Flights data analysis and regression - python code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2015 Flights delays and cancellations"
   ]
  },
  
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this notebook is to perform exploratory data analysis on the flights delay data to get insights on the features and to build models using Linear regression to analyze arrival delay of flights using several features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all the relevant libraries here\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "%matplotlib inline\n",
    "#Reading the csv file\n",
    "flight=pd.read_csv(\"flights.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Description of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YEAR :Year of the Flight Trip<br/>\n",
    "MONTH : Month of the Flight Trip<br/>\n",
    "DAY : Day of the Flight Trip<br/>\n",
    "DAY_OF_WEEK : Day of week of the Flight Trip<br/>\n",
    "AIRLINE : Airline Identifier<br/>\n",
    "FLIGHT_NUMBER : Flight Identifier<br/>\n",
    "TAIL_NUMBER : Aircraft Identifier<br/>\n",
    "ORIGIN_AIRPORT : Starting Airport<br/>\n",
    "DESTINATION_AIRPORT : Destination Airport<br/>\n",
    "SCHEDULED_DEPARTURE : Planned Departure Time<br/>\n",
    "DEPARTURE_TIME : WHEEL_OFF - TAXI_OUT<br/>\n",
    "DEPARTURE_DELAY : Total Delay on Departure<br/>\n",
    "TAXI_OUT : The time duration elapsed between departure from the origin airport gate and wheels off<br/>\n",
    "WHEELS_OFF : The time point that the aircraft's wheels leave the ground<br/>\n",
    "SCHEDULED_TIME : Planned time amount needed for the flight trip<br/>\n",
    "ELAPSED_TIME : AIR_TIME+TAXI_IN+TAXI_OUT<br/>\n",
    "AIR_TIME : The time duration between wheels_off and wheels_on time<br/>\n",
    "DISTANCE : Distance between two airports<br/>\n",
    "WHEELS_ON : The time point that the aircraft's wheels touch on the ground<br/>\n",
    "TAXI_IN : The time duration elapsed between wheels-on and gate arrival at the destination airport<br/>\n",
    "SCHEDULED_ARRIVAL : Planned arrival time<br/>\n",
    "ARRIVAL_TIME : WHEELS_ON+TAXI_IN<br/>\n",
    "ARRIVAL_DELAY : ARRIVAL_TIME-SCHEDULED_ARRIVAL<br/>\n",
    "DIVERTED: Aircraft landed on airport that out of schedule<br/>\n",
    "CANCELLED : Flight Cancelled (1 = cancelled)<br/>\n",
    "CANCELLATION_REASON: Reason for Cancellation of flight: A - Airline/Carrier; B - Weather; C - National Air System; D - Security<br/>\n",
    "AIR_SYSTEM_DELAY : Delay caused by air system<br/>\n",
    "SECURITY_DELAY : Delay caused by security<br/>\n",
    "AIRLINE_DELAY : Delay caused by the airline<br/>\n",
    "LATE_AIRCRAFT_DELAY : Delay caused by aircraft<br/>\n",
    "WEATHER_DELAY : Delay caused by weather<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>EV</td>\n",
       "      <td>4160</td>\n",
       "      <td>N11150</td>\n",
       "      <td>JAX</td>\n",
       "      <td>EWR</td>\n",
       "      <td>540</td>\n",
       "      <td>...</td>\n",
       "      <td>743.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>1635</td>\n",
       "      <td>N025AA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>DFW</td>\n",
       "      <td>625</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>WN</td>\n",
       "      <td>119</td>\n",
       "      <td>N271LV</td>\n",
       "      <td>RSW</td>\n",
       "      <td>ATL</td>\n",
       "      <td>800</td>\n",
       "      <td>...</td>\n",
       "      <td>934.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>EV</td>\n",
       "      <td>4936</td>\n",
       "      <td>N738EV</td>\n",
       "      <td>MSP</td>\n",
       "      <td>IAD</td>\n",
       "      <td>900</td>\n",
       "      <td>...</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>2319</td>\n",
       "      <td>N960DL</td>\n",
       "      <td>LGA</td>\n",
       "      <td>MSP</td>\n",
       "      <td>1010</td>\n",
       "      <td>...</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0  2015      1    1            4      EV           4160      N11150   \n",
       "1  2015      1    1            4      AA           1635      N025AA   \n",
       "2  2015      1    1            4      WN            119      N271LV   \n",
       "3  2015      1    1            4      EV           4936      N738EV   \n",
       "4  2015      1    1            4      DL           2319      N960DL   \n",
       "\n",
       "  ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  ARRIVAL_TIME  \\\n",
       "0            JAX                 EWR                  540  ...         743.0   \n",
       "1            ATL                 DFW                  625  ...           NaN   \n",
       "2            RSW                 ATL                  800  ...         934.0   \n",
       "3            MSP                 IAD                  900  ...        1240.0   \n",
       "4            LGA                 MSP                 1010  ...        1225.0   \n",
       "\n",
       "   ARRIVAL_DELAY  DIVERTED  CANCELLED  CANCELLATION_REASON  AIR_SYSTEM_DELAY  \\\n",
       "0          -14.0         0          0                  NaN               NaN   \n",
       "1            NaN         0          1                    B               NaN   \n",
       "2          -11.0         0          0                  NaN               NaN   \n",
       "3           12.0         0          0                  NaN               NaN   \n",
       "4           -5.0         0          0                  NaN               NaN   \n",
       "\n",
       "   SECURITY_DELAY  AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0             NaN            NaN                  NaN            NaN  \n",
       "1             NaN            NaN                  NaN            NaN  \n",
       "2             NaN            NaN                  NaN            NaN  \n",
       "3             NaN            NaN                  NaN            NaN  \n",
       "4             NaN            NaN                  NaN            NaN  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 5 rows of the dataset\n",
    "flight.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5816</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>UA</td>\n",
       "      <td>436</td>\n",
       "      <td>N69838</td>\n",
       "      <td>IAD</td>\n",
       "      <td>TPA</td>\n",
       "      <td>1230</td>\n",
       "      <td>...</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5817</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>WN</td>\n",
       "      <td>2265</td>\n",
       "      <td>N626SW</td>\n",
       "      <td>TUL</td>\n",
       "      <td>LAS</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>1523.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5818</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>MQ</td>\n",
       "      <td>3083</td>\n",
       "      <td>N513MQ</td>\n",
       "      <td>STL</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1358</td>\n",
       "      <td>...</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5819</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>EV</td>\n",
       "      <td>2775</td>\n",
       "      <td>N624AE</td>\n",
       "      <td>SPI</td>\n",
       "      <td>DFW</td>\n",
       "      <td>1411</td>\n",
       "      <td>...</td>\n",
       "      <td>1605.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5820</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>WN</td>\n",
       "      <td>3479</td>\n",
       "      <td>N719SW</td>\n",
       "      <td>CMH</td>\n",
       "      <td>PHX</td>\n",
       "      <td>1450</td>\n",
       "      <td>...</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "5816  2015     12   31            4      UA            436      N69838   \n",
       "5817  2015     12   31            4      WN           2265      N626SW   \n",
       "5818  2015     12   31            4      MQ           3083      N513MQ   \n",
       "5819  2015     12   31            4      EV           2775      N624AE   \n",
       "5820  2015     12   31            4      WN           3479      N719SW   \n",
       "\n",
       "     ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "5816            IAD                 TPA                 1230  ...   \n",
       "5817            TUL                 LAS                 1345  ...   \n",
       "5818            STL                 ORD                 1358  ...   \n",
       "5819            SPI                 DFW                 1411  ...   \n",
       "5820            CMH                 PHX                 1450  ...   \n",
       "\n",
       "      ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  CANCELLATION_REASON  \\\n",
       "5816        1448.0          -12.0         0          0                  NaN   \n",
       "5817        1523.0           38.0         0          0                  NaN   \n",
       "5818        1525.0            2.0         0          0                  NaN   \n",
       "5819        1605.0          -22.0         0          0                  NaN   \n",
       "5820        1734.0           19.0         0          0                  NaN   \n",
       "\n",
       "      AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  \\\n",
       "5816               NaN             NaN            NaN                  NaN   \n",
       "5817               0.0             0.0            8.0                 30.0   \n",
       "5818               NaN             NaN            NaN                  NaN   \n",
       "5819               NaN             NaN            NaN                  NaN   \n",
       "5820              19.0             0.0            0.0                  0.0   \n",
       "\n",
       "      WEATHER_DELAY  \n",
       "5816            NaN  \n",
       "5817            0.0  \n",
       "5818            NaN  \n",
       "5819            NaN  \n",
       "5820            0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bottom 5 rows of the dataset\n",
    "flight.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total No. of observations : 5821\n"
     ]
    }
   ],
   "source": [
    "#Number of observations\n",
    "print(\"Total No. of observations : {}\".format(len(flight)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total No. of features : 31\n"
     ]
    }
   ],
   "source": [
    "#The number of features/columns\n",
    "print(\"Total No. of features : {}\".format(len(flight.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YEAR\n",
      "MONTH\n",
      "DAY\n",
      "DAY_OF_WEEK\n",
      "AIRLINE\n",
      "FLIGHT_NUMBER\n",
      "TAIL_NUMBER\n",
      "ORIGIN_AIRPORT\n",
      "DESTINATION_AIRPORT\n",
      "SCHEDULED_DEPARTURE\n",
      "DEPARTURE_TIME\n",
      "DEPARTURE_DELAY\n",
      "TAXI_OUT\n",
      "WHEELS_OFF\n",
      "SCHEDULED_TIME\n",
      "ELAPSED_TIME\n",
      "AIR_TIME\n",
      "DISTANCE\n",
      "WHEELS_ON\n",
      "TAXI_IN\n",
      "SCHEDULED_ARRIVAL\n",
      "ARRIVAL_TIME\n",
      "ARRIVAL_DELAY\n",
      "DIVERTED\n",
      "CANCELLED\n",
      "CANCELLATION_REASON\n",
      "AIR_SYSTEM_DELAY\n",
      "SECURITY_DELAY\n",
      "AIRLINE_DELAY\n",
      "LATE_AIRCRAFT_DELAY\n",
      "WEATHER_DELAY\n"
     ]
    }
   ],
   "source": [
    "#Names of columns\n",
    "for col in flight.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5821 entries, 0 to 5820\n",
      "Data columns (total 31 columns):\n",
      "YEAR                   5821 non-null int64\n",
      "MONTH                  5821 non-null int64\n",
      "DAY                    5821 non-null int64\n",
      "DAY_OF_WEEK            5821 non-null int64\n",
      "AIRLINE                5821 non-null object\n",
      "FLIGHT_NUMBER          5821 non-null int64\n",
      "TAIL_NUMBER            5805 non-null object\n",
      "ORIGIN_AIRPORT         5821 non-null object\n",
      "DESTINATION_AIRPORT    5821 non-null object\n",
      "SCHEDULED_DEPARTURE    5821 non-null int64\n",
      "DEPARTURE_TIME         5730 non-null float64\n",
      "DEPARTURE_DELAY        5730 non-null float64\n",
      "TAXI_OUT               5728 non-null float64\n",
      "WHEELS_OFF             5728 non-null float64\n",
      "SCHEDULED_TIME         5821 non-null int64\n",
      "ELAPSED_TIME           5713 non-null float64\n",
      "AIR_TIME               5713 non-null float64\n",
      "DISTANCE               5821 non-null int64\n",
      "WHEELS_ON              5727 non-null float64\n",
      "TAXI_IN                5727 non-null float64\n",
      "SCHEDULED_ARRIVAL      5821 non-null int64\n",
      "ARRIVAL_TIME           5727 non-null float64\n",
      "ARRIVAL_DELAY          5713 non-null float64\n",
      "DIVERTED               5821 non-null int64\n",
      "CANCELLED              5821 non-null int64\n",
      "CANCELLATION_REASON    93 non-null object\n",
      "AIR_SYSTEM_DELAY       1072 non-null float64\n",
      "SECURITY_DELAY         1072 non-null float64\n",
      "AIRLINE_DELAY          1072 non-null float64\n",
      "LATE_AIRCRAFT_DELAY    1072 non-null float64\n",
      "WEATHER_DELAY          1072 non-null float64\n",
      "dtypes: float64(15), int64(11), object(5)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#Information about the columns, type and the not null values\n",
    "flight.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Part I : Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perform EDA to investigate patterns, correlation on our dataset. Our main focus is about Arrival and Departure delays. We compare different features to gain insights on the factors responsible for arrival and departure delays. The analysis is limited to the range and variables in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1. Departure and arrival delay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The missing values in departure delays: 91\n",
      "The missing values in Arrival delays: 108\n"
     ]
    }
   ],
   "source": [
    "# Missing values in departure and arrival delays\n",
    "print(\"The missing values in departure delays: {}\" .format(flight['DEPARTURE_DELAY'].isnull().sum()))\n",
    "print(\"The missing values in Arrival delays: {}\" .format(flight['ARRIVAL_DELAY'].isnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>ARRIVAL_DELAY</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>DEPARTURE_DELAY</th>\n",
       "      <th>DEPARTURE_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ARRIVAL_DELAY  ARRIVAL_TIME  DEPARTURE_DELAY  DEPARTURE_TIME\n",
       "1              NaN           NaN              NaN             NaN\n",
       "10             NaN           NaN              NaN             NaN\n",
       "47             NaN           NaN              NaN             NaN\n",
       "115            NaN           NaN              NaN             NaN\n",
       "116            NaN           NaN              NaN             NaN\n",
       "172            NaN           NaN              NaN             NaN\n",
       "174            NaN           NaN              NaN             NaN\n",
       "190            NaN           NaN              NaN             NaN\n",
       "350            NaN           NaN             31.0          1221.0\n",
       "359            NaN           NaN              NaN             NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 10 values where the arrival and departure delays are missing\n",
    "flight[(flight['ARRIVAL_DELAY'].isnull()) | (flight['DEPARTURE_DELAY'].isnull()) ][['ARRIVAL_DELAY','ARRIVAL_TIME','DEPARTURE_DELAY','DEPARTURE_TIME']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values of both correspond to the same observations. The departure and arrival time is also a missing value for the corresponding missing departure and arrival delays in most cases. We can assume that the arrival delays have more missing values than departure delay because- (1) It could be the first time the flight is flying, hence no prior commercial arrival (2) Or simply that entry was missed. It is hard to know for sure without more information.. Hence, we shall drop the missing values from departure and arrival delay from our dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the null values in departure and arrival delay\n",
    "flight.dropna(subset=['DEPARTURE_DELAY','ARRIVAL_DELAY'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5713"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total number of observations after dropping the null values of departure and arrival delay\n",
    "len(flight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The missing values in departure delays: 0\n",
      "The missing values in Arrival delays: 0\n"
     ]
    }
   ],
   "source": [
    "# The current number of missing values in departyure and arrival delays\n",
    "print(\"The missing values in departure delays: {}\" .format(flight['DEPARTURE_DELAY'].isnull().sum()))\n",
    "print(\"The missing values in Arrival delays: {}\" .format(flight['ARRIVAL_DELAY'].isnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot between arrival and departure delay\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.scatterplot(x=\"DEPARTURE_DELAY\",y=\"ARRIVAL_DELAY\",data=flight)\n",
    "plt.xlabel(\"\\nDeparture delay\" ,fontsize=18)\n",
    "plt.ylabel(\"Arrival delay\" ,fontsize=18)\n",
    "plt.title(\"\\nArrival delays vs departure delays\\n\",fontsize=20)\n",
    "plt.tick_params(labelsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a somewhat linear relationship between arrrival delay and departure delay.Which means, the flights that depart late would arrive late and viceversa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average departure delay : 8.886574479257833 \n",
      "Median departure delay : -2.0 \n",
      "Average arrival delay : 3.9882723612812883 \n",
      "Median arrival delay : -5.0 \n"
     ]
    }
   ],
   "source": [
    "# Mean and median of arrival and departure delay\n",
    "print(\"Average departure delay : {} \" .format(flight['DEPARTURE_DELAY'].mean()))\n",
    "print(\"Median departure delay : {} \" .format(flight['DEPARTURE_DELAY'].median()))\n",
    "print(\"Average arrival delay : {} \" .format(flight['ARRIVAL_DELAY'].mean()))\n",
    "print(\"Median arrival delay : {} \" .format(flight['ARRIVAL_DELAY'].median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average departure delay is more than the average arrival delay. As we do not know the definite reason for this, we can make some assumptions as to why the departure delay is higher than the arrival delay-(1) There are many more factors involved in departure, eg: airline delay, security delay, weather delay, compared to arrival delay which is affected by fewer factors like weather delay. (2) Many times, if the distance is more, the distance makes up for the departure delay. Similarly, the median of departure delay is higher than arrival delay suggesting that departure delay is on the higher side compared to arrival delay. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 Departure and arrival delays based on airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The No. of airlines : 14\n",
      "The different airlines and their count : \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>WN</td>\n",
       "      <td>1269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DL</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AA</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>OO</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>EV</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>UA</td>\n",
       "      <td>506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MQ</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B6</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>US</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AS</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NK</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>F9</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VX</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HA</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    COUNT\n",
       "WN   1269\n",
       "DL    918\n",
       "AA    710\n",
       "OO    575\n",
       "EV    546\n",
       "UA    506\n",
       "MQ    269\n",
       "B6    257\n",
       "US    206\n",
       "AS    145\n",
       "NK    118\n",
       "F9     73\n",
       "VX     64\n",
       "HA     57"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#No. of airlines\n",
    "print(\"The No. of airlines : {}\".format(flight['AIRLINE'].nunique()))\n",
    "# Number of observations in each of the 14 airlines\n",
    "print(\"The different airlines and their count : \\n\")\n",
    "count_air=pd.DataFrame(flight['AIRLINE'].value_counts())\n",
    "count_air=count_air.rename(columns={'AIRLINE':'COUNT'})\n",
    "count_air"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,20))# Creating an empty plot \n",
    "ax=sns.countplot(x=flight['AIRLINE'])# Countplot of airlines\n",
    "plt.tick_params(labelsize=25)# changing the label sizes\n",
    "plt.xlabel(\"\\nAirlines\" ,fontsize=25) # Adding x-label \n",
    "plt.ylabel(\"Count\" ,fontsize=25) #Adding y-label\n",
    "plt.title(\"\\nNumber of each airlines\\n\",fontsize=25) # Adding plot title\n",
    "for p in ax.patches:\n",
    "    ax.annotate('{}'.format(p.get_height()),(p.get_x()+0.35,p.get_height()+5)) # Adding the count above the bars\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>AIRLINE</th>\n",
       "      <th>ARRIVAL/DEPARTURE DELAY</th>\n",
       "      <th>DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>EV</td>\n",
       "      <td>DEPARTURE_DELAY</td>\n",
       "      <td>-9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>WN</td>\n",
       "      <td>DEPARTURE_DELAY</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>EV</td>\n",
       "      <td>DEPARTURE_DELAY</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>DL</td>\n",
       "      <td>DEPARTURE_DELAY</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>DEPARTURE_DELAY</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AIRLINE ARRIVAL/DEPARTURE DELAY  DELAY\n",
       "0      EV         DEPARTURE_DELAY   -9.0\n",
       "1      WN         DEPARTURE_DELAY   -6.0\n",
       "2      EV         DEPARTURE_DELAY    1.0\n",
       "3      DL         DEPARTURE_DELAY    0.0\n",
       "4      DL         DEPARTURE_DELAY   -2.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using melt() on arrival and departure delay to create one boxplot for both. Makes it easy to compare the two\n",
    "flightmelt=flight[['AIRLINE','DEPARTURE_DELAY','ARRIVAL_DELAY']]\n",
    "flightmelt=pd.melt(flightmelt,id_vars=['AIRLINE'],var_name=['ARRIVAL/DEPARTURE DELAY'],value_name='DELAY')\n",
    "flightmelt.head() # Top 5 rows after melting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplot of arrival delays and departure delays group by the airlines\n",
    "plt.figure(figsize=(30,20))\n",
    "sns.boxplot(x=flightmelt['AIRLINE'],y=flightmelt['DELAY'],hue=flightmelt['ARRIVAL/DEPARTURE DELAY'],linewidth=3,fliersize=5)# using the melted dataframe instead of the original dataframe\n",
    "plt.tick_params(labelsize=25)\n",
    "plt.xlabel(\"\\nAirlines\" ,fontsize=25)\n",
    "plt.ylabel(\"Delay\" ,fontsize=25)\n",
    "plt.legend(fontsize=20)\n",
    "plt.ylim(-50,100,10) #To get better visibilit of the IQR, we limit the yaxis to 100.We will compropmise on few outliers.\n",
    "plt.title(\"\\nDelays for different airlines\\n\",fontsize=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DEPARTURE DELAY SUMMARY BASED ON AIRLINES: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>Min</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Median</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIRLINE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>UA</td>\n",
       "      <td>13.851779</td>\n",
       "      <td>506.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>14.00</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WN</td>\n",
       "      <td>9.894405</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B6</td>\n",
       "      <td>13.645914</td>\n",
       "      <td>257.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VX</td>\n",
       "      <td>8.593750</td>\n",
       "      <td>64.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>3.25</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AA</td>\n",
       "      <td>8.349296</td>\n",
       "      <td>710.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DL</td>\n",
       "      <td>7.238562</td>\n",
       "      <td>918.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NK</td>\n",
       "      <td>15.228814</td>\n",
       "      <td>118.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>EV</td>\n",
       "      <td>7.461538</td>\n",
       "      <td>546.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HA</td>\n",
       "      <td>7.964912</td>\n",
       "      <td>57.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MQ</td>\n",
       "      <td>7.278810</td>\n",
       "      <td>269.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>OO</td>\n",
       "      <td>5.702609</td>\n",
       "      <td>575.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>US</td>\n",
       "      <td>7.393204</td>\n",
       "      <td>206.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AS</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>145.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>F9</td>\n",
       "      <td>14.835616</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean   count   Min   Q1  Median     Q3    Max\n",
       "AIRLINE                                                    \n",
       "UA       13.851779   506.0 -12.0 -3.0     1.5  14.00  332.0\n",
       "WN        9.894405  1269.0 -10.0 -3.0     0.0  10.00  224.0\n",
       "B6       13.645914   257.0 -18.0 -5.0    -1.0  11.00  330.0\n",
       "VX        8.593750    64.0  -9.0 -4.0    -1.5   3.25  230.0\n",
       "AA        8.349296   710.0 -14.0 -5.0    -2.0   7.00  289.0\n",
       "DL        7.238562   918.0 -14.0 -4.0    -2.0   3.00  419.0\n",
       "NK       15.228814   118.0 -14.0 -6.0    -2.0  20.00  353.0\n",
       "EV        7.461538   546.0 -15.0 -6.0    -3.0   4.00  382.0\n",
       "HA        7.964912    57.0 -12.0 -6.0    -3.0   1.00  202.0\n",
       "MQ        7.278810   269.0 -13.0 -5.0    -3.0   6.00  311.0\n",
       "OO        5.702609   575.0 -23.0 -7.0    -3.0   2.00  306.0\n",
       "US        7.393204   206.0 -11.0 -5.0    -3.0   2.75  345.0\n",
       "AS        2.800000   145.0 -27.0 -8.0    -4.0   2.00  186.0\n",
       "F9       14.835616    73.0 -15.0 -7.0    -4.0   4.00  650.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Departure delay summary grouped by airlines\n",
    "depart_airline_5=flight.groupby('AIRLINE')['DEPARTURE_DELAY'].describe()\n",
    "depart_airline_5=depart_airline_5[['mean','count','min','25%','50%','75%','max']] #Adding only the information I need to the dataframe\n",
    "depart_airline_5=depart_airline_5.rename(columns={'min':'Min','25%':'Q1','50%':'Median','75%':'Q3','max':'Max'}) # renaming few column names\n",
    "depart_airline_5=depart_airline_5.sort_values(by='Median',ascending=False) #Rearranging in descending order of median\n",
    "print(\"\\nDEPARTURE DELAY SUMMARY BASED ON AIRLINES: \")\n",
    "depart_airline_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ARRIVAL DELAY SUMMARY BASED ON AIRLINE : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>Min</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Median</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIRLINE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>F9</td>\n",
       "      <td>19.150685</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>-9.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HA</td>\n",
       "      <td>10.894737</td>\n",
       "      <td>57.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NK</td>\n",
       "      <td>14.881356</td>\n",
       "      <td>118.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-10.75</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>OO</td>\n",
       "      <td>4.269565</td>\n",
       "      <td>575.0</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>-12.00</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>EV</td>\n",
       "      <td>5.686813</td>\n",
       "      <td>546.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-12.00</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>US</td>\n",
       "      <td>5.067961</td>\n",
       "      <td>206.0</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>-13.00</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WN</td>\n",
       "      <td>3.422380</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-12.00</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B6</td>\n",
       "      <td>9.653696</td>\n",
       "      <td>257.0</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-15.00</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>UA</td>\n",
       "      <td>4.693676</td>\n",
       "      <td>506.0</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-15.00</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>10.00</td>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AA</td>\n",
       "      <td>2.807042</td>\n",
       "      <td>710.0</td>\n",
       "      <td>-46.0</td>\n",
       "      <td>-15.00</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AS</td>\n",
       "      <td>-0.282759</td>\n",
       "      <td>145.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>-14.00</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VX</td>\n",
       "      <td>4.765625</td>\n",
       "      <td>64.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>-15.00</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MQ</td>\n",
       "      <td>3.070632</td>\n",
       "      <td>269.0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>-14.00</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DL</td>\n",
       "      <td>0.135076</td>\n",
       "      <td>918.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>-15.00</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>412.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean   count   Min     Q1  Median     Q3    Max\n",
       "AIRLINE                                                      \n",
       "F9       19.150685    73.0 -25.0  -9.00     1.0  15.00  644.0\n",
       "HA       10.894737    57.0 -29.0  -5.00    -1.0  10.00  194.0\n",
       "NK       14.881356   118.0 -36.0 -10.75    -2.0  23.00  354.0\n",
       "OO        4.269565   575.0 -42.0 -12.00    -3.0   8.00  304.0\n",
       "EV        5.686813   546.0 -36.0 -12.00    -4.0   8.00  386.0\n",
       "US        5.067961   206.0 -42.0 -13.00    -4.0  11.00  334.0\n",
       "WN        3.422380  1269.0 -53.0 -12.00    -4.0   8.00  273.0\n",
       "B6        9.653696   257.0 -45.0 -15.00    -5.0  14.00  339.0\n",
       "UA        4.693676   506.0 -53.0 -15.00    -5.5  10.00  337.0\n",
       "AA        2.807042   710.0 -46.0 -15.00    -6.0   7.75  268.0\n",
       "AS       -0.282759   145.0 -38.0 -14.00    -6.0   2.00  183.0\n",
       "VX        4.765625    64.0 -32.0 -15.00    -6.0   5.25  233.0\n",
       "MQ        3.070632   269.0 -44.0 -14.00    -7.0   8.00  292.0\n",
       "DL        0.135076   918.0 -55.0 -15.00    -8.0   3.00  412.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Arrival delay summary grouped by airlines\n",
    "arr_airline_5=flight.groupby('AIRLINE')['ARRIVAL_DELAY'].describe()\n",
    "arr_airline_5=arr_airline_5[['mean','count','min','25%','50%','75%','max']]  #Adding only the information I need to the dataframe\n",
    "arr_airline_5=arr_airline_5.rename(columns={'min':'Min','25%':'Q1','50%':'Median','75%':'Q3','max':'Max'}) # renaming few column names\n",
    "arr_airline_5=arr_airline_5.sort_values(by='Median',ascending=False) #Rearranging in descending order of median\n",
    "print(\"\\nARRIVAL DELAY SUMMARY BASED ON AIRLINE : \")\n",
    "arr_airline_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot details the departure and arrival delay for each airline. We can make several inferences from the above boxplot- (1) The median for both departure and arrival delay for most of the airlines is negative, which means most of the flights reach or depart early , which is a good thing. (2) There are many outliers for both the delays, which mean that the high outlier delays are not that common and it could happen due to some unexpected reason (we don’t know for sure). (3) Frontier Airlines Inc.(F9) has high average departure and arrival delay and didn’t have many flights (count is 73) in 2015.The maximum departure delay was also by F9, which is 650 minutes (an outlier). This value could have skewed the graph increasing the mean.                       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.  Departure and arrival delay based on airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DEPARTURE DELAY SUMMARY GROUPEDBY AIRPORT : \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>Min</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Median</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>FAR</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>161.00</td>\n",
       "      <td>161.0</td>\n",
       "      <td>161.00</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12898</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.00</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.00</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BMI</td>\n",
       "      <td>101.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>154.50</td>\n",
       "      <td>311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ERI</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>92.00</td>\n",
       "      <td>92.0</td>\n",
       "      <td>92.00</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MYR</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>2.5</td>\n",
       "      <td>94.25</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14576</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>88.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14696</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>88.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10157</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>42.25</td>\n",
       "      <td>87.5</td>\n",
       "      <td>132.75</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12992</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12206</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>59.25</td>\n",
       "      <td>67.5</td>\n",
       "      <td>75.75</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean  count    Min      Q1  Median      Q3    Max\n",
       "ORIGIN_AIRPORT                                                         \n",
       "FAR             161.000000    1.0  161.0  161.00   161.0  161.00  161.0\n",
       "12898           119.000000    1.0  119.0  119.00   119.0  119.00  119.0\n",
       "BMI             101.333333    3.0   -5.0   -3.50    -2.0  154.50  311.0\n",
       "ERI              92.000000    1.0   92.0   92.00    92.0   92.00   92.0\n",
       "MYR              88.000000    4.0   -6.0   -3.75     2.5   94.25  353.0\n",
       "14576            88.000000    1.0   88.0   88.00    88.0   88.00   88.0\n",
       "14696            88.000000    1.0   88.0   88.00    88.0   88.00   88.0\n",
       "10157            87.500000    2.0   -3.0   42.25    87.5  132.75  178.0\n",
       "12992            80.000000    1.0   80.0   80.00    80.0   80.00   80.0\n",
       "12206            67.500000    2.0   51.0   59.25    67.5   75.75   84.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Departure delay summary grouped by airports\n",
    "depart_airport=flight.groupby('ORIGIN_AIRPORT')['DEPARTURE_DELAY'].describe()\n",
    "depart_airport=depart_airport[['mean','count','min','25%','50%','75%','max']]\n",
    "depart_airport=depart_airport.rename(columns={'min':'Min','25%':'Q1','50%':'Median','75%':'Q3','max':'Max'})# renaming few column names\n",
    "depart_airport=depart_airport.sort_values(by='mean',ascending=False)\n",
    "depart_airport=depart_airport.head(10) #We will only need the top 10 rows of our dataset for airports with high departure delay\n",
    "print(\"\\nDEPARTURE DELAY SUMMARY GROUPEDBY AIRPORT : \")\n",
    "depart_airport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() #Creating an empty figure\n",
    "ax = depart_airport['mean'].plot(kind='bar', use_index=True,color='Red',fontsize=12,legend=False) # creating a plot of first y axis for mean\n",
    "ax2 = ax.twinx() # A second y axis for \"count\"\n",
    "ax2.plot(ax.get_xticks(),depart_airport['count'].values, linestyle='-', marker='o', linewidth=2.0,color='gold',label=\"Number of flights\") #creating a plot for second y axis for count\n",
    "ax.set_title(\"Average and count of departure grouped by Airport(top10)\\n\")\n",
    "ax.set_xlabel(\"Airports\")\n",
    "ax.set_ylabel(\"Average departure delays\")\n",
    "ax2.set_ylabel(\"Count\")\n",
    "lines, labels = ax.get_legend_handles_labels() #Adding legend for 1st y axis\n",
    "lines2, labels2 = ax2.get_legend_handles_labels() #adding legend for 2nd y axis\n",
    "ax2.legend(lines + lines2, labels + labels2, loc=\"best\",bbox_to_anchor=(1.6, 1),fontsize=12) # positioning/sizing the legends\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph and table displays the top 10 airports with the highest average delays. One factor that the above airports have in common is that the number of flights departing from the airport is very few. Since the number of flights is very less, one late flight will increase the average delay. Hector International airport(FAR) seems like an unpopular airport with the highest average departure delay of 161 minutes but only one single flight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 Departure and arrival delays based on distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3        1.0\n",
       "7       32.0\n",
       "12      13.0\n",
       "14      12.0\n",
       "15       6.0\n",
       "        ... \n",
       "5813    22.0\n",
       "5814     4.0\n",
       "5817    38.0\n",
       "5818     8.0\n",
       "5820     2.0\n",
       "Name: DEPARTURE_DELAY, Length: 2115, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a dataframe for only the departure delays (positive values)\n",
    "depart_delay_positive=flight[(flight['DEPARTURE_DELAY']>0) ]\n",
    "depart_delay_positive['DEPARTURE_DELAY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>DEPARTURE_DELAY</th>\n",
       "      <th>DISTANCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2583</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>4983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1529</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>4983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1684</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3511</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2820</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4522</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5762</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3969</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3602</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>2994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5176</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>2994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5814</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5316</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>2917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5530</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DEPARTURE_DELAY  DISTANCE\n",
       "2583             -7.0      4983\n",
       "1529             -9.0      4983\n",
       "3495              1.0      4817\n",
       "1280              0.0      4817\n",
       "1684             -3.0      4502\n",
       "3511             23.0      3801\n",
       "2820             19.0      3417\n",
       "4522              5.0      3365\n",
       "5762              8.0      3329\n",
       "3969             25.0      3043\n",
       "3602             -2.0      2994\n",
       "5176            -12.0      2994\n",
       "5814              4.0      2979\n",
       "5316            -11.0      2917\n",
       "5530             13.0      2845"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting departure delay and distance from flight dataset and sorting in descending order by distance first and then by departure delay \n",
    "depart_dist=flight[['DEPARTURE_DELAY','DISTANCE']].sort_values(by=['DISTANCE','DEPARTURE_DELAY'],ascending=False)\n",
    "depart_dist.head(15) # Top 15 long distance flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPARTURE_DELAY</th>\n",
       "      <th>DISTANCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>609</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5234</td>\n",
       "      <td>2.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1432</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5190</td>\n",
       "      <td>78.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>24.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3695</td>\n",
       "      <td>15.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>678</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5169</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3472</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4338</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1675</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DEPARTURE_DELAY  DISTANCE\n",
       "609              -3.0        73\n",
       "557               0.0        69\n",
       "5234              2.0        68\n",
       "3473              0.0        68\n",
       "1432             -8.0        68\n",
       "5190             78.0        67\n",
       "187              24.0        67\n",
       "3695             15.0        67\n",
       "2190              0.0        67\n",
       "678              -9.0        67\n",
       "5169            -10.0        67\n",
       "3472            -12.0        67\n",
       "4338            -13.0        67\n",
       "1675            -11.0        31\n",
       "100             -13.0        31"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depart_dist.tail(15) # Top 15 short distance flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=flight['DISTANCE'],y=flight['DEPARTURE_DELAY']) #scatter plot between distance and departure delay\n",
    "plt.xlim(0,2000,500) # Limiting x axis for better view of data points\n",
    "plt.ylim(0,200,50) # Limiting y axis for better view of data points\n",
    "plt.xlabel(\"\\nDistance\" ,fontsize=12)\n",
    "plt.ylabel(\"Departure Delay\" ,fontsize=12)\n",
    "plt.title(\"Departure delay vs Distance\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=flight['DISTANCE'],y=flight['ARRIVAL_DELAY']) #scatter plot between distance and arrival delay\n",
    "plt.xlim(0,2000,500) # Limiting x axis for better view of data points\n",
    "plt.ylim(0,200,50) # Limiting y axis for better view of data points\n",
    "plt.xlabel(\"\\nDistance\" ,fontsize=12)\n",
    "plt.ylabel(\"Arrival Delay\" ,fontsize=12)\n",
    "plt.title(\"Arrival delay vs Distance\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>DEPARTURE_DELAY</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DISTANCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>DEPARTURE_DELAY</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.936069</td>\n",
       "      <td>0.023095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ARRIVAL_DELAY</td>\n",
       "      <td>0.936069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DISTANCE</td>\n",
       "      <td>0.023095</td>\n",
       "      <td>-0.027935</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DEPARTURE_DELAY  ARRIVAL_DELAY  DISTANCE\n",
       "DEPARTURE_DELAY         1.000000       0.936069  0.023095\n",
       "ARRIVAL_DELAY           0.936069       1.000000 -0.027935\n",
       "DISTANCE                0.023095      -0.027935  1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flight[['DEPARTURE_DELAY','ARRIVAL_DELAY','DISTANCE']].corr() #Correlation of distance, departure delay, arrival delay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a significant amount of variability associated with arrival delay and distance as well as departure delay and distance. Distance doesn't seem like a good predictor of delay. There seems to very less correlation between departure delay and distance as well as arrival delay and distance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>DEPARTURE_DELAY</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DISTANCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>4817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3511</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2820</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4522</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>3365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5762</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>3329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3969</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5814</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>2979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5530</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>2845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>728</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>2717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2513</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>2704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DEPARTURE_DELAY  ARRIVAL_DELAY  DISTANCE\n",
       "3495              1.0          -18.0      4817\n",
       "3511             23.0           10.0      3801\n",
       "2820             19.0           48.0      3417\n",
       "4522              5.0          -13.0      3365\n",
       "5762              8.0          -19.0      3329\n",
       "3969             25.0           26.0      3043\n",
       "5814              4.0          -13.0      2979\n",
       "5530             13.0           -6.0      2845\n",
       "728               1.0          -38.0      2717\n",
       "2513              4.0          -23.0      2704"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 long distance flights with departure delay(positive) and their corresponding arrival delay\n",
    "depart_delay_positive[['DEPARTURE_DELAY','ARRIVAL_DELAY','DISTANCE']].sort_values(by='DISTANCE',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>DEPARTURE_DELAY</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DISTANCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1053</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3442</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>503</td>\n",
       "      <td>99.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2818</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4062</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5234</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5190</td>\n",
       "      <td>78.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3695</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DEPARTURE_DELAY  ARRIVAL_DELAY  DISTANCE\n",
       "81               25.0           31.0        83\n",
       "1053             17.0           15.0        74\n",
       "3442             33.0           23.0        74\n",
       "503              99.0           95.0        73\n",
       "2818              1.0           -3.0        73\n",
       "4062             29.0           17.0        73\n",
       "5234              2.0           -8.0        68\n",
       "5190             78.0           64.0        67\n",
       "187              24.0           20.0        67\n",
       "3695             15.0           18.0        67"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 shorty distance flights with departure delay(positive) and their corresponding arrival delay\n",
    "depart_delay_positive[['DEPARTURE_DELAY','ARRIVAL_DELAY','DISTANCE']].sort_values(by='DISTANCE',ascending=False).tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D # 3d plot\n",
    "fig=plt.figure(figsize=(10,6))\n",
    "ax=fig.add_subplot(111,projection='3d')\n",
    "ax.scatter(depart_delay_positive['DISTANCE'],depart_delay_positive['ARRIVAL_DELAY'],depart_delay_positive['DEPARTURE_DELAY'])\n",
    "ax.set_xlabel('Distance')\n",
    "ax.set_ylabel('Arrival delay')\n",
    "ax.set_zlabel('Departure delay')\n",
    "plt.xlim(0,3000,500)\n",
    "plt.ylim(0,400,50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though the distance is quite long, the distance doesn’t make up for the departure delay. This can be seen by the many arrival delays (positive values).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 Departure and arrival delays based on the day of the week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DEPARTURE DELAY SUMMARY GROUPED BY DAY OF WEEK : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>Min</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Median</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>9.786826</td>\n",
       "      <td>835.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8.995006</td>\n",
       "      <td>801.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7.488971</td>\n",
       "      <td>816.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9.390443</td>\n",
       "      <td>858.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9.661148</td>\n",
       "      <td>906.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.125894</td>\n",
       "      <td>699.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>9.385965</td>\n",
       "      <td>798.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean  count   Min   Q1  Median   Q3    Max\n",
       "DAY_OF_WEEK                                                \n",
       "1            9.786826  835.0 -17.0 -5.0    -2.0  8.0  382.0\n",
       "2            8.995006  801.0 -18.0 -5.0    -2.0  6.0  330.0\n",
       "3            7.488971  816.0 -16.0 -5.0    -2.0  6.0  345.0\n",
       "4            9.390443  858.0 -18.0 -4.0    -1.0  8.0  419.0\n",
       "5            9.661148  906.0 -16.0 -4.0    -1.0  8.0  311.0\n",
       "6            7.125894  699.0 -27.0 -5.0    -2.0  5.0  353.0\n",
       "7            9.385965  798.0 -23.0 -5.0    -1.0  9.0  650.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe to obtain the summary of departure delay grouped by day of week\n",
    "depart_dow=flight.groupby('DAY_OF_WEEK')['DEPARTURE_DELAY'].describe() \n",
    "depart_dow=depart_dow[['mean','count','min','25%','50%','75%','max']] # extracting the parameter we need\n",
    "depart_dow=depart_dow.rename(columns={'min':'Min','25%':'Q1','50%':'Median','75%':'Q3','max':'Max'}) # Renaming them\n",
    "print(\"\\nDEPARTURE DELAY SUMMARY GROUPED BY DAY OF WEEK : \")\n",
    "depart_dow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a plot with x as day of week, Y1 as mean and y2 a count\n",
    "fig = plt.figure()\n",
    "ax = depart_dow['mean'].plot(kind='bar', use_index=True,color='Red',fontsize=12,legend=False)\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(ax.get_xticks(),depart_dow['count'].values, linestyle='-', marker='o', linewidth=2.0,color='gold',label=\"Number of people\")\n",
    "ax.set_title(\"Average and count of departure grouped by Day of week\\n\")\n",
    "ax.set_xlabel(\"Month\")\n",
    "ax.set_ylabel(\"Average departure delays\")\n",
    "ax2.set_ylabel(\"Count\")\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc=\"best\",bbox_to_anchor=(1.6, 1),fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the above table and graph , the best day of the week to travel to experience least departure delay is Saturday-6 with a mean of 7.12. The highest departure delay is on Monday-1 with a mean of 9.7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ARRIVAL DELAY SUMMARY GROUPED BY DAY OF WEEK : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>Min</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Median</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.104192</td>\n",
       "      <td>835.0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>-13.00</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.863920</td>\n",
       "      <td>801.0</td>\n",
       "      <td>-43.0</td>\n",
       "      <td>-14.00</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.458333</td>\n",
       "      <td>816.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>-14.00</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.627040</td>\n",
       "      <td>858.0</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>-12.00</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>4.716336</td>\n",
       "      <td>906.0</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-13.00</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.133047</td>\n",
       "      <td>699.0</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-15.00</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4.422306</td>\n",
       "      <td>798.0</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-13.75</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>644.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean  count   Min     Q1  Median    Q3    Max\n",
       "DAY_OF_WEEK                                                   \n",
       "1            5.104192  835.0 -44.0 -13.00    -5.0   7.0  386.0\n",
       "2            3.863920  801.0 -43.0 -14.00    -6.0   8.0  339.0\n",
       "3            2.458333  816.0 -55.0 -14.00    -6.0   8.0  334.0\n",
       "4            5.627040  858.0 -42.0 -12.00    -4.5  10.0  412.0\n",
       "5            4.716336  906.0 -45.0 -13.00    -5.0   9.0  304.0\n",
       "6            1.133047  699.0 -53.0 -15.00    -6.0   4.0  354.0\n",
       "7            4.422306  798.0 -53.0 -13.75    -5.0   9.0  644.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe to obtain the summary of arrival delay grouped by day of week\n",
    "arr_dow=flight.groupby('DAY_OF_WEEK')['ARRIVAL_DELAY'].describe()\n",
    "arr_dow=arr_dow[['mean','count','min','25%','50%','75%','max']]\n",
    "arr_dow=arr_dow.rename(columns={'min':'Min','25%':'Q1','50%':'Median','75%':'Q3','max':'Max'})\n",
    "print(\"\\nARRIVAL DELAY SUMMARY GROUPED BY DAY OF WEEK : \")\n",
    "arr_dow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a plot with x as day of week, Y1 as mean and y2 a count\n",
    "fig = plt.figure()\n",
    "ax = arr_dow['mean'].plot(kind='bar', use_index=True,color='Red',fontsize=12,legend=False)\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(ax.get_xticks(),depart_dow['count'].values, linestyle='-', marker='o', linewidth=2.0,color='gold',label=\"Number of people\")\n",
    "ax.set_title(\"Average and count of departure grouped by Day of week\\n\")\n",
    "ax.set_xlabel(\"Day of week\")\n",
    "ax.set_ylabel(\"Average departure delays\")\n",
    "ax2.set_ylabel(\"Count\")\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc=\"best\",bbox_to_anchor=(1.6, 1),fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the above table and graph , the best day of the week to travel to experience least arrival delay is Saturday-6 with a mean of 1.13 minutes. The highest departure delay is on Thursday-4 with a mean of 5.62 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6. Analysing the different delay types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AIR_SYSTEM_DELAY</td>\n",
       "      <td>14.319963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SECURITY_DELAY</td>\n",
       "      <td>0.113806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AIRLINE_DELAY</td>\n",
       "      <td>18.119403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LATE_AIRCRAFT_DELAY</td>\n",
       "      <td>20.514925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WEATHER_DELAY</td>\n",
       "      <td>3.616604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mean\n",
       "AIR_SYSTEM_DELAY     14.319963\n",
       "SECURITY_DELAY        0.113806\n",
       "AIRLINE_DELAY        18.119403\n",
       "LATE_AIRCRAFT_DELAY  20.514925\n",
       "WEATHER_DELAY         3.616604"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#average of different delay types\n",
    "delay_type=pd.DataFrame(flight['AIR_SYSTEM_DELAY'].describe())\n",
    "delay_type['SECURITY_DELAY']=pd.DataFrame(flight['SECURITY_DELAY'].describe())\n",
    "delay_type['AIRLINE_DELAY']=pd.DataFrame(flight['AIRLINE_DELAY'].describe())\n",
    "delay_type['LATE_AIRCRAFT_DELAY']=pd.DataFrame(flight['LATE_AIRCRAFT_DELAY'].describe())\n",
    "delay_type['WEATHER_DELAY']=pd.DataFrame(flight['WEATHER_DELAY'].describe())\n",
    "delay_type=pd.DataFrame(delay_type.iloc[1])\n",
    "delay_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(delay_type)\n",
    "plt.tick_params(labelsize=15)\n",
    "plt.xlabel(\"\\nTypes of delay\" ,fontsize=15)\n",
    "plt.ylabel(\"Average Delay\" ,fontsize=15)\n",
    "plt.title(\"\\nAverage delay\\n\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest delay is due to delay caused by the aircraft with an average of 20.51 minutes. Whereas, the least delay is caused due to security with an average of 0.11 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.7. Month vs Arrival delay "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ARRIVAL DELAY SUMMARY GROUPED BY MONTH : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>Min</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Median</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.663438</td>\n",
       "      <td>413.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.921717</td>\n",
       "      <td>396.0</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7.189555</td>\n",
       "      <td>517.0</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.601578</td>\n",
       "      <td>507.0</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.843612</td>\n",
       "      <td>454.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>8.571695</td>\n",
       "      <td>537.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7.605159</td>\n",
       "      <td>504.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>6.121951</td>\n",
       "      <td>492.0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>-2.253165</td>\n",
       "      <td>474.0</td>\n",
       "      <td>-46.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>-1.015595</td>\n",
       "      <td>513.0</td>\n",
       "      <td>-51.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>-0.015385</td>\n",
       "      <td>455.0</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>3.988914</td>\n",
       "      <td>451.0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean  count   Min    Q1  Median    Q3    Max\n",
       "MONTH                                                  \n",
       "1      2.663438  413.0 -40.0 -13.0    -5.0   7.0  395.0\n",
       "2      5.921717  396.0 -42.0 -13.0    -4.0   9.0  292.0\n",
       "3      7.189555  517.0 -53.0 -13.0    -4.0  11.0  412.0\n",
       "4      2.601578  507.0 -45.0 -13.0    -6.0   7.0  334.0\n",
       "5      5.843612  454.0 -36.0 -13.0    -4.0  10.0  226.0\n",
       "6      8.571695  537.0 -55.0 -12.0    -3.0  12.0  312.0\n",
       "7      7.605159  504.0 -36.0 -12.0    -3.0  13.0  644.0\n",
       "8      6.121951  492.0 -44.0 -12.0    -4.0   8.0  354.0\n",
       "9     -2.253165  474.0 -46.0 -16.0    -8.0   1.0  268.0\n",
       "10    -1.015595  513.0 -51.0 -15.0    -7.0   3.0  212.0\n",
       "11    -0.015385  455.0 -42.0 -14.0    -5.0   6.5  337.0\n",
       "12     3.988914  451.0 -44.0 -15.0    -7.0  10.0  273.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe to obtain the summary of arrival delay grouped by month\n",
    "arr_mon=flight.groupby('MONTH')['ARRIVAL_DELAY'].describe()\n",
    "arr_mon=arr_mon[['mean','count','min','25%','50%','75%','max']]\n",
    "arr_mon=arr_mon.rename(columns={'min':'Min','25%':'Q1','50%':'Median','75%':'Q3','max':'Max'})\n",
    "print(\"\\nARRIVAL DELAY SUMMARY GROUPED BY MONTH : \")\n",
    "arr_mon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a plot with x as month, Y1 as mean and y2 a count\n",
    "fig = plt.figure()\n",
    "ax = arr_mon['mean'].plot(kind='bar', use_index=True,color='Red',fontsize=12,legend=False)\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(ax.get_xticks(),arr_mon['count'].values, linestyle='-', marker='o', linewidth=2.0,color='gold',label=\"Number of people\")\n",
    "ax.set_title(\"Average and count of arrival delay grouped by month\\n\")\n",
    "ax.set_xlabel(\"Month\")\n",
    "ax.set_ylabel(\"Average Arrival delays\")\n",
    "ax2.set_ylabel(\"Count\")\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc=\"best\",bbox_to_anchor=(1.6, 1),fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We analyze the arrival delay grouped month wise. It is interesting to note that, September (9), October (10) and November (11)   have a negative average arrival delay. This means that on those months on an average the flights have reached earlier than scheduled. The high average delay is on June (6) with a average delay of 8.57 minutes. We can make assumptions for why this is the case – (1) More number of people travel in the summer months as schools are off, so this can increase air-traffic or airports can be small to accommodate so many aircrafts or airlines may be short staffed. (2) A similar reason as to why there is negative delays in months -9,10,11, not many people are travelling, so fewer aircrafts, easier to handle and reduce delays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DEPARTURE DELAY SUMMARY GROUPED BY MONTH : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>Min</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Median</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7.532688</td>\n",
       "      <td>413.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>378.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9.603535</td>\n",
       "      <td>396.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>12.353965</td>\n",
       "      <td>517.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>7.252465</td>\n",
       "      <td>507.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>10.544053</td>\n",
       "      <td>454.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>9.75</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>11.832402</td>\n",
       "      <td>537.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>12.644841</td>\n",
       "      <td>504.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>10.008130</td>\n",
       "      <td>492.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-4.25</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>4.854430</td>\n",
       "      <td>474.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>4.947368</td>\n",
       "      <td>513.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>5.345055</td>\n",
       "      <td>455.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>9.050998</td>\n",
       "      <td>451.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean  count   Min    Q1  Median     Q3    Max\n",
       "MONTH                                                    \n",
       "1       7.532688  413.0 -18.0 -5.00    -1.0   8.00  378.0\n",
       "2       9.603535  396.0 -15.0 -5.00    -1.0   8.00  303.0\n",
       "3      12.353965  517.0 -16.0 -4.00    -1.0  12.00  419.0\n",
       "4       7.252465  507.0 -23.0 -5.00    -2.0   7.00  345.0\n",
       "5      10.544053  454.0 -15.0 -5.00    -1.0   9.75  212.0\n",
       "6      11.832402  537.0 -14.0 -4.00    -1.0  10.00  322.0\n",
       "7      12.644841  504.0 -18.0 -4.00     0.0  13.00  650.0\n",
       "8      10.008130  492.0 -18.0 -4.25    -1.0   7.00  353.0\n",
       "9       4.854430  474.0 -23.0 -6.00    -3.0   1.00  289.0\n",
       "10      4.947368  513.0 -27.0 -5.00    -2.0   2.00  222.0\n",
       "11      5.345055  455.0 -16.0 -5.00    -2.0   5.00  332.0\n",
       "12      9.050998  451.0 -16.0 -5.00    -2.0   6.00  246.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe to obtain the summary of departure delay grouped by month\n",
    "depart_mon=flight.groupby('MONTH')['DEPARTURE_DELAY'].describe()\n",
    "depart_mon=depart_mon[['mean','count','min','25%','50%','75%','max']]\n",
    "depart_mon=depart_mon.rename(columns={'min':'Min','25%':'Q1','50%':'Median','75%':'Q3','max':'Max'})\n",
    "print(\"\\nDEPARTURE DELAY SUMMARY GROUPED BY MONTH : \")\n",
    "depart_mon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a plot with x as month, Y1 as mean and y2 a count\n",
    "fig = plt.figure()\n",
    "ax = depart_mon['mean'].plot(kind='bar', use_index=True,color='Red',fontsize=12,legend=False)\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(ax.get_xticks(),arr_mon['count'].values, linestyle='-', marker='o', linewidth=2.0,color='gold',label=\"Number of people\")\n",
    "ax.set_title(\"Average and count of departure grouped by month\\n\")\n",
    "ax.set_xlabel(\"Month\")\n",
    "ax.set_ylabel(\"Average departure delays\")\n",
    "ax2.set_ylabel(\"Count\")\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc=\"best\",bbox_to_anchor=(1.6, 1),fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4 Regression Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 Subpart 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Response variable is ARRIVAL_DELAY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "flight.dropna(subset=[\"WEATHER_DELAY\"],how=\"all\",inplace=True) #removing the missing values in WEATHER_DELAY\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YEAR                      0\n",
       "MONTH                     0\n",
       "DAY                       0\n",
       "DAY_OF_WEEK               0\n",
       "AIRLINE                   0\n",
       "FLIGHT_NUMBER             0\n",
       "TAIL_NUMBER               0\n",
       "ORIGIN_AIRPORT            0\n",
       "DESTINATION_AIRPORT       0\n",
       "SCHEDULED_DEPARTURE       0\n",
       "DEPARTURE_TIME            0\n",
       "DEPARTURE_DELAY           0\n",
       "TAXI_OUT                  0\n",
       "WHEELS_OFF                0\n",
       "SCHEDULED_TIME            0\n",
       "ELAPSED_TIME              0\n",
       "AIR_TIME                  0\n",
       "DISTANCE                  0\n",
       "WHEELS_ON                 0\n",
       "TAXI_IN                   0\n",
       "SCHEDULED_ARRIVAL         0\n",
       "ARRIVAL_TIME              0\n",
       "ARRIVAL_DELAY             0\n",
       "DIVERTED                  0\n",
       "CANCELLED                 0\n",
       "CANCELLATION_REASON    1072\n",
       "AIR_SYSTEM_DELAY          0\n",
       "SECURITY_DELAY            0\n",
       "AIRLINE_DELAY             0\n",
       "LATE_AIRCRAFT_DELAY       0\n",
       "WEATHER_DELAY             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flight.isnull().sum() #the remaining null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>Airline_UA</th>\n",
       "      <th>Airline_US</th>\n",
       "      <th>Airline_VX</th>\n",
       "      <th>Airline_WN</th>\n",
       "      <th>Dayofweek_2</th>\n",
       "      <th>Dayofweek_3</th>\n",
       "      <th>Dayofweek_4</th>\n",
       "      <th>Dayofweek_5</th>\n",
       "      <th>Dayofweek_6</th>\n",
       "      <th>Dayofweek_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>OO</td>\n",
       "      <td>5354</td>\n",
       "      <td>N472CA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>MBS</td>\n",
       "      <td>1317</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>UA</td>\n",
       "      <td>1062</td>\n",
       "      <td>N73291</td>\n",
       "      <td>DCA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1603</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>US</td>\n",
       "      <td>2065</td>\n",
       "      <td>N534UW</td>\n",
       "      <td>CLT</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1120</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>OO</td>\n",
       "      <td>5211</td>\n",
       "      <td>N943SW</td>\n",
       "      <td>IDA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1338</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>HA</td>\n",
       "      <td>335</td>\n",
       "      <td>N477HA</td>\n",
       "      <td>OGG</td>\n",
       "      <td>HNL</td>\n",
       "      <td>1503</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "7   2015      1    1            4      OO           5354      N472CA   \n",
       "9   2015      1    1            4      UA           1062      N73291   \n",
       "19  2015      1    2            5      US           2065      N534UW   \n",
       "21  2015      1    2            5      OO           5211      N943SW   \n",
       "22  2015      1    2            5      HA            335      N477HA   \n",
       "\n",
       "   ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  Airline_UA  \\\n",
       "7             ORD                 MBS                 1317  ...           0   \n",
       "9             DCA                 DEN                 1603  ...           1   \n",
       "19            CLT                 IAH                 1120  ...           0   \n",
       "21            IDA                 DEN                 1338  ...           0   \n",
       "22            OGG                 HNL                 1503  ...           0   \n",
       "\n",
       "    Airline_US  Airline_VX  Airline_WN  Dayofweek_2  Dayofweek_3  Dayofweek_4  \\\n",
       "7            0           0           0            0            0            1   \n",
       "9            0           0           0            0            0            1   \n",
       "19           1           0           0            0            0            0   \n",
       "21           0           0           0            0            0            0   \n",
       "22           0           0           0            0            0            0   \n",
       "\n",
       "    Dayofweek_5  Dayofweek_6  Dayofweek_7  \n",
       "7             0            0            0  \n",
       "9             0            0            0  \n",
       "19            1            0            0  \n",
       "21            1            0            0  \n",
       "22            1            0            0  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Airline_dummy=pd.get_dummies(flight['AIRLINE'],prefix='Airline') #Geting dummy variables for \"Airline\" and prefixing it with airline for clarity\n",
    "del Airline_dummy['Airline_AA'] #droping the first column of Airline \"AA\"\n",
    "dayweek_dummy=pd.get_dummies(flight['DAY_OF_WEEK'],prefix='Dayofweek') #Geting dummy variables for \"Day of week\" and prefixing it with Dayofweek for clarity\n",
    "del dayweek_dummy['Dayofweek_1'] #droping the first column of dayofweek \"1\"\n",
    "flight=pd.concat([flight,Airline_dummy,dayweek_dummy],axis=1) #Adding the dummy variables to the original dataset\n",
    "flight.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
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      ],
      "text/plain": [
       "    LATE_AIRCRAFT_DELAY  AIRLINE_DELAY  AIR_SYSTEM_DELAY  WEATHER_DELAY  \\\n",
       "7                  21.0           11.0               4.0            0.0   \n",
       "9                   0.0            0.0              23.0            0.0   \n",
       "19                  0.0            8.0              13.0            0.0   \n",
       "21                 50.0            0.0              31.0            0.0   \n",
       "22                 89.0           25.0               0.0            0.0   \n",
       "\n",
       "    DEPARTURE_TIME  DEPARTURE_DELAY  DISTANCE  Airline_AS  Airline_B6  \\\n",
       "7           1349.0             32.0       222           0           0   \n",
       "9           1603.0              0.0      1476           0           0   \n",
       "19          1128.0              8.0       912           0           0   \n",
       "21          1428.0             50.0       458           0           0   \n",
       "22          1644.0            101.0       100           0           0   \n",
       "\n",
       "    Airline_DL  ...  Airline_UA  Airline_US  Airline_VX  Airline_WN  \\\n",
       "7            0  ...           0           0           0           0   \n",
       "9            0  ...           1           0           0           0   \n",
       "19           0  ...           0           1           0           0   \n",
       "21           0  ...           0           0           0           0   \n",
       "22           0  ...           0           0           0           0   \n",
       "\n",
       "    Dayofweek_2  Dayofweek_3  Dayofweek_4  Dayofweek_5  Dayofweek_6  \\\n",
       "7             0            0            1            0            0   \n",
       "9             0            0            1            0            0   \n",
       "19            0            0            0            1            0   \n",
       "21            0            0            0            1            0   \n",
       "22            0            0            0            1            0   \n",
       "\n",
       "    Dayofweek_7  \n",
       "7             0  \n",
       "9             0  \n",
       "19            0  \n",
       "21            0  \n",
       "22            0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cat_1=flight.iloc[:,31:] # Adding only the categorical dummy variables \n",
    "X_num_1=flight[['LATE_AIRCRAFT_DELAY','AIRLINE_DELAY','AIR_SYSTEM_DELAY','WEATHER_DELAY','DEPARTURE_TIME','DEPARTURE_DELAY','DISTANCE']]#Adding only the nuermical variables\n",
    "X_1=pd.concat([X_num_1,X_cat_1],axis=1) #Combining them\n",
    "X_1.head() #the final predictor variabes for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1=flight['ARRIVAL_DELAY'] #response variable for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dira9\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>ARRIVAL_DELAY</td>  <th>  R-squared:         </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>4.273e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 26 Feb 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:37:12</td>     <th>  Log-Likelihood:    </th> <td> -2140.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1072</td>      <th>  AIC:               </th> <td>   4335.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1045</td>      <th>  BIC:               </th> <td>   4470.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    26</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>               <td>    0.5743</td> <td>    0.300</td> <td>    1.916</td> <td> 0.056</td> <td>   -0.014</td> <td>    1.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LATE_AIRCRAFT_DELAY</th> <td>    0.9814</td> <td>    0.004</td> <td>  251.351</td> <td> 0.000</td> <td>    0.974</td> <td>    0.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AIRLINE_DELAY</th>       <td>    0.9820</td> <td>    0.004</td> <td>  258.523</td> <td> 0.000</td> <td>    0.975</td> <td>    0.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AIR_SYSTEM_DELAY</th>    <td>    0.9853</td> <td>    0.003</td> <td>  311.685</td> <td> 0.000</td> <td>    0.979</td> <td>    0.992</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WEATHER_DELAY</th>       <td>    0.9846</td> <td>    0.004</td> <td>  239.180</td> <td> 0.000</td> <td>    0.977</td> <td>    0.993</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DEPARTURE_TIME</th>      <td>   -0.0001</td> <td>    0.000</td> <td>   -0.923</td> <td> 0.356</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DEPARTURE_DELAY</th>     <td>    0.0158</td> <td>    0.003</td> <td>    4.647</td> <td> 0.000</td> <td>    0.009</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DISTANCE</th>            <td>    0.0001</td> <td>    0.000</td> <td>    1.106</td> <td> 0.269</td> <td>-8.74e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_AS</th>          <td>    1.8908</td> <td>    0.434</td> <td>    4.354</td> <td> 0.000</td> <td>    1.039</td> <td>    2.743</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_B6</th>          <td>    0.0009</td> <td>    0.277</td> <td>    0.003</td> <td> 0.997</td> <td>   -0.542</td> <td>    0.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_DL</th>          <td>   -0.2385</td> <td>    0.224</td> <td>   -1.062</td> <td> 0.288</td> <td>   -0.679</td> <td>    0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_EV</th>          <td>   -0.1519</td> <td>    0.241</td> <td>   -0.629</td> <td> 0.529</td> <td>   -0.625</td> <td>    0.322</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_F9</th>          <td>    0.0010</td> <td>    0.445</td> <td>    0.002</td> <td> 0.998</td> <td>   -0.872</td> <td>    0.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_HA</th>          <td>   -0.1183</td> <td>    0.548</td> <td>   -0.216</td> <td> 0.829</td> <td>   -1.194</td> <td>    0.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_MQ</th>          <td>   -0.1080</td> <td>    0.295</td> <td>   -0.366</td> <td> 0.715</td> <td>   -0.687</td> <td>    0.471</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_NK</th>          <td>    0.4677</td> <td>    0.333</td> <td>    1.404</td> <td> 0.161</td> <td>   -0.186</td> <td>    1.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_OO</th>          <td>   -0.1077</td> <td>    0.241</td> <td>   -0.448</td> <td> 0.654</td> <td>   -0.580</td> <td>    0.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_UA</th>          <td>   -0.3509</td> <td>    0.233</td> <td>   -1.505</td> <td> 0.133</td> <td>   -0.808</td> <td>    0.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_US</th>          <td>   -0.1699</td> <td>    0.319</td> <td>   -0.533</td> <td> 0.594</td> <td>   -0.796</td> <td>    0.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_VX</th>          <td>   -0.1395</td> <td>    0.546</td> <td>   -0.256</td> <td> 0.798</td> <td>   -1.210</td> <td>    0.931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Airline_WN</th>          <td>   -0.1731</td> <td>    0.199</td> <td>   -0.870</td> <td> 0.384</td> <td>   -0.563</td> <td>    0.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dayofweek_2</th>         <td>   -0.2517</td> <td>    0.207</td> <td>   -1.214</td> <td> 0.225</td> <td>   -0.659</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dayofweek_3</th>         <td>    0.0896</td> <td>    0.208</td> <td>    0.431</td> <td> 0.667</td> <td>   -0.319</td> <td>    0.498</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dayofweek_4</th>         <td>   -0.2768</td> <td>    0.197</td> <td>   -1.404</td> <td> 0.161</td> <td>   -0.664</td> <td>    0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dayofweek_5</th>         <td>   -0.2329</td> <td>    0.198</td> <td>   -1.175</td> <td> 0.240</td> <td>   -0.622</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dayofweek_6</th>         <td>   -0.2907</td> <td>    0.238</td> <td>   -1.222</td> <td> 0.222</td> <td>   -0.757</td> <td>    0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dayofweek_7</th>         <td>   -0.1769</td> <td>    0.206</td> <td>   -0.859</td> <td> 0.390</td> <td>   -0.581</td> <td>    0.227</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2216.606</td> <th>  Durbin-Watson:     </th>  <td>   2.019</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4284200.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>16.511</td>  <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>310.936</td> <th>  Cond. No.          </th>  <td>2.21e+04</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.21e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          ARRIVAL_DELAY   R-squared:                       0.999\n",
       "Model:                            OLS   Adj. R-squared:                  0.999\n",
       "Method:                 Least Squares   F-statistic:                 4.273e+04\n",
       "Date:                Wed, 26 Feb 2020   Prob (F-statistic):               0.00\n",
       "Time:                        14:37:12   Log-Likelihood:                -2140.7\n",
       "No. Observations:                1072   AIC:                             4335.\n",
       "Df Residuals:                    1045   BIC:                             4470.\n",
       "Df Model:                          26                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "const                   0.5743      0.300      1.916      0.056      -0.014       1.163\n",
       "LATE_AIRCRAFT_DELAY     0.9814      0.004    251.351      0.000       0.974       0.989\n",
       "AIRLINE_DELAY           0.9820      0.004    258.523      0.000       0.975       0.989\n",
       "AIR_SYSTEM_DELAY        0.9853      0.003    311.685      0.000       0.979       0.992\n",
       "WEATHER_DELAY           0.9846      0.004    239.180      0.000       0.977       0.993\n",
       "DEPARTURE_TIME         -0.0001      0.000     -0.923      0.356      -0.000       0.000\n",
       "DEPARTURE_DELAY         0.0158      0.003      4.647      0.000       0.009       0.023\n",
       "DISTANCE                0.0001      0.000      1.106      0.269   -8.74e-05       0.000\n",
       "Airline_AS              1.8908      0.434      4.354      0.000       1.039       2.743\n",
       "Airline_B6              0.0009      0.277      0.003      0.997      -0.542       0.544\n",
       "Airline_DL             -0.2385      0.224     -1.062      0.288      -0.679       0.202\n",
       "Airline_EV             -0.1519      0.241     -0.629      0.529      -0.625       0.322\n",
       "Airline_F9              0.0010      0.445      0.002      0.998      -0.872       0.874\n",
       "Airline_HA             -0.1183      0.548     -0.216      0.829      -1.194       0.957\n",
       "Airline_MQ             -0.1080      0.295     -0.366      0.715      -0.687       0.471\n",
       "Airline_NK              0.4677      0.333      1.404      0.161      -0.186       1.122\n",
       "Airline_OO             -0.1077      0.241     -0.448      0.654      -0.580       0.364\n",
       "Airline_UA             -0.3509      0.233     -1.505      0.133      -0.808       0.107\n",
       "Airline_US             -0.1699      0.319     -0.533      0.594      -0.796       0.456\n",
       "Airline_VX             -0.1395      0.546     -0.256      0.798      -1.210       0.931\n",
       "Airline_WN             -0.1731      0.199     -0.870      0.384      -0.563       0.217\n",
       "Dayofweek_2            -0.2517      0.207     -1.214      0.225      -0.659       0.155\n",
       "Dayofweek_3             0.0896      0.208      0.431      0.667      -0.319       0.498\n",
       "Dayofweek_4            -0.2768      0.197     -1.404      0.161      -0.664       0.110\n",
       "Dayofweek_5            -0.2329      0.198     -1.175      0.240      -0.622       0.156\n",
       "Dayofweek_6            -0.2907      0.238     -1.222      0.222      -0.757       0.176\n",
       "Dayofweek_7            -0.1769      0.206     -0.859      0.390      -0.581       0.227\n",
       "==============================================================================\n",
       "Omnibus:                     2216.606   Durbin-Watson:                   2.019\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          4284200.685\n",
       "Skew:                          16.511   Prob(JB):                         0.00\n",
       "Kurtosis:                     310.936   Cond. No.                     2.21e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.21e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Linear regression using statsmodel\n",
    "X_con_1=sm.add_constant(X_1)\n",
    "arr_del_stats_1=sm.OLS(y_1,X_con_1).fit()\n",
    "arr_del_stats_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Coef</th>\n",
       "      <th>p-values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>const</td>\n",
       "      <td>0.574268</td>\n",
       "      <td>0.055692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LATE_AIRCRAFT_DELAY</td>\n",
       "      <td>0.981362</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AIRLINE_DELAY</td>\n",
       "      <td>0.982036</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AIR_SYSTEM_DELAY</td>\n",
       "      <td>0.985325</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WEATHER_DELAY</td>\n",
       "      <td>0.984608</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DEPARTURE_TIME</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>0.356129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DEPARTURE_DELAY</td>\n",
       "      <td>0.015849</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DISTANCE</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.269123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_AS</td>\n",
       "      <td>1.890803</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_B6</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.997490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_DL</td>\n",
       "      <td>-0.238513</td>\n",
       "      <td>0.288272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_EV</td>\n",
       "      <td>-0.151854</td>\n",
       "      <td>0.529198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_F9</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>0.998139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_HA</td>\n",
       "      <td>-0.118301</td>\n",
       "      <td>0.829124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_MQ</td>\n",
       "      <td>-0.107958</td>\n",
       "      <td>0.714705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_NK</td>\n",
       "      <td>0.467720</td>\n",
       "      <td>0.160737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_OO</td>\n",
       "      <td>-0.107665</td>\n",
       "      <td>0.654494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_UA</td>\n",
       "      <td>-0.350858</td>\n",
       "      <td>0.132570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_US</td>\n",
       "      <td>-0.169925</td>\n",
       "      <td>0.594466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_VX</td>\n",
       "      <td>-0.139537</td>\n",
       "      <td>0.798189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Airline_WN</td>\n",
       "      <td>-0.173068</td>\n",
       "      <td>0.384351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dayofweek_2</td>\n",
       "      <td>-0.251671</td>\n",
       "      <td>0.225087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dayofweek_3</td>\n",
       "      <td>0.089576</td>\n",
       "      <td>0.666804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dayofweek_4</td>\n",
       "      <td>-0.276833</td>\n",
       "      <td>0.160761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dayofweek_5</td>\n",
       "      <td>-0.232930</td>\n",
       "      <td>0.240380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dayofweek_6</td>\n",
       "      <td>-0.290745</td>\n",
       "      <td>0.221801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dayofweek_7</td>\n",
       "      <td>-0.176871</td>\n",
       "      <td>0.390433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Coef  p-values\n",
       "const                0.574268  0.055692\n",
       "LATE_AIRCRAFT_DELAY  0.981362  0.000000\n",
       "AIRLINE_DELAY        0.982036  0.000000\n",
       "AIR_SYSTEM_DELAY     0.985325  0.000000\n",
       "WEATHER_DELAY        0.984608  0.000000\n",
       "DEPARTURE_TIME      -0.000107  0.356129\n",
       "DEPARTURE_DELAY      0.015849  0.000004\n",
       "DISTANCE             0.000113  0.269123\n",
       "Airline_AS           1.890803  0.000015\n",
       "Airline_B6           0.000871  0.997490\n",
       "Airline_DL          -0.238513  0.288272\n",
       "Airline_EV          -0.151854  0.529198\n",
       "Airline_F9           0.001038  0.998139\n",
       "Airline_HA          -0.118301  0.829124\n",
       "Airline_MQ          -0.107958  0.714705\n",
       "Airline_NK           0.467720  0.160737\n",
       "Airline_OO          -0.107665  0.654494\n",
       "Airline_UA          -0.350858  0.132570\n",
       "Airline_US          -0.169925  0.594466\n",
       "Airline_VX          -0.139537  0.798189\n",
       "Airline_WN          -0.173068  0.384351\n",
       "Dayofweek_2         -0.251671  0.225087\n",
       "Dayofweek_3          0.089576  0.666804\n",
       "Dayofweek_4         -0.276833  0.160761\n",
       "Dayofweek_5         -0.232930  0.240380\n",
       "Dayofweek_6         -0.290745  0.221801\n",
       "Dayofweek_7         -0.176871  0.390433"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting only thecoefficient and p-values from the model\n",
    "arr_del_stats_1p=pd.DataFrame(arr_del_stats_1.params) \n",
    "arr_del_stats_1p.columns=['Coef']\n",
    "arr_del_stats_1p['p-values']=arr_del_stats_1.pvalues\n",
    "arr_del_stats_1p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "R-squared: 99.90602987866627% \n"
     ]
    }
   ],
   "source": [
    "print(\"\\nR-squared: {}% \".format(arr_del_stats_1.rsquared*100)) #R-squared of the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.1. Interpretations\n",
    "With everything else constant, one minute increase in late aircraft delay, increases arrival delay by 0.981362 minutes\n",
    "\n",
    "With everything else fixed,  one minute increase in airline delay, increases arrival delay by 0.982036 minutes\n",
    "\n",
    "As the p values of departure time, airlines and day of week are really high, we will not interpret any of those results, as it might not reveal the truth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.2 Model diagnostics\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_1=arr_del_stats_1.fittedvalues # obtaining the fitted values\n",
    "y_minus_yhat_1=arr_del_stats_1.resid #obtaining the residual values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot for residual vs fitted values \n",
    "plt.scatter(y_hat_1,y_minus_yhat_1)\n",
    "plt.xlabel('Predicted arrival delay (Fitted Values)')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residuals vs Fitted Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#QQ plot\n",
    "qq = sm.qqplot(arr_del_stats_1.resid)\n",
    "plt.title(\"QQ plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use Residual plots and QQplot for identifying the three potentisl problems in our dataset- Non-linearity, Non constant variance of the error terms,Non-Normality of error\n",
    "\n",
    "•\tThe residuals then fall within a horizontal band centered around 0, but it displays slight tendencies to be positive. Hence, Linearity is not met. <br/>\n",
    "•\tResidual plot exhibits slight funnel shape, constant variance is not satisfied.<br/>\n",
    "•\tThe points fall nowhere close to a straight line inthe qqplot . The distribution of the error terms departs substantially from a normal distribution. Normality is not satisfied\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2 Subpart II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x257229eda88>,\n",
       "  <matplotlib.lines.Line2D at 0x25722c601c8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x25722c60148>,\n",
       "  <matplotlib.lines.Line2D at 0x25722a4a5c8>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x257229ed088>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x25722a4a148>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x25722c45548>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#analysing the outliers in arrival delay\n",
    "plt.boxplot(flight['ARRIVAL_DELAY'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1= flight['ARRIVAL_DELAY'].quantile(0.25) #Q1 region\n",
    "Q3=flight['ARRIVAL_DELAY'].quantile(0.75) #Q2 region\n",
    "IQR=Q3-Q1 #Inter quartile region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.25"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1072"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(flight) # current number of observations in flight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "flight.drop(flight[ (flight['ARRIVAL_DELAY']>Q3+1.5*IQR) | (flight['ARRIVAL_DELAY']<Q1-1.5*IQR)].index,inplace=True) # dropping the outliers in arrival delay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "986"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(flight) #  number of observations in flight after dropping the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "flight['log(y)'] = np.log(flight['ARRIVAL_DELAY'] +0.00001 ) # transforming the response variable (adding 0.000001 to avoid error incase a log 0 is present)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2=flight[['LATE_AIRCRAFT_DELAY','AIRLINE_DELAY','AIR_SYSTEM_DELAY','WEATHER_DELAY','DEPARTURE_DELAY']] #predictor variable for the new model\n",
    "y_2=flight['log(y)'] # response variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dira9\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>log(y)</td>      <th>  R-squared:         </th> <td>   0.917</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2161.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 26 Feb 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:37:13</td>     <th>  Log-Likelihood:    </th> <td>  343.09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   986</td>      <th>  AIC:               </th> <td>  -674.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   980</td>      <th>  BIC:               </th> <td>  -644.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>               <td>    2.7076</td> <td>    0.010</td> <td>  268.092</td> <td> 0.000</td> <td>    2.688</td> <td>    2.727</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LATE_AIRCRAFT_DELAY</th> <td>    0.0191</td> <td>    0.000</td> <td>   40.192</td> <td> 0.000</td> <td>    0.018</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AIRLINE_DELAY</th>       <td>    0.0193</td> <td>    0.000</td> <td>   39.368</td> <td> 0.000</td> <td>    0.018</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AIR_SYSTEM_DELAY</th>    <td>    0.0203</td> <td>    0.000</td> <td>   58.918</td> <td> 0.000</td> <td>    0.020</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WEATHER_DELAY</th>       <td>    0.0195</td> <td>    0.001</td> <td>   28.038</td> <td> 0.000</td> <td>    0.018</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DEPARTURE_DELAY</th>     <td>    0.0009</td> <td>    0.000</td> <td>    2.234</td> <td> 0.026</td> <td>    0.000</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>36.792</td> <th>  Durbin-Watson:     </th> <td>   1.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  49.677</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.367</td> <th>  Prob(JB):          </th> <td>1.63e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.819</td> <th>  Cond. No.          </th> <td>    108.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 log(y)   R-squared:                       0.917\n",
       "Model:                            OLS   Adj. R-squared:                  0.916\n",
       "Method:                 Least Squares   F-statistic:                     2161.\n",
       "Date:                Wed, 26 Feb 2020   Prob (F-statistic):               0.00\n",
       "Time:                        14:37:13   Log-Likelihood:                 343.09\n",
       "No. Observations:                 986   AIC:                            -674.2\n",
       "Df Residuals:                     980   BIC:                            -644.8\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "const                   2.7076      0.010    268.092      0.000       2.688       2.727\n",
       "LATE_AIRCRAFT_DELAY     0.0191      0.000     40.192      0.000       0.018       0.020\n",
       "AIRLINE_DELAY           0.0193      0.000     39.368      0.000       0.018       0.020\n",
       "AIR_SYSTEM_DELAY        0.0203      0.000     58.918      0.000       0.020       0.021\n",
       "WEATHER_DELAY           0.0195      0.001     28.038      0.000       0.018       0.021\n",
       "DEPARTURE_DELAY         0.0009      0.000      2.234      0.026       0.000       0.002\n",
       "==============================================================================\n",
       "Omnibus:                       36.792   Durbin-Watson:                   1.914\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               49.677\n",
       "Skew:                          -0.367   Prob(JB):                     1.63e-11\n",
       "Kurtosis:                       3.819   Cond. No.                         108.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Linear regression using statsmodel\n",
    "X_con_2=sm.add_constant(X_2)\n",
    "arr_del_stats_2=sm.OLS(y_2,X_con_2).fit()\n",
    "arr_del_stats_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Coef</th>\n",
       "      <th>p-values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>const</td>\n",
       "      <td>2.707571</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LATE_AIRCRAFT_DELAY</td>\n",
       "      <td>0.019111</td>\n",
       "      <td>1.781589e-209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AIRLINE_DELAY</td>\n",
       "      <td>0.019317</td>\n",
       "      <td>4.990089e-204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AIR_SYSTEM_DELAY</td>\n",
       "      <td>0.020272</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WEATHER_DELAY</td>\n",
       "      <td>0.019478</td>\n",
       "      <td>1.745813e-127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DEPARTURE_DELAY</td>\n",
       "      <td>0.000854</td>\n",
       "      <td>2.570335e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Coef       p-values\n",
       "const                2.707571   0.000000e+00\n",
       "LATE_AIRCRAFT_DELAY  0.019111  1.781589e-209\n",
       "AIRLINE_DELAY        0.019317  4.990089e-204\n",
       "AIR_SYSTEM_DELAY     0.020272   0.000000e+00\n",
       "WEATHER_DELAY        0.019478  1.745813e-127\n",
       "DEPARTURE_DELAY      0.000854   2.570335e-02"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting only he pvalues and coefficients\n",
    "arr_del_stats_2p=pd.DataFrame(arr_del_stats_2.params)\n",
    "arr_del_stats_2p.columns=['Coef']\n",
    "arr_del_stats_2p['p-values']=arr_del_stats_2.pvalues\n",
    "arr_del_stats_2p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "R-squared: 91.68525991830104% \n"
     ]
    }
   ],
   "source": [
    "print(\"\\nR-squared: {}% \".format(arr_del_stats_2.rsquared*100)) #R-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balance_Hat = 2.70757128343479 + 0.019111162332402735*LATE_AIRCRAFT_DELAY +0.019317385511321186 * AIRLINE_DELAY+ 0.020272263585622848*AIR_SYSTEM_DELAY+ 0.019478108752049868*WEATHER_DELAY +0.0008544833163888557*DEPARTURE_DELAY  \n"
     ]
    }
   ],
   "source": [
    "#Regression line\n",
    "print(\"Balance_Hat = {a} + {b}*LATE_AIRCRAFT_DELAY +{c} * AIRLINE_DELAY+ {d}*AIR_SYSTEM_DELAY+ {e}*WEATHER_DELAY +{f}*DEPARTURE_DELAY  \" \n",
    "      .format(a=arr_del_stats_2.params[0],b=arr_del_stats_2.params[1], c=arr_del_stats_2.params[2], d=arr_del_stats_2.params[3], e=arr_del_stats_2.params[4], f=arr_del_stats_2.params[5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_2=arr_del_stats_2.fittedvalues#obtaining the fitted values\n",
    "y_minus_yhat_2=arr_del_stats_2.resid #obtaining the residual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8DOyTeS7pfuAzEbG0wtc1M7MBKrdmcSbwOvDRiPgNSd/C3EpeOCI2A+cDi4AngFsi4jFJV0o6rZJzm5lZdSlpXRo+pkyZEkuXuvJhZjYQkpZFRMFm/qLNUJJeIavTOXsXEBGxZ4XlMzOzJlA0WEREpZ3YZmY2DJTbwQ2ApH2BXTLPI2JN1UtkZmYNp9xJeadJehJ4GvgJ8AzwoxqWy8zMGki5o6GuIpnn8KuIOBg4HnigZqUyM7OGUm6w6IuIF4EWSS0RcR8wqYblMjOzBlJun0WPpN2BnwI3S3oB2Fy7YpmZWSMpt2YxHegFPgXcBfwaOLVWhTIzs8ZSVs0iIl7Lenp9jcpiZmYNqqxgkTM5byeSvFCveVKemdnIUG7NYrvJeZJmkCxeZGZmI8CgVsqLiC7g3VUui5mZNahym6Hel/W0BZhC/pxRZmY2DJU7dDZ75NNmkhnc+dbLNjOzYajcPou/rHVBzMyscZVKUf4vFGluiohPVL1EZmbWcErVLDKrCE0FDgPmp8/PAJbVqlBmubqWdzN30SrW9fSyV3sbEmzY2EerRH8EnR3tHPfmMfzw0efp6e0DoEWwJdi6775frmddTy8HdLQza9pEZkzurPNVmTWPslbKk3QfcFJE9KXP24DFEXFcRS8unQx8DWgFro2IOTn7Pw18jKSfZD3wVxHxbLFzeqW85nVJ10q++9Bz9EfQKnH20QcCcNOS6mfCb2sRu+8yip6NfezS1sLrm7ewJdj6ulfPOKLqr2nWyEqtlFdusFgFvDMiXkqfjwaWRMTECgrWCvwKOBFYCzwMnB0Rj2cdcxzwUERslPQ3wLERcWax8zpYNI/s2kJri9i8pfEG2GVqJwAd7W1cftrhrpHYsFTRsqpZ5gDL0xoGwJ8Bl1dYtqOA1RHxFICk75GMsNoaLNLsthlLgHMrfE2rk+zA0LFrGz0b+7brDGvEQAHbAgVAT28fF8xfwQXzVwCuhdjIUu5oqP+S9CPg6HTT7Ij4TYWv3Qk8l/V8bdb58/koBRZckjQTmAkwbty4Cotl1ZAJDt09vTvs27Cxrw4lqr7+CG5asoa7H/sND118Yr2LY1ZTpUZDvTkifinpbemmzJf7AZIOiIhHKnht5dmW9/ZS0rkkEwH/LN/+iJgHzIOkGaqCMjW87Dv0RuyovaRrJTcvWTOiZmz+9pVNHHrxQvr6oyE/E7NqKFWz+DTJHftX8uwLKkv5sRY4MOv5WGBd7kGSTgAuBv4sIl6v4PWaXtfybi66fSW9ff0AdPf0ctHtKwHq/uXUtbybi+9YyWub+utajnrZ1J+Ex+6e3u2aqloEHzx6XFlNVY1+I2AjW1kd3DV5YWkUSQf38UA3SQf3ByPisaxjJgO3ASdHxJPlnHc4d3BPnXNv3madzo52Hphdv1RdXcu7t345Wn4T9t2NjZu2FAwEuTcCAO1trXzxfUc4YNiQqEoHt6QzgLsi4hVJlwBvA66KiOWDLVhEbJZ0PrCIZOjsf0bEY5KuBJZGxAJgLrA7cKskgDURcdpgX7PZrcsTKIptr7VLulbWZFjrcPTkC9uWhMmufYzetY3LTj2cuYtWbRcoAHr7+pm7aJWDhTWEckdD/WNE3CrpT4BpwJeBb1G8Q7qkiFgILMzZdmnW4xMqOf9wc0BHe96axQEd7UNajnO+/SAP/PqlIX3N4WrDxr6itbJ63QiY5So3RXnmlucU4N8i4vskiyDZEJo1bSLtba3bbWtva2XWtEFPdxmQc779IONn3+lAMYSG+kbArJByaxbdkv4dOAH4kqSdGeRaGDZ4meaIoe4EdU2ifta/8ge6lne7KcrqrtwZ3LsCJwMrI+JJSfsDR0TE4loXcKCGcwd3PThQbNPWIvrqPHkw08fh4GHVVqqDu6zaQURsBF4A/iTdtBkoa3SSNa6u5d1MnXMvB8++k6lz7qVreffWffVucupob6OtdcepOLl/sG0tyntcOTSAX+tob2PuGUfS2dGOSGZv10Omj+Pgi+7kkq6VdSmDjUzl1iwuI5kUNzEiDpV0AHBrREytdQEHyjWL8hQbqnnr0jV1rU1khgLnm3cAOzbDZW/r2LWNiCQ1RyYjbe6/nTnNd4WGJGe0tYq5px+53d38wbPvLGviYXZuqVpx2hGrhmolElwBTAYeiYjJ6bafR8Rbq1bSKnGwKE+hL8j2thZ6+7ZU9bXaWsRRB4/m//36pe2+YNtaBcF2TTv1mFuQL3BmFGr2KRVgsn8fhibFyc6jWvjS+9/qJioblGolEtwUESEp0pPuVpXSWd0UGpJZ7UCxa1sLX3hf8gVWbk1hqL/sBjNwYNa0iQUDTLaejX08PecUoPwAM1ivb97CBfNX8Lnbf771PTerlnKDxS3paKgOSR8H/gq4tnbFslorNGejEu1tLey9284Fv3BnTO7M+wXWCF9qhcpW7HjYFmBa0iauXNlDX4dqzsTGviRoLH32JTdNWdWUne5D0onASSQJABdFxN21LNhguRmquOxssKLImrkD1NYi5p5xZEN88ddDOek6al2zKKa9rYUvurZhRVSlzyLPSVuBsyLi5koKVwsOFoUVa5uvhBcFSpRKBFir938gBJxzTHmJDW1kqShYSNoT+DuStScWAHenz2cBKyJienWLWzkHi8JqcWf7TNoeb+XpWt7Nhbc8mrfJaig5aFiuSudZ3AhMBFaSrIW9GDgDmN6IgcKKq3abeadTUQzYjMmdfOUDR+6QtmWoBcna5odevHC7+TVmhZTq4P6jiDgCQNK1wO+AcRHxSs1LZlVXzU7tocxJNdzkdo7v1d7Gps39bMwzEq0WQ5mzbeqPrRlwc+efmGUrFSy2Dg6PiH5JTztQNK/j3jymKinF/aVSuXyjrwr1eUy6YjE9vTvO02gtMAJrsDKp029duoabP/7Oqp3XhodSfRb9QCYRv4B2YGP6OCJiz5qXcIDcZ5FftRYoqvdCSyNRsZFWhdY5rwbfFIwsFU3Ki4j6NqxaVbz54oX8ob86d6BeX2HoFZs0+KkarlDomoZlK3dSXk1IOhn4GslKeddGxJyc/TsDNwBvB14EzoyIZ4a6nM2smoECvL5CvRSaNFisH6pVYksEe7W38drrfQy26+OBX7/EJV0rPXJqhKvbmhTpXI1vAO8BDgPOlnRYzmEfBTZExCHAV4EvDW0pm99gA8WEfXcjN6+qO7UbT7HPY0sET885hRWXncSTXziFc48ZR8sgk+V+5yEvnzvS1XMBo6OA1RHxVERsAr4H5A7HnQ5cnz6+DTheqlNu6CY0fvadA/6dtlZxzZmTuPvTx/LVMydtTcnd2dE+5An+rLQZkzu3JivMlVsLvHrGETz1xVO45sxJA07rviWSv6fxs+90evQRqp7NUJ3Ac1nP17Ljmt5bj4mIzZJeBt5AMoR3K0kzgZkA48aNG3SBSs3AbSaDCRQAu+00aus1DzRfktXHZacenrcDvFCtI/OZXr7gsbyjrEqJYOuoOjdNjRz1DBb5bm1y20zKOYaImAfMg2Q01GAKkzvipLunl4tuT+6emukLs9KV7V4exJeH1ddgl9t9bdPmil735ofWcN8v1w+LmysrrZ7BYi1wYNbzscC6AseslTQK2Auoyao8cxet2iFnT29fP3MXrWqa/wDVWALVHdjNaaC1wLmLVtFX4cCHCLZ2rmdGTmWGZ5/rVCLDTj2DxcPABEkHA93AWcAHc45ZAHwEeBA4Hbg3BpP5sAyFhoQ2+lDR7KazSt8Yd2CPHLX+u75pyRpuWrLGa4YPI3Xr4I6IzcD5wCLgCeCWiHhM0pWSTksP+w/gDZJWA58GZteqPIXuqBv5TjvTdNZdhUDRKrkDewQp9nfd2dFetbxfGzb2Meu2R51/ahio52goImJhRBwaEW+KiM+n2y6NiAXp4z9ExBkRcUhEHBURT9WqLLOmTdwhuVuj32nnazobjPa2Vr7ygZG7FsVINGvaxLwjotpaxKxpE5P9gx1nm6OvP7jwFgeMZlfXYNFIZkzu5IvvO6KphopW0pSg9KcZrtOqb8bkTuaefuR2w2472tu2LmA1Y3Inu+9SvVbq/gguun2lA0YTq+sM7kbTbENFB5tFtgX45zMnNdW1WvWV+nvv2VjdkXHNNmDEtudg0cRmTZs4qOSADhRWjnJuRgaa+ba7p5dzvv0gz7zY6yG3TWZQy6o2suGcdTZ30uBgahWZVmj/J7VSylkGVsDTc07h8Evv4rVNg+8/222nVj7/F24OradKV8qzBpE78mmwaakj/clMOnQbshWS3Y9XSGZU1ef/4ogBpxDJ9tqmfi6Yv8JpRBqYg0WTqNbIp2yZNmSzQmZM7uSB2e/mmjMnFR0tmOkw322nylY1uGnJGgeMBuVg0SRqNYmq0ScdWmMoZ7TgjMmdPHblyZx7zODzs0ESMMbPvpPJVy52zbeBuM+iSUydc++gm57a21rZpa2FDXlGt3jlO6uVruXdfO72n+ddW3wgdm1r4Qvve6v7M2rMfRbDRL5Jg+X64vuO4LJTD2+6SYfW3GZM7uTxq95T8Xk29m3xLPAG4GDRJGZM7uT9bx/cnVVmPH2zTTq04aEaqUP6+oML5q9g6px7HTTqxPMsmkTX8m5uXjLw1cqyZ+g226RDGx5mTZtYcghuuZp16YDhwDWLJvHZ2x4dcLLAtlZx2amH16Q8ZuXKrdV2tLexa9vgv3p6+/q54gePVa+AVhbXLJrEpkGsPTD3dCcHtMZQqFZbzsS/fDZs7GPSFYt5ubfPE0yHiINFg8vM2h4M/+exRlfJ/KHMkrBumhoaboZqYNmztgcqu6/CrFFVa55Pb18/ly9w01QtuWbRwAZ71+W+CmsWg81xlk9Pr5umask1iwY2mLuuzo5291VY06hk/lA+Pb19zn1WI3UJFpL2lnS3pCfTf0fnOWaSpAclPSbp55LOrEdZ62mgS7qee8w4Hpj9bgcKaxrlJCsc7IJ9vX1JcsJJVzhtSDXUq2YxG7gnIiYA95B/be2NwIcj4nDgZOAaSR1DWMa6G8js6nOPGcfVM46oYWnMaqNUssJ//sAknplzCs/MOYWO9oH3xfX09jHrVs8Ar1S9gsV04Pr08fXAjNwDIuJXEfFk+ngd8AIwZshK2ADKXdhI4EBhTa+cLAMv9w5u9b6+LeEMyxWqVwf3fhHxPEBEPC9p32IHSzoK2An4dYH9M4GZAOPGVZbxslGMn31n2ccOr1SQNpKVyjJQSYe4MyxXpmY1C0k/lvSLPD/TB3ie/YEbgb+MiLzpKyNiXkRMiYgpY8aMqMqH2YhSSYd4pg+wa3k3U+fcy8Gz73SuqQGoWc0iIk4otE/SbyXtn9Yq9idpYsp33J7AncAlEbGkRkVtepUuOGPWLDK1juzlhTN9e3MXrSpY62hrEbOmTdxhxnh3Ty+fmr+CC+avoNPDbYuqVzPUAuAjwJz03+/nHiBpJ+AO4IaIuHVoi1c/A52x3doiPv8X7q+wkaNQU1VmW9fybq74wWNb12/paG/j8tMOZ8bkTiZdsXiHuUuZZlzPBC+uXsFiDnCLpI8Ca4AzACRNAf46Ij4GfAB4F/AGSeelv3deRJTX69uEBponx3dCZjsqloeqp0QHeWYmuP9P7aguwSIiXgSOz7N9KfCx9PFNwE1DXLS6GuiMba9wZ1a+cmvsPb19dC3vdsDI4RncDWQgozUGM97cbCQbyP8v55nakYNFAyl3xnZbi7j8NOd+MhuIgWREKNVcNRI5WDSQcmdszz3DuZ/MBmrWtIm0DSB3yKQrFjN+9p2Mn30nk690yhAHiwbiAGBWOzMmdzL3jCO3a8ItFjqyaxcbNvYx67aRnTJEEcNr/u+UKVNi6dKl9S7GgHUt7y47vUdnR7s7t82qoGt5N7Nue5S+MleiHM7/9yQti4gphfa7ZtEABhIowGkLzKplxuRO5p5+5Hb5qIoZyf/3vPhRAxhogrOBpi43s8Jy52VMnXNvwZngHbu2MXXOvdvNHh8pzccOFg1gIHcr7W2tA0pdbmbl61rezcZNm/Pua20RL2/s2zozvLunl1m3PgqMjP5GN0M1gHJrCvlSNptZdWQyKGSCQbbRu7axU6vIzWTatyX41C0rRkRSQgeLBlBuTeG4N49xoJ1uG+8AAA7SSURBVDCrkUIZFDo72ll+6Un09uVNek0EI2IpVweLBlBuALh5yZph+4doVm+FmoO7e3qZOufess6RWcp1ONYyHCwaxDNzTil5TDDwznAzK0+h5mDBgBdc6u7pHXbzMhwsGsgzc05hvz12KnrMSB66Z1ZL+RZWEoNfibKvP7jiB8Mnx5SDRYN56OITmbDvbgX3e9isWW3kWwO80inL+TrLm5VncDeoS7pWcvOSNdv9sba3tXo0lNkQKjbnolyCppiTUWoGt4NFA8usmjcSJwCZNYKBLkhWTFur2G2nUbzc29eQ/59LBQtPymtghVb8MrOhkbvmd4tE/yBvsPv6Y2tywmZcwrUufRaS9pZ0t6Qn039HFzl2T0ndkv51KMtoZgbJl/kDs9/N03NO4SsfOHKHTvD2tlauOXMS15w5aWt/Rzl6+/qbanRjvTq4ZwP3RMQE4J70eSFXAT8ZklKZmRWRrxM804+YHVRKJSTMyMzhaIYhtvVqhpoOHJs+vh64H/iH3IMkvR3YD7gLKNiWZmY2VMppHp41bWLZfR2ZORmZczequnRwS+qJiI6s5xsiYnTOMS3AvcCHgOOBKRFxfoHzzQRmAowbN+7tzz77bM3KbmZWjq7l3Vx4y6MD7uPoaG/j8tMOH/LAUbcObkk/Bt6YZ9fFZZ7ib4GFEfGcVLwVMCLmAfMgGQ01kHKamdVC5st+oKOpenr7GjKbbc2CRUScUGifpN9K2j8inpe0P/BCnsPeCfyppL8Fdgd2kvRqRBTr3zAzaxjZo6kGMl+jb0twwfwVXL7gsbrUMvKpVzPUXODFiJgjaTawd0R8tsjx51GkGSrbcJpnYWbDx8Gz7xz0jPDRu7Zx2am1DRqNuqzqHOBESU8CJ6bPkTRF0rV1KpOZWc1Ukqpnw8a+uqc/r0uwiIgXI+L4iJiQ/vtSun1pRHwsz/HXlVOrMDNrVPkSFQ5EvedlOJGgmdkQyDdHIzOZb/SubWWdo57zMpwbysysAUy+cnHZWWprkVS0UfsszMwsy2WnHk5ba3nJQnr7+rnwlqFdXMmJBM3MGkBu0sJSbT79EUOajNDNUGZmDWiga2lUOrzWzVBmZk1ooKOnNmzs48Jba9c05WBhZtaAskdPlat/S3DxHStrUh73WZiZNajsDLflrtr32qZ+upZ3V70fwzULM7MmMJCaRi0m7zlYmJk1icwCS9ecOanocesG0DFeLgcLM7MmM2NyJ+ceM67g/kryUBXiPgszsyZ09YwjALh5yZrt5mS0t7Uya9rEqr+eaxZmZk3q6hlH8NUzJ+VdE7zaXLMwM2ti5awJXg2uWZiZWUkOFmZmVlJdgoWkvSXdLenJ9N/RBY4bJ2mxpCckPS5p/NCW1MzMoH41i9nAPRExAbgnfZ7PDcDciPhj4CjghSEqn5mZZalXsJgOXJ8+vh6YkXuApMOAURFxN0BEvBoRG4euiGZmllGvYLFfRDwPkP67b55jDgV6JN0uabmkuZLypmCUNFPSUklL169fX8Nim5mNTDUbOivpx8Ab8+y6uMxTjAL+FJgMrAHmA+cB/5F7YETMA+ZBsp7FIIprZmZF1CxYRMQJhfZJ+q2k/SPieUn7k78vYi2wPCKeSn+nCziGPMEi27Jly34n6dkKil5v+wC/q3chasjX19x8fc2t2PUdVOwX6zUpbwHwEWBO+u/38xzzMDBa0piIWA+8Gyi5BF5EjKlmQYeapKXFVqtqdr6+5ubra26VXF+9+izmACdKehI4MX2OpCmSrgWIiH7gM8A9klYCAr5dp/KamY1odalZRMSLwPF5ti8FPpb1/G7grUNYNDMzy8MzuBvPvHoXoMZ8fc3N19fcBn19ivDgITMzK841CzMzK8nBwszMSnKwGGKSdpH0v5IelfSYpCvyHLOzpPmSVkt6qNkSKJZ5jedJWi9pRfrzsXznalSSWtPMAj/Ms6+pP7+MEtfY7J/fM5JWpmXfYUi+El9PP8OfS3pbPco5WGVc37GSXs76/C4tdU4vfjT0XgfeHRGvSmoD/kfSjyJiSdYxHwU2RMQhks4CvgScWY/CDlI51wgwPyLOr0P5quGTwBPAnnn2Nfvnl1HsGqG5Pz+A4yKi0AS19wAT0p+jgX9L/20mxa4P4GcR8eflnsw1iyEWiVfTp23pT+4og+xEi7cBx0vSEBWxYmVeY9OSNBY4Bbi2wCFN/flBWdc43E0Hbkj/lpcAHWm2iRHLwaIO0ur9CpI0J3dHxEM5h3QCzwFExGbgZeANQ1vKypRxjQDvT6v4t0k6cIiLWIlrgM8CWwrsb/rPj9LXCM37+UFy87JY0jJJM/Ps3/oZptam25pFqesDeGfaVPwjSYeXOqGDRR1ERH9ETALGAkdJekvOIfnuQpvqzryMa/wBMD4i3gr8mG134g1N0p8DL0TEsmKH5dnWNJ9fmdfYlJ9flqkR8TaS5qa/k/SunP1N/RlS+voeAQ6KiCOBfwG6Sp3QwaKOIqIHuB84OWfXWuBAAEmjgL2Al4a0cFVS6Boj4sWIeD19+m3g7UNctMGaCpwm6Rnge8C7Jd2Uc0yzf34lr7GJPz8AImJd+u8LwB0ki6tl2/oZpsYC64amdJUrdX0R8ftMU3FELATaJO1T7JwOFkNM0hhJHenjduAE4Jc5h2USLQKcDtwbTTR7spxrzGn/PY2kI7XhRcRFETE2IsYDZ5F8NufmHNbUn18519isnx+ApN0k7ZF5DJwE/CLnsAXAh9NRUccAL2fW4Gl05VyfpDdm+tEkHUUSC14sdl6Phhp6+wPXK1nIqQW4JSJ+KOlKYGlELCBJw36jpNUkd6Rn1a+4g1LONX5C0mnAZpJrPK9upa2CYfb55TWMPr/9gDvS78pRwHci4i5Jfw0QEd8CFgLvBVYDG4G/rFNZB6Oc6zsd+BtJm4Fe4KxSNzRO92FmZiW5GcrMzEpysDAzs5IcLMzMrCQHCzMzK8nBwszMSnKwMCT1p5knfyHpVkm7VnCuYzNZSiWdJml2kWM7JP3tIF7jckmfKbL/UUnfHeA5i5a1kvJkHfdqNY4ZQJm6s7KKZta5v1bSYenjz2UdX7XPIv0beDBn2yhJvy2WX6nc99Hqw8HCAHojYlJEvAXYBPx19s50YtKA/1YiYkFEzClySAcw4C+oYiT9Mcnf9bvSCUn5jhmV+7yMsjajr6af66SImA0QER+LiMfT/Z/LOraan8VPgbHaPjX7CcAvmmVim+3IwcJy/Qw4RNJ4SU9I+iZJHpkDJZ0k6UFJj6Q1kN0BJJ0s6ZeS/gd4X+ZEStY8+Nf08X6S7kjv+h+V9H+AOcCb0jvfuelxsyQ9rCRB3RVZ57pY0ipJPwYmFin/B4EbgcUkM4szv3+/pC9I+gnwSUnXSfpnSfcBX8qUVdJeStYCaEl/b1dJz0lqk/TxtGyPSvrvUjUwSQen79fDkq7K2Zf3OrP27y7pnvS9Xilperr9KkmfzDru85I+UawcOee9X9KUtKbRnr73N1PFzyIitgC3sn1a9rOA76a/X/J9zJQzfbyPktQjmQSVc7PK9X/T7ftL+qm21ZD/tNz3xMoUEf4Z4T/Aq+m/o4DvA38DjCfJOHpMum8fkjvG3dLn/wBcCuxCkp1zAknytVuAH6bHnAf8a/p4PnBB+riVJF/SeJK7zUw5TiJZUF4kNzI/BN5FkndoJbArydoKq4HPFLiWXwEHpedakLX9fuCbWc+vS8/fmqes3ydZCwCSL7xr08dvyPr9q4G/Tx9fnq88pCkj0sd/l/U+573OPJ/Fnlnv/er0+PHAI+n2FuDX2eXKeu3LgW5gRfozLet9mJL9Wunjqn4WwDuA5enjnUmyD48u933MKec+wDPp45nAJVnnXQocDFwIXJz197VHvf9fDbcfp/swSO8w08c/I0lXcQDwbGxbsOgY4DDgASVpBHYCHgTeDDwdEU8CKEk4ly8l8ruBD0OSkRZ4WdLonGNOSn+Wp893JwlCewB3RMTG9DUW5LsISe8A1kfEs5LWAv8paXREbEgPmZ/zK7emZck1nyRI3EdyR/zNdPtbJF1N0mSzO7AoXzmyTAXenz6+kWQRpGLX+dPsywG+oCRb6BaS9Nj7RcQzkl6UNJkkrcPyiCiU0+erEfHlEmUspKLPIiIeTmtHE4E/BpZkfQ4DfR9zy/VWSaenz/dKy/UwyefdBnRFxIpCJ7DBcbAwSPsssjekAeG17E0k61KcnXPcJKqXulnAFyPi33Ne44IyX+Ns4M2ZJguSO9/3s20Bn9dyjs99nrEA+KKkvUnupO9Nt18HzIiIRyWdBxxbRpnylTvvdeY4BxgDvD0i+tJr2iXddy1JTeiNwH9C0hxFslgRuZ/lIFX6WUCSsfYskmCRPeDgOkq/j5vZ1ky+S9Z2kdREdggwaWA9hSQv19yIuKHMcloZ3Gdh5VoCTJV0CGxtyz+UJJvswZLelB53doHfv4ekeSvT7rwn8ArJnWrGIuCvtK0vpFPSviR33H8hqV1JNs1Tc0+e9jGcAbw1IsZHkjF1epHyFBRJ6ub/Bb5G0qSWqX3sATyf3r2eU8apHmBbEsHs4wtdZ7a9SNaU6JN0HEnTWsYdJCnf35Gei4i4ONLO7HKvE+hLrwWq+Flk+S5wLkmtMrsGUs77+Azb0p6fnrV9EUkCvLa0XIcqybJ6EMn79W2SmnFTrZndDFyzsLJExPr0LvC7knZON18SEb9SshLXnZJ+B/wPkLvQESTrOc+T9FGgH/ibiHhQ0gOSfgH8KCJmKRnN9GBas3kVODciHpE0n6Tt/VmSprJc7wK6I6I7a9tPgcM0uOUw55N00h6bte0fgYfSMqxk+y/XfD4JfCftkP7vzMaIWJzvOkna9TNuBn4gaSnJdf8y6/c3KemY7ynQjFauecDPJT0SEedU8bPIlPNxSRuBZRGRXYsr5338MnCLpA+xrWYHSa1qPPCIkoKtB2aQfE6zJPWlZf3wAN8LK8FZZ82aTFqLegQ4I9NXZFZrboYyayJKJtStBu5xoLCh5JqFmZmV5JqFmZmV5GBhZmYlOViYmVlJDhZmZlaSg4WZmZX0/wGKVnUxlV9jtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_hat_2,y_minus_yhat_2)\n",
    "plt.xlabel('Predicted Arrival delay-Fitted Values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residuals vs Fitted Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qq = sm.qqplot(arr_del_stats_2.resid)\n",
    "plt.title(\"QQ plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.1 Model diagnostics\n",
    "\n",
    "We will use Residual plots and QQplot for identifying the three potential problems in our dataset- Non-linearity, Non constant variance of the error terms,Non-Normality of error\n",
    "\n",
    "•The residuals depart from zero in a systematic fashion, they are negative for smaller values, positive for medium values and negative again for larger values. This tendency indicates the lack of linearity and constant variance of the regression function. <br/>\n",
    "\n",
    "•The points fall reasonably close to a straight line initially but the points quickly departs from normality. The distribution of the error terms  departs substantially from a normal distribution since the points are not in a straight line. Hence, normality is not satisfied\n",
    "\n",
    "•No improvement from the previous model in sec 4.1. The normality improved slightly, but linearity and constant variance has gotten worse\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3. Suggestions on how to improve model fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1)\tTukey's transformation - Performing multiple transformation, till linearity is achieved (at least max level possible) <br/>\n",
    "2)\tSuitable variable selection technique <br/>\n",
    "3)\tAdding interaction terms to the model <br/>\n",
    "4)\tAdding polynomial terms to the model\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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 },
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}