      "25%          6.650000\n",
      "50%         11.183333\n",
      "75%         18.383333\n",
      "max         88.783333\n",
      "Name: duration, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Impute the high outliers\n",
    "outlier_imputer(['duration'], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3a. Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `mean_distance` column\n",
    "\n",
    "When deployed, the model will not know the duration of a trip until after the trip occurs, so 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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100 231\n",
       "1     186 43\n",
       "Name: pickup_dropoff, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create `pickup_dropoff` column\n",
    "df['pickup_dropoff'] = df['PULocationID'].astype(str) + ' ' + df['DOLocationID'].astype(str)\n",
    "df['pickup_dropoff'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, use a `groupby()` statement to group each row by the new `pickup_dropoff` column, compute the mean, and capture the values only in the `trip_distance` column. Assign the results to a variable named `grouped`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>2.433333</td>\n",
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       "      <th>100 1</th>\n",
       "      <td>16.890000</td>\n",
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       "      <th>100 100</th>\n",
       "      <td>0.253333</td>\n",
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       "      <th>100 107</th>\n",
       "      <td>1.180000</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                trip_distance\n",
       "pickup_dropoff               \n",
       "1 1                  2.433333\n",
       "10 148              15.700000\n",
       "100 1               16.890000\n",
       "100 100              0.253333\n",
       "100 107              1.180000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('pickup_dropoff').mean(numeric_only=True)[['trip_distance']]\n",
    "grouped[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`grouped` is an object of the `DataFrame` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Convert `grouped` to a dictionary\n",
    "grouped_dict = grouped.to_dict()\n",
    "\n",
    "# 2. Reassign to only contain the inner dictionary\n",
    "grouped_dict = grouped_dict['trip_distance']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Create a `mean_distance` column that is a copy of the `pickup_dropoff` helper column.\n",
    "\n",
    "2. Use the [`map()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html#pandas-series-map) method on the `mean_distance` series. Pass `grouped_dict` as its argument. Reassign the result back to the `mean_distance` series.\n",
    "</br></br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3.521667</td>\n",
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       "      <td>3.521667</td>\n",
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       "      <td>3.521667</td>\n",
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       "      <td>3.521667</td>\n",
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       "      <th>20581</th>\n",
       "      <td>3.521667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       mean_distance\n",
       "0           3.521667\n",
       "4909        3.521667\n",
       "16636       3.521667\n",
       "18134       3.521667\n",
       "19761       3.521667\n",
       "20581       3.521667"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Create a mean_distance column that is a copy of the pickup_dropoff helper column\n",
    "df['mean_distance'] = df['pickup_dropoff']\n",
    "\n",
    "# 2. Map `grouped_dict` to the `mean_distance` column\n",
    "df['mean_distance'] = df['mean_distance'].map(grouped_dict)\n",
    "\n",
    "# Confirm that it worked\n",
    "df[(df['PULocationID']==100) & (df['DOLocationID']==231)][['mean_distance']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `mean_duration` column\n",
    "\n",
    "Repeat the process used to create the `mean_distance` column to create a `mean_duration` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_duration</th>\n",
       "    </tr>\n",
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       "      <td>22.847222</td>\n",
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       "       mean_duration\n",
       "0          22.847222\n",
       "4909       22.847222\n",
       "16636      22.847222\n",
       "18134      22.847222\n",
       "19761      22.847222\n",
       "20581      22.847222"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('pickup_dropoff').mean(numeric_only=True)[['duration']]\n",
    "grouped\n",
    "\n",
    "# Create a dictionary where keys are unique pickup_dropoffs and values are\n",
    "# mean trip duration for all trips with those pickup_dropoff combos\n",
    "grouped_dict = grouped.to_dict()\n",
    "grouped_dict = grouped_dict['duration']\n",
    "\n",
    "df['mean_duration'] = df['pickup_dropoff']\n",
    "df['mean_duration'] = df['mean_duration'].map(grouped_dict)\n",
    "\n",
    "# Confirm that it worked\n",
    "df[(df['PULocationID']==100) & (df['DOLocationID']==231)][['mean_duration']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `day` and `month` columns\n",
    "\n",
    "Create two new columns, `day` (name of day) and `month` (name of month) by extracting the relevant information from the `tpep_pickup_datetime` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 'day' col\n",
    "df['day'] = df['tpep_pickup_datetime'].dt.day_name().str.lower()\n",
    "\n",
    "# Create 'month' col\n",
    "df['month'] = df['tpep_pickup_datetime'].dt.strftime('%b').str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `rush_hour` column\n",
    "\n",
    "Define rush hour as:\n",
    "* Any weekday (not Saturday or Sunday) AND\n",
    "* Either from 06:00&ndash;10:00 or from 16:00&ndash;20:00\n",
    "\n",
    "Create a binary `rush_hour` column that contains a 1 if the ride was during rush hour and a 0 if it was not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 'rush_hour' col\n",
    "df['rush_hour'] = df['tpep_pickup_datetime'].dt.hour\n",
    "\n",
    "# If day is Saturday or Sunday, impute 0 in `rush_hour` column\n",
    "df.loc[df['day'].isin(['saturday', 'sunday']), 'rush_hour'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rush_hourizer(hour):\n",
    "    if 6 <= hour['rush_hour'] < 10:\n",
    "        val = 1\n",
    "    elif 16 <= hour['rush_hour'] < 20:\n",
    "        val = 1\n",
    "    else:\n",
    "        val = 0\n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
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       "      <th>passenger_count</th>\n",
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       "      <th>DOLocationID</th>\n",
       "      <th>...</th>\n",
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       "      <th>pickup_dropoff</th>\n",
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       "      <th>mean_duration</th>\n",
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       "      <td>6</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>262 236</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>friday</td>\n",
       "      <td>dec</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-07 13:17:59</td>\n",
       "      <td>2017-05-07 13:48:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>188 97</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>sunday</td>\n",
       "      <td>may</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30841670</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-15 23:32:20</td>\n",
       "      <td>2017-04-15 23:49:03</td>\n",
       "      <td>1</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "      <td>16.716667</td>\n",
       "      <td>4 112</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>apr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "4    30841670         2  2017-04-15 23:32:20   2017-04-15 23:49:03   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "4                1           4.37           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  ...  tolls_amount  improvement_surcharge  \\\n",
       "0           100           231  ...           0.0                    0.3   \n",
       "1           186            43  ...           0.0                    0.3   \n",
       "2           262           236  ...           0.0                    0.3   \n",
       "3           188            97  ...           0.0                    0.3   \n",
       "4             4           112  ...           0.0                    0.3   \n",
       "\n",
       "   total_amount   duration  pickup_dropoff  mean_distance  mean_duration  \\\n",
       "0         16.56  14.066667         100 231       3.521667      22.847222   \n",
       "1         20.80  26.500000          186 43       3.108889      24.470370   \n",
       "2          8.75   7.200000         262 236       0.881429       7.250000   \n",
       "3         27.69  30.250000          188 97       3.700000      30.250000   \n",
       "4         17.80  16.716667           4 112       4.435000      14.616667   \n",
       "\n",
       "        day  month rush_hour  \n",
       "0  saturday    mar         0  \n",
       "1   tuesday    apr         0  \n",
       "2    friday    dec         1  \n",
       "3    sunday    may         0  \n",
       "4  saturday    apr         0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the `rush_hourizer()` function to the new column\n",
    "df.loc[(df.day != 'saturday') & (df.day != 'sunday'), 'rush_hour'] = df.apply(rush_hourizer, axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4. Scatter plot\n",
    "\n",
    "Create a scatterplot to visualize the relationship between `mean_duration` and `fare_amount`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of duration and trip_distance, with a line of best fit\n",
    "sns.set(style='whitegrid')\n",
    "f = plt.figure()\n",
    "f.set_figwidth(5)\n",
    "f.set_figheight(5)\n",
    "sns.regplot(x=df['mean_duration'], y=df['fare_amount'],\n",
    "            scatter_kws={'alpha':0.5, 's':5},\n",
    "            line_kws={'color':'red'})\n",
    "plt.ylim(0, 70)\n",
    "plt.xlim(0, 70)\n",
    "plt.title('Mean duration x fare amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `mean_duration` variable correlates with the target variable. But what are the horizontal lines around fare amounts of 52 dollars and 63 dollars? What are the values and how many are there?\n",
    "\n",
    "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.0    514\n",
       "62.5     84\n",
       "59.0      9\n",
       "50.5      9\n",
       "57.5      8\n",
       "Name: fare_amount, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['fare_amount'] > 50]['fare_amount'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 514 trips whose fares were \\$52.\n",
    "\n",
    "Examine the first 30 of these trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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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>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>pickup_dropoff</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18600059</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-05 19:15:30</td>\n",
       "      <td>2017-03-05 19:52:18</td>\n",
       "      <td>2</td>\n",
       "      <td>18.90</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.58</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>72.92</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>236 132</td>\n",
       "      <td>19.211667</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>sunday</td>\n",
       "      <td>mar</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>47959795</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-03 14:24:57</td>\n",
       "      <td>2017-06-03 15:31:48</td>\n",
       "      <td>1</td>\n",
       "      <td>18.00</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>66.850000</td>\n",
       "      <td>132 163</td>\n",
       "      <td>19.229000</td>\n",
       "      <td>52.941667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>jun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>95729204</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-11 20:16:16</td>\n",
       "      <td>2017-11-11 20:17:14</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>132 132</td>\n",
       "      <td>2.255862</td>\n",
       "      <td>3.021839</td>\n",
       "      <td>saturday</td>\n",
       "      <td>nov</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>103404868</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-12-06 23:37:08</td>\n",
       "      <td>2017-12-07 00:06:19</td>\n",
       "      <td>1</td>\n",
       "      <td>18.93</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>29.183333</td>\n",
       "      <td>132 79</td>\n",
       "      <td>19.431667</td>\n",
       "      <td>47.275000</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>80479432</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-24 23:45:45</td>\n",
       "      <td>2017-09-25 00:15:14</td>\n",
       "      <td>1</td>\n",
       "      <td>17.99</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.64</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>73.20</td>\n",
       "      <td>29.483333</td>\n",
       "      <td>132 234</td>\n",
       "      <td>17.654000</td>\n",
       "      <td>49.833333</td>\n",
       "      <td>sunday</td>\n",
       "      <td>sep</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>16226157</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-02-28 18:30:05</td>\n",
       "      <td>2017-02-28 19:09:55</td>\n",
       "      <td>1</td>\n",
       "      <td>18.40</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>62.84</td>\n",
       "      <td>39.833333</td>\n",
       "      <td>132 48</td>\n",
       "      <td>18.761905</td>\n",
       "      <td>58.246032</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>55253442</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-05 12:51:58</td>\n",
       "      <td>2017-06-05 13:07:35</td>\n",
       "      <td>1</td>\n",
       "      <td>4.73</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>228</td>\n",
       "      <td>88</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>15.616667</td>\n",
       "      <td>228 88</td>\n",
       "      <td>4.730000</td>\n",
       "      <td>15.616667</td>\n",
       "      <td>monday</td>\n",
       "      <td>jun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>65900029</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-03 22:47:14</td>\n",
       "      <td>2017-08-03 23:32:41</td>\n",
       "      <td>2</td>\n",
       "      <td>18.21</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>45.450000</td>\n",
       "      <td>132 48</td>\n",
       "      <td>18.761905</td>\n",
       "      <td>58.246032</td>\n",
       "      <td>thursday</td>\n",
       "      <td>aug</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>80904240</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-26 13:48:26</td>\n",
       "      <td>2017-09-26 14:31:17</td>\n",
       "      <td>1</td>\n",
       "      <td>17.27</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>42.850000</td>\n",
       "      <td>186 132</td>\n",
       "      <td>17.096000</td>\n",
       "      <td>42.920000</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>sep</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>33706214</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-23 21:34:48</td>\n",
       "      <td>2017-04-23 22:46:23</td>\n",
       "      <td>6</td>\n",
       "      <td>18.34</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>57.80</td>\n",
       "      <td>71.583333</td>\n",
       "      <td>132 148</td>\n",
       "      <td>17.994286</td>\n",
       "      <td>46.340476</td>\n",
       "      <td>sunday</td>\n",
       "      <td>apr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>99259872</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-22 21:31:32</td>\n",
       "      <td>2017-11-22 22:00:25</td>\n",
       "      <td>1</td>\n",
       "      <td>18.65</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>63.36</td>\n",
       "      <td>28.883333</td>\n",
       "      <td>132 144</td>\n",
       "      <td>18.537500</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>nov</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>61050418</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-07-18 13:29:06</td>\n",
       "      <td>2017-07-18 13:29:19</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>230</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>230 161</td>\n",
       "      <td>0.685484</td>\n",
       "      <td>7.965591</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>jul</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>54444647</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-26 13:39:12</td>\n",
       "      <td>2017-06-26 14:34:54</td>\n",
       "      <td>1</td>\n",
       "      <td>17.76</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>211</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>55.700000</td>\n",
       "      <td>211 132</td>\n",
       "      <td>16.580000</td>\n",
       "      <td>61.691667</td>\n",
       "      <td>monday</td>\n",
       "      <td>jun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>94424289</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-07 22:15:00</td>\n",
       "      <td>2017-11-07 22:45:32</td>\n",
       "      <td>2</td>\n",
       "      <td>16.97</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>30.533333</td>\n",
       "      <td>132 170</td>\n",
       "      <td>17.203000</td>\n",
       "      <td>37.113333</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>nov</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>103094220</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-06 05:19:50</td>\n",
       "      <td>2017-12-06 05:53:52</td>\n",
       "      <td>1</td>\n",
       "      <td>20.80</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.85</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>64.41</td>\n",
       "      <td>34.033333</td>\n",
       "      <td>132 239</td>\n",
       "      <td>20.901250</td>\n",
       "      <td>44.862500</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>66115834</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-08-04 17:53:34</td>\n",
       "      <td>2017-08-04 18:50:56</td>\n",
       "      <td>1</td>\n",
       "      <td>21.60</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>12.60</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>75.66</td>\n",
       "      <td>57.366667</td>\n",
       "      <td>264 264</td>\n",
       "      <td>3.191516</td>\n",
       "      <td>15.618773</td>\n",
       "      <td>friday</td>\n",
       "      <td>aug</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>55934137</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-09 09:31:25</td>\n",
       "      <td>2017-06-09 10:24:10</td>\n",
       "      <td>2</td>\n",
       "      <td>18.81</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>163</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>66.00</td>\n",
       "      <td>52.750000</td>\n",
       "      <td>163 132</td>\n",
       "      <td>17.275833</td>\n",
       "      <td>52.338889</td>\n",
       "      <td>friday</td>\n",
       "      <td>jun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>13731926</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-21 06:11:03</td>\n",
       "      <td>2017-02-21 06:59:39</td>\n",
       "      <td>5</td>\n",
       "      <td>16.94</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>60.34</td>\n",
       "      <td>48.600000</td>\n",
       "      <td>132 170</td>\n",
       "      <td>17.203000</td>\n",
       "      <td>37.113333</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>52277743</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-20 08:15:18</td>\n",
       "      <td>2017-06-20 10:24:37</td>\n",
       "      <td>1</td>\n",
       "      <td>17.77</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>88.783333</td>\n",
       "      <td>132 246</td>\n",
       "      <td>18.515000</td>\n",
       "      <td>66.316667</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>jun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>2684305</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-01-10 22:29:47</td>\n",
       "      <td>2017-01-10 23:06:46</td>\n",
       "      <td>1</td>\n",
       "      <td>18.57</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>66.00</td>\n",
       "      <td>36.983333</td>\n",
       "      <td>132 48</td>\n",
       "      <td>18.761905</td>\n",
       "      <td>58.246032</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>jan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>90860814</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-27 21:50:00</td>\n",
       "      <td>2017-10-27 22:35:04</td>\n",
       "      <td>1</td>\n",
       "      <td>22.43</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>45.066667</td>\n",
       "      <td>132 163</td>\n",
       "      <td>19.229000</td>\n",
       "      <td>52.941667</td>\n",
       "      <td>friday</td>\n",
       "      <td>oct</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>106575186</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-16 06:39:59</td>\n",
       "      <td>2017-12-16 07:07:59</td>\n",
       "      <td>2</td>\n",
       "      <td>17.80</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>75</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>64.56</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>75 132</td>\n",
       "      <td>18.442500</td>\n",
       "      <td>36.204167</td>\n",
       "      <td>saturday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>110495611</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-12-30 05:25:29</td>\n",
       "      <td>2017-12-30 06:01:29</td>\n",
       "      <td>6</td>\n",
       "      <td>18.23</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>68</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>68 132</td>\n",
       "      <td>18.785000</td>\n",
       "      <td>58.041667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>87017503</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-15 22:39:12</td>\n",
       "      <td>2017-10-15 23:14:22</td>\n",
       "      <td>1</td>\n",
       "      <td>21.80</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>261</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>35.166667</td>\n",
       "      <td>132 261</td>\n",
       "      <td>22.115000</td>\n",
       "      <td>51.493750</td>\n",
       "      <td>sunday</td>\n",
       "      <td>oct</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>12762608</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-17 20:39:42</td>\n",
       "      <td>2017-02-17 21:13:29</td>\n",
       "      <td>1</td>\n",
       "      <td>19.57</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.67</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.01</td>\n",
       "      <td>33.783333</td>\n",
       "      <td>132 140</td>\n",
       "      <td>19.293333</td>\n",
       "      <td>36.791667</td>\n",
       "      <td>friday</td>\n",
       "      <td>feb</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>71264442</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-08-23 18:23:26</td>\n",
       "      <td>2017-08-23 19:18:29</td>\n",
       "      <td>1</td>\n",
       "      <td>16.70</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>42.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>99.59</td>\n",
       "      <td>55.050000</td>\n",
       "      <td>132 230</td>\n",
       "      <td>18.571200</td>\n",
       "      <td>59.598000</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>aug</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>11006300</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-07 17:20:19</td>\n",
       "      <td>2017-02-07 17:34:41</td>\n",
       "      <td>1</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>170</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>62.84</td>\n",
       "      <td>14.366667</td>\n",
       "      <td>170 48</td>\n",
       "      <td>1.265789</td>\n",
       "      <td>14.135965</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>68882036</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-14 23:01:15</td>\n",
       "      <td>2017-08-14 23:03:35</td>\n",
       "      <td>5</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>265</td>\n",
       "      <td>265</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>265 265</td>\n",
       "      <td>0.753077</td>\n",
       "      <td>3.411538</td>\n",
       "      <td>monday</td>\n",
       "      <td>aug</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>74720333</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-09-06 10:46:17</td>\n",
       "      <td>2017-09-06 11:44:41</td>\n",
       "      <td>1</td>\n",
       "      <td>19.10</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>15.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>68.60</td>\n",
       "      <td>58.400000</td>\n",
       "      <td>239 132</td>\n",
       "      <td>19.795000</td>\n",
       "      <td>50.562500</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>sep</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>51937907</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-19 06:23:13</td>\n",
       "      <td>2017-06-19 07:03:53</td>\n",
       "      <td>6</td>\n",
       "      <td>19.77</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>238</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17.57</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>76.13</td>\n",
       "      <td>40.666667</td>\n",
       "      <td>238 132</td>\n",
       "      <td>19.470000</td>\n",
       "      <td>53.861111</td>\n",
       "      <td>monday</td>\n",
       "      <td>jun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "11      18600059         2  2017-03-05 19:15:30   2017-03-05 19:52:18   \n",
       "110     47959795         1  2017-06-03 14:24:57   2017-06-03 15:31:48   \n",
       "161     95729204         2  2017-11-11 20:16:16   2017-11-11 20:17:14   \n",
       "247    103404868         2  2017-12-06 23:37:08   2017-12-07 00:06:19   \n",
       "379     80479432         2  2017-09-24 23:45:45   2017-09-25 00:15:14   \n",
       "388     16226157         1  2017-02-28 18:30:05   2017-02-28 19:09:55   \n",
       "406     55253442         2  2017-06-05 12:51:58   2017-06-05 13:07:35   \n",
       "449     65900029         2  2017-08-03 22:47:14   2017-08-03 23:32:41   \n",
       "468     80904240         2  2017-09-26 13:48:26   2017-09-26 14:31:17   \n",
       "520     33706214         2  2017-04-23 21:34:48   2017-04-23 22:46:23   \n",
       "569     99259872         2  2017-11-22 21:31:32   2017-11-22 22:00:25   \n",
       "572     61050418         2  2017-07-18 13:29:06   2017-07-18 13:29:19   \n",
       "586     54444647         2  2017-06-26 13:39:12   2017-06-26 14:34:54   \n",
       "692     94424289         2  2017-11-07 22:15:00   2017-11-07 22:45:32   \n",
       "717    103094220         1  2017-12-06 05:19:50   2017-12-06 05:53:52   \n",
       "719     66115834         1  2017-08-04 17:53:34   2017-08-04 18:50:56   \n",
       "782     55934137         2  2017-06-09 09:31:25   2017-06-09 10:24:10   \n",
       "816     13731926         2  2017-02-21 06:11:03   2017-02-21 06:59:39   \n",
       "818     52277743         2  2017-06-20 08:15:18   2017-06-20 10:24:37   \n",
       "835      2684305         2  2017-01-10 22:29:47   2017-01-10 23:06:46   \n",
       "840     90860814         2  2017-10-27 21:50:00   2017-10-27 22:35:04   \n",
       "861    106575186         1  2017-12-16 06:39:59   2017-12-16 07:07:59   \n",
       "881    110495611         2  2017-12-30 05:25:29   2017-12-30 06:01:29   \n",
       "958     87017503         1  2017-10-15 22:39:12   2017-10-15 23:14:22   \n",
       "970     12762608         2  2017-02-17 20:39:42   2017-02-17 21:13:29   \n",
       "984     71264442         1  2017-08-23 18:23:26   2017-08-23 19:18:29   \n",
       "1082    11006300         2  2017-02-07 17:20:19   2017-02-07 17:34:41   \n",
       "1097    68882036         2  2017-08-14 23:01:15   2017-08-14 23:03:35   \n",
       "1110    74720333         1  2017-09-06 10:46:17   2017-09-06 11:44:41   \n",
       "1179    51937907         2  2017-06-19 06:23:13   2017-06-19 07:03:53   \n",
       "\n",
       "      passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "11                  2          18.90           2                  N   \n",
       "110                 1          18.00           2                  N   \n",
       "161                 1           0.23           2                  N   \n",
       "247                 1          18.93           2                  N   \n",
       "379                 1          17.99           2                  N   \n",
       "388                 1          18.40           2                  N   \n",
       "406                 1           4.73           2                  N   \n",
       "449                 2          18.21           2                  N   \n",
       "468                 1          17.27           2                  N   \n",
       "520                 6          18.34           2                  N   \n",
       "569                 1          18.65           2                  N   \n",
       "572                 1           0.00           2                  N   \n",
       "586                 1          17.76           2                  N   \n",
       "692                 2          16.97           2                  N   \n",
       "717                 1          20.80           2                  N   \n",
       "719                 1          21.60           2                  N   \n",
       "782                 2          18.81           2                  N   \n",
       "816                 5          16.94           2                  N   \n",
       "818                 1          17.77           2                  N   \n",
       "835                 1          18.57           2                  N   \n",
       "840                 1          22.43           2                  N   \n",
       "861                 2          17.80           2                  N   \n",
       "881                 6          18.23           2                  N   \n",
       "958                 1          21.80           2                  N   \n",
       "970                 1          19.57           2                  N   \n",
       "984                 1          16.70           2                  N   \n",
       "1082                1           1.09           2                  N   \n",
       "1097                5           2.12           2                  N   \n",
       "1110                1          19.10           2                  N   \n",
       "1179                6          19.77           2                  N   \n",
       "\n",
       "      PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "11             236           132             1         52.0    0.0      0.5   \n",
       "110            132           163             1         52.0    0.0      0.5   \n",
       "161            132           132             2         52.0    0.0      0.5   \n",
       "247            132            79             2         52.0    0.0      0.5   \n",
       "379            132           234             1         52.0    0.0      0.5   \n",
       "388            132            48             2         52.0    4.5      0.5   \n",
       "406            228            88             2         52.0    0.0      0.5   \n",
       "449            132            48             2         52.0    0.0      0.5   \n",
       "468            186           132             2         52.0    0.0      0.5   \n",
       "520            132           148             1         52.0    0.0      0.5   \n",
       "569            132           144             1         52.0    0.0      0.5   \n",
       "572            230           161             1         52.0    0.0      0.5   \n",
       "586            211           132             1         52.0    0.0      0.5   \n",
       "692            132           170             1         52.0    0.0      0.5   \n",
       "717            132           239             1         52.0    0.0      0.5   \n",
       "719            264           264             1         52.0    4.5      0.5   \n",
       "782            163           132             1         52.0    0.0      0.5   \n",
       "816            132           170             1         52.0    0.0      0.5   \n",
       "818            132           246             1         52.0    0.0      0.5   \n",
       "835            132            48             1         52.0    0.0      0.5   \n",
       "840            132           163             2         52.0    0.0      0.5   \n",
       "861             75           132             1         52.0    0.0      0.5   \n",
       "881             68           132             2         52.0    0.0      0.5   \n",
       "958            132           261             2         52.0    0.0      0.5   \n",
       "970            132           140             1         52.0    0.0      0.5   \n",
       "984            132           230             1         52.0    4.5      0.5   \n",
       "1082           170            48             2         52.0    4.5      0.5   \n",
       "1097           265           265             2         52.0    0.0      0.5   \n",
       "1110           239           132             1         52.0    0.0      0.5   \n",
       "1179           238           132             1         52.0    0.0      0.5   \n",
       "\n",
       "      tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "11         14.58          5.54                    0.3         72.92   \n",
       "110         0.00          0.00                    0.3         52.80   \n",
       "161         0.00          0.00                    0.3         52.80   \n",
       "247         0.00          0.00                    0.3         52.80   \n",
       "379        14.64          5.76                    0.3         73.20   \n",
       "388         0.00          5.54                    0.3         62.84   \n",
       "406         0.00          5.76                    0.3         58.56   \n",
       "449         0.00          5.76                    0.3         58.56   \n",
       "468         0.00          5.76                    0.3         58.56   \n",
       "520         5.00          0.00                    0.3         57.80   \n",
       "569        10.56          0.00                    0.3         63.36   \n",
       "572        11.71          5.76                    0.3         70.27   \n",
       "586        11.71          5.76                    0.3         70.27   \n",
       "692        11.71          5.76                    0.3         70.27   \n",
       "717         5.85          5.76                    0.3         64.41   \n",
       "719        12.60          5.76                    0.3         75.66   \n",
       "782        13.20          0.00                    0.3         66.00   \n",
       "816         2.00          5.54                    0.3         60.34   \n",
       "818        11.71          5.76                    0.3         70.27   \n",
       "835        13.20          0.00                    0.3         66.00   \n",
       "840         0.00          5.76                    0.3         58.56   \n",
       "861         6.00          5.76                    0.3         64.56   \n",
       "881         0.00          0.00                    0.3         52.80   \n",
       "958         0.00          0.00                    0.3         52.80   \n",
       "970        11.67          5.54                    0.3         70.01   \n",
       "984        42.29          0.00                    0.3         99.59   \n",
       "1082        0.00          5.54                    0.3         62.84   \n",
       "1097        0.00          0.00                    0.3         52.80   \n",
       "1110       15.80          0.00                    0.3         68.60   \n",
       "1179       17.57          5.76                    0.3         76.13   \n",
       "\n",
       "       duration pickup_dropoff  mean_distance  mean_duration        day month  \\\n",
       "11    36.800000        236 132      19.211667      40.500000     sunday   mar   \n",
       "110   66.850000        132 163      19.229000      52.941667   saturday   jun   \n",
       "161    0.966667        132 132       2.255862       3.021839   saturday   nov   \n",
       "247   29.183333         132 79      19.431667      47.275000  wednesday   dec   \n",
       "379   29.483333        132 234      17.654000      49.833333     sunday   sep   \n",
       "388   39.833333         132 48      18.761905      58.246032    tuesday   feb   \n",
       "406   15.616667         228 88       4.730000      15.616667     monday   jun   \n",
       "449   45.450000         132 48      18.761905      58.246032   thursday   aug   \n",
       "468   42.850000        186 132      17.096000      42.920000    tuesday   sep   \n",
       "520   71.583333        132 148      17.994286      46.340476     sunday   apr   \n",
       "569   28.883333        132 144      18.537500      37.000000  wednesday   nov   \n",
       "572    0.216667        230 161       0.685484       7.965591    tuesday   jul   \n",
       "586   55.700000        211 132      16.580000      61.691667     monday   jun   \n",
       "692   30.533333        132 170      17.203000      37.113333    tuesday   nov   \n",
       "717   34.033333        132 239      20.901250      44.862500  wednesday   dec   \n",
       "719   57.366667        264 264       3.191516      15.618773     friday   aug   \n",
       "782   52.750000        163 132      17.275833      52.338889     friday   jun   \n",
       "816   48.600000        132 170      17.203000      37.113333    tuesday   feb   \n",
       "818   88.783333        132 246      18.515000      66.316667    tuesday   jun   \n",
       "835   36.983333         132 48      18.761905      58.246032    tuesday   jan   \n",
       "840   45.066667        132 163      19.229000      52.941667     friday   oct   \n",
       "861   28.000000         75 132      18.442500      36.204167   saturday   dec   \n",
       "881   36.000000         68 132      18.785000      58.041667   saturday   dec   \n",
       "958   35.166667        132 261      22.115000      51.493750     sunday   oct   \n",
       "970   33.783333        132 140      19.293333      36.791667     friday   feb   \n",
       "984   55.050000        132 230      18.571200      59.598000  wednesday   aug   \n",
       "1082  14.366667         170 48       1.265789      14.135965    tuesday   feb   \n",
       "1097   2.333333        265 265       0.753077       3.411538     monday   aug   \n",
       "1110  58.400000        239 132      19.795000      50.562500  wednesday   sep   \n",
       "1179  40.666667        238 132      19.470000      53.861111     monday   jun   \n",
       "\n",
       "      rush_hour  \n",
       "11            0  \n",
       "110           0  \n",
       "161           0  \n",
       "247           0  \n",
       "379           0  \n",
       "388           1  \n",
       "406           0  \n",
       "449           0  \n",
       "468           0  \n",
       "520           0  \n",
       "569           0  \n",
       "572           0  \n",
       "586           0  \n",
       "692           0  \n",
       "717           0  \n",
       "719           1  \n",
       "782           1  \n",
       "816           1  \n",
       "818           1  \n",
       "835           0  \n",
       "840           0  \n",
       "861           0  \n",
       "881           0  \n",
       "958           0  \n",
       "970           0  \n",
       "984           1  \n",
       "1082          1  \n",
       "1097          0  \n",
       "1110          0  \n",
       "1179          1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set pandas to display all columns\n",
    "pd.set_option('display.max_columns', None)\n",
    "df[df['fare_amount']==52].head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that almost all of the trips in the first 30 rows where the fare amount was \\$52 either begin or end at location 132, and all of them have a `RatecodeID` of 2.\n",
    "\n",
    "There is no readily apparent reason why PULocation 132 should have so many fares of 52 dollars. They seem to occur on all different days, at different times, with both vendors, in all months. However, there are many toll amounts of $5.76 and \\\\$5.54. This would seem to indicate that location 132 is in an area that frequently requires tolls to get to and from. It's likely this is an airport.\n",
    "\n",
    "\n",
    "The data dictionary says that `RatecodeID` of 2 indicates trips for JFK, which is John F. Kennedy International Airport. A quick Google search for \"new york city taxi flat rate \\$52\" indicates that in 2017 (the year that this data was collected) there was indeed a flat fare for taxi trips between JFK airport (in Queens) and Manhattan.\n",
    "\n",
    "Because `RatecodeID` is known from the data dictionary, the values for this rate code can be imputed back into the data after the model makes its predictions. This way you know that those data points will always be correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5. Isolate modeling variables\n",
    "\n",
    "Drop features that are redundant, irrelevant, or that will not be available in a deployed environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 25 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   Unnamed: 0             22699 non-null  int64         \n",
      " 1   VendorID               22699 non-null  int64         \n",
      " 2   tpep_pickup_datetime   22699 non-null  datetime64[ns]\n",
      " 3   tpep_dropoff_datetime  22699 non-null  datetime64[ns]\n",
      " 4   passenger_count        22699 non-null  int64         \n",
      " 5   trip_distance          22699 non-null  float64       \n",
      " 6   RatecodeID             22699 non-null  int64         \n",
      " 7   store_and_fwd_flag     22699 non-null  object        \n",
      " 8   PULocationID           22699 non-null  int64         \n",
      " 9   DOLocationID           22699 non-null  int64         \n",
      " 10  payment_type           22699 non-null  int64         \n",
      " 11  fare_amount            22699 non-null  float64       \n",
      " 12  extra                  22699 non-null  float64       \n",
      " 13  mta_tax                22699 non-null  float64       \n",
      " 14  tip_amount             22699 non-null  float64       \n",
      " 15  tolls_amount           22699 non-null  float64       \n",
      " 16  improvement_surcharge  22699 non-null  float64       \n",
      " 17  total_amount           22699 non-null  float64       \n",
      " 18  duration               22699 non-null  float64       \n",
      " 19  pickup_dropoff         22699 non-null  object        \n",
      " 20  mean_distance          22699 non-null  float64       \n",
      " 21  mean_duration          22699 non-null  float64       \n",
      " 22  day                    22699 non-null  object        \n",
      " 23  month                  22699 non-null  object        \n",
      " 24  rush_hour              22699 non-null  int64         \n",
      "dtypes: datetime64[ns](2), float64(11), int64(8), object(4)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   VendorID         22699 non-null  int64  \n",
      " 1   passenger_count  22699 non-null  int64  \n",
      " 2   fare_amount      22699 non-null  float64\n",
      " 3   mean_distance    22699 non-null  float64\n",
      " 4   mean_duration    22699 non-null  float64\n",
      " 5   rush_hour        22699 non-null  int64  \n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "\n",
    "df2 = df2.drop(['Unnamed: 0', 'tpep_dropoff_datetime', 'tpep_pickup_datetime',\n",
    "               'trip_distance', 'RatecodeID', 'store_and_fwd_flag', 'PULocationID', 'DOLocationID',\n",
    "               'payment_type', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge',\n",
    "               'total_amount', 'tpep_dropoff_datetime', 'tpep_pickup_datetime', 'duration',\n",
    "               'pickup_dropoff', 'day', 'month'\n",
    "               ], axis=1)\n",
    "\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 6. Pair plot\n",
    "\n",
    "Create a pairplot to visualize pairwise relationships between `fare_amount`, `mean_duration`, and `mean_distance`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pairplot to visualize pairwise relationships between variables in the data\n",
    "### YOUR CODE HERE ###\n",
    "\n",
    "sns.pairplot(df2[['fare_amount', 'mean_duration', 'mean_distance']],\n",
    "             plot_kws={'alpha':0.4, 'size':5},\n",
    "             );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These variables all show linear correlation with each other. Investigate this further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 7. Identify correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, code a correlation matrix to help determine most correlated variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VendorID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266463</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>0.001876</td>\n",
       "      <td>-0.002874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>0.266463</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014942</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>-0.022035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.014942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910185</td>\n",
       "      <td>0.859105</td>\n",
       "      <td>-0.020075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_distance</th>\n",
       "      <td>0.004741</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.910185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.874864</td>\n",
       "      <td>-0.039725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_duration</th>\n",
       "      <td>0.001876</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>0.859105</td>\n",
       "      <td>0.874864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.021583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rush_hour</th>\n",
       "      <td>-0.002874</td>\n",
       "      <td>-0.022035</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.039725</td>\n",
       "      <td>-0.021583</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 VendorID  passenger_count  fare_amount  mean_distance  \\\n",
       "VendorID         1.000000         0.266463     0.001045       0.004741   \n",
       "passenger_count  0.266463         1.000000     0.014942       0.013428   \n",
       "fare_amount      0.001045         0.014942     1.000000       0.910185   \n",
       "mean_distance    0.004741         0.013428     0.910185       1.000000   \n",
       "mean_duration    0.001876         0.015852     0.859105       0.874864   \n",
       "rush_hour       -0.002874        -0.022035    -0.020075      -0.039725   \n",
       "\n",
       "                 mean_duration  rush_hour  \n",
       "VendorID              0.001876  -0.002874  \n",
       "passenger_count       0.015852  -0.022035  \n",
       "fare_amount           0.859105  -0.020075  \n",
       "mean_distance         0.874864  -0.039725  \n",
       "mean_duration         1.000000  -0.021583  \n",
       "rush_hour            -0.021583   1.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create correlation matrix containing pairwise correlation of columns, using pearson correlation coefficient\n",
    "df2.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize a correlation heatmap of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": [
    "`mean_duration` and `mean_distance` are both highly correlated with the target variable of `fare_amount` They're also both correlated with each other, with a Pearson correlation of 0.87.\n",
    "\n",
    "Recall that highly correlated predictor variables can be bad for linear regression models when you want to be able to draw statistical inferences about the data from the model. However, correlated predictor variables can still be used to create an accurate predictor if the prediction itself is more important than using the model as a tool to learn about your data."
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "07UJJm41ajgf"
   },
   "source": [
    "### Task 8a. Split data into outcome variable and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 288
    },
    "id": "AzcDgLRET4d7",
    "outputId": "17e373f7-1716-4dfa-d76b-b63818f1bfa0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   VendorID         22699 non-null  int64  \n",
      " 1   passenger_count  22699 non-null  int64  \n",
      " 2   fare_amount      22699 non-null  float64\n",
      " 3   mean_distance    22699 non-null  float64\n",
      " 4   mean_duration    22699 non-null  float64\n",
      " 5   rush_hour        22699 non-null  int64  \n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your X and y variables. X represents the features and y represents the outcome (target) variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the target column from the features\n",
    "X = df2.drop(columns=['fare_amount'])\n",
    "\n",
    "# Set y variable\n",
    "y = df2[['fare_amount']]\n",
    "\n",
    "# Display first few rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o3ArC_5xa7Oi"
   },
   "source": [
    "### Task 8b. Pre-process data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dummy encode categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "QvDpwcQm0f35",
    "outputId": "308b7689-9ff9-4347-9a2a-4050eb7432e8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "      <th>VendorID_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  mean_distance  mean_duration  rush_hour  VendorID_2\n",
       "0                6       3.521667      22.847222          0           1\n",
       "1                1       3.108889      24.470370          0           0\n",
       "2                1       0.881429       7.250000          1           0\n",
       "3                1       3.700000      30.250000          0           1\n",
       "4                1       4.435000      14.616667          0           1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert VendorID to string\n",
    "X['VendorID'] = X['VendorID'].astype(str)\n",
    "\n",
    "# Get dummies\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iDYyjWssbnBG"
   },
   "source": [
    "### Split data into training and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jo706RzDN2ql"
   },
   "source": [
    "Create training and testing sets. The test set should contain 20% of the total samples. Set `random_state=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "A2BNUvacwaZY"
   },
   "outputs": [],
   "source": [
    "# Create training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jI4w3gXP5bhl"
   },
   "source": [
    "### Standardize the data\n",
    "\n",
    "Use `StandardScaler()`, `fit()`, and `transform()` to standardize the `X_train` variables. Assign the results to a variable called `X_train_scaled`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4Y3T2poF28fP",
    "outputId": "e22f38e7-42af-4e96-ec5b-e2fe64935b90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train scaled: [[-0.50301524  0.8694684   0.17616665 -0.64893329  0.89286563]\n",
      " [-0.50301524 -0.60011281 -0.69829589  1.54099045  0.89286563]\n",
      " [ 0.27331093 -0.47829156 -0.57301906 -0.64893329 -1.11998936]\n",
      " ...\n",
      " [-0.50301524 -0.45121122 -0.6788917  -0.64893329 -1.11998936]\n",
      " [-0.50301524 -0.58944763 -0.85743597  1.54099045 -1.11998936]\n",
      " [ 1.82596329  0.83673851  1.13212101 -0.64893329  0.89286563]]\n"
     ]
    }
   ],
   "source": [
    "# Standardize the X variables\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "print('X_train scaled:', X_train_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wk0rjKeO3JLv"
   },
   "source": [
    "### Fit the model\n",
    "\n",
    "Instantiate your model and fit it to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SClNm5hWotj6",
    "outputId": "8df935cb-95ae-4fd8-8c88-50931f492141"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit your model to the training data\n",
    "lr=LinearRegression()\n",
    "lr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMj6QkK1cLmS"
   },
   "source": [
    "### Task 8c. Evaluate model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AromLx7t5hjt"
   },
   "source": [
    "### Train data\n",
    "\n",
    "Evaluate your model performance by calculating the residual sum of squares and the explained variance score (R^2). Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "33rE1x9e3U6t",
    "outputId": "7cd2e70f-38c7-415a-b31b-927c2b59a3ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination: 0.8398434585044773\n",
      "R^2: 0.8398434585044773\n",
      "MAE: 2.186666416775414\n",
      "MSE: 17.88973296349268\n",
      "RMSE: 4.229625629236313\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model performance on the training data\n",
    "r_sq = lr.score(X_train_scaled, y_train)\n",
    "print('Coefficient of determination:', r_sq)\n",
    "y_pred_train = lr.predict(X_train_scaled)\n",
    "print('R^2:', r2_score(y_train, y_pred_train))\n",
    "print('MAE:', mean_absolute_error(y_train, y_pred_train))\n",
    "print('MSE:', mean_squared_error(y_train, y_pred_train))\n",
    "print('RMSE:',np.sqrt(mean_squared_error(y_train, y_pred_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test data\n",
    "\n",
    "Calculate the same metrics on the test data. Remember to scale the `X_test` data using the scaler that was fit to the training data. Do not refit the scaler to the testing data, just transform it. Call the results `X_test_scaled`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the X_test data\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sCyrnw_nyAcK",
    "outputId": "ef5b979a-6598-446b-def9-9463aa0e44dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination: 0.8682583641795454\n",
      "R^2: 0.8682583641795454\n",
      "MAE: 2.1336549840593864\n",
      "MSE: 14.326454156998944\n",
      "RMSE: 3.785030271609323\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model performance on the testing data\n",
    "r_sq_test = lr.score(X_test_scaled, y_test)\n",
    "print('Coefficient of determination:', r_sq_test)\n",
    "y_pred_test = lr.predict(X_test_scaled)\n",
    "print('R^2:', r2_score(y_test, y_pred_test))\n",
    "print('MAE:', mean_absolute_error(y_test,y_pred_test))\n",
    "print('MSE:', mean_squared_error(y_test, y_pred_test))\n",
    "print('RMSE:',np.sqrt(mean_squared_error(y_test, y_pred_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vB3DOXfIySe3"
   },
   "source": [
    "The model performance is high on both training and test sets, suggesting that there is little bias in the model and that the model is not overfit. In fact, the test scores were even better than the training scores.\n",
    "\n",
    "For the test data, an R<sup>2</sup> of 0.868 means that 86.8% of the variance in the `fare_amount` variable is described by the model.\n",
    "\n",
    "The mean absolute error is informative here because, for the purposes of the model, an error of two is not more than twice as bad as an error of one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cp7ojoBldEYy"
   },
   "source": [
    "<img src=\"images/Execute.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Execute**\n",
    "\n",
    "Consider the questions in your PACE Strategy Document to reflect on the Execute stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F_l3bkxQdJ3a"
   },
   "source": [
    "### Task 9a. Results\n",
    "\n",
    "Use the code cell below to get `actual`,`predicted`, and `residual` for the testing set, and store them as columns in a `results` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "cSl5gbXfBPBN",
    "outputId": "ca3ca3db-4f77-402f-9e98-14986d4fbf2c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>predicted</th>\n",
       "      <th>residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5818</th>\n",
       "      <td>14.0</td>\n",
       "      <td>12.356503</td>\n",
       "      <td>1.643497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18134</th>\n",
       "      <td>28.0</td>\n",
       "      <td>16.314595</td>\n",
       "      <td>11.685405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4655</th>\n",
       "      <td>5.5</td>\n",
       "      <td>6.726789</td>\n",
       "      <td>-1.226789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7378</th>\n",
       "      <td>15.5</td>\n",
       "      <td>16.227206</td>\n",
       "      <td>-0.727206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13914</th>\n",
       "      <td>9.5</td>\n",
       "      <td>10.536408</td>\n",
       "      <td>-1.036408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual  predicted   residual\n",
       "5818     14.0  12.356503   1.643497\n",
       "18134    28.0  16.314595  11.685405\n",
       "4655      5.5   6.726789  -1.226789\n",
       "7378     15.5  16.227206  -0.727206\n",
       "13914     9.5  10.536408  -1.036408"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a `results` dataframe\n",
    "results = pd.DataFrame(data={'actual': y_test['fare_amount'],\n",
    "                             'predicted': y_pred_test.ravel()})\n",
    "results['residual'] = results['actual'] - results['predicted']\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mwRmSDS3eyeH"
   },
   "source": [
    "### Task 9b. Visualize model results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y3vQ-mB51dfd"
   },
   "source": [
    "Create a scatterplot to visualize `actual` vs. `predicted`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 887
    },
    "id": "IBFU_dicBjwQ",
    "outputId": "7216a9e5-2f17-4352-a0a9-809132756acf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot to visualize `predicted` over `actual`\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "sns.set(style='whitegrid')\n",
    "sns.scatterplot(x='actual',\n",
    "                y='predicted',\n",
    "                data=results,\n",
    "                s=20,\n",
    "                alpha=0.5,\n",
    "                ax=ax\n",
    ")\n",
    "# Draw an x=y line to show what the results would be if the model were perfect\n",
    "plt.plot([0,60], [0,60], c='red', linewidth=2)\n",
    "plt.title('Actual vs. predicted');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IbO71S_R9IcY"
   },
   "source": [
    "Visualize the distribution of the `residuals` using a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "3a0UYoEr9Nx6",
    "outputId": "4a3b91b1-7f30-43d6-8f2f-24e12217a866"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of the `residuals`\n",
    "sns.histplot(results['residual'], bins=np.arange(-15,15.5,0.5))\n",
    "plt.title('Distribution of the residuals')\n",
    "plt.xlabel('residual value')\n",
    "plt.ylabel('count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.01544262152868053"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['residual'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the residuals is approximately normal and has a mean of -0.015. The residuals represent the variance in the outcome variable that is not explained by the model. A normal distribution around zero is good, as it demonstrates that the model's errors are evenly distributed and unbiased."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCnELck-9h5M"
   },
   "source": [
    "Create a scatterplot of `residuals` over `predicted`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "7Kmr2U8A95fY",
    "outputId": "da0e2714-e9be-4d19-8a5e-11d16a437ce2",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot of `residuals` over `predicted`\n",
    "\n",
    "sns.scatterplot(x='predicted', y='residual', data=results)\n",
    "plt.axhline(0, c='red')\n",
    "plt.title('Scatterplot of residuals over predicted values')\n",
    "plt.xlabel('predicted value')\n",
    "plt.ylabel('residual value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The model's residuals are evenly distributed above and below zero, with the exception of the sloping lines from the upper-left corner to the lower-right corner, which you know are the imputed maximum of \\\\$62.50 and the flat rate of \\\\$52 for JFK airport trips."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 9c. Coefficients\n",
    "\n",
    "Use the `coef_` attribute to get the model's coefficients. The coefficients are output in the order of the features that were used to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "      <th>VendorID_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.030825</td>\n",
       "      <td>7.133867</td>\n",
       "      <td>2.812115</td>\n",
       "      <td>0.110233</td>\n",
       "      <td>-0.054373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  mean_distance  mean_duration  rush_hour  VendorID_2\n",
       "0         0.030825       7.133867       2.812115   0.110233   -0.054373"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get model coefficients\n",
    "coefficients = pd.DataFrame(lr.coef_, columns=X.columns)\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficients reveal that `mean_distance` was the feature with the greatest weight in the model's final prediction. Be careful here! A common misinterpretation is that for every mile traveled, the fare amount increases by a mean of \\\\$7.13. This is incorrect. Remember, the data used to train the model was standardized with `StandardScaler()`. As such, the units are no longer miles. In other words, you cannot say \"for every mile traveled...\", as stated above. The correct interpretation of this coefficient is: controlling for other variables, *for every +1 change in standard deviation*, the fare amount increases by a mean of \\\\$7.13. \n",
    "\n",
    "Note also that because some highly correlated features were not removed, the confidence interval of this assessment is wider.\n",
    "\n",
    "So, translate this back to miles instead of standard deviation (i.e., unscale the data). \n",
    "\n",
    "1. Calculate the standard deviation of `mean_distance` in the `X_train` data.\n",
    "\n",
    "2. Divide the coefficient (7.133867) by the result to yield a more intuitive interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.574812975256415\n",
      "1.9955916713344426\n"
     ]
    }
   ],
   "source": [
    "# 1. Calculate SD of `mean_distance` in X_train data\n",
    "print(X_train['mean_distance'].std())\n",
    "\n",
    "# 2. Divide the model coefficient by the standard deviation\n",
    "print(7.133867 / X_train['mean_distance'].std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can make a more intuitive interpretation: for every 3.57 miles traveled, the fare increased by a mean of \\\\$7.13. Or, reduced: for every 1 mile traveled, the fare increased by a mean of \\\\$2.00."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P6AlDDyhdzmG"
   },
   "source": [
    "### Task 9d. Conclusion\n",
    "\n",
    "**What are the key takeaways from this notebook?**\n",
    "\n",
    "* Multiple linear regression is a powerful tool to estimate a dependent continous variable from several independent variables.\n",
    "* Exploratory data analysis is useful for selecting both numeric and categorical features for multiple linear regression.\n",
    "* Fitting multiple linear regression models may require trial and error to select variables that fit an accurate model while maintaining model assumptions (or not, depending on your use case)."
   ]
  }
 ],
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