ML micro Projects/Rforest_Taxi Fare Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"center\" width=100%>\n",
    "    <tr>\n",
    "        <td width=\"15%\">\n",
    "            <img src=\"taxi.jpg\">\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": {
    "collapsed": true
   },
   "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": {
    "collapsed": true
   },
   "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": {
    "collapsed": true
   },
   "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 = 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": {},
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52:16.0</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "</div>"
      ],
      "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('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": {
    "collapsed": true
   },
   "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": {},
   "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": {},
   "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": {
    "collapsed": true
   },
   "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": {},
   "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": {
    "collapsed": true
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "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": {
    "collapsed": true
   },
   "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": {
    "collapsed": true
   },
   "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": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <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>4.500000</td>\n",
       "      <td>2009-06-15 17:26:21+00:00</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",
       "      <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>16.900000</td>\n",
       "      <td>2010-01-05 16:52:16+00:00</td>\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>5.700000</td>\n",
       "      <td>2011-08-18 00:35:00+00:00</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",
       "      <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>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": 13,
     "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": 14,
   "metadata": {
    "collapsed": true
   },
   "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": 15,
   "metadata": {},
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>40.734371</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    40.734371   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": 16,
     "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": 17,
   "metadata": {
    "collapsed": true
   },
   "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": 18,
   "metadata": {},
   "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 tarvel distance more than 130 km, as that is the limit for trips in and around NYC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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",
    "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": 20,
   "metadata": {
    "collapsed": true
   },
   "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": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_taxi.drop(incorrect_coordinates, inplace = True, errors = 'ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percentage of Missing Values</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>travel_dist_km</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>float64</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>year</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>day</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>no_of_passenger</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>int64</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>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_pickup</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>amount</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",
       "travel_dist_km            0                      0.000000  float64\n",
       "dayofweek                 0                      0.000000    int64\n",
       "year                      0                      0.000000    int64\n",
       "month                     0                      0.000000    int64\n",
       "day                       0                      0.000000    int64\n",
       "hour                      0                      0.000000    int64\n",
       "no_of_passenger           0                      0.000000    int64\n",
       "latitude_of_dropoff       0                      0.000000  float64\n",
       "longitude_of_dropoff      0                      0.000000  float64\n",
       "latitude_of_pickup        0                      0.000000  float64\n",
       "longitude_of_pickup       0                      0.000000  float64\n",
       "amount                    0                      0.000000  float64"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {
    "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 25,
     "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": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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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": 27,
   "metadata": {},
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe8cc975390>"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe8cb6fb5d0>"
      ]
     },
     "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": {},
     "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": 30,
   "metadata": {
    "collapsed": true
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "**2. Plot the mean fare**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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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": 32,
   "metadata": {},
   "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": 33,
   "metadata": {},
   "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": 34,
   "metadata": {
    "collapsed": true
   },
   "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": 35,
   "metadata": {
    "collapsed": true
   },
   "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": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 36,
     "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": 37,
   "metadata": {},
   "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": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=10, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 38,
     "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": 39,
   "metadata": {
    "collapsed": true
   },
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 2.0686233979335547\n",
      "Mean Squared Error (MSE): 19.590860734865448\n",
      "Root Mean Squared Error (RMSE): 4.426156429100247\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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2.068623</td>\n",
       "      <td>19.590861</td>\n",
       "      <td>4.426156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model      MAE       MSE     RMSE\n",
       "0  Random Forest  2.068623 19.590861 4.426156"
      ]
     },
     "execution_count": 41,
     "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": 42,
   "metadata": {
    "collapsed": true
   },
   "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": 43,
   "metadata": {},
   "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": 44,
   "metadata": {
    "collapsed": true
   },
   "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": 45,
   "metadata": {
    "collapsed": true
   },
   "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": 46,
   "metadata": {},
   "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2.068623</td>\n",
       "      <td>19.590861</td>\n",
       "      <td>4.426156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</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  2.068623 19.590861 4.426156\n",
       "1  Random Forest  2.108016 19.952916 4.466869"
      ]
     },
     "execution_count": 47,
     "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 \",\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"conclusion\"> </a>\n",
    "# 6. Conclusion and Interpretation"
   ]
  },
  {
   "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 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>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}