Airline_Ticket_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import our libraries \n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read our dataset\n",
    "train_data = pd.read_excel('Data_Train.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#since we have very less missing value so we gonna drop these missing values \n",
    "train_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check now!\n",
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            object\n",
       "Date_of_Journey    object\n",
       "Source             object\n",
       "Destination        object\n",
       "Route              object\n",
       "Dep_Time           object\n",
       "Arrival_Time       object\n",
       "Duration           object\n",
       "Total_Stops        object\n",
       "Additional_Info    object\n",
       "Price               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check datatypes of the dataset to deal with cleaning data step \n",
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#since we have three columns(Date_of_Journey , Dep_Time , Arrival_Time ) so we gonna change these columns in datetime datatype\n",
    "#define our function which gonna do that because our model will not be able to understand Theses string values,it just understand Time-stamp\n",
    "def convert_into_datetime(col):\n",
    "    train_data[col]=pd.to_datetime(train_data[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
       "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops',\n",
       "       'Additional_Info', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['Date_of_Journey','Dep_Time','Arrival_Time']:\n",
    "    convert_into_datetime(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                    object\n",
       "Date_of_Journey    datetime64[ns]\n",
       "Source                     object\n",
       "Destination                object\n",
       "Route                      object\n",
       "Dep_Time           datetime64[ns]\n",
       "Arrival_Time       datetime64[ns]\n",
       "Duration                   object\n",
       "Total_Stops                object\n",
       "Additional_Info            object\n",
       "Price                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now check after we converted it \n",
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We have to split the date column into sub columns so that our model can understand it that this is day and that is month and so on \n",
    "train_data['Journey_day']=train_data['Date_of_Journey'].dt.day\n",
    "train_data['Journey_month']=train_data['Date_of_Journey'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2021-07-02 22:20:00</td>\n",
       "      <td>2021-03-22 01:10:00</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2021-07-02 05:50:00</td>\n",
       "      <td>2021-07-02 13:15:00</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2021-07-02 09:25:00</td>\n",
       "      <td>2021-06-10 04:25:00</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>2021-07-02 18:05:00</td>\n",
       "      <td>2021-07-02 23:30:00</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>2021-07-02 16:50:00</td>\n",
       "      <td>2021-07-02 21:35:00</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India      2019-01-05   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways      2019-09-06     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      2019-12-05   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      2019-01-03  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "             Dep_Time        Arrival_Time Duration Total_Stops  \\\n",
       "0 2021-07-02 22:20:00 2021-03-22 01:10:00   2h 50m    non-stop   \n",
       "1 2021-07-02 05:50:00 2021-07-02 13:15:00   7h 25m     2 stops   \n",
       "2 2021-07-02 09:25:00 2021-06-10 04:25:00      19h     2 stops   \n",
       "3 2021-07-02 18:05:00 2021-07-02 23:30:00   5h 25m      1 stop   \n",
       "4 2021-07-02 16:50:00 2021-07-02 21:35:00   4h 45m      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  \n",
       "0         No info   3897           24              3  \n",
       "1         No info   7662            5              1  \n",
       "2         No info  13882            6              9  \n",
       "3         No info   6218            5             12  \n",
       "4         No info  13302            3              1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Since we have converted Date_of_Journey column into integers, Now we can drop as it is of no use.\n",
    "train_data.drop('Date_of_Journey', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2021-07-02 22:20:00</td>\n",
       "      <td>2021-03-22 01:10:00</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2021-07-02 05:50:00</td>\n",
       "      <td>2021-07-02 13:15:00</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2021-07-02 09:25:00</td>\n",
       "      <td>2021-06-10 04:25:00</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>2021-07-02 18:05:00</td>\n",
       "      <td>2021-07-02 23:30:00</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>2021-07-02 16:50:00</td>\n",
       "      <td>2021-07-02 21:35:00</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "             Dep_Time        Arrival_Time Duration Total_Stops  \\\n",
       "0 2021-07-02 22:20:00 2021-03-22 01:10:00   2h 50m    non-stop   \n",
       "1 2021-07-02 05:50:00 2021-07-02 13:15:00   7h 25m     2 stops   \n",
       "2 2021-07-02 09:25:00 2021-06-10 04:25:00      19h     2 stops   \n",
       "3 2021-07-02 18:05:00 2021-07-02 23:30:00   5h 25m      1 stop   \n",
       "4 2021-07-02 16:50:00 2021-07-02 21:35:00   4h 45m      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  \n",
       "0         No info   3897           24              3  \n",
       "1         No info   7662            5              1  \n",
       "2         No info  13882            6              9  \n",
       "3         No info   6218            5             12  \n",
       "4         No info  13302            3              1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if we looked on arrival_time and dep_time our model not gonna understand it cuz it's only understand numbers so all we have to do it to extract the information from that column\n",
    "#define function to extract hour \n",
    "def extract_hour(df,col):\n",
    "    df[col+\"_hour\"]=df[col].dt.hour\n",
    "    \n",
    "#define another one for minute \n",
    "def extract_min(df,col):\n",
    "    df[col+\"_minute\"]=df[col].dt.minute\n",
    "\n",
    "#define one for dropping columns\n",
    "def drop_column(df,col):\n",
    "    df.drop(col,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Departure time is when a plane leaves the gate. \n",
    "# Similar to Date_of_Journey we can extract values from Dep_Time\n",
    "extract_hour(train_data,'Dep_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Minutes\n",
    "extract_min(train_data,'Dep_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we can drop Dep_Time as it is of no use\n",
    "drop_column(train_data,'Dep_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2021-03-22 01:10:00</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2021-07-02 13:15:00</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2021-06-10 04:25:00</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>2021-07-02 23:30:00</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>2021-07-02 21:35:00</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "         Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0 2021-03-22 01:10:00   2h 50m    non-stop         No info   3897   \n",
       "1 2021-07-02 13:15:00   7h 25m     2 stops         No info   7662   \n",
       "2 2021-06-10 04:25:00      19h     2 stops         No info  13882   \n",
       "3 2021-07-02 23:30:00   5h 25m      1 stop         No info   6218   \n",
       "4 2021-07-02 21:35:00   4h 45m      1 stop         No info  13302   \n",
       "\n",
       "   Journey_day  Journey_month  Dep_Time_hour  Dep_Time_minute  \n",
       "0           24              3             22               20  \n",
       "1            5              1              5               50  \n",
       "2            6              9              9               25  \n",
       "3            5             12             18                5  \n",
       "4            3              1             16               50  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrival time is when the plane pulls up to the gate.\n",
    "# Similar to Date_of_Journey we can extract values from Arrival_Time\n",
    "\n",
    "# Extracting Hours\n",
    "extract_hour(train_data,'Arrival_Time')\n",
    "\n",
    "# Extracting minutes\n",
    "extract_min(train_data,'Arrival_Time')\n",
    "\n",
    "# Now we can drop Arrival_Time as it is of no use\n",
    "drop_column(train_data,'Arrival_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
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       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
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       "      <td>22</td>\n",
       "      <td>20</td>\n",
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       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            5              1   \n",
       "2     2 stops         No info  13882            6              9   \n",
       "3      1 stop         No info   6218            5             12   \n",
       "4      1 stop         No info  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \n",
       "0             22               20                  1                   10  \n",
       "1              5               50                 13                   15  \n",
       "2              9               25                  4                   25  \n",
       "3             18                5                 23                   30  \n",
       "4             16               50                 21                   35  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2h', '50m']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'2h 50m'.split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Lets Apply pre-processing on duration column,Separate Duration hours and minute from duration\n",
    "\n",
    "duration=list(train_data['Duration'])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split(' '))==2:\n",
    "        pass\n",
    "    else:\n",
    "        if 'h' in duration[i]:                   # Check if duration contains only hour\n",
    "            duration[i]=duration[i] + ' 0m'      # Adds 0 minute\n",
    "        else:\n",
    "            duration[i]='0h '+ duration[i]       # if duration contains only second, Adds 0 hour\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Duration']=duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "      <td>No info</td>\n",
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       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h 0m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK   19h 0m   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            5              1   \n",
       "2     2 stops         No info  13882            6              9   \n",
       "3      1 stop         No info   6218            5             12   \n",
       "4      1 stop         No info  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \n",
       "0             22               20                  1                   10  \n",
       "1              5               50                 13                   15  \n",
       "2              9               25                  4                   25  \n",
       "3             18                5                 23                   30  \n",
       "4             16               50                 21                   35  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'50'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'2h 50m'.split(' ')[1][0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now we need to extract this information so we gonna define two fuctions do it as dep_time and arrival time Notice: here we are dealing with strings not datetime datatype\n",
    "\n",
    "def hour(x):\n",
    "    return x.split(' ')[0][0:-1]\n",
    "\n",
    "def min(x):\n",
    "    return x.split(' ')[1][0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Duration_hours']=train_data['Duration'].apply(hour)\n",
    "train_data['Duration_minutes'] = train_data['Duration'].apply(min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h 0m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK   19h 0m   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            5              1   \n",
       "2     2 stops         No info  13882            6              9   \n",
       "3      1 stop         No info   6218            5             12   \n",
       "4      1 stop         No info  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \\\n",
       "0             22               20                  1                   10   \n",
       "1              5               50                 13                   15   \n",
       "2              9               25                  4                   25   \n",
       "3             18                5                 23                   30   \n",
       "4             16               50                 21                   35   \n",
       "\n",
       "  Duration_hours Duration_minutes  \n",
       "0              2               50  \n",
       "1              7               25  \n",
       "2             19                0  \n",
       "3              5               25  \n",
       "4              4               45  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## No need for duration column now \n",
    "train_data.drop('Duration',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                object\n",
       "Source                 object\n",
       "Destination            object\n",
       "Route                  object\n",
       "Total_Stops            object\n",
       "Additional_Info        object\n",
       "Price                   int64\n",
       "Journey_day             int64\n",
       "Journey_month           int64\n",
       "Dep_Time_hour           int64\n",
       "Dep_Time_minute         int64\n",
       "Arrival_Time_hour       int64\n",
       "Arrival_Time_minute     int64\n",
       "Duration_hours         object\n",
       "Duration_minutes       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We still have those two columns after extraction in object datatype which is not understandable by our model so we need to convert them\n",
    "\n",
    "train_data['Duration_hours']=train_data['Duration_hours'].astype(int)\n",
    "train_data['Duration_minutes']=train_data['Duration_minutes'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                object\n",
       "Source                 object\n",
       "Destination            object\n",
       "Route                  object\n",
       "Total_Stops            object\n",
       "Additional_Info        object\n",
       "Price                   int64\n",
       "Journey_day             int64\n",
       "Journey_month           int64\n",
       "Dep_Time_hour           int64\n",
       "Dep_Time_minute         int64\n",
       "Arrival_Time_hour       int64\n",
       "Arrival_Time_minute     int64\n",
       "Duration_hours          int32\n",
       "Duration_minutes        int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check again !\n",
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Airline', 'Source', 'Destination', 'Route', 'Total_Stops', 'Additional_Info']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now let's extract our categorical columns to deal with \n",
    "cat_col=[col for col in train_data.columns if train_data[col].dtype=='O']\n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Price',\n",
       " 'Journey_day',\n",
       " 'Journey_month',\n",
       " 'Dep_Time_hour',\n",
       " 'Dep_Time_minute',\n",
       " 'Arrival_Time_hour',\n",
       " 'Arrival_Time_minute',\n",
       " 'Duration_hours',\n",
       " 'Duration_minutes']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same to numerical columns \n",
    "cont_col=[col for col in train_data.columns if train_data[col].dtype!='O']\n",
    "cont_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  \n",
       "0         No info  \n",
       "1         No info  \n",
       "2         No info  \n",
       "3         No info  \n",
       "4         No info  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Handling Categorical Data\n",
    "##We are using 2 main Encoding Techniques to convert Categorical data into some numerical format\n",
    "##Nominal data --> data are not in any order --> OneHotEncoder is used in this case\n",
    "##Ordinal data --> data are in order -->       LabelEncoder is used in this case\n",
    "\n",
    "categorical=train_data[cat_col]\n",
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1751\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              818\n",
       "Vistara                               479\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Airline'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Airline', ylabel='Price'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.boxplot(y='Price',x='Airline',data=train_data.sort_values('Price',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conclusion--> From graph we can see that Jet Airways Business have the highest Price., Apart from the first Airline almost all are having similar median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total_Stops', ylabel='Price'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.boxplot(y='Price',x='Total_Stops',data=train_data.sort_values('Price',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(categorical['Airline'].unique())"
   ]
  },
  {
   "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>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Trujet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Air India  GoAir  IndiGo  Jet Airways  Jet Airways Business  \\\n",
       "0          0      0       1            0                     0   \n",
       "1          1      0       0            0                     0   \n",
       "2          0      0       0            1                     0   \n",
       "3          0      0       1            0                     0   \n",
       "4          0      0       1            0                     0   \n",
       "\n",
       "   Multiple carriers  Multiple carriers Premium economy  SpiceJet  Trujet  \\\n",
       "0                  0                                  0         0       0   \n",
       "1                  0                                  0         0       0   \n",
       "2                  0                                  0         0       0   \n",
       "3                  0                                  0         0       0   \n",
       "4                  0                                  0         0       0   \n",
       "\n",
       "   Vistara  Vistara Premium economy  \n",
       "0        0                        0  \n",
       "1        0                        0  \n",
       "2        0                        0  \n",
       "3        0                        0  \n",
       "4        0                        0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "Airline=pd.get_dummies(categorical['Airline'], drop_first=True)\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4536\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x217368b96d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source vs Price\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.catplot(y='Price',x='Source',data=train_data.sort_values('Price',ascending=False),kind='boxen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Chennai  Delhi  Kolkata  Mumbai\n",
       "0        0      0        0       0\n",
       "1        0      0        1       0\n",
       "2        0      1        0       0\n",
       "3        0      0        1       0\n",
       "4        0      0        0       0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "\n",
    "Source=pd.get_dummies(categorical['Source'], drop_first=True)\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cochin  Delhi  Hyderabad  Kolkata  New Delhi\n",
       "0       0      0          0        0          1\n",
       "1       0      0          0        0          0\n",
       "2       1      0          0        0          0\n",
       "3       0      0          0        0          0\n",
       "4       0      0          0        0          1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination=pd.get_dummies(categorical['Destination'], drop_first=True)\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    BLR → DEL\n",
       "1        CCU → IXR → BBI → BLR\n",
       "2        DEL → LKO → BOM → COK\n",
       "3              CCU → NAG → BLR\n",
       "4              BLR → NAG → DEL\n",
       "                 ...          \n",
       "10678                CCU → BLR\n",
       "10679                CCU → BLR\n",
       "10680                BLR → DEL\n",
       "10681                BLR → DEL\n",
       "10682    DEL → GOI → BOM → COK\n",
       "Name: Route, Length: 10682, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Route']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-51-103bd018a128>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_1']=categorical['Route'].str.split('→').str[0]\n",
      "<ipython-input-51-103bd018a128>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_2']=categorical['Route'].str.split('→').str[1]\n",
      "<ipython-input-51-103bd018a128>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_3']=categorical['Route'].str.split('→').str[2]\n",
      "<ipython-input-51-103bd018a128>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_4']=categorical['Route'].str.split('→').str[3]\n",
      "<ipython-input-51-103bd018a128>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_5']=categorical['Route'].str.split('→').str[4]\n"
     ]
    }
   ],
   "source": [
    "categorical['Route_1']=categorical['Route'].str.split('→').str[0]\n",
    "categorical['Route_2']=categorical['Route'].str.split('→').str[1]\n",
    "categorical['Route_3']=categorical['Route'].str.split('→').str[2]\n",
    "categorical['Route_4']=categorical['Route'].str.split('→').str[3]\n",
    "categorical['Route_5']=categorical['Route'].str.split('→').str[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>BLR</td>\n",
       "      <td>DEL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>CCU</td>\n",
       "      <td>IXR</td>\n",
       "      <td>BBI</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>DEL</td>\n",
       "      <td>LKO</td>\n",
       "      <td>BOM</td>\n",
       "      <td>COK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>CCU</td>\n",
       "      <td>NAG</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NAG</td>\n",
       "      <td>DEL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info Route_1 Route_2 Route_3 Route_4 Route_5  \n",
       "0         No info    BLR      DEL     NaN     NaN     NaN  \n",
       "1         No info    CCU     IXR     BBI      BLR     NaN  \n",
       "2         No info    DEL     LKO     BOM      COK     NaN  \n",
       "3         No info    CCU     NAG      BLR     NaN     NaN  \n",
       "4         No info    BLR     NAG      DEL     NaN     NaN  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical['Route_1'].fillna('None',inplace=True)\n",
    "categorical['Route_2'].fillna('None',inplace=True)\n",
    "categorical['Route_3'].fillna('None',inplace=True)\n",
    "categorical['Route_4'].fillna('None',inplace=True)\n",
    "categorical['Route_5'].fillna('None',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>BLR</td>\n",
       "      <td>DEL</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>CCU</td>\n",
       "      <td>IXR</td>\n",
       "      <td>BBI</td>\n",
       "      <td>BLR</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>DEL</td>\n",
       "      <td>LKO</td>\n",
       "      <td>BOM</td>\n",
       "      <td>COK</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>CCU</td>\n",
       "      <td>NAG</td>\n",
       "      <td>BLR</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NAG</td>\n",
       "      <td>DEL</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info Route_1 Route_2 Route_3 Route_4 Route_5  \n",
       "0         No info    BLR      DEL    None    None    None  \n",
       "1         No info    CCU     IXR     BBI      BLR    None  \n",
       "2         No info    DEL     LKO     BOM      COK    None  \n",
       "3         No info    CCU     NAG      BLR    None    None  \n",
       "4         No info    BLR     NAG      DEL    None    None  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_column(categorical,'Route')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Source             0\n",
       "Destination        0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Route_1            0\n",
       "Route_2            0\n",
       "Route_3            0\n",
       "Route_4            0\n",
       "Route_5            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airline has total 12 categories \n",
      "\n",
      "Source has total 5 categories \n",
      "\n",
      "Destination has total 6 categories \n",
      "\n",
      "Total_Stops has total 5 categories \n",
      "\n",
      "Additional_Info has total 10 categories \n",
      "\n",
      "Route_1 has total 5 categories \n",
      "\n",
      "Route_2 has total 45 categories \n",
      "\n",
      "Route_3 has total 30 categories \n",
      "\n",
      "Route_4 has total 14 categories \n",
      "\n",
      "Route_5 has total 6 categories \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Now we need to print how many categories in each column\n",
    "for feature in categorical.columns:\n",
    "    print('{} has total {} categories \\n' .format(feature,len(categorical[feature].value_counts())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Source', 'Destination', 'Total_Stops', 'Additional_Info',\n",
       "       'Route_1', 'Route_2', 'Route_3', 'Route_4', 'Route_5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['Route_1', 'Route_2', 'Route_3', 'Route_4','Route_5']:\n",
    "    categorical[i]=encoder.fit_transform(categorical[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Total_Stops Additional_Info  Route_1  \\\n",
       "0       IndiGo  Banglore   New Delhi    non-stop         No info        0   \n",
       "1    Air India   Kolkata    Banglore     2 stops         No info        2   \n",
       "2  Jet Airways     Delhi      Cochin     2 stops         No info        3   \n",
       "3       IndiGo   Kolkata    Banglore      1 stop         No info        2   \n",
       "4       IndiGo  Banglore   New Delhi      1 stop         No info        0   \n",
       "\n",
       "   Route_2  Route_3  Route_4  Route_5  \n",
       "0       13       29       13        5  \n",
       "1       25        1        3        5  \n",
       "2       32        4        5        5  \n",
       "3       34        3       13        5  \n",
       "4       34        8       13        5  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info,so we can drop this column\n",
    "drop_column(categorical,'Additional_Info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Total_Stops  Route_1  Route_2  Route_3  \\\n",
       "0       IndiGo  Banglore   New Delhi    non-stop        0       13       29   \n",
       "1    Air India   Kolkata    Banglore     2 stops        2       25        1   \n",
       "2  Jet Airways     Delhi      Cochin     2 stops        3       32        4   \n",
       "3       IndiGo   Kolkata    Banglore      1 stop        2       34        3   \n",
       "4       IndiGo  Banglore   New Delhi      1 stop        0       34        8   \n",
       "\n",
       "   Route_4  Route_5  \n",
       "0       13        5  \n",
       "1        3        5  \n",
       "2        5        5  \n",
       "3       13        5  \n",
       "4       13        5  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      5625\n",
       "non-stop    3491\n",
       "2 stops     1520\n",
       "3 stops       45\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Total_Stops'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "# Here Values are assigned with corresponding key\n",
    "\n",
    "dict={'non-stop':0, '2 stops':2, '1 stop':1, '3 stops':3, '4 stops':4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical['Total_Stops']=categorical['Total_Stops'].map(dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Route_1  Route_2  Route_3  \\\n",
       "0       IndiGo  Banglore   New Delhi            0        0       13       29   \n",
       "1    Air India   Kolkata    Banglore            2        2       25        1   \n",
       "2  Jet Airways     Delhi      Cochin            2        3       32        4   \n",
       "3       IndiGo   Kolkata    Banglore            1        2       34        3   \n",
       "4       IndiGo  Banglore   New Delhi            1        0       34        8   \n",
       "\n",
       "   Route_4  Route_5  \n",
       "0       13        5  \n",
       "1        3        5  \n",
       "2        5        5  \n",
       "3       13        5  \n",
       "4       13        5  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10678</th>\n",
       "      <td>4107</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10679</th>\n",
       "      <td>4145</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10680</th>\n",
       "      <td>7229</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10681</th>\n",
       "      <td>12648</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10682</th>\n",
       "      <td>11753</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10682 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price  Journey_day  Journey_month  Dep_Time_hour  Dep_Time_minute  \\\n",
       "0       3897           24              3             22               20   \n",
       "1       7662            5              1              5               50   \n",
       "2      13882            6              9              9               25   \n",
       "3       6218            5             12             18                5   \n",
       "4      13302            3              1             16               50   \n",
       "...      ...          ...            ...            ...              ...   \n",
       "10678   4107            4              9             19               55   \n",
       "10679   4145           27              4             20               45   \n",
       "10680   7229           27              4              8               20   \n",
       "10681  12648            3              1             11               30   \n",
       "10682  11753            5              9             10               55   \n",
       "\n",
       "       Arrival_Time_hour  Arrival_Time_minute  Duration_hours  \\\n",
       "0                      1                   10               2   \n",
       "1                     13                   15               7   \n",
       "2                      4                   25              19   \n",
       "3                     23                   30               5   \n",
       "4                     21                   35               4   \n",
       "...                  ...                  ...             ...   \n",
       "10678                 22                   25               2   \n",
       "10679                 23                   20               2   \n",
       "10680                 11                   20               3   \n",
       "10681                 14                   10               2   \n",
       "10682                 19                   15               8   \n",
       "\n",
       "       Duration_minutes  \n",
       "0                    50  \n",
       "1                    25  \n",
       "2                     0  \n",
       "3                    25  \n",
       "4                    45  \n",
       "...                 ...  \n",
       "10678                30  \n",
       "10679                35  \n",
       "10680                 0  \n",
       "10681                40  \n",
       "10682                20  \n",
       "\n",
       "[10682 rows x 9 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[cont_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Route_1  Route_2  Route_3  \\\n",
       "0       IndiGo  Banglore   New Delhi            0        0       13       29   \n",
       "1    Air India   Kolkata    Banglore            2        2       25        1   \n",
       "2  Jet Airways     Delhi      Cochin            2        3       32        4   \n",
       "3       IndiGo   Kolkata    Banglore            1        2       34        3   \n",
       "4       IndiGo  Banglore   New Delhi            1        0       34        8   \n",
       "\n",
       "   Route_4  Route_5  Air India  ...  New Delhi  Price  Journey_day  \\\n",
       "0       13        5          0  ...          1   3897           24   \n",
       "1        3        5          1  ...          0   7662            5   \n",
       "2        5        5          0  ...          0  13882            6   \n",
       "3       13        5          0  ...          0   6218            5   \n",
       "4       13        5          0  ...          1  13302            3   \n",
       "\n",
       "   Journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0              3             22               20                  1   \n",
       "1              1              5               50                 13   \n",
       "2              9              9               25                  4   \n",
       "3             12             18                5                 23   \n",
       "4              1             16               50                 21   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_minutes  \n",
       "0                   10               2                50  \n",
       "1                   15               7                25  \n",
       "2                   25              19                 0  \n",
       "3                   30               5                25  \n",
       "4                   35               4                45  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate dataframe --> categorical + Airline + Source + Destination\n",
    "\n",
    "data_train=pd.concat([categorical,Airline,Source,Destination,train_data[cont_col]],axis=1)\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_column(data_train,'Airline')\n",
    "drop_column(data_train,'Source')\n",
    "drop_column(data_train,'Destination')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Total_Stops</th>\n",
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       "      <td>7662</td>\n",
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       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Route_1  Route_2  Route_3  Route_4  Route_5  Air India  GoAir  \\\n",
       "0            0        0       13       29       13        5          0      0   \n",
       "1            2        2       25        1        3        5          1      0   \n",
       "2            2        3       32        4        5        5          0      0   \n",
       "3            1        2       34        3       13        5          0      0   \n",
       "4            1        0       34        8       13        5          0      0   \n",
       "\n",
       "   IndiGo  Jet Airways  ...  New Delhi  Price  Journey_day  Journey_month  \\\n",
       "0       1            0  ...          1   3897           24              3   \n",
       "1       0            0  ...          0   7662            5              1   \n",
       "2       0            1  ...          0  13882            6              9   \n",
       "3       1            0  ...          0   6218            5             12   \n",
       "4       1            0  ...          1  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \\\n",
       "0             22               20                  1                   10   \n",
       "1              5               50                 13                   15   \n",
       "2              9               25                  4                   25   \n",
       "3             18                5                 23                   30   \n",
       "4             16               50                 21                   35   \n",
       "\n",
       "   Duration_hours  Duration_minutes  \n",
       "0               2                50  \n",
       "1               7                25  \n",
       "2              19                 0  \n",
       "3               5                25  \n",
       "4               4                45  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Total_Stops  Route_1  Route_2  Route_3  Route_4  Route_5  Air India  GoAir  \\\n",
       "0            0        0       13       29       13        5          0      0   \n",
       "1            2        2       25        1        3        5          1      0   \n",
       "2            2        3       32        4        5        5          0      0   \n",
       "3            1        2       34        3       13        5          0      0   \n",
       "4            1        0       34        8       13        5          0      0   \n",
       "\n",
       "   IndiGo  Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0       1            0                     0                  0   \n",
       "1       0            0                     0                  0   \n",
       "2       0            1                     0                  0   \n",
       "3       1            0                     0                  0   \n",
       "4       1            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Trujet  Vistara  \\\n",
       "0                                  0         0       0        0   \n",
       "1                                  0         0       0        0   \n",
       "2                                  0         0       0        0   \n",
       "3                                  0         0       0        0   \n",
       "4                                  0         0       0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      0        0       0       0      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      0        1       0       0      0   \n",
       "4                        0        0      0        0       0       0      0   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  Price  Journey_day  Journey_month  \\\n",
       "0          0        0          1   3897           24              3   \n",
       "1          0        0          0   7662            5              1   \n",
       "2          0        0          0  13882            6              9   \n",
       "3          0        0          0   6218            5             12   \n",
       "4          0        0          1  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \\\n",
       "0             22               20                  1                   10   \n",
       "1              5               50                 13                   15   \n",
       "2              9               25                  4                   25   \n",
       "3             18                5                 23                   30   \n",
       "4             16               50                 21                   35   \n",
       "\n",
       "   Duration_hours  Duration_minutes  \n",
       "0               2                50  \n",
       "1               7                25  \n",
       "2              19                 0  \n",
       "3               5                25  \n",
       "4               4                45  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Route_1', 'Route_2', 'Route_3', 'Route_4', 'Route_5',\n",
       "       'Air India', 'GoAir', 'IndiGo', 'Jet Airways', 'Jet Airways Business',\n",
       "       'Multiple carriers', 'Multiple carriers Premium economy', 'SpiceJet',\n",
       "       'Trujet', 'Vistara', 'Vistara Premium economy', 'Chennai', 'Delhi',\n",
       "       'Kolkata', 'Mumbai', 'Cochin', 'Delhi', 'Hyderabad', 'Kolkata',\n",
       "       'New Delhi', 'Price', 'Journey_day', 'Journey_month', 'Dep_Time_hour',\n",
       "       'Dep_Time_minute', 'Arrival_Time_hour', 'Arrival_Time_minute',\n",
       "       'Duration_hours', 'Duration_minutes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#outlier detection\n",
    "def plot(df,col):\n",
    "    fig,(ax1,ax2)=plt.subplots(2,1)\n",
    "    sns.distplot(df[col],ax=ax1)\n",
    "    sns.boxplot(df[col],ax=ax2)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2160x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SxlnTgo5GRDJNQr/tnHM9ZvYb4DfOOX+me50g9h9tC3y5nKHKzspiclEuuw/Hvt2CiMhgBj3H4517ucPMGoCtwDYzO2Bm/+RPeKNf3eFjlE/IvAU3TyrKY+9R3RlDRIYu3uSCbwIfA851zpU45yYC5wMfM7NvpTq4E0H9oWOUj8/AxFOcR1NbF63t/s/EE5HMFi/xXAN8zjnXe89jbzWBL3qfyQh0dfew92hbRh7xTPFujb23SUc9IjI08RLPGOdcQ/9C7zyP/3ctG2X2NbXT3eMoH18QdChDFkk8+44o8YjI0MRLPINdqKGLOEao/lD45HwmHvEU5WWTPybEvqPtQYciIhkm3qy2D5tZrEW5DMicC0/SVP3hVoCMPMdjZkzRBAMRGYZBE49zLjTY5zIyvUc8GZh4AKYU5fLGrsM45zJqOriIBCvRJXMkBeoPH6NkbA75OZmZ308qzqO9q4d6Xc8jIkOgxBOg+sOZOaMt4iRvgsHWPU0BRyIimUSJJ0D1h1qZWpy5iaesOB8D3qo7HHQoIpJBlHgC4pyjPkNXLYjIyc7ipOI8Xt91OOhQRCSDKPEE5GBLB22dPRk7sSBi+oQC3th1mJ6eeHfJEBEJU+IJSM3+ZgBmThobcCQjM31iPk1tXbzX0BJ0KCKSIZR4AlK9J3x51NyyooAjGZnpE8KrLry+81DAkYhIplDiCUj1nqNMHJvDpAy/kVrpuFzG5WXzhs7ziEiClHgCUr2niTPKxmX8hZdZZnxk+ng2vq8jHhFJTObc9nIU+fmf3qd6z1EumFnCI6/uDDqcEbvotEnc9dtqdjS0UFGa2eesRCT1dMQTgIbmdrp6HCcVj47l7pbMKwPgt5v2BByJiGQCJZ4ARBbWLBsliad8fD5nzxjPb99S4hGR+JR4ArD3SBshs4yfWBDtinllbNlzlO2aVi0icSjxBGDnwVamFOWSnTV6uv+Ks8oIZRkrXt4RdCgikuY0ucBnrR1d7DzYyoUzS4IOJWkiEyTOnj6en7/yPlOK8ijOH8Pnz58RcGQiko5Gz5/cGWLD9oN09zhOnVwYdChJd/GcyeBg/db9QYciImlMicdnf6xtIJRlVJSMvmnHEwpyOPeUCVS9f5DdukePiAxAicdnL9Y0cHJJATnZo7PrF51xEvk52fzX6/V0a+FQEYlhdP72S1MHmtrZureJWZNG3zBbRH5OiE/NK6P+8DEefPG9oMMRkTSkxOOjp9/cDcCcDF8YNJ6zphUzt6yIH/xuG5vqjgQdjoikGSUeH/1qYx1nTSvuvWX0aGVmfOacckoLc7nxkT+z90hb0CGJSBpR4vHJ5vojVO85ymfnTws6FF8U5GTzr184h4MtHfztA3/ShaUi0kuJxye/fG0XOaEs/vLDU4MOxTdnz5jAyq+ex8GWDi79f3/gzqff5t0DzUGHJSIB0wWkPqjZ18SjG3bymXPKGV+QE3Q4volcWHrjglms2bKPFS/v4OE/7mD+yRP47PxpXHFWGePyxgQcpYj4LaVHPGa22My2mVmtmS2P8bmZ2b3e52+Z2Tnx2prZRDNbY2Y13vOEqM9u8epvM7PLosrnm9km77N7zceb4DjnuPU3mxmbm813F8/xa7dppSh/DFfNn8Z3Fs/hliVzOHKsk+VPbuK8u9fx7cff4MWaAxxq6aC7x9HW2U1zexeHWjrY39RGS3sXzg1/WnZndw/7j7axv6mN9q7uJH4rERmulB3xmFkI+AmwCKgDXjOzVc65LVHVlgCzvcf5wP3A+XHaLgfWOefu8RLScuC7ZjYXuBr4EDAVWGtmpznnur3tXge8AqwGFgPPpuq7RzS1dfKdJ95iw/aD3POZeZQUjp5FQYejyDu6+fKFFew6dIyN7x/it2/t4ck/1w/ariAnxIyJBVSUjOXkkgJOLhlLRUkBk7317kJZRltnN3WHj1F/6BjvN7ZQu7+Zmv3N1B364EJWAyaOzWFKUR5TivK4an45c04aR0XJWLJDGnUW8Usqh9rOA2qdc+8BmNljwFIgOvEsBVa68J+0r5jZeDMrAyoGabsUWOC1XwH8HviuV/6Yc64d2G5mtcB5ZrYDKHLO/cnb1krg06Qo8fz4hRreb2xl79E2qnYcoqO7h1uWzOFvz52eit1lJDNjxsQCZkws4Ip5ZWxvaOFAUxvt3T2EzAhlhR9ZZnR09dDU1kljSwcbdx5iTfW+uBemZmeFV/6eNC6X06aMY1xeNs5Bc3sX+4+2se9oO9V7jrJ+W3hpn5xQFqdOLqR8fB652SFyvYt727t6aOvsprWjm6b2Tprauujs6iE/J8TY3Gzyx4QoyAmRnxMix0tcPQ4c4SNdB+CgxzmcA0f4GcJ3bjULP+M9G5Bl4f4JF5v3PlI/0ib6sw/qhnf3Qd/0P1CMPnJ0/erEate/TnRpbx0X/Um/z/rsu99+vH6K9E2P119ZZoQMsrKMkIX/DWRlhb9jPIkOYyS2rfiVkhtTcgZhkvXdbrl8DmNS+MdYKhNPObAr6n0d4aOaeHXK47Sd4pzbA+Cc22Nmk6O29UqMbXV6r/uXH8fMriN8ZATQbGbbBvpyCSgFGgCu/x5cP4INnQB6+ypZ3h1i/Zpk7jx1kt5Po5T6KTED9tPtI9vuyfEqpDLxxEqr/f9UHahOIm0T3V/C23LOPQA8EGc/CTGzKudcZTK2NdqprxKjfkqM+ikxQfZTKge264Do8aVpwO4E6wzWdp83HIf3HFkKebBtTYtRLiIiAUhl4nkNmG1mp5hZDuET/6v61VkFXOPNbrsAOOINow3WdhWwzHu9DHgqqvxqM8s1s1MIT1jY4G2vycwu8GazXRPVRkREfJayoTbnXJeZfR14HggBDznn3jaz673Pf0p4htnlQC3QCnx5sLbepu8BHjezrwI7gc96bd42s8cJT0DoAm70ZrQBfA34DyCf8KSClM9oI0lDdicI9VVi1E+JUT8lJrB+spFcIyEiIjJUunhBRER8pcQjIiK+UuJJgXhLBY02ZjbdzNabWbWZvW1mN3vlSVveyJs08kuv/FUzq/D9iyaJmYXM7HUze8Z7r36Kwbug/Akz2+r92/qo+up4ZvYt7//dZjN71Mzy0r6fnHN6JPFBeDLEu8BMIAd4E5gbdFwp/s5lwDne63HAO8Bc4PvAcq98OfDP3uu5Xr/kAqd4/RXyPtsAfJTw9VfPAku88huAn3qvrwZ+GfT3HkF/fRt4BHjGe69+it1PK4C/817nAOPVV8f1UTmwHcj33j8OfCnd+ynwjhttD+8H93zU+1uAW4KOy+c+eIrwOnvbgDKvrAzYFqtPCM9e/KhXZ2tU+eeAn0XX8V5nE77i2oL+rsPom2nAOuCSqMSjfjq+n4q8X6jWr1x91bc/Iqu8TPS+wzPApeneTxpqS76BlgE6IXiH4WcDr9JveSMgenmjgZZKGmh5o942zrku4AhQkpIvkVr/AnwH6IkqUz8dbyZwAHjYG5Z80MzGor7qwzlXD/yA8KUlewhfC/k70ryflHiSbzjL/YwKZlYI/Br4pnPu6GBVY5TFW94o4/vVzD4F7HfObUy0SYyyUd9PnmzgHOB+59zZQAvhIaOBnJB95Z27WUp42GwqMNbMvjhYkxhlvveTEk/yJbJU0KhjZmMIJ53/dM496RUnc3mj3jZmlg0UAweT/01S6mPAlRZeMf0x4BIz+wXqp1jqgDrn3Kve+ycIJyL1VV8Lge3OuQPOuU7gSeBC0ryflHiSL5GlgkYVb/bLvwPVzrkfRn2UzOWNorf118ALzht0zhTOuVucc9OccxWE/1284Jz7Iuqn4zjn9gK7zOx0r+iThFclUV/1tRO4wMwKvO/3SaCadO+noE+OjcYH4WWA3iE8Y+TWoOPx4ft+nPCh91vAG97jcsLjwOsI33VgHTAxqs2tXv9sw5s945VXApu9z37MB6tr5AG/Iry80gZgZtDfe4R9toAPJheon2L30UeAKu/f1W+ACeqrmP10J7DV+44/JzxjLa37SUvmiIiIrzTUJiIivlLiERERXynxiIiIr1J56+uMVlpa6ioqKoIOQ0Qko2zcuLHBOTdpsDpKPAOoqKigqqoq6DBERDKKmb0fr46G2kRExFdKPCIi4islHhER8ZUSj4iI+EqJR0REfKXEIyIivlLiERERX+k6njRx3333UVtbm3D9+vp6AMrLh3dz01mzZnHTTTcNq62IyEgo8aSJ2tpa3thcTXfBxITqh1qPALC3feg/wlBrpt3rSkRGEyWeNNJdMJFjcy5PqG7+1tUACdeP1VZEJAg6xyMiIr5S4hEREV8p8YiIiK+UeERExFdKPCIi4islHhER8ZUSj4iI+EqJR0REfKXEIyIivlLiERERXynxiIiIr5R4RETEV0o8IiLiKyUeERHxlRKPiIj4SolHRER8pcQjIiK+UuJJsvvuu4/77rsv6DBGBfWlyOikW18nWW1tbdAhjBrqS5HRSUc8IiLiKyUeERHxlRKPiIj4SolHRER8pcQjIiK+UuIRERFfKfGIiIivlHhERMRXSjwiIuIrJR4REfGVEo+IiPhKiUdERHylxCMiIr5S4hEREV8p8Uhaa21tZfHixSxYsIBLL72USy+9lMWLF3PttddSW1vLN77xDaqqqliwYAELFizgiiuuoLa2losvvri3zYIFC1i/fj133nknCxYsYOnSpTQ2Nvbuo7GxkW984xt9yqI1NjZy1VVXsWDBAh566KEBY21sbORrX/sa1157LTfccENvfJHt1tbW9sYXb59BSFVMkX654YYb4m67qqqKSy65hI0bN/oWZzr+LILiV18o8Uha27lzJ21tbQB0dHTQ0dFBW1sbNTU13HXXXWzatIk77rijt35LSwt33XUXzrneNgB3330369evB+DIkSOsXLmyt82KFSvYtGlTn7JoK1as6P2POFCdSL3q6mpqamrYsmVLb3yRNnfddVdvfPH2GYRUxRTply1btsTd9h133EFPTw+33367b3Gm488iKH71hRKPpK3W1lba29sH/HzHjh0452hubj6uvL+urq4+75966ikaGxtpbGzkueeewznHc889d9xfeo2NjaxatapPWayjnsbGRp599tmY8T333HNUVVX1xrVjxw5Wr1494D6DEK8fRrLd6H559tlnB9x2VVVV78+yubk55lFPsuNM1ffORH72he5AmmT19fUcO3aMm2++eUjtamtryepwKYqqr6y2o9TWNg05Rr+l+g6kK1euxDlHT08PAN3d3axcuZJvfetbvXVWrFgRs91XvvKVPmUrVqw4LrlFdHd39zkqgw8SYax9BmHFihWD9sNIthvdL52dnQNuu38f3X777TzzzDMpjTNV3zsT+dkXOuKJYmbXmVmVmVUdOHAg6HBOeJHhslRZs2YNa9eu7f3F2NXVxZo1a/rUWbt2bULbWrt27YDxdnV1HXdUFv1Z/30GIV4/jGS70f3inBtw2/37KFafJTvOVH3vTORnX+iIJ4pz7gHgAYDKysph/dYrLy8H4Ec/+tGQ2t18881sfG/fcHY5ZD15RcyaOWXIMfrtsssuG3SobaQWLVqEc47Vq1fT1dVFdnY2ixYt6lNn4cKFxw21xbJw4UKefvrpmMknOzubvLy8mL9IY+0zCAsXLhy0H0ay3eh+MbMBt11YWNinjwoLC1MeZ6q+dybysy90xCNpa8aMGSnd/jXXXMOyZcvIygr/NwiFQlxzzTV96ixbtixmu/6WLVtGdnbsv+NCodBxw0iRurH2GYR4/TCS7Ub3y5gxYwbcdv8+uvPOO1MeZ6q+dybysy+UeCRtFRQUkJubO+DnFRUVmNlxfxlXVFQcV7d/Uli6dCklJSWUlJSwePFizIzFixdTUlLSp15JSQlXXnlln7L+53ci9ZYsWRIzvsWLF1NZWdkbV0VFBZdffvmA+wxCvH4YyXaj+2XJkiUDbruysrL3Z1lYWMj8+fNTHmeqvncm8rMvlHgkrc2YMYO8vDwAcnJyyMnJIS8vj9mzZ3Pbbbcxb968Pn8pjx07lttuuw0z620DcOutt3LxxRcDUFxc3OevuWXLljFv3rwB/8JbtmxZ73/Cwf4KXLZsGWeccQazZ89m7ty5vfFF2tx222298cXbZxBSFVOkX+bOnRt323fccQdZWVkxj3ZSFWc6/iyC4ldfWKpP4GaqyspKV1VVNeR2kZliwz3Hc2zO5QnVz9+6GiDh+v3bzs+AczzD7UsRCY6ZbXTOVQ5WR0c8IiLiKyUeERHxlRKPiIj4SolHRER8pcQjIiK+UuIRERFfKfGIiIivlHhERMRXSjwiIuIrJR4REfGVEo+IiPhKiUdERHylxCMiIr5S4hEREV/p1tdJNmvWrKBDGDXUlyKjkxJPkt10001BhzBqqC9FRicNtYmIiK+UeERExFdKPCIi4islHhER8ZUSj4iI+EqJR0REfKXEIyIivlLiERERXynxiIiIr5R4RETEV0o8IiLiKyUeERHxlRKPiIj4SolHRER8pcQjIiK+UuIRERFfKfGIiIivdAfSNBJqPUj+1tUJ1m0ESLh+//3AlCG3ExFJBiWeNDFr1qwh1a+v7wKgvHw4CWTKkPcnIpIsSjxp4qabbgo6BBERX+gcj4iI+EqJR0REfKXEIyIivlLiERERXynxiIiIr5R4RETEV0o8IiLiKyUeERHxlTnngo4hLZnZAeD9QaqUAg0+hTNUim140jk2SO/4FNvwjMbYTnbOTRqsghLPMJlZlXOuMug4YlFsw5POsUF6x6fYhudEjU1DbSIi4islHhER8ZUSz/A9EHQAg1Bsw5POsUF6x6fYhueEjE3neERExFc64hEREV8p8YiIiK+UeIbIzBab2TYzqzWz5Sncz0Nmtt/MNkeVTTSzNWZW4z1PiPrsFi+mbWZ2WVT5fDPb5H12r5mZV55rZr/0yl81s4ohxDbdzNabWbWZvW1mN6dLfGaWZ2YbzOxNL7Y70yW2qO2GzOx1M3smDWPb4W33DTOrSqf4zGy8mT1hZlu9f3sfTYfYzOx0r78ij6Nm9s10iM1r+y3v/8JmM3vUwv9Hgo3NOadHgg8gBLwLzARygDeBuSna118A5wCbo8q+Dyz3Xi8H/tl7PdeLJRc4xYsx5H22AfgoYMCzwBKv/Abgp97rq4FfDiG2MuAc7/U44B0vhsDj87ZT6L0eA7wKXJAOsUXF+G3gEeCZdPq5em12AKX9ytIiPmAF8Hfe6xxgfLrE1u93xF7g5HSIDSgHtgP53vvHgS8FHVvgv8wz6eF1+vNR728Bbknh/irom3i2AWXe6zJgW6w4gOe9WMuArVHlnwN+Fl3He51N+AplG2acTwGL0i0+oAD4M3B+usQGTAPWAZfwQeJJi9i8Njs4PvEEHh9QRPgXqKVbbP3iuRT4Y7rERjjx7AImeu2e8WIMNDYNtQ1N5IcYUeeV+WWKc24PgPc8OU5c5d7r/uV92jjnuoAjQMlQA/IOq88mfGSRFvF5Q1lvAPuBNc65tIkN+BfgO0BPVFm6xAbggN+Z2UYzuy6N4psJHAAe9oYpHzSzsWkSW7SrgUe914HH5pyrB34A7AT2AEecc78LOjYlnqGxGGXpMB99oLgGi3fE38XMCoFfA990zh1Nl/icc93OuY8QPro4z8zOTIfYzOxTwH7n3MZ4df2OLcrHnHPnAEuAG83sL9IkvmzCQ8/3O+fOBloIDxGlQ2zhxmY5wJXAr+JV9Ss279zNUsLDZlOBsWb2xaBjU+IZmjpgetT7acBuH/e/z8zKALzn/XHiqvNe9y/v08bMsoFi4GCigZjZGMJJ5z+dc0+mW3wAzrnDwO+BxWkS28eAK81sB/AYcImZ/SJNYgPAObfbe94P/BdwXprEVwfUeUevAE8QTkTpEFvEEuDPzrl93vt0iG0hsN05d8A51wk8CVwYdGxKPEPzGjDbzE7x/rq5Gljl4/5XAcu818sIn1uJlF/tzS45BZgNbPAOoZvM7AJvBso1/dpEtvXXwAvOG6SNx9vWvwPVzrkfplN8ZjbJzMZ7r/MJ/8fbmg6xOeducc5Nc85VEP6384Jz7ovpEBuAmY01s3GR14TPBWxOh/icc3uBXWZ2ulf0SWBLOsQW5XN8MMzWf3tBxbYTuMDMCrxtfhKoDjy2oZw408MBXE54Fte7wK0p3M+jhMdkOwn/RfFVwuOm64Aa73liVP1bvZi24c028corCf/yeBf4MR+sVpFHeEiglvBslZlDiO3jhA+l3wLe8B6Xp0N8wFnA615sm4F/8soDj61fnAv4YHJBWsRG+DzKm97j7ci/7zSK7yNAlfez/Q0wIY1iKwAageKosnSJ7U7Cf3xtBn5OeMZaoLFpyRwREfGVhtpERMRXSjwiIuIrJR4REfGVEo+IiPhKiUdERHylxCOSBsys28IrG282s1+ZWcEA9V72OzaRZFPiEUkPx5xzH3HOnQl0ANdHf2hmIQDn3IVBBCeSTEo8IunnRWCWmS2w8H2PHgE2AZhZc6SSmX3Huz/Km2Z2j1d2qpk95y3y+aKZzQnmK4gMLDvoAETkA95aV0uA57yi84AznXPb+9VbAnwaON8512pmE72PHgCud87VmNn5wL8SvgWDSNpQ4hFJD/nerRwgfMTz74QXc9zQP+l4FgIPO+daAZxzB73Vwi8EfhVeTgsIL48iklaUeETSwzEXvpVDLy95tAxQ3zh+6fks4HD/7YikG53jEclMvwO+Epn9ZmYTXfieSNvN7LNemZnZh4MMUiQWJR6RDOSce47wcvRV3hDd33sffQH4qplFVpheGkyEIgPT6tQiIuIrHfGIiIivlHhERMRXSjwiIuIrJR4REfGVEo+IiPhKiUdERHylxCMiIr76/7+eYh2bYOOlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,20))\n",
    "plot(data_train,'Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['Price']=np.where(data_train['Price']>=40000,data_train['Price'].median(),data_train['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2160x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure(figsize=(30,20))\n",
    "plot(data_train,'Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "### separate your independent & dependent data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Trujet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</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",
       "      <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>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>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</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",
       "      <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>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>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Route_1  Route_2  Route_3  Route_4  Route_5  Air India  GoAir  \\\n",
       "0            0        0       13       29       13        5          0      0   \n",
       "1            2        2       25        1        3        5          1      0   \n",
       "2            2        3       32        4        5        5          0      0   \n",
       "3            1        2       34        3       13        5          0      0   \n",
       "4            1        0       34        8       13        5          0      0   \n",
       "\n",
       "   IndiGo  Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0       1            0                     0                  0   \n",
       "1       0            0                     0                  0   \n",
       "2       0            1                     0                  0   \n",
       "3       1            0                     0                  0   \n",
       "4       1            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Trujet  Vistara  \\\n",
       "0                                  0         0       0        0   \n",
       "1                                  0         0       0        0   \n",
       "2                                  0         0       0        0   \n",
       "3                                  0         0       0        0   \n",
       "4                                  0         0       0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      0        0       0       0      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      0        1       0       0      0   \n",
       "4                        0        0      0        0       0       0      0   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  Journey_day  Journey_month  Dep_Time_hour  \\\n",
       "0          0        0          1           24              3             22   \n",
       "1          0        0          0            5              1              5   \n",
       "2          0        0          0            6              9              9   \n",
       "3          0        0          0            5             12             18   \n",
       "4          0        0          1            3              1             16   \n",
       "\n",
       "   Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  Duration_hours  \\\n",
       "0               20                  1                   10               2   \n",
       "1               50                 13                   15               7   \n",
       "2               25                  4                   25              19   \n",
       "3                5                 23                   30               5   \n",
       "4               50                 21                   35               4   \n",
       "\n",
       "   Duration_minutes  \n",
       "0                50  \n",
       "1                25  \n",
       "2                 0  \n",
       "3                25  \n",
       "4                45  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=data_train.drop('Price',axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3897.0\n",
       "1         7662.0\n",
       "2        13882.0\n",
       "3         6218.0\n",
       "4        13302.0\n",
       "          ...   \n",
       "10678     4107.0\n",
       "10679     4145.0\n",
       "10680     7229.0\n",
       "10681    12648.0\n",
       "10682    11753.0\n",
       "Name: Price, Length: 10682, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data_train['Price']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Selection\n",
    "#Finding out the best feature which will contribute and have good relation with target variable. \n",
    "#Why to apply Feature Selection?\n",
    "#To select important features to get rid of curse of dimensionality ie..to get rid of duplicate features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.15196723e+00, 1.99648921e+00, 2.76311324e+00, 2.24918759e+00,\n",
       "       1.51873818e+00, 6.93544472e-01, 7.55300600e-01, 1.09751656e-01,\n",
       "       6.70043337e-01, 9.21387599e-01, 0.00000000e+00, 5.71676618e-01,\n",
       "       1.09690488e-02, 3.07541858e-01, 0.00000000e+00, 2.26983974e-01,\n",
       "       1.31471344e-03, 1.65446061e-01, 1.53139878e+00, 8.71314943e-01,\n",
       "       2.83473994e-01, 1.53373559e+00, 3.98826786e-01, 2.98562908e-01,\n",
       "       1.59689958e-01, 3.57246433e-01, 1.10540805e+00, 8.82494846e-01,\n",
       "       1.43506804e+00, 1.20028384e+00, 1.84763539e+00, 1.54528839e+00,\n",
       "       1.80384688e+00, 1.05291180e+00])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info_classif(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>2.147181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_1</th>\n",
       "      <td>2.042995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_2</th>\n",
       "      <td>2.780275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_3</th>\n",
       "      <td>2.286378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_4</th>\n",
       "      <td>1.523966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_5</th>\n",
       "      <td>0.806043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air India</th>\n",
       "      <td>0.779176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoAir</th>\n",
       "      <td>0.109269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndiGo</th>\n",
       "      <td>0.677722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways</th>\n",
       "      <td>0.934453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers</th>\n",
       "      <td>0.573608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpiceJet</th>\n",
       "      <td>0.328867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trujet</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara</th>\n",
       "      <td>0.212691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>0.174323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1.533073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.874347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.288992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cochin</th>\n",
       "      <td>1.532932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.394280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>0.304092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.172615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi</th>\n",
       "      <td>0.370464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey_day</th>\n",
       "      <td>1.076528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey_month</th>\n",
       "      <td>0.870901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>1.408081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <td>1.203586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>1.848298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <td>1.538661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_hours</th>\n",
       "      <td>1.779438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_minutes</th>\n",
       "      <td>1.082460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          0\n",
       "Total_Stops                        2.147181\n",
       "Route_1                            2.042995\n",
       "Route_2                            2.780275\n",
       "Route_3                            2.286378\n",
       "Route_4                            1.523966\n",
       "Route_5                            0.806043\n",
       "Air India                          0.779176\n",
       "GoAir                              0.109269\n",
       "IndiGo                             0.677722\n",
       "Jet Airways                        0.934453\n",
       "Jet Airways Business               0.000000\n",
       "Multiple carriers                  0.573608\n",
       "Multiple carriers Premium economy  0.000000\n",
       "SpiceJet                           0.328867\n",
       "Trujet                             0.000000\n",
       "Vistara                            0.212691\n",
       "Vistara Premium economy            0.000000\n",
       "Chennai                            0.174323\n",
       "Delhi                              1.533073\n",
       "Kolkata                            0.874347\n",
       "Mumbai                             0.288992\n",
       "Cochin                             1.532932\n",
       "Delhi                              0.394280\n",
       "Hyderabad                          0.304092\n",
       "Kolkata                            0.172615\n",
       "New Delhi                          0.370464\n",
       "Journey_day                        1.076528\n",
       "Journey_month                      0.870901\n",
       "Dep_Time_hour                      1.408081\n",
       "Dep_Time_minute                    1.203586\n",
       "Arrival_Time_hour                  1.848298\n",
       "Arrival_Time_minute                1.538661\n",
       "Duration_hours                     1.779438\n",
       "Duration_minutes                   1.082460"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp=pd.DataFrame(mutual_info_classif(X,y),index=X.columns)\n",
    "imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Route_2</th>\n",
       "      <td>2.780275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_3</th>\n",
       "      <td>2.286378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>2.147181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_1</th>\n",
       "      <td>2.042995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>1.848298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_hours</th>\n",
       "      <td>1.779438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <td>1.538661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1.533073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cochin</th>\n",
       "      <td>1.532932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_4</th>\n",
       "      <td>1.523966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>1.408081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <td>1.203586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_minutes</th>\n",
       "      <td>1.082460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey_day</th>\n",
       "      <td>1.076528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways</th>\n",
       "      <td>0.934453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.874347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey_month</th>\n",
       "      <td>0.870901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_5</th>\n",
       "      <td>0.806043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air India</th>\n",
       "      <td>0.779176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndiGo</th>\n",
       "      <td>0.677722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers</th>\n",
       "      <td>0.573608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.394280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi</th>\n",
       "      <td>0.370464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpiceJet</th>\n",
       "      <td>0.328867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>0.304092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.288992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara</th>\n",
       "      <td>0.212691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>0.174323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.172615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoAir</th>\n",
       "      <td>0.109269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trujet</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   importance\n",
       "Route_2                              2.780275\n",
       "Route_3                              2.286378\n",
       "Total_Stops                          2.147181\n",
       "Route_1                              2.042995\n",
       "Arrival_Time_hour                    1.848298\n",
       "Duration_hours                       1.779438\n",
       "Arrival_Time_minute                  1.538661\n",
       "Delhi                                1.533073\n",
       "Cochin                               1.532932\n",
       "Route_4                              1.523966\n",
       "Dep_Time_hour                        1.408081\n",
       "Dep_Time_minute                      1.203586\n",
       "Duration_minutes                     1.082460\n",
       "Journey_day                          1.076528\n",
       "Jet Airways                          0.934453\n",
       "Kolkata                              0.874347\n",
       "Journey_month                        0.870901\n",
       "Route_5                              0.806043\n",
       "Air India                            0.779176\n",
       "IndiGo                               0.677722\n",
       "Multiple carriers                    0.573608\n",
       "Delhi                                0.394280\n",
       "New Delhi                            0.370464\n",
       "SpiceJet                             0.328867\n",
       "Hyderabad                            0.304092\n",
       "Mumbai                               0.288992\n",
       "Vistara                              0.212691\n",
       "Chennai                              0.174323\n",
       "Kolkata                              0.172615\n",
       "GoAir                                0.109269\n",
       "Trujet                               0.000000\n",
       "Jet Airways Business                 0.000000\n",
       "Multiple carriers Premium economy    0.000000\n",
       "Vistara Premium economy              0.000000"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.columns=['importance']\n",
    "imp.sort_values(by='importance',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "##dump your model using pickle so that we will re-use\n",
    "import pickle\n",
    "def predict(ml_model,dump):\n",
    "    model=ml_model.fit(X_train,y_train)\n",
    "    print('Training score : {}'.format(model.score(X_train,y_train)))\n",
    "    y_prediction=model.predict(X_test)\n",
    "    print('predictions are: \\n {}'.format(y_prediction))\n",
    "    print('\\n')\n",
    "    r2_score=metrics.r2_score(y_test,y_prediction)\n",
    "    print('r2 score: {}'.format(r2_score))\n",
    "    print('MAE:',metrics.mean_absolute_error(y_test,y_prediction))\n",
    "    print('MSE:',metrics.mean_squared_error(y_test,y_prediction))\n",
    "    print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test,y_prediction)))\n",
    "    sns.distplot(y_test-y_prediction)\n",
    "    \n",
    "    if dump==1:\n",
    "        ##dump your model using pickle so that we will re-use\n",
    "        file=open(r'C:\\Users\\gamal\\Downloads\\Real World Projects (ML)\\Airline Ticketmodel.pkl','wb')\n",
    "        pickle.dump(model,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score : 0.9557929755692159\n",
      "predictions are: \n",
      " [12051.7075      6091.48        3557.77233333 ... 10058.13\n",
      " 11568.265       3995.39247619]\n",
      "\n",
      "\n",
      "r2 score: 0.8045789001485932\n",
      "MAE: 1205.729909934967\n",
      "MSE: 3906967.6578015215\n",
      "RMSE: 1976.6050839258512\n"
     ]
    },
    {
     "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": [
    "predict(RandomForestRegressor(),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now let's play with more algorithms !\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score : 0.9688174373384579\n",
      "predictions are: \n",
      " [11263.5  5636.   3543.  ...  8446.  11841.   3841. ]\n",
      "\n",
      "\n",
      "r2 score: 0.689869455202506\n",
      "MAE: 1380.8132584620182\n",
      "MSE: 6200302.879993501\n",
      "RMSE: 2490.0407386212582\n"
     ]
    },
    {
     "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": [
    "predict(DecisionTreeRegressor(),0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score : 0.6173098560580006\n",
      "predictions are: \n",
      " [11629.56935967  6137.92079089  3037.53370422 ...  9889.63983861\n",
      " 11720.74845864  4436.46749021]\n",
      "\n",
      "\n",
      "r2 score: 0.6094230704195011\n",
      "MAE: 1981.8402896432387\n",
      "MSE: 7808631.887317909\n",
      "RMSE: 2794.392937172206\n"
     ]
    },
    {
     "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": [
    "predict(LinearRegression(),0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score : 0.7831270977234973\n",
      "predictions are: \n",
      " [12586.6  6164.8  6929.4 ... 12252.6 11789.2  3977.4]\n",
      "\n",
      "\n",
      "r2 score: 0.6534501464315339\n",
      "MAE: 1757.2182498830136\n",
      "MSE: 6928418.020046794\n",
      "RMSE: 2632.1888268220414\n"
     ]
    },
    {
     "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": [
    "predict(KNeighborsRegressor(),0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hyperparameter Tuning\n",
    "#1.Choose following method for hyperparameter tuning\n",
    "#    a.RandomizedSearchCV --> Fast way to Hypertune model\n",
    "#    b.GridSearchCV--> Slow way to hypertune my model\n",
    "\n",
    "#2.Assign hyperparameters in form of dictionary\n",
    "#3.Fit the model\n",
    "#4.Check best paramters and best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators=[int(x) for x in np.linspace(start=100,stop=1200,num=6)]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features=['auto','sqrt']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth=[int(x) for x in np.linspace(5,30,num=4)]\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split=[5,10,15,100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_grid={\n",
    "    'n_estimators':n_estimators,\n",
    "    'max_features':max_features,\n",
    "    'max_depth':max_depth,\n",
    "    'min_samples_split':min_samples_split\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': [100, 320, 540, 760, 980, 1200],\n",
       " 'max_features': ['auto', 'sqrt'],\n",
       " 'max_depth': [5, 13, 21, 30],\n",
       " 'min_samples_split': [5, 10, 15, 100]}"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation\n",
    "\n",
    "rf_random=RandomizedSearchCV(estimator=reg_rf,param_distributions=random_grid,cv=3,verbose=2,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:   19.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [5, 13, 21, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_split': [5, 10, 15, 100],\n",
       "                                        'n_estimators': [100, 320, 540, 760,\n",
       "                                                         980, 1200]},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 540,\n",
       " 'min_samples_split': 5,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 21}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 135,
     "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": [
    "sns.distplot(y_test-prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8211327842541764"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 1159.4442280162477\n",
      "MSE 3576013.171000018\n",
      "RMSE 1891.0349470594185\n"
     ]
    }
   ],
   "source": [
    "print('MAE',metrics.mean_absolute_error(y_test,prediction))\n",
    "print('MSE',metrics.mean_squared_error(y_test,prediction))\n",
    "print('RMSE',np.sqrt(metrics.mean_squared_error(y_test,prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open a file, where you want to store the data\n",
    "file=open('rf_random.pkl','wb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dump information to that file\n",
    "pickle.dump(rf_random,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=open('rf_random.pkl','rb')\n",
    "forest=pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction=forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12474.68620226,  6245.90242363,  3575.9767198 , ...,\n",
       "       10315.94137061, 11302.2835941 ,  3943.23256724])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8211327842541764"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test,y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}