predicting-flight-ticket-prices/predicting_flight_ticket_prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Flight Ticket Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing basic libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.set_printoptions(precision=4)\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('mode.chained_assignment', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.rc('figure', figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "from dateutil.parser import parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('data/Data_Train.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing and Modifying the training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      "Airline            10683 non-null object\n",
      "Date_of_Journey    10683 non-null object\n",
      "Source             10683 non-null object\n",
      "Destination        10683 non-null object\n",
      "Route              10682 non-null object\n",
      "Dep_Time           10683 non-null object\n",
      "Arrival_Time       10683 non-null object\n",
      "Duration           10683 non-null object\n",
      "Total_Stops        10682 non-null object\n",
      "Additional_Info    10683 non-null object\n",
      "Price              10683 non-null int64\n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 500.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9039</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>09:45</td>\n",
       "      <td>09:25 07 May</td>\n",
       "      <td>23h 40m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No info</td>\n",
       "      <td>7480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        Airline Date_of_Journey Source Destination Route Dep_Time  \\\n",
       "9039  Air India       6/05/2019  Delhi      Cochin   NaN    09:45   \n",
       "\n",
       "      Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "9039  09:25 07 May  23h 40m         NaN         No info   7480  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Total_Stops.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.Total_Stops.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Airline.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Airline.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Date_of_Journey.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Source.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Destination.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Destination.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Route.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Dep_Time.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1343"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Arrival_Time.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "368"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Duration.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Total_Stops.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Total_Stops.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "      <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>0</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</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</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</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</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            0         No info   3897  \n",
       "1    05:50         13:15   7h 25m            2         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h            2         No info  13882  \n",
       "3    18:05         23:30   5h 25m            1         No info   6218  \n",
       "4    16:50         21:35   4h 45m            1         No info  13302  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting to ordinal values \n",
    "stops = {'non-stop': 0, '1 stop': 1, '2 stops': 2, '3 stops': 3, '4 stops': 4}\n",
    "df.Total_Stops = df.Total_Stops.map(stops)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    5625\n",
       "0    3491\n",
       "2    1520\n",
       "3      45\n",
       "4       1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Total_Stops.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Additional_Info.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No info                         8344\n",
       "In-flight meal not included     1982\n",
       "No check-in baggage included     320\n",
       "1 Long layover                    19\n",
       "Change airports                    7\n",
       "Business class                     4\n",
       "No Info                            3\n",
       "1 Short layover                    1\n",
       "Red-eye flight                     1\n",
       "2 Long layover                     1\n",
       "Name: Additional_Info, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Additional_Info.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No info                         8347\n",
       "In-flight meal not included     1982\n",
       "No check-in baggage included     320\n",
       "1 Long layover                    19\n",
       "Change airports                    7\n",
       "Business class                     4\n",
       "1 Short layover                    1\n",
       "Red-eye flight                     1\n",
       "2 Long layover                     1\n",
       "Name: Additional_Info, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging two values which are different only in the case of the word 'Info' ie. 'info'/'Info'\n",
    "df.Additional_Info = df.Additional_Info.str.replace('Info', 'info')\n",
    "df.Additional_Info.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# A longer way to convert Duration into minutes\n",
    "'''\n",
    "df['Duration_In_Minutes'] = df['Duration'].str.replace('m', '')\n",
    "df['Duration_In_Minutes'] = df['Duration_In_Minutes'].str.replace('h ', ' ')\n",
    "df['Duration_In_Minutes'] = df['Duration_In_Minutes'].str.replace('h', ' 00')\n",
    "\n",
    "def duration_in_mins(text):\n",
    "    if ' ' in text:\n",
    "        t = [x.strip() for x in text.split(' ')]\n",
    "        duration = int(t[0]) * 60 + int(t[1])\n",
    "    else:\n",
    "        duration = int(text)\n",
    "    \n",
    "    return duration\n",
    "\n",
    "df['Duration_In_Minutes'] = df['Duration_In_Minutes'].apply(lambda x: duration_in_mins(x))\n",
    "df.head()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert Duration into minutes.\n",
    "def duration_in_mins(text):\n",
    "    td = pd.Timedelta(text)\n",
    "    duration = (td.days * 24 * 60 ) + (td.seconds / 60)\n",
    "    return duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>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>Duration_In_Minutes</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>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>170.0</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>445.0</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>1140.0</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>325.0</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>285.0</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            0         No info   3897   \n",
       "1    05:50         13:15   7h 25m            2         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h            2         No info  13882   \n",
       "3    18:05         23:30   5h 25m            1         No info   6218   \n",
       "4    16:50         21:35   4h 45m            1         No info  13302   \n",
       "\n",
       "   Duration_In_Minutes  \n",
       "0                170.0  \n",
       "1                445.0  \n",
       "2               1140.0  \n",
       "3                325.0  \n",
       "4                285.0  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting Duration into minutes.\n",
    "df['Duration_In_Minutes'] = df['Duration'].apply(lambda x: duration_in_mins(x))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.0, 75.0, 80.0, 85.0, 90.0, 95.0, 135.0, 140.0, 145.0, 150.0]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for anomalies in Duration_In_Minutes.\n",
    "sorted(df.Duration_In_Minutes.unique())[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>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>Duration_In_Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6474</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → GOI → PNQ → HYD</td>\n",
       "      <td>16:50</td>\n",
       "      <td>16:55</td>\n",
       "      <td>5m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>17327</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline Date_of_Journey  Source Destination                  Route  \\\n",
       "6474  Air India       6/03/2019  Mumbai   Hyderabad  BOM → GOI → PNQ → HYD   \n",
       "\n",
       "     Dep_Time Arrival_Time Duration  Total_Stops Additional_Info  Price  \\\n",
       "6474    16:50        16:55       5m            2         No info  17327   \n",
       "\n",
       "      Duration_In_Minutes  \n",
       "6474                  5.0  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Duration_In_Minutes == 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the anomalies in Duration_In_Minutes.\n",
    "# df = df.drop([6474], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>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>Duration_In_Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3821</th>\n",
       "      <td>Air India</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → GOI → PNQ → HYD</td>\n",
       "      <td>21:10</td>\n",
       "      <td>16:55</td>\n",
       "      <td>4h 15m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>16172</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5309</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → GOI → PNQ → HYD</td>\n",
       "      <td>21:10</td>\n",
       "      <td>16:55</td>\n",
       "      <td>4h 15m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>17327</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5942</th>\n",
       "      <td>Air India</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → GOI → PNQ → HYD</td>\n",
       "      <td>05:05</td>\n",
       "      <td>16:55</td>\n",
       "      <td>11h 50m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>16172</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6474</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → GOI → PNQ → HYD</td>\n",
       "      <td>16:50</td>\n",
       "      <td>16:55</td>\n",
       "      <td>5m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>17327</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7456</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → GOI → PNQ → HYD</td>\n",
       "      <td>05:05</td>\n",
       "      <td>16:55</td>\n",
       "      <td>11h 50m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>16697</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8478</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → GOI → PNQ → HYD</td>\n",
       "      <td>13:30</td>\n",
       "      <td>16:55</td>\n",
       "      <td>3h 25m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>18797</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline Date_of_Journey  Source Destination                  Route  \\\n",
       "3821  Air India      12/03/2019  Mumbai   Hyderabad  BOM → GOI → PNQ → HYD   \n",
       "5309  Air India       6/03/2019  Mumbai   Hyderabad  BOM → GOI → PNQ → HYD   \n",
       "5942  Air India      12/03/2019  Mumbai   Hyderabad  BOM → GOI → PNQ → HYD   \n",
       "6474  Air India       6/03/2019  Mumbai   Hyderabad  BOM → GOI → PNQ → HYD   \n",
       "7456  Air India       6/03/2019  Mumbai   Hyderabad  BOM → GOI → PNQ → HYD   \n",
       "8478  Air India       6/03/2019  Mumbai   Hyderabad  BOM → GOI → PNQ → HYD   \n",
       "\n",
       "     Dep_Time Arrival_Time Duration  Total_Stops Additional_Info  Price  \\\n",
       "3821    21:10        16:55   4h 15m            2         No info  16172   \n",
       "5309    21:10        16:55   4h 15m            2         No info  17327   \n",
       "5942    05:05        16:55  11h 50m            2         No info  16172   \n",
       "6474    16:50        16:55       5m            2         No info  17327   \n",
       "7456    05:05        16:55  11h 50m            2         No info  16697   \n",
       "8478    13:30        16:55   3h 25m            2         No info  18797   \n",
       "\n",
       "      Duration_In_Minutes  \n",
       "3821                255.0  \n",
       "5309                255.0  \n",
       "5942                710.0  \n",
       "6474                  5.0  \n",
       "7456                710.0  \n",
       "8478                205.0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instead of removing the row with anomaly, replacing its anomalies with a similar sample.\n",
    "df[(df.Source == 'Mumbai') & (df.Destination == 'Hyderabad') & (df.Route == 'BOM → GOI → PNQ → HYD')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[6474, 5] = '21:10'\n",
    "df.iloc[6474, 7] = '4h 15m'\n",
    "df.iloc[6474, 11] = 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>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>Duration_In_Minutes</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</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>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>170.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>6</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>445.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-09</td>\n",
       "      <td>6</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>325.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-12</td>\n",
       "      <td>6</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>285.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>4</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            0         No info   3897   \n",
       "1    05:50         13:15   7h 25m            2         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h            2         No info  13882   \n",
       "3    18:05         23:30   5h 25m            1         No info   6218   \n",
       "4    16:50         21:35   4h 45m            1         No info  13302   \n",
       "\n",
       "   Duration_In_Minutes  Day  Month  Year        Date  Weekday  \n",
       "0                170.0   24      3  2019  2019-03-24        6  \n",
       "1                445.0    1      5  2019  2019-05-01        2  \n",
       "2               1140.0    9      6  2019  2019-06-09        6  \n",
       "3                325.0   12      5  2019  2019-05-12        6  \n",
       "4                285.0    1      3  2019  2019-03-01        4  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a bunch of new features from the Date_Of_Journey column.\n",
    "df['Day'] = [datetime.strptime(x, '%d/%m/%Y').day for x in df.Date_of_Journey]\n",
    "df['Month'] = [datetime.strptime(x, '%d/%m/%Y').month for x in df.Date_of_Journey]\n",
    "df['Year'] = [datetime.strptime(x, '%d/%m/%Y').year for x in df.Date_of_Journey]\n",
    "df['Date'] = [datetime.strptime(x, '%d/%m/%Y').date() for x in df.Date_of_Journey]\n",
    "df['Weekday'] = [datetime.strptime(x, '%d/%m/%Y').weekday() for x in df.Date_of_Journey]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new feature for the weekday of the Date_Of_Journey.\n",
    "weekday = {0: 'Monday', 1: 'Tuesday', 2: 'Wednesday', 3: 'Thursday', 4: 'Friday', 5: 'Saturday', 6: 'Sunday'}\n",
    "df['Day_Of_Week'] = df['Weekday'].map(weekday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Duration_In_Minutes</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Day_Of_Week</th>\n",
       "      <th>Weekend_Or_Not</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>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>170.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>445.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-09</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>325.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-12</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>285.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0</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            0         No info   3897   \n",
       "1    05:50         13:15   7h 25m            2         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h            2         No info  13882   \n",
       "3    18:05         23:30   5h 25m            1         No info   6218   \n",
       "4    16:50         21:35   4h 45m            1         No info  13302   \n",
       "\n",
       "   Duration_In_Minutes  Day  Month  Year        Date  Weekday Day_Of_Week  \\\n",
       "0                170.0   24      3  2019  2019-03-24        6      Sunday   \n",
       "1                445.0    1      5  2019  2019-05-01        2   Wednesday   \n",
       "2               1140.0    9      6  2019  2019-06-09        6      Sunday   \n",
       "3                325.0   12      5  2019  2019-05-12        6      Sunday   \n",
       "4                285.0    1      3  2019  2019-03-01        4      Friday   \n",
       "\n",
       "   Weekend_Or_Not  \n",
       "0               1  \n",
       "1               0  \n",
       "2               1  \n",
       "3               1  \n",
       "4               0  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a feature assessing whether the weekday is weekend or not.\n",
    "weekend_or_not = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 1, 6: 1}\n",
    "df['Weekend_Or_Not'] = df['Weekday'].map(weekend_or_not)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Not gonna use these columns. Keeping them in comments for future reference.\n",
    "'''\n",
    "df['Time_Of_Departure'] = [parse(x).time() for x in df.Dep_Time]\n",
    "df['Time_Of_Arrival'] = [parse(x).time() for x in df.Arrival_Time]\n",
    "df.head()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Date_Of_Arrival from Arrival_Time.\n",
    "# Full of '' values as of now, will try to work with it later.\n",
    "df['Date_Of_Arrival'] = df.Arrival_Time.str.replace('[\\d]+:[\\d]+[ ]?', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replaced '' with 'NaN' for clarity. Still unusable.\n",
    "df['Date_Of_Arrival'] = df['Date_Of_Arrival'].str.replace('\\A\\Z', 'NaN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Duration_In_Minutes</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Day_Of_Week</th>\n",
       "      <th>Weekend_Or_Not</th>\n",
       "      <th>Date_Of_Arrival</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</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>170.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>22 Mar</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>445.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</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</td>\n",
       "      <td>19h</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-09</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>10 Jun</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>325.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-12</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>285.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</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   2h 50m            0         No info   3897   \n",
       "1    05:50        13:15   7h 25m            2         No info   7662   \n",
       "2    09:25        04:25      19h            2         No info  13882   \n",
       "3    18:05        23:30   5h 25m            1         No info   6218   \n",
       "4    16:50        21:35   4h 45m            1         No info  13302   \n",
       "\n",
       "   Duration_In_Minutes  Day  Month  Year        Date  Weekday Day_Of_Week  \\\n",
       "0                170.0   24      3  2019  2019-03-24        6      Sunday   \n",
       "1                445.0    1      5  2019  2019-05-01        2   Wednesday   \n",
       "2               1140.0    9      6  2019  2019-06-09        6      Sunday   \n",
       "3                325.0   12      5  2019  2019-05-12        6      Sunday   \n",
       "4                285.0    1      3  2019  2019-03-01        4      Friday   \n",
       "\n",
       "   Weekend_Or_Not Date_Of_Arrival  \n",
       "0               1          22 Mar  \n",
       "1               0             NaN  \n",
       "2               1          10 Jun  \n",
       "3               1             NaN  \n",
       "4               0             NaN  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clearing the Date_Of_Arrival from Arrival_Time.\n",
    "df['Arrival_Time'] = df['Arrival_Time'].str.extract('(\\d+:\\d+)')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a feature which assigns a particular time period to Dep_Time and Arrival_Time \n",
    "bins = [-1, 3, 7, 11, 15, 19, 23]\n",
    "time_periods = ['Late_Night', 'Early_Morning', 'Morninig_To_Noon', 'Noon_To_Evening', 'Evening_To_Night', 'Night']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dep_Time_Timeperiod divides Dep_Time into 6 different time periods.\n",
    "df['Dep_Time_Timeperiod'] = pd.cut(df.Dep_Time.str.extract('(\\A\\d{2}):', expand=False).astype(int), bins, labels=time_periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Morninig_To_Noon    2728\n",
       "Early_Morning       2353\n",
       "Evening_To_Night    2177\n",
       "Night               1692\n",
       "Noon_To_Evening     1437\n",
       "Late_Night           295\n",
       "Name: Dep_Time_Timeperiod, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Dep_Time_Timeperiod.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Arrival_Time_Timeperiod divides Arrival_Time into 6 different time periods.\n",
    "df['Arrival_Time_Timeperiod'] = pd.cut(df.Arrival_Time.str.extract('(\\A\\d{2}):', expand=False).astype(int), bins, labels=time_periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>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>...</th>\n",
       "      <th>Year</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Day_Of_Week</th>\n",
       "      <th>Weekend_Or_Not</th>\n",
       "      <th>Date_Of_Arrival</th>\n",
       "      <th>Dep_Time_Timeperiod</th>\n",
       "      <th>Arrival_Time_Timeperiod</th>\n",
       "      <th>Dep_Time_Timeperiod_Encoded</th>\n",
       "      <th>Arrival_Time_Timeperiod_Encoded</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</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>22 Mar</td>\n",
       "      <td>Night</td>\n",
       "      <td>Late_Night</td>\n",
       "      <td>5</td>\n",
       "      <td>0</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>Noon_To_Evening</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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</td>\n",
       "      <td>19h</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-09</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>10 Jun</td>\n",
       "      <td>Morninig_To_Noon</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-12</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Evening_To_Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>4</td>\n",
       "      <td>5</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</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Evening_To_Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\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  \\\n",
       "0    22:20        01:10   2h 50m            0         No info   \n",
       "1    05:50        13:15   7h 25m            2         No info   \n",
       "2    09:25        04:25      19h            2         No info   \n",
       "3    18:05        23:30   5h 25m            1         No info   \n",
       "4    16:50        21:35   4h 45m            1         No info   \n",
       "\n",
       "                ...                Year        Date  Weekday  Day_Of_Week  \\\n",
       "0               ...                2019  2019-03-24        6       Sunday   \n",
       "1               ...                2019  2019-05-01        2    Wednesday   \n",
       "2               ...                2019  2019-06-09        6       Sunday   \n",
       "3               ...                2019  2019-05-12        6       Sunday   \n",
       "4               ...                2019  2019-03-01        4       Friday   \n",
       "\n",
       "   Weekend_Or_Not Date_Of_Arrival  Dep_Time_Timeperiod  \\\n",
       "0               1          22 Mar                Night   \n",
       "1               0             NaN        Early_Morning   \n",
       "2               1          10 Jun     Morninig_To_Noon   \n",
       "3               1             NaN     Evening_To_Night   \n",
       "4               0             NaN     Evening_To_Night   \n",
       "\n",
       "  Arrival_Time_Timeperiod  Dep_Time_Timeperiod_Encoded  \\\n",
       "0              Late_Night                            5   \n",
       "1         Noon_To_Evening                            1   \n",
       "2           Early_Morning                            2   \n",
       "3                   Night                            4   \n",
       "4                   Night                            4   \n",
       "\n",
       "  Arrival_Time_Timeperiod_Encoded  \n",
       "0                               0  \n",
       "1                               3  \n",
       "2                               1  \n",
       "3                               5  \n",
       "4                               5  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding the different time periods into numeric values, because LabelEncoder won't handle categorical dtype.\n",
    "time_period_mapping = {'Late_Night':0, 'Early_Morning':1, 'Morninig_To_Noon':2, 'Noon_To_Evening':3, 'Evening_To_Night':4, 'Night':5}\n",
    "df['Dep_Time_Timeperiod_Encoded'] = df['Dep_Time_Timeperiod'].map(time_period_mapping)\n",
    "df['Arrival_Time_Timeperiod_Encoded'] = df['Arrival_Time_Timeperiod'].map(time_period_mapping)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7cbfdb0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xPYWzurq6ZLTUTSgUSsnPyUTZ3LuU3f3Te3b2LmV3//RO7+mSlID27LPP6t1339W9996r/fv3q6OjQ/n5+dqzZ4/OOussbdiwQV//+tfV2Niol19+Wf/+7/+umpoajRo1Sn6/Xx6PJ66xPSkvL09GS93U1dWl5OdkomzuXcru/uk9O3uXsrt/eqf3ZKquru71taQEtJkzZ+ruu+/W9ddfL4fDoYceekhOp1N33nmnDMNQRUWFzj//fJ133nnauHGjZs+eLcuy9NBDD0mS7rvvvrjHAgAA2E1SAprX69V3vvOd4+pr1qzp9rnT6dT9999/3Lhx48bFPRYAAMBu2KgWAAAgwxDQAAAAMgwBDQAAIMMQ0AAAADIMAQ0AACDDENAAAAAyDAENAE4gFDXUHDIUihrpngqALMKDLAGgF7sOBrS2pl4NB1q0+dB2zRhXqmED/H1/IQB8TKygAUAPQlFDa2vq5ctxa6DPLV+OW2tr6llJA5ASBDQA6EEwHFPUMJXv7TzRkO91K2qYCoZjaZ4ZgGxAQAOAHvhy3PK4nGqPdAay9khMHpdTvhyuDAGQfAQ0AOhBrselGeNKFQzH1BSMKRiOaca4UuV6XOmeGoAswFtBAOjFsAF+3TpthLZsjWj8mBGEMwApwwoaAJxArsel4lwX4QxAShHQAAAAMgwBDQAAIMMQ0AAAADIMAQ0AACDDENAAAAAyDAENAAAgwxDQAAAAMgwBDQAAIMPEHdB27dqlV199VY2NjbIsK5lzAgAAyGpxPepp9erVeuGFF9TS0qLPfe5z2rNnj6qqqpI9NwAAgKwU1wra7373O/3sZz9TQUGBbrzxRr355pvJnhcAAEDWiiugHTul6XA4JElerzd5MwIAAMhycZ3ivOqqq3TDDTeovr5eX/7yl3XZZZcle14AAABZK66A9oUvfEFTpkzRe++9p+HDh+sTn/hEsucFAACQteI6xblmzRqtWbNGn/3sZ7V8+XL9+te/Tva8AAAAslZcAe2pp57SHXfcIUl67LHH9NRTTyV1UgAAANksroDmdDqVk5MjSfJ4PF03C5zIoUOHNG3aNG3fvl27d+/W9ddfrzlz5mjx4sUyTVOStGrVKs2cOVOzZ8/WW2+9JUkJjQUAALCjuK5Bu/TSSzVnzhyNHTtWtbW1uuSSS044PhqNqqqqSrm5uZKkpUuXat68eZo8ebKqqqq0bt06lZaWavPmzXrmmWfU0NCgyspKPffccwmNBQAAsKO4AtrXvvY1XXzxxdq5c6c+97nP6Zxzzjnh+OXLl2v27Nl6/PHHJUm1tbWaNGmSJGnq1KnauHGjysrKVFFRIYfDodLSUhmGocOHDyc0tqSk5OP0DgAAkJFOeIrzmWeekSR95zvf0R/+8Adt27ZNv//977Vy5cpev+b5559XSUmJLrrooq6aZVldp0V9Pp/a2toUCATk9/u7xhyrJzIWAADAjk64gnb66adLkoYOHSqXyxXXN3zuuefkcDj02muvqa6uTvPnz9fhw4e7Xg8GgyosLJTf71cwGOxWLygokNPpjHtsT+rq6uKa58cRCoVS8nMyUTb3LmV3//Senb1L2d0/vdN7upwwoB1bBfv973+vn/zkJ3F9w1/+8pddf547d67uvfderVixQps2bdLkyZO1fv16TZkyRUOGDNGKFSt08803q7GxUaZpqqSkRKNHj457bE/Ky8vj7f1fVldXl5Kfk4myuXcpu/un9+zsXcru/umd3pOpurq619fiugatoKBAL774osrKyrpWuMrKyuKewPz587Vo0SKtXLlSw4cP1/Tp0+VyuTRx4kTNmjVLpml2PXw9kbEAAAB21GdACwQC2rt3r37+85931RwOh37xi1/0+c2feOKJrj+vXr36uNcrKytVWVnZrVZWVhb3WAAAADs6YUBbvXq1fvKTn8jlcun222/X1KlTUzUvAACArHXCuzh/+9vf6o9//KOefvrpuFbMAAAA8PGdMKB5vV55vV6VlJQoGo2mak4AAABZLa5HPUmde5kBAAAg+U54Ddr777+vO+64Q5Zldf35mO985ztJnxyQSUJRQ80hQ6GooVxPfPsCAgDwrzhhQPvud7/b9efZs2cnfTJAptp1MKC1NfVqONCizYe2a8a4Ug0b4O/7CwEA+BecMKAdeyYmkM1CUUNra+rly3FroM8tX45ba2vqdeu0EaykAQCSIu5r0IBsFQzHFDVMSdLh9pgkKWqYCoZj6ZwWAMDG4nqSAJDNfDlu7TvSrt+82aBIJCLvB7s1YUiRfDn87wMASA5W0IA+hKOGdh5sl8vpkNftkMvp0M6D7QpHjXRPDQBgUwQ0oA9NbWG5XQ6dW1qos4o8Ore0UG6XQ01t4XRPDQBgUwQ0oA8DC3LkcTq7rjkLhmPyOJ0aWJCT5pkBAOyKgAb0oSjfq2svKNW7+9u0rSmsd/e36doLSlWU70331AAANsVVzkAfQlFD+5rDmvXJs9R4oEmnDxqofc1hNqzNEmxQDCAdWEED+nBsm43CPK/yPE4V5nnZZiNL7DoY0A9eel9P/+OIfvDS+9p1MJDuKQHIEqygAX3w5bgVCMX01t79am/v0Lst+zV8gI9tNmwuFDX087/u0vamoNrbozocbVYgHNX8z5azkgYg6VhBA+LgcJz4c9jP4WBEtfWt8ue6VZDjkj/Xrdr6Vh0ORtI9NQBZgCUAoA/BcEy+HLcuLT9NH9Q36KzS09TUFlIwHGMlxdasox8c3T8eqwNAErGCBvTBl+OWx+VU1DCV5+n86HE5OcVpcyW+HI0+o1CBcFRtYUOBcFSjzyhUiY/tVQAkHwEN6EOux6UZ40oVDMfUFIwpGI5pxrhSVs9sLtfj0o3/NkznDy7S4CKPzh9cpBv/bRjHHUBKsAQAxGHYAL9unTZCW7ZGNH7MCP6RzhLDBvh12yUjtWVrTOPHjOS4A0gZVtCAOOV6XCrOdfGPdJbhuANIBwIaAABAhiGgJejDu4oDAAAkA9egJWDXwYDW1tSr4UCLNh/arhnjSjVsgD/d00KKvF3frN9ta5ZV1KzRpcXpng5ShEc9AUgHVtDiFIoaWltTL1+OWwN9bvly3FpbU89KWpb41pq/66qHN+qRTYd11cMb9a01f0/3lJACuw4G9Nir27W2rkWPvbqdRz0BSBkCWpyOPY8x39u56JjvdfM8xizxdn2znt3SIJdD8rodcjmkZ7c06O365nRPDUnEmzIA6URAi9OxzUrbI52BrD0SY7PSLPH3XUdkWZLb1Xl6y+1yybI667Av3pQBSCcCWpzYrDR7TRjWTw6HFDM6V05ihiGHo7MO++JNGYB0IqAl4NhmpTPKi3TrtBHcIJAlRpcWa+b4M2RYUiRmybCkmePP4EYBm+NNGYB0SspbQcMwdM8992jnzp1yuVxaunSpLMvSggUL5HA4NHLkSC1evFhOp1OrVq3SK6+8IrfbrYULF2rs2LHavXt33GNTjU0rs9O3r5ugGyua9btNdbpycjnhLEvwBAkA6ZKUgPbyyy9Lkn71q19p06ZNXQFt3rx5mjx5sqqqqrRu3TqVlpZq8+bNeuaZZ9TQ0KDKyko999xzWrp0adxjgVQZXVosxznFKiecZRXelAFIh6QEtMsuu0yf/vSnJUn19fUaMGCAXnnlFU2aNEmSNHXqVG3cuFFlZWWqqKiQw+FQaWmpDMPQ4cOHVVtbG/fYkpKSZLQAAACQNkm7Bs3tdmv+/Pl64IEHNH36dFmWJYfDIUny+Xxqa2tTIBCQ3//P67iO1RMZC6QKT5EAAKRKUm9HWr58ue68805dd911CofDXfVgMKjCwkL5/X4Fg8Fu9YKCAjmdzrjHflRdXV2SuvmnUCiUkp+TibK19/rWiF7eEVAoGtPvtr2ui4f7VVroTfe0Uipbj72U3b1L2d0/vdN7uiQloP3617/W/v37deuttyovL08Oh0NjxozRpk2bNHnyZK1fv15TpkzRkCFDtGLFCt18881qbGyUaZoqKSnR6NGj4x77UeXl5cloqZu6urqU/JxMlI29h6KG/vzqdg0bXKCWw00qKhmorW0x/duE7LpoPBuP/THZ3LuU3f3TO70nU3V1da+vJSWgXXHFFbr77rt1ww03KBaLaeHChRoxYoQWLVqklStXavjw4Zo+fbpcLpcmTpyoWbNmyTRNVVVVSZLmz58f91gg2T68YWmLOjcsbW6PKBiOZVVAAwCkTlICWn5+vr73ve8dV1+9evVxtcrKSlVWVnarlZWVxT0WSLZjG5a2dETUETXV0hFhw1IAQFKxUS3Qh1yPSxcMLdbzWz7Qs1sP6/ktH+iCocWsngEAkoaABvQhFDV0+5PVem9/u5pD0nv723X7k9XczQkASBoCGtCHP/5jnw51mN1qhzpM/fEf+9I0IwCA3RHQgD5s2nE4oToAAB8XAQ3ow+WjT0+oDgDAx0VAA/ow8nR/QnXYC0+QAJAOBDSgD+82BhKqwz52HQzoBy+9p6f/cUQ/eOk97TrIMQeQGgQ0oA+/f3NvQnXYQyhq6Gcbd+nNvS3a2xLVm3tb9LONu1hJA5ASBLQEcboj+2zc3vPNAL3VYQ+Hg2G93dAqf45HBTku+XM8eruhVYeD4b6/GAA+JgJaAjjdkZ2mnzsooTrswnH0g9X947E6ACQRAS1OnO7IXndfdV5CddhDic+rc0sL1RwM60AgquZgWOeWFqrE50331ABkAQJanDjdkb1625CWjWrtLdfj0rmlBdp1qF37WqPadahd55YW8IgvAClBQIsbpzuyFRvVZqeW9ohWv/6BvG6X/F6nvG6XVr/+gVraI+meGoAsQECL07HTHYFQTG1hQ4FQjNMdWYKNarPT3uYO7W8Ny5/rls/rkj/Xrf2tYe1t7kj31ABkAQJanHI9Ln3pU8N0/uBiDS7y6PzBxfrSp4ZxuiMLXDL6dI05Pa9bbczpebqEgGZr+V6nnA4pEjMVMy1FYqacjs46ACSbO90TOJUMG+DXbZecrS1boxo/5mzCWRZZ9YVJ+v9eeFdb9zRpzJCB+ublo9I9JSTZGUX5Gn2GX5t2HlE0ZsjjNjS5rJ/OKMpP99QAZAHeCiYo1+NSca6LcJZFQlFDa2vqdc4Zhbp4RKHOOaNQa2vquYM3GzgcsiRZDsk6+jkApAIraEAfguGYDgbC2n2oXYdbgippcWpo/3wFwzGCuo01tHTorb0tcrsckuWQ2+XQW3tb1NDSobIBPIcVQHIR0IA+uJ0OvbHzsBpbwzKMmPa2tehAa0huJ6spdtbcHlZLR0ymZcm0LDkNQx0OU83tYUkENADJxSlOoA+H2yM60BZWOGYoYlgKxwwdaAvrMNst2Jrb4ZJhmrIsS05JlmXJME25HayaAkg+AhrQh+b2sEKx7tebhWLG0ZUU2JXTJeV5nLLkUMySLDmU53HKST4DkAKc4gT6kO/1KGaYihmdF4o7DFNuV2cd9jXQnyOv26VQNKrOs9mWvG63Bvpz0j01AFmAFTSgL5bkdEhm5x9lqvNzWSf+MpzaYqbkz3HLefRaQ6fTIX+OWzEzzRMDkBVYQQP6ELNMhWLda6FYZx32FY4ZihqWzijKU0corLzcHEVipsIxtlcBkHysoCWopT2iPc0RnseXRd7YcTChOuwhx+2UZGnPkXYdaDe050i7JOtoHdmA3/dIJ1bQErDhvQN6eN37OhIIqt+bQVVeerYqRg5K97SQZOvebuq1fmPF2SmeDVLFME0daY90/pI8uqPKkfaIDJOV02yw4b0D+sFL29USDOpnb7XrtktG8PseKcVbwTi1tEf0//70rpoCEYVjlpoCnZ/zzsr+powoTqgOe9jfEpbL4VDMlMJm5zVpLodD+1u4e9fuWtoj+sFL2+XLdes0v0e+XHdnWOP3PVKIgBanvc3t2t8aVr7XrVy3U/let/a3hrW3uT3dU0OSfbq8NKE67KHY51FbxNSxK84MSW0RU8U+7t61u6a2sKKmKX9O57H253gUNU01tRHOkToEtDjle91yOv55YXjMMuV0dNZhbwP9Xg3weY6d5ZJD0gCfRwP93nROC0m252DPb756q8M+BhbkyON0KhCOSpIC4ag8TqcGFrDFClKHgBanM4ryNHl4P3WEDbXA2/SZAAAaEUlEQVSGDHWEDU0e3k9nFOWle2pIsphpyTCMrl01LEmGYShmss+Gne04GEioDvsoyvfqtktGKBiKaX8gqmAoptsuGaGifN6UIXVO+vJPNBrVwoULtW/fPkUiEX31q1/V2WefrQULFsjhcGjkyJFavHixnE6nVq1apVdeeUVut1sLFy7U2LFjtXv37rjHplKux6VvXDpKT2/+QLsaD2jY6YM0a9JZPCw7CzQcCaklbMrtOLofmiW1hE01HAmptDg/3dNDkuR6en7/2lsd9lIxcpDOO7NYr79ZpynnlxPOkHInPaD93//9n4qLi7VixQodOXJE//mf/6lzzjlH8+bN0+TJk1VVVaV169aptLRUmzdv1jPPPKOGhgZVVlbqueee09KlS+Mem2rDBvh1++WjtGWrofFjRhHOskRTIKRj+9IaRxfNHEfrsK/ebtbkJs7sUZTv1ZBiL+EMaXHSA9pnPvMZTZ8+vetzl8ul2tpaTZo0SZI0depUbdy4UWVlZaqoqJDD4VBpaakMw9Dhw4cTGltSUnKyp9+ncLTzFGc4ahDQssTZp/nldEiGKTkckmVJLmdnHfZ1bmnPd+n2VgeAk+mkr9X7fD75/X4FAgF94xvf0Lx582RZlhwOR9frbW1tCgQC8vv93b6ura0tobGptuG9A/rKL/6m72zs/LjhvQMpnwNSr19+jorzXDLVuYJmSirOc6lfPhcM29mgop5XTXqrA8DJlJRbEBsaGnTbbbdpzpw5uvrqq7VixYqu14LBoAoLC+X3+xUMBrvVCwoK5HQ64x7bk7q6uiR0JAUiMT3w5wYFIqZcTmnvoVY98OsaLbniDPmz6E7OUCiUtL/jTLXjcEgHg90f73MwaOj1N9/W8JLcNM0q9bLt2P9lR89vAv/w2tu6aHjPv3/sKtuO/YfRO72ny0lPFgcPHtRNN92kqqoqXXjhhZKk0aNHa9OmTZo8ebLWr1+vKVOmaMiQIVqxYoVuvvlmNTY2yjRNlZSUJDS2J+Xl5Se7JUlSbX2zWqMH1L8gX9FISB5vrg4FwsofeJbKs+iUR11dXdL+jjPV3zftkqX6bjVLUrO7n8rLh6VlTumQbcf+5YZ3JB3/FIlYXpHKyz+R+gmlUbYd+w+jd3pPpurq6l5fO+kB7dFHH1Vra6seeeQRPfLII5Kk//7v/9aSJUu0cuVKDR8+XNOnT5fL5dLEiRM1a9YsmaapqqoqSdL8+fO1aNGiuMam0rF90KLHtlcwDPZByxLv1jcnVIc9WL3cDNBbHQBOppOeLu655x7dc889x9VXr159XK2yslKVlZXdamVlZXGPTaUzivI0utSvTTuOKGoY8rjYBy1bbGsMJlSHPbR29LxrfG912E8oaqg5ZCjETWFIAzb0SYijj89hR+eW+hKqwx7qGnvekLa3Ouxl18GAHnt1u9bWteixV7drFxsUI8UIaHFqaOnQ2/VtOqM4T2cWenVGcZ7erm9TQ0tHuqeGJIuZPQfx3uqwh+L8np+52Vsd9hGKGlpbUy9fjlsDfW75ctxaW1OvUNTo+4uBk4SAFqf2SEymJTkdDsVMS06HQ6bVWYe91TW0JlSHPVhWzwG8tzrsIxiOKWqYXdcY53vdihqmgmF+32eDD5/aTicCWpwGF+crz+vUzqaA6tui2tkUUJ7XqcE86sf2Rg3seUPa3uqwhwnD+iVUh334ctzyuJxdb8DbIzF5XE75crgpzO4y6dQ2AS0BXqdDOV6XclydH71O3klng0+UFiZUhz18asTAhOqwj1yPSzPGlSoYjqkpGFMwHNOMcaXcKGBzmXZqm4AWp6a2sPx5Hv3biIEqH5SnfxsxUP48j5rauKPL7n748raE6rCHbb2cwu6tDnsZNsCvW6eN0IzyIt06bYSGDWDF3O4y7dQ2AS1OAwty5HE6FTEM+bydHz1OpwYW8Lgfu2voZYW7tzrs4ZV3GhOqw35yPS4V57pYOcsSmXZqm4AWp6J8r267ZISCoZj2B6IKhmK67ZIRKsrnuXyAHdU19rwRcW912E9Le0R7miNqaY+keypIgUw7tc0VjwmoGDlIo04r0Pq/12nqhHINKmSTWsCuGpt7vnyhtzrsZcN7B/SDl7arJRjUz95q122XjFDFyEHpnhaS7Nip7S1bIxo/ZkRaV09ZQUvAroMBPfHaHm3YE9QTr+1h48Is0a+XtzG91WEPZxb1fPlCb3XYR0t7RD94abt8uW6d5vfIl+vuDGuspGWFTDm1TUCLUyhq6Od/3aU39zZrb0tUb+5t1s//uivt+6Qg+Y70cn1ob3XYQ3NHzw/d7K0O+2hqCytqmjIMS42tERmGpahpclMYUoo1gDgdDkZUW9+qfj6vjIhTeV6XautbdTgYUWkxpzoBu+mIRBOqwz4GFuRoz8GADgQ734XVHWzSIJ+bm8KQUgS0uFnqiMR0JBhVJBqRtz2oXI9DkpXuiQFIAo/bKYWPXy3zuDnxYHdNgZAOtXdfIj/UHlNTIMSNYUgZftPEyed1y+V0yrA6A5lhWXI5nfJ5ybiAHe0P9nwqs7c67GPLriMyLMmhf/5nWJ11IFVIF3GKmZbOG1ykvUc6dKTVUL/CfA3ul6eYyQoaANjJwMLOU5mWJKdDOvZr/lgdSAUCWpx8OW4N8Oeov8+rvY0RDT69SA6Hg2ezAYDNDCzMVb7Hqfao2RXO8j1ODSzMTe/EkFU4xRmnXI9LZxbnaM0bH+jF7a1a88YHOrM4J+234QJIjt7+KeafaPsbXJyvEp+nW63E59Hg4vw0zQjZiIAWp5b2iH68fqeCEUMxQwpGDP14/U72xQFsKpRgHfYRjhlq7YjJ5ZRcDsnllFo7YgrH2FYJqUNAi9POpqD2NHcoZhgyLEsxw9Ce5g7tbAqme2oAgJPo/f0BmZbUP9+jghyn+ud7ZFqddSBVuIAqTuGYoUjUlNPpkNR5R49pmryjAgCb6e/3yjAtHQpG5ZBkhU15XU7197PFBlKHgBanYp9HHrdTkZjZ+T+sLHndThV/5DoFAMCprZ/PK5fTkvGhrdBcTkv9fAQ0pA6nOONUkOvVaQVeOR2dq2dOh3RagVcFufwPCwB2crAtoo/uoGRanXUgVVhBi5PP61JzR1TRo3tURk2puSMqn5e7OAHATlo6ImqPWnKocxXDlNQetdTSQUBD6rCCFqfa+mY1d3S/3qy5w1BtfXOaZgQASIaWjqPXnkkyjn50HK0DqUJAi9PrOw4lVAcAnJpOL8rtesqy4+hH62gdSBUCWpxqenkGW291AMCpKWZY8nkdXatoDkk+r0Mxg0f7IXW4Bi1O7ze1JVQHAJyahvTPlz/HK7cjqphpyu10Ktfr0ZD+PEkAqcMKWpyG9Ov5f8ze6gCAU1NhnldjzyyQHE5ZlkNyODX2zAIV5nHXPlKHgBanS889I6E6AODUFAzHdHpxvob0y1Wex9KQfrk6vThfwXCs7y8GThJOccZp1GkFCdUBAKcmt9OhtX/fo9aju2oc6gho96GA7rziE+mdGLIKK2hxOhAIJ1QHAJyaavYc6Qpnx7RGOutAqiQtoL355puaO3euJGn37t26/vrrNWfOHC1evFim2bnb66pVqzRz5kzNnj1bb731VsJjU6kj0vP+N73VAQCnphe2NSZUB5IhKQHtRz/6ke655x6Fw52rS0uXLtW8efP05JNPyrIsrVu3TrW1tdq8ebOeeeYZrVy5Uvfdd1/CY1Np4rD+CdUBAKemfE/PT4jprQ4kQ1IC2pAhQ/Twww93fV5bW6tJkyZJkqZOnaq//vWvqq6uVkVFhRwOh0pLS2UYhg4fPpzQ2FRqau35VGZvdQDAqelz4wcnVAeSISk3CUyfPl179+7t+tyyLDkcnfsx+3w+tbW1KRAIqLi4uGvMsXoiY0tKSo772XV1dcloSS++2fMTA16seU+DrNSGxXQKhUJJ+zs+FWXT3wXH/p+y7e8h2469Q9Llw3P1wo5QV+3y4blytDSorqUhfRNLsWw77h+WCb2n5C5Op/OfC3XBYFCFhYXy+/0KBoPd6gUFBQmN7Ul5eXkSOpC8774pqeX4el5B0n5mJqqrq8uqfjvt6PWVbPq7yL5jz3E/JvuOvfQld381hrfpQHO7BhXn60sXn6PykYPSPa2Uysbjfkyqeq+uru71tZTcxTl69Ght2rRJkrR+/XpNnDhREyZM0IYNG2Sapurr62WapkpKShIam0rVe3p+KHpvdQDAqamlPaIfvLRdA/25GjkgRwP9ufrBS9vV0h7p+4uBkyQlK2jz58/XokWLtHLlSg0fPlzTp0+Xy+XSxIkTNWvWLJmmqaqqqoTHplJ/X887SPdWBwCcmprawmoLR3WkI6qOUFR54ZDczs56UT6/85EaSQtogwcP1po1ayRJZWVlWr169XFjKisrVVlZ2a2WyNhU6oiYCdUBAKemwjy3mtujyvO6let2yjAtBUIxFeaxtztSh41q49Qv35NQHcCprbcNFdhowf5cTqcuGNpPMdNUa8hQzDR1wdB+cjn5JxOpw9uBOJ1enJdQHcCpLc8hBaye67A3X45bwwf6dfYgvxoPNOn0QQNlWp11IFV4OxCn//nrnoTqAE5tubmJ1WEfuR6XZowrlWlJhiWZljRjXKly2agWKcTbAQDoQXG+Vwc7jr9rr5iLxLPCsAF+3TpthLZsjWj8mBGEM6QcK2gA0IMiX89LZb3VYT+5HpeKc12EM6QFAQ0AevCZc09LqA4AJxMBDQB6cFZJz08r6a0OACcTAQ0AelC980BCdQA4mQhoANCDzbuOJFQHgJOJgAYAPRhY0PPNAL3VAeBkIqABAABkGAIaAPRg6qiBCdUB4GQioAFAD4b27/luzd7qAHAyEdAAoAc7DrYlVAeAk4mABgA9OLe0KKE6AJxMBDQA6EGJL0cled0f8VOS51KJLydNMwKQTXhYOgD0YGBBjj5xepEM09CRQLv6+fPlcro0sICABiD5WEEDgB4U5Xt12yUj5HK65HE55XK6dNslI1SU70331ABkAVbQgD6c4ZMagj3XYW8VIwfpvDOL9fqbdZpyfjnhDEDKsIIG9KFi5OkJ1WEvRfleDSn2Es4ApBQBDejDwMK8hOoAAHxcBDSgD1eNK02oDgDAx0VAA/owurRY1004Q46jnzskXTfhDI0uLU7ntAAANsZNAkAcvn3dBN1Y0azfbarTlZPLCWcAgKQioAFxGl1aLMc5xSonnAEAkoxTnAAAABmGgAYAAJBhCGhxmlCan1AdAADgX0VAi9Pz37g4oToAAMC/ioCWgF3LruxaMZtQmq9dy65M84wAAIAdnVJ3cZqmqXvvvVfvvPOOvF6vlixZoqFDh6Z0Ds9/42LV1dWpvLw8pT8XAABkj1NqBe3FF19UJBLR008/rTvuuEPLli1L95QAAABOulMqoFVXV+uiiy6SJI0bN05bt25N84wAAABOvlPqFGcgEJDf7+/63OVyKRaLye3+Zxt1dXVJn0coFErJz8lE2dy7lN3903t29i5ld//0Tu/pckoFNL/fr2Aw2PW5aZrdwpmklFwbls3XoGVz71J290/v2dm7lN390zu9J1N1dXWvr51SpzgnTJig9evXS5Jqamo0atSoNM8IAADg5DulVtAuv/xybdy4UbNnz5ZlWXrooYfSPSUAAICT7pQKaE6nU/fff3+6pwEAAJBUp9QpTgAAgGxAQAMAAMgwBDQAAIAM47Asy0r3JE6WE92uCgAAkGkuuOCCHuu2CmgAAAB2wClOAACADENAAwAAyDAEtBMwTVNVVVWaNWuW5s6dq927d3d7fc2aNbrmmmt03XXX6eWXX07TLJOjr96XLFmia665RnPnztXcuXPV1taWppkmz5tvvqm5c+ceV3/ppZd07bXXatasWVqzZk0aZpZ8vfX+05/+VFdeeWXXcd+xY0caZpc80WhUd911l+bMmaOZM2dq3bp13V6387Hvq3c7H3vDMHT33Xdr9uzZuuGGG7Rnz55ur9v5uEt992/nY3/MoUOHNG3aNG3fvr1bPa3H3kKv/vSnP1nz58+3LMuytmzZYv3Xf/1X12sHDhywrrrqKiscDlutra1df7aLE/VuWZY1e/Zs69ChQ+mYWko8/vjj1lVXXWV9/vOf71aPRCLWZZddZjU3N1vhcNi65pprrAMHDqRplsnRW++WZVl33HGH9Y9//CMNs0qNZ5991lqyZIllWZZ1+PBha9q0aV2v2f3Yn6h3y7L3sX/hhResBQsWWJZlWa+//nq333d2P+6WdeL+Lcvex96yOo/x1772NeuKK66w3n///W71dB57VtBOoLq6WhdddJEkady4cdq6dWvXa2+99ZbGjx8vr9ergoICDRkyRNu2bUvXVE+6E/VumqZ2796tqqoqzZ49W88++2y6ppk0Q4YM0cMPP3xcffv27RoyZIiKiork9Xp1wQUX6G9/+1saZpg8vfUuSbW1tXr88cd1/fXX67HHHkvxzJLvM5/5jG6//fauz10uV9ef7X7sT9S7ZO9jf9lll+mBBx6QJNXX12vAgAFdr9n9uEsn7l+y97GXpOXLl2v27NkaNGhQt3q6jz0B7QQCgYD8fn/X5y6XS7FYrOu1goKCrtd8Pp8CgUDK55gsJ+q9vb1dX/jCF7RixQr9+Mc/1pNPPmmrcCpJ06dPl9t9/JPQ7H7cpd57l6Qrr7xS9957r37+85+rurradqf2fT6f/H6/AoGAvvGNb2jevHldr9n92J+od8n+x97tdmv+/Pl64IEHNH369K663Y/7Mb31L9n72D///PMqKSnpWpD4sHQfewLaCfj9fgWDwa7PTdPs+ofro68Fg8FuB/JUd6Le8/Ly9MUvflF5eXny+/2aMmWK7QJab+x+3E/Esix96UtfUklJibxer6ZNm6a333473dM66RoaGvTFL35RM2bM0NVXX91Vz4Zj31vv2XLsly9frj/96U9atGiR2tvbJWXHcT+mp/7tfuyfe+45/fWvf9XcuXNVV1en+fPnq6mpSVL6jz0B7QQmTJig9evXS5Jqamo0atSortfGjh2r6upqhcNhtbW1afv27d1eP9WdqPddu3Zpzpw5MgxD0WhUf//733Xuueema6opNWLECO3evVvNzc2KRCL629/+pvHjx6d7WikRCAR01VVXKRgMyrIsbdq0SWPGjEn3tE6qgwcP6qabbtJdd92lmTNndnvN7sf+RL3b/dj/+te/7jp1l5eXJ4fD0XWK1+7HXTpx/3Y/9r/85S+1evVqPfHEEyovL9fy5cs1cOBASek/9j2fx4Ak6fLLL9fGjRs1e/ZsWZalhx56SD/96U81ZMgQXXrppZo7d67mzJkjy7L0zW9+Uzk5Oeme8knTV+9XX321rrvuOnk8Hs2YMUMjR45M95ST6je/+Y3a29s1a9YsLViwQDfffLMsy9K1116r0047Ld3TS6oP9/7Nb35TX/ziF+X1enXhhRdq2rRp6Z7eSfXoo4+qtbVVjzzyiB555BFJ0uc//3l1dHTY/tj31budj/0VV1yhu+++WzfccINisZgWLlyoP//5z1nz/3xf/dv52PckU37f8yQBAACADMMpTgAAgAxDQAMAAMgwBDQAAIAMQ0ADAADIMAQ0AACADMM2GwBOWcuWLVNtba2ampoUCoV01llnqV+/fvr+979/3Ni9e/fqvffe08UXX9zj99q9e7cWLFigp556qtef9+yzz+o3v/mNnE6nLMvSrbfeqgsvvLDP7w0AiSKgAThlLViwQFLn41p27NihO++8s9exr732mvbu3fsvh6jm5mb96Ec/0m9+8xt5vV41NjZq1qxZeuWVVz729waAjyKgAbCdBx98UDU1NZKkGTNm6LrrrtOPf/xjRSIRjR8/Xjk5OfrhD38oSQqHw1qxYkWf39Pv9ysSiejpp5/Wpz/9aZ111ll64YUXFI1Gu33v/v3768EHH5Tb7VZOTo6WLFmiSCSiu+66SyUlJWpsbNTFF1+s22+/XX/4wx/0P//zP3K73Ro6dKiWLVsmh8OR1L8bAKcGrkEDYCsvvviiDhw4oDVr1uiXv/ylnn/+ee3Zs0e33HKL/uM//kOf/vSn9f7772vlypX6xS9+oYsuukh//OMf+/y+brdbP/nJT7Rjxw7ddNNNuuSSS/T888/L6/V2+96LFi3Sfffdp9WrV+u6667Tt7/9bUmdp1i//e1v67nnntNf/vIXbdu2Tb/97W91yy236Fe/+pWmTJliy4dwA/jXsIIGwFa2b9+uiRMnyuFwyOv16vzzz9f27du7jTnttNN0//33Kz8/X42NjZo0aVKf33f//v2KRqNavHixJGnHjh265ZZb9MlPfrLbuIMHD+oTn/iEJOmTn/ykVq1aJUkqLy9XYWGhpM5n+e7cuVMLFy7U448/rtWrV+vss8/WFVdc8bH7B2APrKABsJURI0aourpakhSJRFRTU6OhQ4fK4XDo2JPtFi1apGXLlmnZsmXq37+/4nni3f79+3XXXXeptbVVknTmmWequLhYbre72/ceMGCA3nvvPUnS5s2bNWzYMEmdwTEUCikWi+mtt97S2WefrTVr1uj222/X6tWrFYlEtG7dupP91wHgFMUKGgBbufTSS7V582bNnj1bkUhEV111lc455xxFo1H96Ec/Unl5ua6++mrNnDlThYWF6t+/vw4cONDn9x07dqyuv/56zZ07V3l5eTIMQ9dff72GDh2q1tbWru+9ZMmSrlU2t9uthx56SIZhyO12q7KyUocOHdKVV16pkSNH6rzzztONN96ooqIiFRQU2P4h1ADix8PSASDJ4tnCAwA+jBU0APiQ73//+3rjjTeOqy9fvlylpaVpmBGAbMQKGgAAQIbhJgEAAIAMQ0ADAADIMAQ0AACADENAAwAAyDAENAAAgAxDQAMAAMgw/z+1AMp92WhHKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter('Total_Stops', 'Price', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7d8b250>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter('Duration_In_Minutes', 'Price', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7d1ac30>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter('Weekday', 'Price', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7e28d90>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter('Weekend_Or_Not', 'Price', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7cfb610>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter('Day', 'Price', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7cfb390>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter('Month', 'Price', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Price</th>\n",
       "      <th>Duration_In_Minutes</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Weekend_Or_Not</th>\n",
       "      <th>Dep_Time_Timeperiod_Encoded</th>\n",
       "      <th>Arrival_Time_Timeperiod_Encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.603897</td>\n",
       "      <td>0.738186</td>\n",
       "      <td>-0.009451</td>\n",
       "      <td>0.054383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036750</td>\n",
       "      <td>-0.019548</td>\n",
       "      <td>-0.045127</td>\n",
       "      <td>0.032074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.603897</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506586</td>\n",
       "      <td>-0.153774</td>\n",
       "      <td>-0.103643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.051570</td>\n",
       "      <td>0.020275</td>\n",
       "      <td>0.020116</td>\n",
       "      <td>0.022946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_In_Minutes</th>\n",
       "      <td>0.738186</td>\n",
       "      <td>0.506586</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022481</td>\n",
       "      <td>0.014769</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.012751</td>\n",
       "      <td>-0.004173</td>\n",
       "      <td>0.024640</td>\n",
       "      <td>0.045036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>-0.009451</td>\n",
       "      <td>-0.153774</td>\n",
       "      <td>-0.022481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.091245</td>\n",
       "      <td>-0.104187</td>\n",
       "      <td>-0.004639</td>\n",
       "      <td>-0.002531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>0.054383</td>\n",
       "      <td>-0.103643</td>\n",
       "      <td>0.014769</td>\n",
       "      <td>-0.038359</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.083345</td>\n",
       "      <td>-0.013189</td>\n",
       "      <td>0.036593</td>\n",
       "      <td>-0.004209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekday</th>\n",
       "      <td>-0.036750</td>\n",
       "      <td>0.051570</td>\n",
       "      <td>-0.012751</td>\n",
       "      <td>-0.091245</td>\n",
       "      <td>-0.083345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.815513</td>\n",
       "      <td>-0.015155</td>\n",
       "      <td>-0.004500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekend_Or_Not</th>\n",
       "      <td>-0.019548</td>\n",
       "      <td>0.020275</td>\n",
       "      <td>-0.004173</td>\n",
       "      <td>-0.104187</td>\n",
       "      <td>-0.013189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.815513</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008848</td>\n",
       "      <td>-0.002309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_Timeperiod_Encoded</th>\n",
       "      <td>-0.045127</td>\n",
       "      <td>0.020116</td>\n",
       "      <td>0.024640</td>\n",
       "      <td>-0.004639</td>\n",
       "      <td>0.036593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.015155</td>\n",
       "      <td>-0.008848</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_Timeperiod_Encoded</th>\n",
       "      <td>0.032074</td>\n",
       "      <td>0.022946</td>\n",
       "      <td>0.045036</td>\n",
       "      <td>-0.002531</td>\n",
       "      <td>-0.004209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.004500</td>\n",
       "      <td>-0.002309</td>\n",
       "      <td>0.012049</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Total_Stops     Price  Duration_In_Minutes  \\\n",
       "Total_Stops                         1.000000  0.603897             0.738186   \n",
       "Price                               0.603897  1.000000             0.506586   \n",
       "Duration_In_Minutes                 0.738186  0.506586             1.000000   \n",
       "Day                                -0.009451 -0.153774            -0.022481   \n",
       "Month                               0.054383 -0.103643             0.014769   \n",
       "Year                                     NaN       NaN                  NaN   \n",
       "Weekday                            -0.036750  0.051570            -0.012751   \n",
       "Weekend_Or_Not                     -0.019548  0.020275            -0.004173   \n",
       "Dep_Time_Timeperiod_Encoded        -0.045127  0.020116             0.024640   \n",
       "Arrival_Time_Timeperiod_Encoded     0.032074  0.022946             0.045036   \n",
       "\n",
       "                                      Day     Month  Year   Weekday  \\\n",
       "Total_Stops                     -0.009451  0.054383   NaN -0.036750   \n",
       "Price                           -0.153774 -0.103643   NaN  0.051570   \n",
       "Duration_In_Minutes             -0.022481  0.014769   NaN -0.012751   \n",
       "Day                              1.000000 -0.038359   NaN -0.091245   \n",
       "Month                           -0.038359  1.000000   NaN -0.083345   \n",
       "Year                                  NaN       NaN   NaN       NaN   \n",
       "Weekday                         -0.091245 -0.083345   NaN  1.000000   \n",
       "Weekend_Or_Not                  -0.104187 -0.013189   NaN  0.815513   \n",
       "Dep_Time_Timeperiod_Encoded     -0.004639  0.036593   NaN -0.015155   \n",
       "Arrival_Time_Timeperiod_Encoded -0.002531 -0.004209   NaN -0.004500   \n",
       "\n",
       "                                 Weekend_Or_Not  Dep_Time_Timeperiod_Encoded  \\\n",
       "Total_Stops                           -0.019548                    -0.045127   \n",
       "Price                                  0.020275                     0.020116   \n",
       "Duration_In_Minutes                   -0.004173                     0.024640   \n",
       "Day                                   -0.104187                    -0.004639   \n",
       "Month                                 -0.013189                     0.036593   \n",
       "Year                                        NaN                          NaN   \n",
       "Weekday                                0.815513                    -0.015155   \n",
       "Weekend_Or_Not                         1.000000                    -0.008848   \n",
       "Dep_Time_Timeperiod_Encoded           -0.008848                     1.000000   \n",
       "Arrival_Time_Timeperiod_Encoded       -0.002309                     0.012049   \n",
       "\n",
       "                                 Arrival_Time_Timeperiod_Encoded  \n",
       "Total_Stops                                             0.032074  \n",
       "Price                                                   0.022946  \n",
       "Duration_In_Minutes                                     0.045036  \n",
       "Day                                                    -0.002531  \n",
       "Month                                                  -0.004209  \n",
       "Year                                                         NaN  \n",
       "Weekday                                                -0.004500  \n",
       "Weekend_Or_Not                                         -0.002309  \n",
       "Dep_Time_Timeperiod_Encoded                             0.012049  \n",
       "Arrival_Time_Timeperiod_Encoded                         1.000000  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mat = df.corr()\n",
    "corr_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing test set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_excel('data/Test_set.xlsx')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing and modifying the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          897\n",
       "IndiGo                               511\n",
       "Air India                            440\n",
       "Multiple carriers                    347\n",
       "SpiceJet                             208\n",
       "Vistara                              129\n",
       "Air Asia                              86\n",
       "GoAir                                 46\n",
       "Multiple carriers Premium economy      3\n",
       "Jet Airways Business                   2\n",
       "Vistara Premium economy                2\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Airline.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       1145\n",
       "Kolkata      710\n",
       "Banglore     555\n",
       "Mumbai       186\n",
       "Chennai       75\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       1145\n",
       "Banglore      710\n",
       "Delhi         317\n",
       "New Delhi     238\n",
       "Hyderabad     186\n",
       "Kolkata        75\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Destination.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_test.Route.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No info                         2148\n",
       "In-flight meal not included      444\n",
       "No check-in baggage included      76\n",
       "1 Long layover                     1\n",
       "Change airports                    1\n",
       "Business class                     1\n",
       "Name: Additional_Info, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Additional_Info.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration  Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m            1                      No info  \n",
       "1    06:20         10:20       4h            1                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m            1  In-flight meal not included  \n",
       "3    08:00         21:00      13h            1                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m            0                      No info  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Total_Stops = df_test.Total_Stops.map(stops)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1431\n",
       "0     849\n",
       "2     379\n",
       "3      11\n",
       "4       1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Total_Stops.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Duration_In_Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>1425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration  Total_Stops              Additional_Info  \\\n",
       "0    17:30  04:25 07 Jun  10h 55m            1                      No info   \n",
       "1    06:20         10:20       4h            1                      No info   \n",
       "2    19:15  19:00 22 May  23h 45m            1  In-flight meal not included   \n",
       "3    08:00         21:00      13h            1                      No info   \n",
       "4    23:55  02:45 25 Jun   2h 50m            0                      No info   \n",
       "\n",
       "   Duration_In_Minutes  \n",
       "0                655.0  \n",
       "1                240.0  \n",
       "2               1425.0  \n",
       "3                780.0  \n",
       "4                170.0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['Duration_In_Minutes'] = df_test['Duration'].apply(lambda x: duration_in_mins(x))\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.0, 75.0, 80.0, 85.0, 90.0, 95.0, 135.0, 140.0, 145.0, 150.0]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df_test.Duration_In_Minutes.unique())[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>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>Duration_In_Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2660</th>\n",
       "      <td>Air India</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → GOI → PNQ → HYD</td>\n",
       "      <td>16:50</td>\n",
       "      <td>16:55</td>\n",
       "      <td>5m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline Date_of_Journey  Source Destination                  Route  \\\n",
       "2660  Air India      12/03/2019  Mumbai   Hyderabad  BOM → GOI → PNQ → HYD   \n",
       "\n",
       "     Dep_Time Arrival_Time Duration  Total_Stops Additional_Info  \\\n",
       "2660    16:50        16:55       5m            2         No info   \n",
       "\n",
       "      Duration_In_Minutes  \n",
       "2660                  5.0  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[df_test.Duration_In_Minutes == 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instead of dropping the anomalous sample, replacing the anomalies with a similar sample.\n",
    "# df_test = df_test.drop([2660], axis=0)\n",
    "df_test.iloc[2660, 5] = '21:10'\n",
    "df_test.iloc[2660, 7] = '4h 15m'\n",
    "df_test.iloc[2660, 10] = 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                            Air India\n",
       "Date_of_Journey                   12/03/2019\n",
       "Source                                Mumbai\n",
       "Destination                        Hyderabad\n",
       "Route                  BOM → GOI → PNQ → HYD\n",
       "Dep_Time                               21:10\n",
       "Arrival_Time                           16:55\n",
       "Duration                              4h 15m\n",
       "Total_Stops                                2\n",
       "Additional_Info                      No info\n",
       "Duration_In_Minutes                      255\n",
       "Name: 2660, dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.iloc[2660]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>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>Duration_In_Minutes</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>655.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-06</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>240.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>780.0</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>170.0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration  Total_Stops              Additional_Info  \\\n",
       "0    17:30  04:25 07 Jun  10h 55m            1                      No info   \n",
       "1    06:20         10:20       4h            1                      No info   \n",
       "2    19:15  19:00 22 May  23h 45m            1  In-flight meal not included   \n",
       "3    08:00         21:00      13h            1                      No info   \n",
       "4    23:55  02:45 25 Jun   2h 50m            0                      No info   \n",
       "\n",
       "   Duration_In_Minutes  Day  Month  Year        Date  Weekday  \n",
       "0                655.0    6      6  2019  2019-06-06        3  \n",
       "1                240.0   12      5  2019  2019-05-12        6  \n",
       "2               1425.0   21      5  2019  2019-05-21        1  \n",
       "3                780.0   21      5  2019  2019-05-21        1  \n",
       "4                170.0   24      6  2019  2019-06-24        0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using a different method than train set to extract different features from the Date_Of_Journey\n",
    "df_test['Day'] = [x.day for x in pd.to_datetime(df_test.Date_of_Journey, dayfirst=True)]\n",
    "df_test['Month'] = [x.month for x in pd.to_datetime(df_test.Date_of_Journey, dayfirst=True)]\n",
    "df_test['Year'] = [x.year for x in pd.to_datetime(df_test.Date_of_Journey, dayfirst=True)]\n",
    "df_test['Date'] = [x.date() for x in pd.to_datetime(df_test.Date_of_Journey, dayfirst=True)]\n",
    "df_test['Weekday'] = [x.weekday() for x in pd.to_datetime(df_test.Date_of_Journey, dayfirst=True)]\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['Day_Of_Week'] = df_test['Weekday'].map(weekday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>Duration_In_Minutes</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Day_Of_Week</th>\n",
       "      <th>Weekend_Or_Not</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>655.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-06</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>240.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-12</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>780.0</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>170.0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration  Total_Stops              Additional_Info  \\\n",
       "0    17:30  04:25 07 Jun  10h 55m            1                      No info   \n",
       "1    06:20         10:20       4h            1                      No info   \n",
       "2    19:15  19:00 22 May  23h 45m            1  In-flight meal not included   \n",
       "3    08:00         21:00      13h            1                      No info   \n",
       "4    23:55  02:45 25 Jun   2h 50m            0                      No info   \n",
       "\n",
       "   Duration_In_Minutes  Day  Month  Year        Date  Weekday Day_Of_Week  \\\n",
       "0                655.0    6      6  2019  2019-06-06        3    Thursday   \n",
       "1                240.0   12      5  2019  2019-05-12        6      Sunday   \n",
       "2               1425.0   21      5  2019  2019-05-21        1     Tuesday   \n",
       "3                780.0   21      5  2019  2019-05-21        1     Tuesday   \n",
       "4                170.0   24      6  2019  2019-06-24        0      Monday   \n",
       "\n",
       "   Weekend_Or_Not  \n",
       "0               0  \n",
       "1               1  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['Weekend_Or_Not'] = df_test['Weekday'].map(weekend_or_not)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['Date_Of_Arrival'] = df_test.Arrival_Time.str.replace('[\\d]+:[\\d]+[ ]?', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "      <th>Weekday</th>\n",
       "      <th>Day_Of_Week</th>\n",
       "      <th>Weekend_Or_Not</th>\n",
       "      <th>Date_Of_Arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>655.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-06</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>0</td>\n",
       "      <td>07 Jun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>240.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-12</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>22 May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>780.0</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>170.0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "      <td>25 Jun</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time Arrival_Time Duration  Total_Stops              Additional_Info  \\\n",
       "0    17:30        04:25  10h 55m            1                      No info   \n",
       "1    06:20        10:20       4h            1                      No info   \n",
       "2    19:15        19:00  23h 45m            1  In-flight meal not included   \n",
       "3    08:00        21:00      13h            1                      No info   \n",
       "4    23:55        02:45   2h 50m            0                      No info   \n",
       "\n",
       "   Duration_In_Minutes  Day  Month  Year        Date  Weekday Day_Of_Week  \\\n",
       "0                655.0    6      6  2019  2019-06-06        3    Thursday   \n",
       "1                240.0   12      5  2019  2019-05-12        6      Sunday   \n",
       "2               1425.0   21      5  2019  2019-05-21        1     Tuesday   \n",
       "3                780.0   21      5  2019  2019-05-21        1     Tuesday   \n",
       "4                170.0   24      6  2019  2019-06-24        0      Monday   \n",
       "\n",
       "   Weekend_Or_Not Date_Of_Arrival  \n",
       "0               0          07 Jun  \n",
       "1               1                  \n",
       "2               0          22 May  \n",
       "3               0                  \n",
       "4               0          25 Jun  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['Arrival_Time'] = df_test['Arrival_Time'].str.extract('(\\d+:\\d+)')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dep_Time_Timeperiod divides Dep_Time into 6 different time periods.\n",
    "df_test['Dep_Time_Timeperiod'] = pd.cut(df_test.Dep_Time.str.extract('(\\A\\d{2}):', expand=False).astype(int), bins, labels=time_periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Morninig_To_Noon    686\n",
       "Early_Morning       572\n",
       "Evening_To_Night    535\n",
       "Night               436\n",
       "Noon_To_Evening     384\n",
       "Late_Night           58\n",
       "Name: Dep_Time_Timeperiod, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Dep_Time_Timeperiod.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Arrival_Time_Timeperiod divides Arrival_Time into 6 different time periods.\n",
    "df_test['Arrival_Time_Timeperiod'] = pd.cut(df_test.Arrival_Time.str.extract('(\\A\\d{2}):', expand=False).astype(int), bins, labels=time_periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</th>\n",
       "      <th>Year</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Day_Of_Week</th>\n",
       "      <th>Weekend_Or_Not</th>\n",
       "      <th>Date_Of_Arrival</th>\n",
       "      <th>Dep_Time_Timeperiod</th>\n",
       "      <th>Arrival_Time_Timeperiod</th>\n",
       "      <th>Dep_Time_Timeperiod_Encoded</th>\n",
       "      <th>Arrival_Time_Timeperiod_Encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-06</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>0</td>\n",
       "      <td>07 Jun</td>\n",
       "      <td>Evening_To_Night</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-12</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>Morninig_To_Noon</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>22 May</td>\n",
       "      <td>Evening_To_Night</td>\n",
       "      <td>Evening_To_Night</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Morninig_To_Noon</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "      <td>25 Jun</td>\n",
       "      <td>Night</td>\n",
       "      <td>Late_Night</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time Arrival_Time Duration  Total_Stops              Additional_Info  \\\n",
       "0    17:30        04:25  10h 55m            1                      No info   \n",
       "1    06:20        10:20       4h            1                      No info   \n",
       "2    19:15        19:00  23h 45m            1  In-flight meal not included   \n",
       "3    08:00        21:00      13h            1                      No info   \n",
       "4    23:55        02:45   2h 50m            0                      No info   \n",
       "\n",
       "                ...                Year        Date  Weekday  Day_Of_Week  \\\n",
       "0               ...                2019  2019-06-06        3     Thursday   \n",
       "1               ...                2019  2019-05-12        6       Sunday   \n",
       "2               ...                2019  2019-05-21        1      Tuesday   \n",
       "3               ...                2019  2019-05-21        1      Tuesday   \n",
       "4               ...                2019  2019-06-24        0       Monday   \n",
       "\n",
       "  Weekend_Or_Not  Date_Of_Arrival Dep_Time_Timeperiod  \\\n",
       "0              0           07 Jun    Evening_To_Night   \n",
       "1              1                        Early_Morning   \n",
       "2              0           22 May    Evening_To_Night   \n",
       "3              0                     Morninig_To_Noon   \n",
       "4              0           25 Jun               Night   \n",
       "\n",
       "   Arrival_Time_Timeperiod Dep_Time_Timeperiod_Encoded  \\\n",
       "0            Early_Morning                           4   \n",
       "1         Morninig_To_Noon                           1   \n",
       "2         Evening_To_Night                           4   \n",
       "3                    Night                           2   \n",
       "4               Late_Night                           5   \n",
       "\n",
       "  Arrival_Time_Timeperiod_Encoded  \n",
       "0                               1  \n",
       "1                               2  \n",
       "2                               4  \n",
       "3                               5  \n",
       "4                               0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding the different time periods into numeric values, because LabelEncoder won't handle categorical dtype.\n",
    "df_test['Dep_Time_Timeperiod_Encoded'] = df_test['Dep_Time_Timeperiod'].map(time_period_mapping)\n",
    "df_test['Arrival_Time_Timeperiod_Encoded'] = df_test['Arrival_Time_Timeperiod'].map(time_period_mapping)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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",
       "Duration_In_Minutes                0\n",
       "Day                                0\n",
       "Month                              0\n",
       "Year                               0\n",
       "Date                               0\n",
       "Weekday                            0\n",
       "Day_Of_Week                        0\n",
       "Weekend_Or_Not                     0\n",
       "Date_Of_Arrival                    0\n",
       "Dep_Time_Timeperiod                0\n",
       "Arrival_Time_Timeperiod            0\n",
       "Dep_Time_Timeperiod_Encoded        0\n",
       "Arrival_Time_Timeperiod_Encoded    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating new samples on the train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
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       "      <th>Additional_Info</th>\n",
       "      <th>...</th>\n",
       "      <th>Year</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Day_Of_Week</th>\n",
       "      <th>Weekend_Or_Not</th>\n",
       "      <th>Date_Of_Arrival</th>\n",
       "      <th>Dep_Time_Timeperiod</th>\n",
       "      <th>Arrival_Time_Timeperiod</th>\n",
       "      <th>Dep_Time_Timeperiod_Encoded</th>\n",
       "      <th>Arrival_Time_Timeperiod_Encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BOM → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>21:50</td>\n",
       "      <td>16h</td>\n",
       "      <td>3</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>Night</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → VGA → TIR → HYD</td>\n",
       "      <td>08:00</td>\n",
       "      <td>18:10</td>\n",
       "      <td>10h 10m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-06</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Morninig_To_Noon</td>\n",
       "      <td>Evening_To_Night</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → IXC → DEL → HYD</td>\n",
       "      <td>07:15</td>\n",
       "      <td>16:35</td>\n",
       "      <td>9h 20m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-12</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>Evening_To_Night</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air India</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → TRV → DEL</td>\n",
       "      <td>07:30</td>\n",
       "      <td>22:35</td>\n",
       "      <td>15h 5m</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-12</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>Night</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → DEL → BLR</td>\n",
       "      <td>22:30</td>\n",
       "      <td>10:40</td>\n",
       "      <td>12h 10m</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>25 Mar</td>\n",
       "      <td>Night</td>\n",
       "      <td>Morninig_To_Noon</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination  \\\n",
       "0    Air India       1/04/2019   Kolkata    Banglore   \n",
       "2    Air India       6/03/2019    Mumbai   Hyderabad   \n",
       "3  Jet Airways      12/03/2019    Mumbai   Hyderabad   \n",
       "4    Air India      12/03/2019  Banglore   New Delhi   \n",
       "6     Air Asia      24/03/2019   Kolkata    Banglore   \n",
       "\n",
       "                         Route Dep_Time Arrival_Time Duration  Total_Stops  \\\n",
       "0  CCU → IXR → BBI → BOM → BLR    05:50        21:50      16h            3   \n",
       "2        BOM → VGA → TIR → HYD    08:00        18:10  10h 10m            2   \n",
       "3        BOM → IXC → DEL → HYD    07:15        16:35   9h 20m            2   \n",
       "4              BLR → TRV → DEL    07:30        22:35   15h 5m            1   \n",
       "6              CCU → DEL → BLR    22:30        10:40  12h 10m            1   \n",
       "\n",
       "  Additional_Info               ...                Year        Date  Weekday  \\\n",
       "0         No info               ...                2019  2019-04-01        0   \n",
       "2         No info               ...                2019  2019-03-06        2   \n",
       "3         No info               ...                2019  2019-03-12        1   \n",
       "4         No info               ...                2019  2019-03-12        1   \n",
       "6         No info               ...                2019  2019-03-24        6   \n",
       "\n",
       "   Day_Of_Week Weekend_Or_Not  Date_Of_Arrival Dep_Time_Timeperiod  \\\n",
       "0       Monday              0                        Early_Morning   \n",
       "2    Wednesday              0                     Morninig_To_Noon   \n",
       "3      Tuesday              0                        Early_Morning   \n",
       "4      Tuesday              0                        Early_Morning   \n",
       "6       Sunday              1           25 Mar               Night   \n",
       "\n",
       "   Arrival_Time_Timeperiod Dep_Time_Timeperiod_Encoded  \\\n",
       "0                    Night                           1   \n",
       "2         Evening_To_Night                           2   \n",
       "3         Evening_To_Night                           1   \n",
       "4                    Night                           1   \n",
       "6         Morninig_To_Noon                           5   \n",
       "\n",
       "  Arrival_Time_Timeperiod_Encoded  \n",
       "0                               5  \n",
       "2                               4  \n",
       "3                               4  \n",
       "4                               5  \n",
       "6                               2  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding some new routes and dep_time to the train set, which are present in test set.\n",
    "# These samples were found during Label Encoding the test set. Since they were not present in the\n",
    "# training set, the LabelEncoder couldn't encode them in the test set.\n",
    "new_routes = df_test[df_test.Route == 'CCU → IXR → BBI → BOM → BLR']\n",
    "new_routes = pd.concat([new_routes, df_test[df_test.Route == 'BOM → VGA → TIR → HYD'], \n",
    "                        df_test[df_test.Route == 'BOM → IXC → DEL → HYD'], \n",
    "                        df_test[df_test.Route == 'BLR → TRV → DEL'], \n",
    "                        df_test[df_test.Dep_Time == '22:30']], ignore_index=True)\n",
    "new_routes = new_routes.drop([1,5])\n",
    "new_routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14770.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating prices for the new samples from similar samples in training set.\n",
    "df[(df.Airline == 'Air India') & (df.Source == 'Kolkata') & (df.Destination == 'Banglore') \n",
    "   & (df.Total_Stops == 3) & (df.Duration_In_Minutes < 1080)]['Price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12761.142857142857"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Airline == 'Air India') & (df.Source == 'Mumbai') & (df.Destination == 'Hyderabad') \n",
    "   & (df.Total_Stops == 2) & (df.Duration_In_Minutes < 840) \n",
    "   & (df.Duration_In_Minutes > 480)]['Price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23685.5"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Airline == 'Jet Airways') & (df.Source == 'Mumbai') & (df.Destination == 'Hyderabad') \n",
    "   & (df.Total_Stops == 2) & (df.Duration_In_Minutes < 840) \n",
    "   & (df.Duration_In_Minutes > 480)]['Price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9366.689655172413"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Airline == 'Air India') & (df.Source == 'Banglore') & (df.Destination == 'New Delhi') \n",
    "   & (df.Total_Stops == 1) & (df.Duration_In_Minutes < 1080) \n",
    "   & (df.Duration_In_Minutes > 720) & (df.Additional_Info == 'No info')]['Price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6106.65"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Airline == 'Air Asia') & (df.Source == 'Kolkata') & (df.Destination == 'Banglore') \n",
    "   & (df.Total_Stops == 1) & (df.Duration_In_Minutes < 900) \n",
    "   & (df.Duration_In_Minutes > 600) & (df.Additional_Info == 'No info')]['Price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_prices = [14770.0, 12761.14, 23685.5, 9366.69, 6106.65]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inserting new prices into the new_routes dataframe.\n",
    "new_routes.insert(10, 'Price', new_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Appending the new_routes dataframe into df.\n",
    "df = pd.concat([df, new_routes], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing a baseline model and Improving it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, [0,2,3,4,5,6,8,9,11,12,13,14,15,16,18,22,23]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IndiGo', 'Banglore', 'New Delhi', 'BLR → DEL', '22:20', '01:10',\n",
       "       0, 'No info', 170.0, 24, 3, 2019, datetime.date(2019, 3, 24), 6, 1,\n",
       "       5, 0], dtype=object)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.iloc[:, 10].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoding various features.\n",
    "labelencoder_airline = LabelEncoder()\n",
    "X[:, 0] = labelencoder_airline.fit_transform(X[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_source = LabelEncoder()\n",
    "X[:, 1] = labelencoder_source.fit_transform(X[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_destination = LabelEncoder()\n",
    "X[:, 2] = labelencoder_destination.fit_transform(X[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_route = LabelEncoder()\n",
    "X[:, 3] = labelencoder_route.fit_transform(X[:, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_deptime = LabelEncoder()\n",
    "X[:, 4] = labelencoder_deptime.fit_transform(X[:, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_arrivaltime = LabelEncoder()\n",
    "X[:, 5] = labelencoder_arrivaltime.fit_transform(X[:, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_addinfo = LabelEncoder()\n",
    "X[:, 7] = labelencoder_addinfo.fit_transform(X[:, 7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_date = LabelEncoder()\n",
    "X[:, 12] = labelencoder_date.fit_transform(X[:, 12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting dataset into train and validation sets. (80% training, 20% validation)\n",
    "'''\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a baseline Random Forest Regressor.\n",
    "regressor = RandomForestRegressor(n_estimators=200, random_state=42, oob_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performing K-fold Cross-validation.\n",
    "scores = cross_val_score(estimator=regressor, X=X, y=y, cv=5, scoring='r2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8804, 0.8805, 0.8897, 0.9088, 0.8476])"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8814123135037566"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.019845328543575368"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
       "           oob_score=True, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model on whole dataset, for more train samples.\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8797613612355373"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regressor's score on the samples which were not seen by the Random Forest algorithm.\n",
    "# oob_score works as a prediction on a validation or train set.\n",
    "regressor.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "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', 'Duration_In_Minutes', 'Day', 'Month',\n",
       "       'Year', 'Date', 'Weekday', 'Day_Of_Week', 'Weekend_Or_Not',\n",
       "       'Date_Of_Arrival', 'Dep_Time_Timeperiod', 'Arrival_Time_Timeperiod',\n",
       "       'Dep_Time_Timeperiod_Encoded', 'Arrival_Time_Timeperiod_Encoded'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_column_names = df.columns\n",
    "df_column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_column_names = ['Airline', 'Source', 'Destination', 'Route','Dep_Time', 'Arrival_Time', \n",
    "                  'Total_Stops','Additional_Info', 'Duration_In_Minutes', 'Day', 'Month',\n",
    "                  'Year', 'Date', 'Weekday', 'Weekend_Or_Not', 'Dep_Time_Timeperiod_Encoded', \n",
    "                  'Arrival_Time_Timeperiod_Encoded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airline 0.10399521186147237\n",
      "Source 0.006471789115113388\n",
      "Destination 0.006006218421986081\n",
      "Route 0.04517634158811682\n",
      "Dep_Time 0.030013110455937617\n",
      "Arrival_Time 0.028350140512884318\n",
      "Total_Stops 0.04524771391695864\n",
      "Additional_Info 0.05089336182619321\n",
      "Duration_In_Minutes 0.463453835668864\n",
      "Day 0.0187711268050384\n",
      "Month 0.007856905736652106\n",
      "Year 0.0\n",
      "Date 0.16750808942574508\n",
      "Weekday 0.008032699713460524\n",
      "Weekend_Or_Not 0.0024566889206741713\n",
      "Dep_Time_Timeperiod_Encoded 0.007946933954097872\n",
      "Arrival_Time_Timeperiod_Encoded 0.007819832076805205\n"
     ]
    }
   ],
   "source": [
    "# Checking feature importances of various features.\n",
    "for name, score in zip(X_column_names, regressor.feature_importances_):\n",
    "    print(name, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing features with least importances. \n",
    "# Removing Year.\n",
    "X_edit = X[:, [0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,16]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 5, 18, 211, 12, 0, 7, 170.0, 24, 3, 8, 6, 1, 5, 0],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_edit[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(estimator=regressor, X=X_edit, y=y, cv=5, scoring='r2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.881 , 0.8803, 0.8905, 0.9082, 0.8465])"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8812975453732543"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020084282517737414"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Weekend_Or_Not and Year.\n",
    "X_edit = X[:, [0,1,2,3,4,5,6,7,8,9,10,12,13,15,16]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 5, 18, 211, 12, 0, 7, 170.0, 24, 3, 8, 6, 5, 0], dtype=object)"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_edit[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(estimator=regressor, X=X_edit, y=y, cv=5, scoring='r2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8804, 0.8799, 0.8901, 0.9091, 0.8494])"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8817785327434088"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01933455868175054"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final train set. Only Year was removed.\n",
    "X = X[:, [0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,16]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 5, 18, 211, 12, 0, 7, 170.0, 24, 3, 8, 6, 1, 5, 0],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
       "           oob_score=True, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting X and y arrays to the regressor.\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9796461412451257"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regressor's score on train set.\n",
    "regressor.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.879777597583748"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regressor's score on the samples which were not seen by the Random Forest algorithm.\n",
    "regressor.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying Grid Search to find the best model parameters for the regressor.\n",
    "parameters = [{'n_estimators':[200, 300, 400, 500], 'max_features':[2,3,4,6,8,10,12,14,'auto']}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(estimator=regressor, param_grid=parameters, cv=5)\n",
    "grid_search = grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8980150838372084"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 500}"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regressor with the best score in GridSearch.\n",
    "regressor_gs = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=8, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=500, n_jobs=1, oob_score=True, random_state=42,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting X, y to the best regressor, regressor_gs.\n",
    "regressor_gs.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8982595930363942"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# oob_score using regressor_gs.\n",
    "regressor_gs.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making predictions based on the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = df_test.iloc[:, [0,2,3,4,5,6,8,9,10,11,12,13,14,15,17,21,22]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Jet Airways', 'Delhi', 'Cochin', 'DEL → BOM → COK', '17:30',\n",
       "       '04:25', 1, 'No info', 655.0, 6, 6, 2019,\n",
       "       datetime.date(2019, 6, 6), 3, 0, 4, 1], dtype=object)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing features with low feature importance i.e. Year\n",
    "X_test = X_test[:, [0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,16]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Jet Airways', 'Delhi', 'Cochin', 'DEL → BOM → COK', '17:30',\n",
       "       '04:25', 1, 'No info', 655.0, 6, 6, datetime.date(2019, 6, 6), 3,\n",
       "       0, 4, 1], dtype=object)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoding the features.\n",
    "X_test[:, 0] = labelencoder_airline.transform(X_test[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[:, 1] = labelencoder_source.transform(X_test[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[:, 2] = labelencoder_destination.transform(X_test[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[:, 3] = labelencoder_route.transform(X_test[:, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[:, 4] = labelencoder_deptime.transform(X_test[:, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[:, 5] = labelencoder_arrivaltime.transform(X_test[:, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[:, 7] = labelencoder_addinfo.transform(X_test[:, 7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[:, 11] = labelencoder_date.transform(X_test[:, 11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions based on the regressor made using GridSearch.\n",
    "y_pred = regressor_gs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14713.886 ,  4386.092 , 12898.    , ..., 15918.4653, 13914.348 ,\n",
       "        7853.036 ])"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making dataframe of predictions.\n",
    "pred_df  = pd.DataFrame(y_pred, columns=['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting the predictions to an Excel file.\n",
    "pred_df.to_excel('Price_Prediction.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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