9 - Random Forest Project - Taxi Fare Prediction (Regression).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"center\" width=100%>\n",
    "    <tr>\n",
    "        <td width=\"15%\">\n",
    "            <img src=\"taxi.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"center\">\n",
    "                <font color=\"#21618C\" size=24px>\n",
    "                    <b>Taxi Fare Prediction\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "\n",
    "This case study is to predict the taxi fare for a taxi ride in New York City from a given pickup point to the agreed dropoff location. Decision tree and Random Forest regressor is used for the fare prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Definition\n",
    "\n",
    "**unique_id**: Unique identifier or key for each record in the dataset - (string)    \n",
    "\n",
    "**date_time_of_pickup**: time when the ride started - (timestamp)\n",
    "\n",
    "**longitude_of_pickup**: Longitude of the taxi ride pickup point - (float) - (Numerical) \n",
    " \n",
    "**latitude_of_pickup**: Latitude of the taxi ride pickup point - (float) - (Numerical)\n",
    "    \n",
    "**longitude__of_dropoff**: Longitude of the taxi ride dropoff point  - (float) - (Numerical)\n",
    "    \n",
    "**latitude_of_dropoff**: Latitude of the taxi ride dropoff point - (float) - (Numerical)\n",
    "    \n",
    "**no_of_passenger**: count of the passengers during the ride - (integer) - (Numerical)\n",
    "    \n",
    "**amount**: (target variable)dollar amount of the cost of the taxi ride\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Icon Legends\n",
    "<table>\n",
    "  <tr>\n",
    "    <th width=\"25%\"> <img src=\"infer.png\" style=\"width:25%;\"></th>\n",
    "    <th width=\"25%\"> <img src=\"alsoreadicon.png\" style=\"width:25%;\"></th>\n",
    "    <th width=\"25%\"> <img src=\"todo.png\" style=\"width:25%;\"></th>\n",
    "    <th width=\"25%\"> <img src=\"quicktip.png\" style=\"width:25%;\"></th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Inferences from Outcome</b></font></div>\n",
    "    </td>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Additional Reads</b></font></div>\n",
    "    </td>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Lets do it</b></font></div>\n",
    "    </td>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Quick Tips</b></font></div>\n",
    "    </td>\n",
    "\n",
    "</tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "1. **[Import Libraries](#import_lib)**\n",
    "2. **[Set Options](#set_options)**\n",
    "3. **[Read Data](#Read_Data)**\n",
    "4. **[Prepare and Analyze the Data](#data_preparation)**\n",
    "    - 4.1 - [Understand the Data](#Data_Understanding)\n",
    "        - 4.1.1 - [Data Type](#Data_Types)\n",
    "        - 4.1.2 - [Feature Engineering](#Feature_Eng)\n",
    "        - 4.1.3 - [Summary Statistics](#Summary_Statistics)\n",
    "        - 4.1.4 - [Discover Outliers](#outlier)\n",
    "        - 4.1.5 - [Missing Values](#Missing_Values)\n",
    "        - 4.1.6 - [Correlation](#correlation)\n",
    "    - 4.2 - [Exploratory Data Analysis](#EDA)\n",
    "        - 4.2.1 - [Peak hours](#Peak)\n",
    "        - 4.2.2 - [Mean fare for each hour during weekdays and weekends](#Mean_Fare)\n",
    "        - 4.2.3 - [Distribution of key numerical variables](#Distribution)\n",
    "5. **[Random Forest](#Random_Forest)**\n",
    "    - 5.1 - [Random Forest Model](#RF_Model)\n",
    "    - 5.2 - [Random Forest with GridSearchCV ](#RF_CV)\n",
    "6. **[Conclusion and Interpretation](#conclusion)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='import_lib'></a>\n",
    "# 1. Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Let us import the required libraries and functions.</b> \n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:17.760178Z",
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   "outputs": [],
   "source": [
    "# suppress warnings \n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "\n",
    "# 'Os' module provides functions for interacting with the operating system \n",
    "import os\n",
    "\n",
    "# 'Pandas' is used for data manipulation and analysis\n",
    "import pandas as pd \n",
    "\n",
    "# 'Numpy' is used for mathematical operations on large, multi-dimensional arrays and matrices\n",
    "import numpy as np\n",
    "\n",
    "# 'Matplotlib' is a data visualization library for 2D and 3D plots, built on numpy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# 'Seaborn' is based on matplotlib; used for plotting statistical graphics\n",
    "import seaborn as sns\n",
    "\n",
    "from math import radians, cos, sin, sqrt, asin\n",
    "\n",
    "# 'Scikit-learn' (sklearn) emphasizes various regression, classification and clustering algorithms\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:17.790998Z",
     "start_time": "2022-01-26T20:30:17.760178Z"
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   "outputs": [],
   "source": [
    "# set the plot size using 'rcParams'\n",
    "# once the plot size is set using 'rcParams', it sets the size of all the forthcoming plots in the file\n",
    "# pass width and height in inches to 'figure.figsize' \n",
    "plt.rcParams['figure.figsize'] = [15,8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='set_options'></a>\n",
    "# 2. Set Options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Now we make necessary changes to :<br><br>\n",
    "1. Display complete dataframe<br>\n",
    "2. To set the decimal place of a numeric output to 6\n",
    "</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:17.904624Z",
     "start_time": "2022-01-26T20:30:17.790998Z"
    }
   },
   "outputs": [],
   "source": [
    "# display all columns of the dataframe\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "# display all rows of the dataframe\n",
    "pd.options.display.max_rows = 50 # None\n",
    "\n",
    "# returns an output value upto 6 decimals\n",
    "pd.options.display.float_format = '{:.6f}'.format "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Read_Data'></a>\n",
    "# 3. Read Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Read and display data to get insights from the data.\n",
    "                    </b> \n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
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   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>date_time_of_pickup</th>\n",
       "      <th>longitude_of_pickup</th>\n",
       "      <th>latitude_of_pickup</th>\n",
       "      <th>longitude_of_dropoff</th>\n",
       "      <th>latitude_of_dropoff</th>\n",
       "      <th>no_of_passenger</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26:21.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>52:16.0</td>\n",
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       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35:00.0</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30:42.0</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51:00.0</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  unique_id    amount      date_time_of_pickup  longitude_of_pickup  \\\n",
       "0   26:21.0  4.500000  2009-06-15 17:26:21 UTC           -73.844311   \n",
       "1   52:16.0 16.900000  2010-01-05 16:52:16 UTC           -74.016048   \n",
       "2   35:00.0  5.700000  2011-08-18 00:35:00 UTC           -73.982738   \n",
       "3   30:42.0  7.700000  2012-04-21 04:30:42 UTC           -73.987130   \n",
       "4   51:00.0  5.300000  2010-03-09 07:51:00 UTC           -73.968095   \n",
       "\n",
       "   latitude_of_pickup  longitude_of_dropoff  latitude_of_dropoff  \\\n",
       "0           40.721319            -73.841610            40.712278   \n",
       "1           40.711303            -73.979268            40.782004   \n",
       "2           40.761270            -73.991242            40.750562   \n",
       "3           40.733143            -73.991567            40.758092   \n",
       "4           40.768008            -73.956655            40.783762   \n",
       "\n",
       "   no_of_passenger  \n",
       "0                1  \n",
       "1                1  \n",
       "2                2  \n",
       "3                1  \n",
       "4                1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read csv file using pandas\n",
    "df_taxi = pd.read_csv('../Dataset/TaxiFare.csv')\n",
    "\n",
    "# display the top 5 rows of the dataframe\n",
    "df_taxi.head()\n",
    "\n",
    "# Note: To display more rows, example 10, use head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column `unique_id` contains the unique identifier for each observation, which is redundant for further analysis. Thus, we drop this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:18.914740Z",
     "start_time": "2022-01-26T20:30:18.712176Z"
    }
   },
   "outputs": [],
   "source": [
    "# drop the column 'Id' using drop()\n",
    "# 'axis = 1' drops the specified column\n",
    "df_taxi = df_taxi.drop('unique_id', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='data_preparation'></a>\n",
    "# 4. Prepare and Analyze the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Data preparation is the process of cleaning and transforming raw data before building predictive models. <br><br>\n",
    "                        Here we will analyze and prepare data to perform regression techniques:<br>\n",
    "                        1. Check dimensions of the dataframe in terms of rows and columns <br>\n",
    "                        2. Check the data types. If not as per business context, change the data types  <br>\n",
    "                        3. Study Summary Statistics <br>\n",
    "                        4. Distribution of Variables<br>\n",
    "                        5. Analyze relationship between numeric variables <br>\n",
    "                        6. Check for missing values<br>\n",
    "                        7. Study correlation<br>\n",
    "                        8. Analyze relationship between numeric and categoric variables <br><br>\n",
    "                        Note: It is an art to explore data and one will need more and more practice to gain expertise in this area. </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Data_Understanding'></a>\n",
    "## 4.1 Understand the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let us now see the number of variables and observations in the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:19.008227Z",
     "start_time": "2022-01-26T20:30:18.918772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'shape' returns the dimensions of the data\n",
    "df_taxi.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the dataframe has 7 columns and 50000 rows. It means there are 7 variables and 50000 observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Data_Types'></a>\n",
    "### 4.1.1 Data Type\n",
    "\n",
    "Data has a variety of data types. The main types stored in pandas dataframes are object, float, int64, bool and datetime64. In order to learn about each attribute, it is always good for us to know the data type of each column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>In order to know whether the datatype aof each variable is correct, we do the following:<br><br>\n",
    "                        1. Check the data type <br>\n",
    "                        2. Change the incorrect data type <br>\n",
    "                        3. Recheck the data type after the conversion <br>\n",
    "                        4. Feature engineering <br>\n",
    "                        5. Drop the redundant variables <br>\n",
    "                       </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Check the data type.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:19.134518Z",
     "start_time": "2022-01-26T20:30:19.014212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount                  float64\n",
       "date_time_of_pickup      object\n",
       "longitude_of_pickup     float64\n",
       "latitude_of_pickup      float64\n",
       "longitude_of_dropoff    float64\n",
       "latitude_of_dropoff     float64\n",
       "no_of_passenger           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use 'dtypes' to check the data type of each variable\n",
    "df_taxi.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align='left'>\n",
    "    <tr>\n",
    "        <td width='8%'>\n",
    "            <img src='infer.png'>\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align='left', style='font-size:120%'>\n",
    "                <font color='#21618C'>\n",
    "                    <b>From the above output, we see that the data type of 'date_time_of_pickup' is 'object '.<br>\n",
    "\n",
    "But according to data definition, 'date_time_of_pickup 'is a date time stamp variable, which is wrongly interpreted as 'object', so we will convert this variable data type to 'datetime'.</br></b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Change the incorrect data type.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:26.799697Z",
     "start_time": "2022-01-26T20:30:19.145453Z"
    }
   },
   "outputs": [],
   "source": [
    "# convert object to datetime using to_datetime method from pandas\n",
    "#if error is set to'coerce', then invalid parsing will be set as NaT.\n",
    "df_taxi.date_time_of_pickup = pd.to_datetime(df_taxi.date_time_of_pickup, errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Recheck the data type after the conversion.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:26.819804Z",
     "start_time": "2022-01-26T20:30:26.803429Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount                              float64\n",
       "date_time_of_pickup     datetime64[ns, UTC]\n",
       "longitude_of_pickup                 float64\n",
       "latitude_of_pickup                  float64\n",
       "longitude_of_dropoff                float64\n",
       "latitude_of_dropoff                 float64\n",
       "no_of_passenger                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recheck the data type of each column\n",
    "df_taxi.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "           <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Now the data type of each variable is as per the data definition.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Feature_Eng'></a>\n",
    "### 4.1.2 Feature Engineering\n",
    "\n",
    "We will extract date time features from the variable 'date_time_of_pickup'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>For feature engineering, we do the following:<br><br>\n",
    "                        1. Feature addition <br>\n",
    "                        2. Drop the redundant variables <br>\n",
    "                       </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Add new variables**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will extract new features from variable `date_time_of_pickup` . We will also add a variable which measures the distance between the pickup and dropoff point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:27.275829Z",
     "start_time": "2022-01-26T20:30:26.823794Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "      <th>date_time_of_pickup</th>\n",
       "      <th>longitude_of_pickup</th>\n",
       "      <th>latitude_of_pickup</th>\n",
       "      <th>longitude_of_dropoff</th>\n",
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       "      <td>-73.956655</td>\n",
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       "      <td>7</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>49995</th>\n",
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       "    <tr>\n",
       "      <th>49996</th>\n",
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       "    <tr>\n",
       "      <th>49997</th>\n",
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       "      <td>-74.002698</td>\n",
       "      <td>40.739428</td>\n",
       "      <td>-73.998108</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-73.932603</td>\n",
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       "      <td>-73.932603</td>\n",
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       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>50000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         amount       date_time_of_pickup  longitude_of_pickup  \\\n",
       "0      4.500000 2009-06-15 17:26:21+00:00           -73.844311   \n",
       "1     16.900000 2010-01-05 16:52:16+00:00           -74.016048   \n",
       "2      5.700000 2011-08-18 00:35:00+00:00           -73.982738   \n",
       "3      7.700000 2012-04-21 04:30:42+00:00           -73.987130   \n",
       "4      5.300000 2010-03-09 07:51:00+00:00           -73.968095   \n",
       "...         ...                       ...                  ...   \n",
       "49995 15.000000 2013-06-12 23:25:15+00:00           -73.999973   \n",
       "49996  7.500000 2015-06-22 17:19:18+00:00           -73.984756   \n",
       "49997  6.900000 2011-01-30 04:53:00+00:00           -74.002698   \n",
       "49998  4.500000 2012-11-06 07:09:00+00:00           -73.946062   \n",
       "49999 10.900000 2010-01-13 08:13:14+00:00           -73.932603   \n",
       "\n",
       "       latitude_of_pickup  longitude_of_dropoff  latitude_of_dropoff  \\\n",
       "0               40.721319            -73.841610            40.712278   \n",
       "1               40.711303            -73.979268            40.782004   \n",
       "2               40.761270            -73.991242            40.750562   \n",
       "3               40.733143            -73.991567            40.758092   \n",
       "4               40.768008            -73.956655            40.783762   \n",
       "...                   ...                   ...                  ...   \n",
       "49995           40.748531            -74.016899            40.705993   \n",
       "49996           40.768211            -73.987366            40.760597   \n",
       "49997           40.739428            -73.998108            40.759483   \n",
       "49998           40.777567            -73.953450            40.779687   \n",
       "49999           40.763805            -73.932603            40.763805   \n",
       "\n",
       "       no_of_passenger  hour  day  month  year  dayofweek  \n",
       "0                    1    17   15      6  2009          0  \n",
       "1                    1    16    5      1  2010          1  \n",
       "2                    2     0   18      8  2011          3  \n",
       "3                    1     4   21      4  2012          5  \n",
       "4                    1     7    9      3  2010          1  \n",
       "...                ...   ...  ...    ...   ...        ...  \n",
       "49995                1    23   12      6  2013          2  \n",
       "49996                1    17   22      6  2015          0  \n",
       "49997                1     4   30      1  2011          6  \n",
       "49998                2     7    6     11  2012          1  \n",
       "49999                1     8   13      1  2010          2  \n",
       "\n",
       "[50000 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract various date time components as seperate variables\n",
    "df_taxi = df_taxi.assign(hour = df_taxi.date_time_of_pickup.dt.hour, \n",
    "                         day = df_taxi.date_time_of_pickup.dt.day,\n",
    "                        month = df_taxi.date_time_of_pickup.dt.month, \n",
    "                        year = df_taxi.date_time_of_pickup.dt.year, \n",
    "                        dayofweek = df_taxi.date_time_of_pickup.dt.dayofweek)\n",
    "df_taxi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will add a new variable which measures the distance between the pickup and dropoff point. We will use the Haversine formula to compute the distance between two points of the journey, using the logitude and latitude values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"alsoreadicon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>I love to know more:  </b><a href=\"https://en.wikipedia.org/wiki/Haversine_formula\"> What is Haversine </a>\n",
    "</font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:27.305540Z",
     "start_time": "2022-01-26T20:30:27.278784Z"
    }
   },
   "outputs": [],
   "source": [
    "# function to calculate the travel distance from the longitudes and latitudes\n",
    "def distance_transform(longitude1, latitude1, longitude2, latitude2):\n",
    "    travel_dist = []\n",
    "    \n",
    "    for pos in range(len(longitude1)):\n",
    "        long1,lati1,long2,lati2 = map(radians,[longitude1[pos],latitude1[pos],longitude2[pos],latitude2[pos]])\n",
    "        dist_long = long2 - long1\n",
    "        dist_lati = lati2 - lati1\n",
    "        a = sin(dist_lati/2)**2 + cos(lati1) * cos(lati2) * sin(dist_long/2)**2\n",
    "        c = 2 * asin(sqrt(a))*6371\n",
    "        travel_dist.append(c)\n",
    "       \n",
    "    return travel_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:27.421831Z",
     "start_time": "2022-01-26T20:30:27.305540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.distance_transform(longitude1, latitude1, longitude2, latitude2)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:27.726568Z",
     "start_time": "2022-01-26T20:30:27.429499Z"
    }
   },
   "outputs": [],
   "source": [
    "#Add a new variable travel distance\n",
    "\n",
    "df_taxi['travel_dist_km'] = distance_transform(df_taxi['longitude_of_pickup'].to_numpy(),\n",
    "                                                df_taxi['latitude_of_pickup'].to_numpy(),\n",
    "                                                df_taxi['longitude_of_dropoff'].to_numpy(),\n",
    "                                                df_taxi['latitude_of_dropoff'].to_numpy()\n",
    "                                              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:27.747553Z",
     "start_time": "2022-01-26T20:30:27.726568Z"
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    "scrolled": true
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    {
     "data": {
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       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>1.389525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>2012-04-21 04:30:42+00:00</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>2.799270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.300000</td>\n",
       "      <td>2010-03-09 07:51:00+00:00</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1.999157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     amount       date_time_of_pickup  longitude_of_pickup  \\\n",
       "0  4.500000 2009-06-15 17:26:21+00:00           -73.844311   \n",
       "1 16.900000 2010-01-05 16:52:16+00:00           -74.016048   \n",
       "2  5.700000 2011-08-18 00:35:00+00:00           -73.982738   \n",
       "3  7.700000 2012-04-21 04:30:42+00:00           -73.987130   \n",
       "4  5.300000 2010-03-09 07:51:00+00:00           -73.968095   \n",
       "\n",
       "   latitude_of_pickup  longitude_of_dropoff  latitude_of_dropoff  \\\n",
       "0           40.721319            -73.841610            40.712278   \n",
       "1           40.711303            -73.979268            40.782004   \n",
       "2           40.761270            -73.991242            40.750562   \n",
       "3           40.733143            -73.991567            40.758092   \n",
       "4           40.768008            -73.956655            40.783762   \n",
       "\n",
       "   no_of_passenger  hour  day  month  year  dayofweek  travel_dist_km  \n",
       "0                1    17   15      6  2009          0        1.030764  \n",
       "1                1    16    5      1  2010          1        8.450134  \n",
       "2                2     0   18      8  2011          3        1.389525  \n",
       "3                1     4   21      4  2012          5        2.799270  \n",
       "4                1     7    9      3  2010          1        1.999157  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the newly added vaiarbles\n",
    "df_taxi.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Drop the redundant variable.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have extracted new features from variable `date_time_of_pickup` this variable is redundant for further analysis. Hence we drop this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:27.910723Z",
     "start_time": "2022-01-26T20:30:27.749550Z"
    }
   },
   "outputs": [],
   "source": [
    "# drop the column 'date_time_of_pickup' using drop()\n",
    "# 'axis = 1' drops the specified column\n",
    "\n",
    "df_taxi = df_taxi.drop('date_time_of_pickup',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:28.456198Z",
     "start_time": "2022-01-26T20:30:27.910723Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   amount                50000 non-null  float64\n",
      " 1   longitude_of_pickup   50000 non-null  float64\n",
      " 2   latitude_of_pickup    50000 non-null  float64\n",
      " 3   longitude_of_dropoff  50000 non-null  float64\n",
      " 4   latitude_of_dropoff   50000 non-null  float64\n",
      " 5   no_of_passenger       50000 non-null  int64  \n",
      " 6   hour                  50000 non-null  int64  \n",
      " 7   day                   50000 non-null  int64  \n",
      " 8   month                 50000 non-null  int64  \n",
      " 9   year                  50000 non-null  int64  \n",
      " 10  dayofweek             50000 non-null  int64  \n",
      " 11  travel_dist_km        50000 non-null  float64\n",
      "dtypes: float64(6), int64(6)\n",
      "memory usage: 4.6 MB\n"
     ]
    }
   ],
   "source": [
    "df_taxi.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As now have the required features, after having extracted new ones and dropping the redundant variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Summary_Statistics'></a>\n",
    "### 4.1.3 Summary Statistics\n",
    "\n",
    "Here we take a look at the summary of each attribute. This includes the count, mean, the minimum and maximum values as well as some percentiles for numeric variables and count, unique, top, frequency for other variable types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> In our dataset we have both numerical and categorical variables. Now we check for summary statistics of all the variables<br><br>\n",
    "                        1. For numerical variables, use the describe()<br>\n",
    "                        2. For categorical variables use the describe(include='object')\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. For numerical variables, use the describe()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:28.693098Z",
     "start_time": "2022-01-26T20:30:28.456198Z"
    }
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>11.364171</td>\n",
       "      <td>9.685557</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude_of_pickup</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>-72.509756</td>\n",
       "      <td>10.393860</td>\n",
       "      <td>-75.423848</td>\n",
       "      <td>-73.992062</td>\n",
       "      <td>-73.981840</td>\n",
       "      <td>-73.967148</td>\n",
       "      <td>40.783472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude_of_pickup</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>39.933759</td>\n",
       "      <td>6.224857</td>\n",
       "      <td>-74.006893</td>\n",
       "      <td>40.734880</td>\n",
       "      <td>40.752678</td>\n",
       "      <td>40.767360</td>\n",
       "      <td>401.083332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude_of_dropoff</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>-72.504616</td>\n",
       "      <td>10.407570</td>\n",
       "      <td>-84.654241</td>\n",
       "      <td>-73.991152</td>\n",
       "      <td>-73.980082</td>\n",
       "      <td>-73.963584</td>\n",
       "      <td>40.851027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude_of_dropoff</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>39.926251</td>\n",
       "      <td>6.014737</td>\n",
       "      <td>-74.006377</td>\n",
       "      <td>40.734372</td>\n",
       "      <td>40.753372</td>\n",
       "      <td>40.768167</td>\n",
       "      <td>43.415190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_passenger</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.667840</td>\n",
       "      <td>1.289195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>13.489080</td>\n",
       "      <td>6.506935</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>15.672040</td>\n",
       "      <td>8.660789</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>6.273300</td>\n",
       "      <td>3.461157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>2011.739260</td>\n",
       "      <td>1.862639</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayofweek</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>3.029980</td>\n",
       "      <td>1.956936</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>travel_dist_km</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>18.508946</td>\n",
       "      <td>355.564309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.223146</td>\n",
       "      <td>2.120114</td>\n",
       "      <td>3.895570</td>\n",
       "      <td>8667.818812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count        mean        std         min  \\\n",
       "amount               50000.000000   11.364171   9.685557   -5.000000   \n",
       "longitude_of_pickup  50000.000000  -72.509756  10.393860  -75.423848   \n",
       "latitude_of_pickup   50000.000000   39.933759   6.224857  -74.006893   \n",
       "longitude_of_dropoff 50000.000000  -72.504616  10.407570  -84.654241   \n",
       "latitude_of_dropoff  50000.000000   39.926251   6.014737  -74.006377   \n",
       "no_of_passenger      50000.000000    1.667840   1.289195    0.000000   \n",
       "hour                 50000.000000   13.489080   6.506935    0.000000   \n",
       "day                  50000.000000   15.672040   8.660789    1.000000   \n",
       "month                50000.000000    6.273300   3.461157    1.000000   \n",
       "year                 50000.000000 2011.739260   1.862639 2009.000000   \n",
       "dayofweek            50000.000000    3.029980   1.956936    0.000000   \n",
       "travel_dist_km       50000.000000   18.508946 355.564309    0.000000   \n",
       "\n",
       "                             25%         50%         75%         max  \n",
       "amount                  6.000000    8.500000   12.500000  200.000000  \n",
       "longitude_of_pickup   -73.992062  -73.981840  -73.967148   40.783472  \n",
       "latitude_of_pickup     40.734880   40.752678   40.767360  401.083332  \n",
       "longitude_of_dropoff  -73.991152  -73.980082  -73.963584   40.851027  \n",
       "latitude_of_dropoff    40.734372   40.753372   40.768167   43.415190  \n",
       "no_of_passenger         1.000000    1.000000    2.000000    6.000000  \n",
       "hour                    9.000000   14.000000   19.000000   23.000000  \n",
       "day                     8.000000   16.000000   23.000000   31.000000  \n",
       "month                   3.000000    6.000000    9.000000   12.000000  \n",
       "year                 2010.000000 2012.000000 2013.000000 2015.000000  \n",
       "dayofweek               1.000000    3.000000    5.000000    6.000000  \n",
       "travel_dist_km          1.223146    2.120114    3.895570 8667.818812  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the describe() returns the statistical summary of the variables\n",
    "# by default, it returns the summary of numerical variables\n",
    "# use .transpose() for better readability. However it is optional\n",
    "df_taxi.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "<b>The above output illustrates the summary statistics of all the numeric variables like mean, median (50%), standard deviation, minimum, and maximum values, along with the first and third quantiles.<br><br> \n",
    " <br><br>Note that the minimum amount is -5 and maximum is 200. Ideally fare amount should not be less that 2.5 dollars, which is the minimum value set for fares in NYC. Also the minimum count of passengers is 0, which isn't a practical scenario. We will deal with such values as we progress the case study.\n",
    "    </b>     </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. For categorical variables, use the describe(include='object').**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As, there are no categorical varaibles, we skip this step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='outlier'></a>\n",
    "### 4.1.4 Discover Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rv_JBRXxeboi"
   },
   "source": [
    "#### Importance of detecting an outlier\n",
    "An outlier is an observation that appears to deviate distinctly from other observations in the data. If the outliers are not removed, the model accuracy may decrease.\n",
    "Let us detect the extreme values in the data. \n",
    "\n",
    "\n",
    "The following can be considered as outliers in this case study:\n",
    "1. Amount < 2.5\n",
    "2. Trips with travel distance less than or equal to 0, and more than 130Kms\n",
    "3. Trips where 90< latitude <-90, 180 < longitude < -180                     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. We have seen that there are instances of amount less that 0 as well in the data set, where as the minimum fare for any trip in NYC is 2.5 dollars, hence we will remove such observations. We have already seen that the max fare is 200 in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:29.084016Z",
     "start_time": "2022-01-26T20:30:28.695092Z"
    }
   },
   "outputs": [],
   "source": [
    "#We will only keep the observation where fare is between 2.5\n",
    "df_taxi = df_taxi.loc[(df_taxi.amount >= 2.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:29.098942Z",
     "start_time": "2022-01-26T20:30:29.087968Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining observastions in the dataset: (49990, 12)\n"
     ]
    }
   ],
   "source": [
    "print(\"Remaining observastions in the dataset:\", df_taxi.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Remove the observations with travel distance more than 130 kms.As seen from descriptive stats there are obs with travel distance more than 130 km, as that is the limit for trips in and around NYC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:29.286395Z",
     "start_time": "2022-01-26T20:30:29.102929Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining observastions in the dataset: (49990, 12)\n"
     ]
    }
   ],
   "source": [
    "#We will only keep the observation where travel distance is less than or equal to 130\n",
    "\n",
    "df_taxi = df_taxi.loc[(df_taxi.travel_dist_km >= 1) | (df_taxi.travel_dist_km <= 130)]\n",
    "print(\"Remaining observastions in the dataset:\", df_taxi.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Remove the observations with unreal longitude and latitude values , that is , 90< latitude <-90, 180 < longitude < -180."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:29.436090Z",
     "start_time": "2022-01-26T20:30:29.286395Z"
    }
   },
   "outputs": [],
   "source": [
    "incorrect_coordinates = df_taxi.loc[(df_taxi.latitude_of_pickup > 90) |(df_taxi.latitude_of_pickup < -90) |\n",
    "                                   (df_taxi.latitude_of_dropoff > 90) |(df_taxi.latitude_of_dropoff < -90) |\n",
    "                                   (df_taxi.longitude_of_pickup > 180) |(df_taxi.longitude_of_pickup < -180) |\n",
    "                                   (df_taxi.longitude_of_dropoff > 90) |(df_taxi.longitude_of_dropoff < -90)\n",
    "                                    ].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:29.679373Z",
     "start_time": "2022-01-26T20:30:29.436090Z"
    }
   },
   "outputs": [],
   "source": [
    "df_taxi.drop(incorrect_coordinates, inplace = True, errors = 'ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:29.804152Z",
     "start_time": "2022-01-26T20:30:29.679373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining observastions in the dataset: (49989, 12)\n"
     ]
    }
   ],
   "source": [
    "print(\"Remaining observastions in the dataset:\", df_taxi.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> The number of observations is reduced to 48455 from 50000 which suggests that we have removed the observations with extreme or immpractical values.\n",
    "                    </b>   \n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BudwXJ6Kebou"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"alsoreadicon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>I love to know more:  </b><a href=\"https://bit.ly/33bgNpq\">How to use statistics to identify outliers in data</a>\n",
    "</font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Missing_Values'></a>\n",
    "### 4.1.5 Missing Values\n",
    "\n",
    "If the missing values are not handled properly we may end up drawing an inaccurate inference about the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>In order to get the count of missing values in each column.<br><br>\n",
    "                        <ol type=\"1\"><li>Check the missing values</li>\n",
    "                            <li>Visualize missing values using heatmap</li>\n",
    "                            <li>Handle missing values\n",
    "                            <ul type=\"i\">\n",
    "                                <li>For numeric variables</li>\n",
    "                                <li> For categoric variables</li>\n",
    "                            </ul>\n",
    "                        </ol>  </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Check the missing values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:29.943660Z",
     "start_time": "2022-01-26T20:30:29.816119Z"
    }
   },
   "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>Total</th>\n",
       "      <th>Percentage of Missing Values</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude_of_pickup</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude_of_pickup</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude_of_dropoff</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude_of_dropoff</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_passenger</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayofweek</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>travel_dist_km</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Total  Percentage of Missing Values     Type\n",
       "amount                    0                      0.000000  float64\n",
       "longitude_of_pickup       0                      0.000000  float64\n",
       "latitude_of_pickup        0                      0.000000  float64\n",
       "longitude_of_dropoff      0                      0.000000  float64\n",
       "latitude_of_dropoff       0                      0.000000  float64\n",
       "no_of_passenger           0                      0.000000    int64\n",
       "hour                      0                      0.000000    int64\n",
       "day                       0                      0.000000    int64\n",
       "month                     0                      0.000000    int64\n",
       "year                      0                      0.000000    int64\n",
       "dayofweek                 0                      0.000000    int64\n",
       "travel_dist_km            0                      0.000000  float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the variables on the basis of total null values in the variable\n",
    "# 'isnull().sum()' returns the number of missing values in each variable\n",
    "# 'ascending = False' sorts values in the descending order\n",
    "# the variable with highest number of missing values will appear first\n",
    "Total = df_taxi.isnull().sum().sort_values(ascending = False)          \n",
    "\n",
    "# calculate the percentage of missing values\n",
    "# 'ascending = False' sorts values in the descending order\n",
    "# the variable with highest percentage of missing values will appear first\n",
    "Percent = (df_taxi.isnull().sum()*100/df_taxi.isnull().count()).sort_values(ascending = False)   \n",
    "\n",
    "# concat the 'Total' and 'Percent' columns using 'concat' function\n",
    "# 'keys' is the list of column names\n",
    "# 'axis = 1' concats along the columns\n",
    "missing_data = pd.concat([Total, Percent], axis = 1, keys = ['Total', 'Percentage of Missing Values'])    \n",
    "\n",
    "# add the column containing data type of each variable\n",
    "missing_data['Type'] = df_taxi[missing_data.index].dtypes\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>None of the variables contain missing values.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Visualize missing values using heatmap**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:33.579149Z",
     "start_time": "2022-01-26T20:30:29.943660Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap to visualize the null values in each column\n",
    "# 'cbar = False' does not show the color axis \n",
    "sns.heatmap(df_taxi.isnull(), cbar=False)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there are missing values in a dataset then the horizontal lines in the heatmap correspond to the missing values. Since there are no missing values here there are no horizontal lines corresponding to them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Dogz55FZeboP"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> The above plot shows that there are no missing values in the data.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='correlation'></a>\n",
    "### 4.1.6 Study correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K_oo8YEAeboS"
   },
   "source": [
    "Correlation is a statistic that measures the degree to which two variables move with each other. A correlation coefficient near  1  indicates the strong relationship between them; a weak correlation indicates the extent to which one variable increases as the other decreases. Correlation among multiple variables can be represented in the form of a matrix. This allows us to see which variables are correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sAagGX-keboT"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> To check the correlation between numerical variables, we perform the following steps:<br><br>\n",
    "                    1. Compute a correlation matrix  <br>\n",
    "                    2. Plot a heatmap for the correlation matrix\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Compute a correlation matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:33.673032Z",
     "start_time": "2022-01-26T20:30:33.579149Z"
    }
   },
   "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>amount</th>\n",
       "      <th>longitude_of_pickup</th>\n",
       "      <th>latitude_of_pickup</th>\n",
       "      <th>longitude_of_dropoff</th>\n",
       "      <th>latitude_of_dropoff</th>\n",
       "      <th>no_of_passenger</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>travel_dist_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005905</td>\n",
       "      <td>-0.006563</td>\n",
       "      <td>0.004452</td>\n",
       "      <td>-0.005600</td>\n",
       "      <td>0.016506</td>\n",
       "      <td>-0.022009</td>\n",
       "      <td>-0.001417</td>\n",
       "      <td>0.026742</td>\n",
       "      <td>0.121081</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.016451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude_of_pickup</th>\n",
       "      <td>0.005905</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.984132</td>\n",
       "      <td>0.956613</td>\n",
       "      <td>-0.947401</td>\n",
       "      <td>-0.005614</td>\n",
       "      <td>0.004913</td>\n",
       "      <td>0.006008</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>-0.001264</td>\n",
       "      <td>-0.003224</td>\n",
       "      <td>0.143712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude_of_pickup</th>\n",
       "      <td>-0.006563</td>\n",
       "      <td>-0.984132</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.943741</td>\n",
       "      <td>0.961374</td>\n",
       "      <td>0.003982</td>\n",
       "      <td>-0.004313</td>\n",
       "      <td>-0.008884</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>-0.000925</td>\n",
       "      <td>0.001911</td>\n",
       "      <td>-0.130943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude_of_dropoff</th>\n",
       "      <td>0.004452</td>\n",
       "      <td>0.956613</td>\n",
       "      <td>-0.943741</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.982102</td>\n",
       "      <td>-0.004874</td>\n",
       "      <td>0.005506</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>0.001322</td>\n",
       "      <td>-0.000327</td>\n",
       "      <td>-0.002441</td>\n",
       "      <td>0.143172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude_of_dropoff</th>\n",
       "      <td>-0.005600</td>\n",
       "      <td>-0.947401</td>\n",
       "      <td>0.961374</td>\n",
       "      <td>-0.982102</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002898</td>\n",
       "      <td>-0.004530</td>\n",
       "      <td>-0.008012</td>\n",
       "      <td>-0.000341</td>\n",
       "      <td>-0.001817</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>-0.124844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_passenger</th>\n",
       "      <td>0.016506</td>\n",
       "      <td>-0.005614</td>\n",
       "      <td>0.003982</td>\n",
       "      <td>-0.004874</td>\n",
       "      <td>0.002898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011400</td>\n",
       "      <td>0.006787</td>\n",
       "      <td>0.009099</td>\n",
       "      <td>0.006385</td>\n",
       "      <td>0.038581</td>\n",
       "      <td>-0.005371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>-0.022009</td>\n",
       "      <td>0.004913</td>\n",
       "      <td>-0.004313</td>\n",
       "      <td>0.005506</td>\n",
       "      <td>-0.004530</td>\n",
       "      <td>0.011400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008192</td>\n",
       "      <td>-0.001916</td>\n",
       "      <td>0.004167</td>\n",
       "      <td>-0.091267</td>\n",
       "      <td>0.000644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>-0.001417</td>\n",
       "      <td>0.006008</td>\n",
       "      <td>-0.008884</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>-0.008012</td>\n",
       "      <td>0.006787</td>\n",
       "      <td>-0.008192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017266</td>\n",
       "      <td>-0.011155</td>\n",
       "      <td>0.008422</td>\n",
       "      <td>0.010610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.026742</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.001322</td>\n",
       "      <td>-0.000341</td>\n",
       "      <td>0.009099</td>\n",
       "      <td>-0.001916</td>\n",
       "      <td>-0.017266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.115726</td>\n",
       "      <td>-0.010004</td>\n",
       "      <td>-0.009217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.121081</td>\n",
       "      <td>-0.001264</td>\n",
       "      <td>-0.000925</td>\n",
       "      <td>-0.000327</td>\n",
       "      <td>-0.001817</td>\n",
       "      <td>0.006385</td>\n",
       "      <td>0.004167</td>\n",
       "      <td>-0.011155</td>\n",
       "      <td>-0.115726</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006808</td>\n",
       "      <td>0.022136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayofweek</th>\n",
       "      <td>0.000542</td>\n",
       "      <td>-0.003224</td>\n",
       "      <td>0.001911</td>\n",
       "      <td>-0.002441</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>0.038581</td>\n",
       "      <td>-0.091267</td>\n",
       "      <td>0.008422</td>\n",
       "      <td>-0.010004</td>\n",
       "      <td>0.006808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>travel_dist_km</th>\n",
       "      <td>0.016451</td>\n",
       "      <td>0.143712</td>\n",
       "      <td>-0.130943</td>\n",
       "      <td>0.143172</td>\n",
       "      <td>-0.124844</td>\n",
       "      <td>-0.005371</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>0.010610</td>\n",
       "      <td>-0.009217</td>\n",
       "      <td>0.022136</td>\n",
       "      <td>-0.000518</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        amount  longitude_of_pickup  latitude_of_pickup  \\\n",
       "amount                1.000000             0.005905           -0.006563   \n",
       "longitude_of_pickup   0.005905             1.000000           -0.984132   \n",
       "latitude_of_pickup   -0.006563            -0.984132            1.000000   \n",
       "longitude_of_dropoff  0.004452             0.956613           -0.943741   \n",
       "latitude_of_dropoff  -0.005600            -0.947401            0.961374   \n",
       "no_of_passenger       0.016506            -0.005614            0.003982   \n",
       "hour                 -0.022009             0.004913           -0.004313   \n",
       "day                  -0.001417             0.006008           -0.008884   \n",
       "month                 0.026742             0.000402            0.000498   \n",
       "year                  0.121081            -0.001264           -0.000925   \n",
       "dayofweek             0.000542            -0.003224            0.001911   \n",
       "travel_dist_km        0.016451             0.143712           -0.130943   \n",
       "\n",
       "                      longitude_of_dropoff  latitude_of_dropoff  \\\n",
       "amount                            0.004452            -0.005600   \n",
       "longitude_of_pickup               0.956613            -0.947401   \n",
       "latitude_of_pickup               -0.943741             0.961374   \n",
       "longitude_of_dropoff              1.000000            -0.982102   \n",
       "latitude_of_dropoff              -0.982102             1.000000   \n",
       "no_of_passenger                  -0.004874             0.002898   \n",
       "hour                              0.005506            -0.004530   \n",
       "day                               0.005221            -0.008012   \n",
       "month                             0.001322            -0.000341   \n",
       "year                             -0.000327            -0.001817   \n",
       "dayofweek                        -0.002441             0.001626   \n",
       "travel_dist_km                    0.143172            -0.124844   \n",
       "\n",
       "                      no_of_passenger      hour       day     month      year  \\\n",
       "amount                       0.016506 -0.022009 -0.001417  0.026742  0.121081   \n",
       "longitude_of_pickup         -0.005614  0.004913  0.006008  0.000402 -0.001264   \n",
       "latitude_of_pickup           0.003982 -0.004313 -0.008884  0.000498 -0.000925   \n",
       "longitude_of_dropoff        -0.004874  0.005506  0.005221  0.001322 -0.000327   \n",
       "latitude_of_dropoff          0.002898 -0.004530 -0.008012 -0.000341 -0.001817   \n",
       "no_of_passenger              1.000000  0.011400  0.006787  0.009099  0.006385   \n",
       "hour                         0.011400  1.000000 -0.008192 -0.001916  0.004167   \n",
       "day                          0.006787 -0.008192  1.000000 -0.017266 -0.011155   \n",
       "month                        0.009099 -0.001916 -0.017266  1.000000 -0.115726   \n",
       "year                         0.006385  0.004167 -0.011155 -0.115726  1.000000   \n",
       "dayofweek                    0.038581 -0.091267  0.008422 -0.010004  0.006808   \n",
       "travel_dist_km              -0.005371  0.000644  0.010610 -0.009217  0.022136   \n",
       "\n",
       "                      dayofweek  travel_dist_km  \n",
       "amount                 0.000542        0.016451  \n",
       "longitude_of_pickup   -0.003224        0.143712  \n",
       "latitude_of_pickup     0.001911       -0.130943  \n",
       "longitude_of_dropoff  -0.002441        0.143172  \n",
       "latitude_of_dropoff    0.001626       -0.124844  \n",
       "no_of_passenger        0.038581       -0.005371  \n",
       "hour                  -0.091267        0.000644  \n",
       "day                    0.008422        0.010610  \n",
       "month                 -0.010004       -0.009217  \n",
       "year                   0.006808        0.022136  \n",
       "dayofweek              1.000000       -0.000518  \n",
       "travel_dist_km        -0.000518        1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the corr() function to generate the correlation matrix of the numeric variables\n",
    "corr = df_taxi.corr()\n",
    "\n",
    "# print the correlation matrix\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Plot the heatmap for the diagonal correlation matrix**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A correlation matrix is a symmetric matrix. Plot only the upper triangular entries using a heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:35.649061Z",
     "start_time": "2022-01-26T20:30:33.675027Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2880x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the plot size\n",
    "# pass the required height and width to the parameter, 'figsize'  \n",
    "plt.figure(figsize = (40,20))\n",
    "\n",
    "# use 'mask' to plot a upper triangular correlation matrix \n",
    "# 'tril_indices_from' returns the indices for the lower-triangle of matrix\n",
    "# 'k = -1' consider the diagonal of the matrix\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.tril_indices_from(mask, k = -1)] = True\n",
    "\n",
    "# plot the heat map\n",
    "# corr: give the correlation matrix\n",
    "# cmap: color code used for plotting\n",
    "# vmax: gives a maximum range of values for the chart\n",
    "# vmin: gives a minimum range of values for the chart\n",
    "# annot: prints the correlation values in the chart\n",
    "# annot_kws: sets the font size of the annotation\n",
    "# mask: mask the upper traingular matrix values\n",
    "sns.heatmap(corr, cmap = 'RdYlGn', vmax = 1.0, vmin = -1.0, annot = True, annot_kws = {\"size\": 20}, mask = mask)\n",
    "\n",
    "# set the size of x and y axes labels\n",
    "# set text size using 'fontsize'\n",
    "plt.xticks(fontsize = 12)\n",
    "plt.yticks(fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>The diagonal entries are all '1' which represents the correlation of the variable with itself. The dark green squares represent the variables with strong positive correlation. The dark red squares represent the variables with strong negative correlation. \n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the above correlation map is difficult to study. Create a heatmap that consider the variables with strong correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:36.540610Z",
     "start_time": "2022-01-26T20:30:35.652054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set figure size\n",
    "plt.figure(figsize=(50, 20))\n",
    "\n",
    "# plot the heat map\n",
    "# corr: give the correlation matrix\n",
    "# pass the condition to get the strong correlation between the variables\n",
    "# cmap: color code used for plotting\n",
    "# vmax: gives a maximum range of values for the chart\n",
    "# vmin: gives a minimum range of values for the chart\n",
    "# annot: prints the correlation values in the chart\n",
    "# annot_kws: sets the font size of the annotation\n",
    "#sns.set(font_scale=2)\n",
    "sns.heatmap(corr[(corr >= 0.7) | (corr <= -0.7)], cmap = 'RdYlGn', vmax = 1.0, vmin = -1.0, annot = True, \n",
    "            annot_kws={\"size\": 20})\n",
    "\n",
    "# set the size of x and y axes labels\n",
    "# set text size using 'fontsize'\n",
    "plt.xticks(fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>The above plot shows that there is highest positive correlation (= 0.96) between the variables 'longitude_of_pickup' and 'longitude_of_dropoff'. Also there is strong positive correlation between the pair latitude_of_pickup and latitude_of_dropoff. These variables may be involved in multicollinearity.<br>\n",
    "                        Following variable pairs have strong negative correlation in the dataset(longitude_of_pickup,latitude_of_pickup), (latitude_of_dropoff,longitude_of_pickup),(longitude_of_dropoff,latitude_of_pickup) and (longitude_of_dropoff, latitude_of_dropoff).\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pPwoQF_Hebod"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"quicktip.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Correlation does not imply causation. In other words, if two variables are correlated, it does not mean that one variable caused the other.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OET43huSeboe"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"alsoreadicon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>I love to know more:</b> <br><br>\n",
    "                    <a href=\"https://bit.ly/2PBvA8T\">Why correlation does not imply causation </a>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA'></a>\n",
    "## 4.2 Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Peak'></a>\n",
    "### 4.2.1 Peak hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will check for the business during the weekdays and weekends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> We will check the peak hours during the following: <br><br>\n",
    "                        1. Weekdays <br>\n",
    "                        2. Weekends <br>\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Plot a countplot to check the peak hours during weekdays**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot a countplot, we use the `countplot()` from the seaborn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:37.099415Z",
     "start_time": "2022-01-26T20:30:36.540610Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Taxi trips count during week days'}, xlabel='hour', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size, grid type and color palette along with appropraite titel for the plot\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('Taxi trips count during week days')\n",
    "sns.set_style(\"white\")\n",
    "#plot the countplot for weekdays\n",
    "sns.countplot(x='hour', data=df_taxi.loc[(df_taxi.dayofweek >= 0) & (df_taxi.dayofweek <=4)], palette='Set2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that during the weekdays the count of trips start to peak from 8 am and is highest at 9am. In the evening the peak is at 6pm and 7pm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Plot a countplot to check the peak hours during weekends**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:37.626404Z",
     "start_time": "2022-01-26T20:30:37.099415Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Taxi trips count during week ends'}, xlabel='hour', ylabel='count'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size, grid type and color palette along with appropraite titel for the plot\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('Taxi trips count during week ends')\n",
    "sns.set_style(\"white\")\n",
    "#plot the countplot for weekends\n",
    "sns.countplot(x='hour', data=df_taxi.loc[(df_taxi.dayofweek >= 5) & (df_taxi.dayofweek <=6)], palette='Set2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that during the weekends the count of trips is high after 12 midnight to 3am to 4am. This pattern is different from the weekdays. In the evening the demand is almost evenly high after 6pm till late night except."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>From the above plot, we can notice that the pattern for demand of taxi is different for weekdays and weekends.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Mean_Fare'></a>\n",
    "### 4.2.2 Mean fare for each hour during weekdays and weekends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will check the mean fares for each hour during the weekdays and weekends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> We will do the following: <br><br>\n",
    "                        1. Compute the mean hourly fare for weekdays and weekends <br>\n",
    "                        2. Plot the bar graph for the same <br>\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Compute the hourly mean fare for weekdays and weekends**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:37.734505Z",
     "start_time": "2022-01-26T20:30:37.628335Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set the days in the dataset as week days and week ends\n",
    "week_days = df_taxi.loc[(df_taxi.dayofweek >= 0) & (df_taxi.dayofweek <= 4)]\n",
    "week_ends = df_taxi.loc[(df_taxi.dayofweek >= 5) & (df_taxi.dayofweek <= 6)]\n",
    "# compute the mean fare amount over the week day and week end.\n",
    "# use groupby('hour') to get the mean fare for each hour\n",
    "week_days_fare = week_days.groupby(['hour']).amount.mean().to_frame().reset_index()\n",
    "week_ends_fare = week_ends.groupby(['hour']).amount.mean().to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:37.827708Z",
     "start_time": "2022-01-26T20:30:37.734505Z"
    }
   },
   "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>hour</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12.079275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12.522360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10.929956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>11.837477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>14.961357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>15.502702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>11.438986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>10.863713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>10.468992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>10.699696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>11.200024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>11.305669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>11.481385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>11.705997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>12.222706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>12.059795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>12.525304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>11.426644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>11.014698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>10.755850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>10.786913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>11.293807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>11.061567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>11.311165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hour    amount\n",
       "0      0 12.079275\n",
       "1      1 12.522360\n",
       "2      2 10.929956\n",
       "3      3 11.837477\n",
       "4      4 14.961357\n",
       "5      5 15.502702\n",
       "6      6 11.438986\n",
       "7      7 10.863713\n",
       "8      8 10.468992\n",
       "9      9 10.699696\n",
       "10    10 11.200024\n",
       "11    11 11.305669\n",
       "12    12 11.481385\n",
       "13    13 11.705997\n",
       "14    14 12.222706\n",
       "15    15 12.059795\n",
       "16    16 12.525304\n",
       "17    17 11.426644\n",
       "18    18 11.014698\n",
       "19    19 10.755850\n",
       "20    20 10.786913\n",
       "21    21 11.293807\n",
       "22    22 11.061567\n",
       "23    23 11.311165"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_days_fare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Plot the mean fare**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:38.459105Z",
     "start_time": "2022-01-26T20:30:37.829670Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9bdq0yYMPPpiqqqqlluv+++/PYYcdlh49eiT5bN2YhW2/ffv2ad68ec2b93fffTe77rprnn322fnud+WVV2a33XbLvffem7vuuit33XVXLrjggtx+++155513FjtXly5dcuWVV2bu3Lmprq7OFVdckW222abOxzVu3Lim3PkqY9188801M3auvfbatGrVKuuuu+5X2s6XPfDAA+nWrVv69euXjTfeOHfccUfNvt1qq63yj3/8I9XV1ZkzZ06OOOKITJgw4WuN90WbbbZZXn311Tz99NNJkpdffjkTJkzIlltumdatW+fll1/O7NmzM3fu3MVeP+bXv/51rr/++lx//fULFETJZ2XC1ltvnQsuuKCm3EiS7bbbLpdeemm23HLLmgKsc+fOueyyy1IURebMmZNBgwbl8ssvr/N1ttpqq2XzzTfP4MGD89vf/jaffvppunbtmltvvTUff/xxqqur5yuYFvX6XnPNNbPZZptl5MiR2XfffZMkL7zwQnbdddest956OfjggzNgwIA888wzC90fq666arp165Y//OEPady48Xwz6vr27ZuJEyemd+/eOeWUU/Lxxx/n/fffX2AbnTp1ysSJE/PII4/UzKhr3rx5fvjDH+byyy+vKXE222yzvPHGGzWvkYkTJ2bnnXfOlClT0rlz59x00001ayddeeWVNYthJ5+Vd/vvv39atmyZP/7xjzX7/4orrsicOXNSXV2d448/fr5PVVuYRe3nxX2+ALAkzCQC4BtnjTXWyHrrrZeWLVumVatW8932ve99L7/73e9y1FFHpSiKNGnSJP/zP/+TlVdeOYcddljOPPPMnHfeeWnatGk233zzmkVtl4Yjjzwyhx12WFZaaaW0aNEiW2yxxUK336xZs5x//vkZMWJE/vd//zfz5s3Lr3/96/lOTZo2bVr+/e9/59prr53vsVtttVU222yzjBkzZrFn5QwaNChnnHFGdt9998ybNy+bbLJJjj/++Doft8suu6SysjKjR4+e75S+Rdlmm20yYMCA9O/fP9XV1WndunUuvPDCNGq0eH+36t+//wL3Peqoo9K3b98cffTR6dmzZ+bNm5dtttkm//73v1NdXZ3DDz88I0aMyG677Zaqqqr06NEjO+20U81Mna+rdevWOe+883LKKadk1qxZqaioyGmnnZb27dtn7bXXzhZbbJHu3btntdVWS6dOnfLiiy8ulXG7du2a66+/Pt26dau5rnPnzjn22GPzi1/8oua64447LiNGjEjPnj0zd+7cbL311vnlL3+Zpk2b1vo6Gz9+fM3j99hjj9x22205/fTTc/LJJ+fFF1/MnnvumVVWWSUbbrhhPvzwwyR1v74/LzU+L2M23HDDdO/ePXvuuWdWWmmlNG/ePMOHD6/1+fbr1y977713RowYMd/1xxxzTEaOHJlzzz03FRUVOfzww9OuXbsFHr/iiivmu9/9bubOnTvfrLxtt902Z511Vjp16pTks//PP/zhDznzzDMze/bsFEWRM888M+3atUu7du3y//7f/8vAgQNTUVGRFi1a5I9//ON8M8kqKioycuTI7L777tl2221z6KGH5owzzsgee+yRqqqqbLTRRhkyZMgi/2+33XbbWvfz4j5fAFgSFcXXmTMOAAB1qK6uzu9+97ustdZaOeigg8odBwCohdPNAACoNzNmzEinTp3y7rvv5oADDih3HABgEcwkAgAAAMBMIgAAAACURAAAAABESQQAAABAkiblDlCbTp06pW3btuWOAQAAAPCN8fbbb2f8+PELva3BlkRt27bNddddV+4YAAAAAN8YvXv3rvU2p5sBAAAAoCQCAAAAQEkEAAAAQBrwmkQAAADA8mHu3LmZPHlyZs2aVe4o3xjNmzdPu3bt0rRp08V+jJIIAAAAKKvJkyenZcuW+e53v5uKiopyx1nmFUWRqVOnZvLkyWnfvv1iP87pZgAAAEBZzZo1K23atFEQLSUVFRVp06bNV56ZpSQCAAAAyk5BtHQtyf5UEgEAAAANy9Jem6iO7R1wwAF5+umnkyRz5sxJx44dc8kll9Tcvv/+++eFF174SkNus8029XLf+qQkAgAAABqW5s2Tioql99W8+SKH69y5cx599NEkyWOPPZbOnTvnnnvuSZLMnj077777bjbccMP6ftZlpyQCAAAAlmtbb711TUl07733Zq+99sr06dMzffr0PPHEE9lyyy2TJLfcckv22Wef7Lvvvjn77LOTJNOnT88RRxyRysrKVFZW5sUXX5xv26NGjcrJJ5+coihqrquqqsqwYcOy99575+ijj86cOXOSJC+99FIGDhyYAQMGpHfv3nn88cdz//3354gjjqh5bN++ffPee+9lyJAh6devX/bcc8/cfPPNS2U/+HQzAAAAYLn2gx/8IK+++mqKosiECRNy1FFHZauttsqDDz6YF198MV26dMlHH32U0aNH59prr82KK66YY489Ng888EAefPDB/PSnP02/fv3y+uuvZ+jQobnyyiuTJGeccUYqKipy4oknzjfeuHHjMnv27IwdOzbvvPNObrvttiTJpEmTMnjw4GywwQa58cYbc9111+WUU07Jqaeemv/+9795//33s+qqq2allVbK+PHjc+211yZJHnjggaWyH5REAAAAwHKtUaNG2XDDDTNu3ListtpqadasWbp27Zp77rknL7zwQg444IC8+eabmTZtWg466KAkycyZM/PWW2/lpZdeysMPP5xbbrklSfLxxx8nST744IO8+OKLWWeddRYY7+WXX84mm2ySJFlrrbWy5pprJklWX331nH/++WnevHlmzpyZFi1apKKiIr169cq//vWvTJ48OX369EmLFi1y/PHH5/jjj8+MGTPSq1evpbMflspWAAAAAJZh22yzTS688MJ06dIlSdKxY8c8//zzSZJWrVqlXbt2WXPNNXPppZdmzJgx2X///bPpppumQ4cOGTBgQMaMGZNzzz03PXv2TJJ8+9vfziWXXJJJkyZl3Lhx843VoUOHPPnkk0mSKVOmZMqUKUmSESNG5IgjjsgZZ5yR9ddfv+YUtT333DO33nprJkyYkG233TbvvfdennvuufzpT3/KRRddlLPOOivz5s372vvATCIAAABgubf11ltn+PDhOfPMM5MkzZo1S8uWLfODH/wgSdK6desMGDAglZWVqaqqStu2bdO9e/cccsghOe644zJ27NjMmDEjhx9+eM02KyoqMnLkyBx44IEZO3ZsVl111STJjjvumMceeyx77bVX1lprrZrre/XqlUMPPTRt2rTJd77znXz44YdJkjXWWCMrr7xyNttsszRp0iSrrbZa3n///ey+++5ZaaWVMnDgwDRp8vUrnoriiysnNSC9e/fOddddV+4YAAAAQD2bOHFiNtpoo/+7YtasOj+R7CtZ2tsrg4MPPjjDhg3Luuuuu9iPWWC/ZtF9i9PNAAAAgIZlaRc6y3BBNGvWrPTu3TsbbrjhVyqIloTTzQAAAAAaqObNm5fsTCsziQAAAABQEgEAAACgJAIAAAAgSiIAAAAAoiQCAAAAGphZs0q7vQMOOCBPP/10kmTOnDnp2LFjLrnkkprb999//7zwwgtfacxtttnmK+dcHHvvvXcmT55cL9tWEgEAAAANSvPmSUXF0vtq3nzR43Xu3DmPPvpokuSxxx5L586dc8899yRJZs+enXfffTcbbrhhPT/r8mtS7gAAAAAA5bT11lvn/PPPz8CBA3Pvvfdmr732ytlnn53p06fnueeey5ZbbpkkueWWW3LZZZelUaNG6dixY4455phMnz49xx13XD788MMkyfDhw7PBBhvUbHvUqFGZPn16TjjhhFRUVNRcf84552TChAkpiiIDBgxI9+7dU1lZmQ033DAvv/xyZsyYkfPOOy9t27bN73//+9x33335zne+UzPOY489ljPOOCNNmjTJKquskrPPPjstWrT4WvtBSQTQwMyaVfdfOhbnPgAAwOL5wQ9+kFdffTVFUWTChAk56qijstVWW+XBBx/Miy++mC5duuSjjz7K6NGjc+2112bFFVfMsccemwceeCAPPvhgfvrTn6Zfv355/fXXM3To0Fx55ZVJkjPOOCMVFRU58cQT5xvv3nvvzeTJk3PVVVdl9uzZ2XvvvWtOT9tkk01y3HHH5fe//31uuummbLfddpkwYUKuueaafPLJJ9lpp52SJHfccUd+9rOf5cADD8xdd92Vjz/+WEkE8E3z+dTaRSmK0mQBAIDlQaNGjbLhhhtm3LhxWW211dKsWbN07do199xzT1544YUccMABefPNNzNt2rQcdNBBSZKZM2fmrbfeyksvvZSHH344t9xyS5Lk448/TpJ88MEHefHFF7POOussMN5LL72U5557LpWVlUmSefPm5Z133knyWWGVJN/5znfywQcfZNKkSdl4443TqFGjtGjRIuuvv36S5JBDDskFF1yQ/v37Z4011sgmm2zy9ffD194CAAAAwDJum222yYUXXpguXbokSTp27Jjnn38+SdKqVau0a9cua665Zi699NKMGTMm+++/fzbddNN06NAhAwYMyJgxY3LuueemZ8+eSZJvf/vbueSSSzJp0qSMGzduvrE6dOiQTp06ZcyYMfnLX/6S7t27p127dgvN1b59+zz99NOprq7OJ598kkmTJiVJbrzxxuyxxx4ZM2ZMvv/972fs2LFfex+YSQQAAAAs97beeusMHz48Z555ZpKkWbNmadmyZc3MntatW2fAgAGprKxMVVVV2rZtm+7du+eQQw7Jcccdl7Fjx2bGjBk5/PDDa7ZZUVGRkSNH5sADD8zYsWOz6qqrJkm23377PPLII+nXr18++eST7LjjjrWeKrbRRhtll112SZ8+fbL66qunTZs2SZIf/ehHGTJkSFZaaaU0bdo0v/vd7772PqgoioZ50kLv3r1z3XXXlTsGQFk43QwAgOXJxIkTs9FGG9VcXtprcC6va3p+eb8mi+5bnG4GAAAANCh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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hours\n",
    "x = np.array(week_days_fare.hour)\n",
    "# an array of week day fare \n",
    "y = np.array(week_days_fare.amount)\n",
    "# an array of week end fare\n",
    "z = np.array(week_ends_fare.amount)\n",
    "\n",
    "# Set the figure size, title, x and y labels\n",
    "plt.figure(figsize = (20,10))\n",
    "plt.title('Mean Fare Amont For Each Hour - Weekdays Vs Weekends')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Mean Fare')\n",
    "# Pass the three integers. The value of these integer should be less that 10\n",
    "ax=plt.subplot(1,1,1)\n",
    "ax.bar(x-0.2, y, width=0.2, color='red', align='center', label = 'Week days')\n",
    "ax.bar(x, z, width=0.2, color='blue', align='center', label = 'Week ends')\n",
    "plt.xticks(range(0,24))\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that the mean fares for each hour are different for week days and week ends. Mean fare for weekdays during morning hours 6,7 and 8 is lmuch ess compared to weekends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Distribution'></a>\n",
    "### 4.2.3 Distribution of key numerical vairables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will check the distribustion of 'travel_dist_km' and 'amount'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> We will check the distribution of the following variables: <br><br>\n",
    "                        1. 'amount' <br>\n",
    "                        2. 'travel_dist_km' <br>\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Distribution of fare 'amount'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:39.974494Z",
     "start_time": "2022-01-26T20:30:38.459105Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.title(\"Distribution of the fare amount\")\n",
    "plt.xlabel(\"Fare Amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xlim(-10,20)\n",
    "plt.xticks(range(0,200,5))\n",
    "\n",
    "snsplot = sns.kdeplot(df_taxi.amount, shade=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most the trips have a fare amount of 2 to 20 dollars. There are a few small peaks around 50 and 58 dollars as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Distribustion of the travel distance in km**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:41.134013Z",
     "start_time": "2022-01-26T20:30:39.974494Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "sns.style = ('darkgrid')\n",
    "plt.title(\"Distribution of the trip distance\")\n",
    "plt.xlabel(\"Distance(Km)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xlim(-10, 200)\n",
    "plt.xticks(range(0,200,5))\n",
    "\n",
    "sns.plot = sns.kdeplot(df_taxi[df_taxi.travel_dist_km<600].travel_dist_km, shade=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most the trips have a travel distance of 2.5 km to 25 km. There are a few instances of 0 km as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>We have generated new features, removed redundant features and removed the outliers.\n",
    "<br>                    \n",
    "We now progress to build the model.\n",
    "</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Random_Forest'></a>\n",
    "# 5.  Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is an example of the `Bagging` technique. It constructs multiple decision trees on randomly selected data samples. We can use the bootstrap sampling method to select the random samples of the same size from the dataset to construct multiple trees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='RF_Model'></a>\n",
    "## 5.1 Random Forest Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we build a model using random forest regressor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>In order to build the model, we do the following: <br><br>\n",
    "                       1. Split the features and target into seperate data frames<br>\n",
    "                       2. Split the data into training and test sets<br>\n",
    "                       3. Build model<br>\n",
    "                       4. Predict the values using test set <br>\n",
    "                       5. Compute accuracy measures <br>\n",
    "                       6. Tabulate the results\n",
    "</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Split the data into features and target dataframes**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store the amount variable which is the target variable, into y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:41.149970Z",
     "start_time": "2022-01-26T20:30:41.139997Z"
    }
   },
   "outputs": [],
   "source": [
    "# select only the target variable 'amount' and store it in dataframe 'y'\n",
    "y = pd.DataFrame(df_taxi['amount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, use this 'y' as a target variable to build the classification models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:41.273188Z",
     "start_time": "2022-01-26T20:30:41.156952Z"
    }
   },
   "outputs": [],
   "source": [
    "# use 'drop()' to remove the variable 'amount' from df_taxi\n",
    "# 'axis = 1' drops the corresponding column(s)\n",
    "X = df_taxi.drop('amount',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:41.415389Z",
     "start_time": "2022-01-26T20:30:41.275177Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude_of_pickup</th>\n",
       "      <th>latitude_of_pickup</th>\n",
       "      <th>longitude_of_dropoff</th>\n",
       "      <th>latitude_of_dropoff</th>\n",
       "      <th>no_of_passenger</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>travel_dist_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>1.030764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>8.450134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>1.389525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>2.799270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1.999157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude_of_pickup  latitude_of_pickup  longitude_of_dropoff  \\\n",
       "0           -73.844311           40.721319            -73.841610   \n",
       "1           -74.016048           40.711303            -73.979268   \n",
       "2           -73.982738           40.761270            -73.991242   \n",
       "3           -73.987130           40.733143            -73.991567   \n",
       "4           -73.968095           40.768008            -73.956655   \n",
       "\n",
       "   latitude_of_dropoff  no_of_passenger  hour  day  month  year  dayofweek  \\\n",
       "0            40.712278                1    17   15      6  2009          0   \n",
       "1            40.782004                1    16    5      1  2010          1   \n",
       "2            40.750562                2     0   18      8  2011          3   \n",
       "3            40.758092                1     4   21      4  2012          5   \n",
       "4            40.783762                1     7    9      3  2010          1   \n",
       "\n",
       "   travel_dist_km  \n",
       "0        1.030764  \n",
       "1        8.450134  \n",
       "2        1.389525  \n",
       "3        2.799270  \n",
       "4        1.999157  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this 'X' as a set of predictors to build the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Split the data into training and test sets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:30:41.780089Z",
     "start_time": "2022-01-26T20:30:41.418159Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X_train is: (34992, 11)\n",
      "The shape of X_test is: (14997, 11)\n",
      "The shape of y_train is: (34992, 1)\n",
      "The shape of y_test is: (14997, 1)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# split data into train subset and test subset for predictor and target variables\n",
    "# 'test_size' returns the proportion of data to be included in the test set\n",
    "# set 'random_state' to generate the same dataset each time you run the code \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 1)\n",
    "\n",
    "# check the dimensions of the train & test subset for \n",
    "# print dimension of predictors train set\n",
    "print(\"The shape of X_train is:\",X_train.shape)\n",
    "\n",
    "# print dimension of predictors test set\n",
    "print(\"The shape of X_test is:\",X_test.shape)\n",
    "\n",
    "# print dimension of target train set\n",
    "print(\"The shape of y_train is:\",y_train.shape)\n",
    "\n",
    "# print dimension of target test set\n",
    "print(\"The shape of y_test is:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Build model using RandomForestRegressor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:31:14.803390Z",
     "start_time": "2022-01-26T20:30:41.780089Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=10)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#intantiate the regressor\n",
    "rf_reg = RandomForestRegressor(n_estimators=100, random_state=10)\n",
    "\n",
    "# fit the regressor with training dataset\n",
    "rf_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Predict the values using test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:31:15.360618Z",
     "start_time": "2022-01-26T20:31:14.805170Z"
    }
   },
   "outputs": [],
   "source": [
    "# predict the values on test dataset using predict()\n",
    "y_pred = rf_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Compute accuracy measures**\n",
    "\n",
    "Now we calculate accuray measures Root-mean-square-error (RMSE), R-squared and Adjusted R-squared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:31:15.383446Z",
     "start_time": "2022-01-26T20:31:15.360618Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 2.06849660066934\n",
      "Mean Squared Error (MSE): 19.606631425668667\n",
      "Root Mean Squared Error (RMSE): 4.427937604084848\n"
     ]
    }
   ],
   "source": [
    "# Calculate MAE\n",
    "rf_reg_MAE = metrics.mean_absolute_error(y_test, y_pred)\n",
    "print('Mean Absolute Error (MAE):', rf_reg_MAE)\n",
    "\n",
    "# Calculate MSE\n",
    "rf_reg_MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "print('Mean Squared Error (MSE):', rf_reg_MSE)\n",
    "\n",
    "# Calculate RMSE\n",
    "rf_reg_RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error (RMSE):', rf_reg_RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. Tabulate the results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:31:15.563210Z",
     "start_time": "2022-01-26T20:31:15.383446Z"
    }
   },
   "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>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>2.068497</td>\n",
       "      <td>19.606631</td>\n",
       "      <td>4.427938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model      MAE       MSE     RMSE\n",
       "0  Random Forest  2.068497 19.606631 4.427938"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the result table for all accuracy scores\n",
    "# accuracy measures considered for model comparision are RMSE\n",
    "# create a list of column names\n",
    "cols = ['Model', 'MAE', 'MSE', 'RMSE']\n",
    "\n",
    "# create a empty dataframe of the colums\n",
    "# columns: specifies the columns to be selected\n",
    "result_tabulation = pd.DataFrame(columns = cols)\n",
    "\n",
    "# compile the required information\n",
    "rf_reg_metrics = pd.Series({'Model': \"Random Forest \",\n",
    "                     'MAE':rf_reg_MAE,\n",
    "                     'MSE': rf_reg_MSE,\n",
    "                     'RMSE': rf_reg_RMSE     \n",
    "                   })\n",
    "\n",
    "# append our result table using append()\n",
    "# ignore_index=True: does not use the index labels\n",
    "# python can only append a Series if ignore_index=True or if the Series has a name\n",
    "result_tabulation = result_tabulation.append(rf_reg_metrics, ignore_index = True)\n",
    "\n",
    "# print the result table\n",
    "result_tabulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Let us also take a look at building a random forest model with grid search.\n",
    "</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='RF_CV'></a>\n",
    "## 5.2 Random Forest with GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we show how a decision tree is optimized by cross-validation, which is done using the `GridSearchCV()` from sklearn library.\n",
    "\n",
    "The performance of the selected hyperparameters and trained model is then measured on the test set that was not used during the model building."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> Now we build a random forest using the GridSearchCV. We start with our original data set gradually proceeding with our analysis<br><br>\n",
    "                        To build a Random Forest using GridSearchCV, we do the following:<br>\n",
    "                        1. Use GridSearch to obtain the optimal values of hyperparameters <br>\n",
    "                        2. Build the model using the hyperparameters obtained in step 1<br>\n",
    "                        3. Do predictions on the test set<br>\n",
    "                        4. Compute accuracy measures <br>\n",
    "                        5. Tabulate the results <br>                     \n",
    "                      </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Use GridSearch to obtain the optimal values of hyperparameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:31:15.670982Z",
     "start_time": "2022-01-26T20:31:15.563210Z"
    }
   },
   "outputs": [],
   "source": [
    "# create a dictionary with hyperparameters and its values\n",
    "# pass the n_estimators \n",
    "# pass the list of values to 'min_samples_split' that assigns minimum number of samples to split an internal node\n",
    "# pass the list of values to 'max_depth' that assigns maximum depth of the tree\n",
    "# pass the list of values to 'min_samples_leaf' that assigns minimum number of samples required at the terminal/leaf node\n",
    "# pass the list of values to 'max_leaf_nodes' that assigns maximum number of leaf nodes in the tree\n",
    "tuned_paramaters = [{'n_estimators': [ 85, 100],\n",
    "                     'min_samples_split': [15,20],\n",
    "                     'max_depth': [8, 10]\n",
    "                     #'min_samples_leaf': [5,10],\n",
    "                     #'max_leaf_nodes': [10, 15]\n",
    "                    }]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:48:42.849264Z",
     "start_time": "2022-01-26T20:31:15.670982Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for random forest regressor:  {'max_depth': 10, 'min_samples_split': 20, 'n_estimators': 85} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# instantiate the 'RandomForestRegressor' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "rf_reg_CV= RandomForestRegressor(random_state = 10)\n",
    "\n",
    "# use GridSearchCV() to find the optimal value of the hyperparameters\n",
    "# n_estimator: pass the decision tree classifier model\n",
    "# param_grid: pass the list 'tuned_parameters'\n",
    "# cv: number of folds in k-fold i.e. here cv = 10\n",
    "grid = GridSearchCV(estimator = rf_reg_CV, \n",
    "                         param_grid = tuned_paramaters, \n",
    "                         cv = 10)\n",
    "\n",
    "# fit the model on X_train and y_train using fit()\n",
    "dt_grid = grid.fit(X_train, y_train)\n",
    "\n",
    "# get the best parameters\n",
    "print('Best parameters for random forest regressor: ', dt_grid.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Build the model using the hyperparameters obtained in step 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:48:58.169387Z",
     "start_time": "2022-01-26T20:48:42.849264Z"
    }
   },
   "outputs": [],
   "source": [
    "# instantiate the 'RandomForestRegressor'\n",
    "# 'best_params_' returns the dictionary containing best parameter values and parameter name  \n",
    "# 'get()' returns the value of specified parameter\n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "dt_grid_model = RandomForestRegressor(n_estimators = dt_grid.best_params_.get('n_estimators'),\n",
    "                                       max_depth = dt_grid.best_params_.get('max_depth'),\n",
    "                                       #max_leaf_nodes = dt_grid.best_params_.get('max_leaf_nodes'),\n",
    "                                       #min_samples_leaf = dt_grid.best_params_.get('min_samples_leaf'),\n",
    "                                       min_samples_split = dt_grid.best_params_.get('min_samples_split'),\n",
    "                                       random_state = 10)\n",
    "\n",
    "# use fit() to fit the model on the train set\n",
    "dt_grid_model = dt_grid_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Predict the values using test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:48:58.396513Z",
     "start_time": "2022-01-26T20:48:58.169387Z"
    }
   },
   "outputs": [],
   "source": [
    "# predict the class labels using 'X_test'\n",
    "y_pred = dt_grid_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Compute accuracy measures**\n",
    "\n",
    "Now we calculate accuray measures Root-mean-square-error (RMSE), R-squared and Adjusted R-squared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:48:58.427441Z",
     "start_time": "2022-01-26T20:48:58.401508Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 2.108015582329935\n",
      "Mean Squared Error (MSE): 19.952915628853532\n",
      "Root Mean Squared Error (RMSE): 4.466868660354089\n"
     ]
    }
   ],
   "source": [
    "# calculate MAE\n",
    "rf_reg_CV_MAE = metrics.mean_absolute_error(y_test, y_pred)\n",
    "print('Mean Absolute Error (MAE):', rf_reg_CV_MAE)\n",
    "                                         \n",
    "# calculate MSE\n",
    "rf_reg_CV_MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "print('Mean Squared Error (MSE):', rf_reg_CV_MSE)\n",
    "                                         \n",
    "# calculate RMSE\n",
    "rf_reg_CV_RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error (RMSE):', rf_reg_CV_RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. Tabulate the results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T20:48:58.632040Z",
     "start_time": "2022-01-26T20:48:58.430432Z"
    }
   },
   "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>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>2.068497</td>\n",
       "      <td>19.606631</td>\n",
       "      <td>4.427938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest with Grid Search</td>\n",
       "      <td>2.108016</td>\n",
       "      <td>19.952916</td>\n",
       "      <td>4.466869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Model      MAE       MSE     RMSE\n",
       "0                   Random Forest  2.068497 19.606631 4.427938\n",
       "1  Random Forest with Grid Search  2.108016 19.952916 4.466869"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the result table for all accuracy scores\n",
    "# accuracy measures considered for model comparision are MAE, MSE, RMSE\n",
    "# create a list of column names\n",
    "#cols = ['Model', 'MAE', 'MSE', 'RMSE']\n",
    "\n",
    "# create a empty dataframe of the colums\n",
    "# columns: specifies the columns to be selected\n",
    "#result_tabulation = pd.DataFrame(columns = cols)\n",
    "\n",
    "# compile the required information\n",
    "rf_reg_metrics = pd.Series({'Model': \"Random Forest with Grid Search \",\n",
    "                     'MAE':rf_reg_CV_MAE,\n",
    "                     'MSE': rf_reg_CV_MSE,\n",
    "                     'RMSE': rf_reg_CV_RMSE     \n",
    "                   })\n",
    "\n",
    "# append our result table using append()\n",
    "# ignore_index=True: does not use the index labels\n",
    "# python can only append a Series if ignore_index=True or if the Series has a name\n",
    "result_tabulation = result_tabulation.append(rf_reg_metrics, ignore_index = True)\n",
    "\n",
    "# print the result table\n",
    "result_tabulation"
   ]
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    "<a id=\"conclusion\"> </a>\n",
    "# 6. Conclusion and Interpretation"
   ]
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    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> The regression algorithms named in the above table have been implemented on the given dataset. The performance of the models were evaluated using MAE, MSE,RMSE. <br><br>\n",
    "                        The above result shows that the RMSE value for both the model is much less than the standard deviation of the 'amount' (target) variable. Both the models have performed well. We can further fine tune the model for various hyperparameter values.\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
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