Flight Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0f95116d",
   "metadata": {},
   "source": [
    "# Flight price prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9a9b83a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "095bd85e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_excel(\"./Data_Train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3630ec8b",
   "metadata": {},
   "outputs": [
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6def30b0",
   "metadata": {},
   "outputs": [
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       "    <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_excel(\"./Test_set.xlsx\")\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "78fa5e64",
   "metadata": {},
   "outputs": [
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       "      <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>3897.0</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 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</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 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</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 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</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 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302.0</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    non-stop         No info   3897.0  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662.0  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882.0  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218.0  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = train_df.append(test_df)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0446e70",
   "metadata": {},
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       "      <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>Price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2666</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → DEL → BLR</td>\n",
       "      <td>20:30</td>\n",
       "      <td>20:25 07 Jun</td>\n",
       "      <td>23h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2667</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>27/03/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>14:20</td>\n",
       "      <td>16:55</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>21:50</td>\n",
       "      <td>04:25 07 Mar</td>\n",
       "      <td>6h 35m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2669</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>04:00</td>\n",
       "      <td>19:15</td>\n",
       "      <td>15h 15m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2670</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>15/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>04:55</td>\n",
       "      <td>19:15</td>\n",
       "      <td>14h 20m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Airline Date_of_Journey   Source Destination            Route  \\\n",
       "2666          Air India       6/06/2019  Kolkata    Banglore  CCU → DEL → BLR   \n",
       "2667             IndiGo      27/03/2019  Kolkata    Banglore        CCU → BLR   \n",
       "2668        Jet Airways       6/03/2019    Delhi      Cochin  DEL → BOM → COK   \n",
       "2669          Air India       6/03/2019    Delhi      Cochin  DEL → BOM → COK   \n",
       "2670  Multiple carriers      15/06/2019    Delhi      Cochin  DEL → BOM → COK   \n",
       "\n",
       "     Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "2666    20:30  20:25 07 Jun  23h 55m      1 stop         No info    NaN  \n",
       "2667    14:20         16:55   2h 35m    non-stop         No info    NaN  \n",
       "2668    21:50  04:25 07 Mar   6h 35m      1 stop         No info    NaN  \n",
       "2669    04:00         19:15  15h 15m      1 stop         No info    NaN  \n",
       "2670    04:55         19:15  14h 20m      1 stop         No info    NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NAN values in price is because of test dataset merging\n",
    "final_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe7b8d64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13354 entries, 0 to 2670\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Airline          13354 non-null  object \n",
      " 1   Date_of_Journey  13354 non-null  object \n",
      " 2   Source           13354 non-null  object \n",
      " 3   Destination      13354 non-null  object \n",
      " 4   Route            13353 non-null  object \n",
      " 5   Dep_Time         13354 non-null  object \n",
      " 6   Arrival_Time     13354 non-null  object \n",
      " 7   Duration         13354 non-null  object \n",
      " 8   Total_Stops      13353 non-null  object \n",
      " 9   Additional_Info  13354 non-null  object \n",
      " 10  Price            10683 non-null  float64\n",
      "dtypes: float64(1), object(10)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ff2901f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10683.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9087.064121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4611.359167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79512.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price\n",
       "count  10683.000000\n",
       "mean    9087.064121\n",
       "std     4611.359167\n",
       "min     1759.000000\n",
       "25%     5277.000000\n",
       "50%     8372.000000\n",
       "75%    12373.000000\n",
       "max    79512.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa0c469c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature engineering to extract date from the object\n",
    "final_df['date'] = final_df['Date_of_Journey'].str.split('/').str[0]\n",
    "final_df['month'] = final_df['Date_of_Journey'].str.split('/').str[1]\n",
    "final_df['year'] = final_df['Date_of_Journey'].str.split('/').str[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "347727c7",
   "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",
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       "    }\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>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</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>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>03</td>\n",
       "      <td>2019</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>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>05</td>\n",
       "      <td>2019</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>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>06</td>\n",
       "      <td>2019</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>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>05</td>\n",
       "      <td>2019</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>13302.0</td>\n",
       "      <td>01</td>\n",
       "      <td>03</td>\n",
       "      <td>2019</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 date  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897.0   24   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662.0    1   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882.0    9   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218.0   12   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302.0   01   \n",
       "\n",
       "  month  year  \n",
       "0    03  2019  \n",
       "1    05  2019  \n",
       "2    06  2019  \n",
       "3    05  2019  \n",
       "4    03  2019  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f837133",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['date']=final_df['date'].astype(int)\n",
    "final_df['month']=final_df['month'].astype(int)\n",
    "final_df['year']=final_df['year'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b05ebd67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13354 entries, 0 to 2670\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Airline          13354 non-null  object \n",
      " 1   Date_of_Journey  13354 non-null  object \n",
      " 2   Source           13354 non-null  object \n",
      " 3   Destination      13354 non-null  object \n",
      " 4   Route            13353 non-null  object \n",
      " 5   Dep_Time         13354 non-null  object \n",
      " 6   Arrival_Time     13354 non-null  object \n",
      " 7   Duration         13354 non-null  object \n",
      " 8   Total_Stops      13353 non-null  object \n",
      " 9   Additional_Info  13354 non-null  object \n",
      " 10  Price            10683 non-null  float64\n",
      " 11  date             13354 non-null  int64  \n",
      " 12  month            13354 non-null  int64  \n",
      " 13  year             13354 non-null  int64  \n",
      "dtypes: float64(1), int64(3), object(10)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba51424c",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop(\"Date_of_Journey\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ac1c2ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>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>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>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>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>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>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>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>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>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>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    22:20   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL    16:50   \n",
       "\n",
       "   Arrival_Time Duration Total_Stops Additional_Info    Price  date  month  \\\n",
       "0  01:10 22 Mar   2h 50m    non-stop         No info   3897.0    24      3   \n",
       "1         13:15   7h 25m     2 stops         No info   7662.0     1      5   \n",
       "2  04:25 10 Jun      19h     2 stops         No info  13882.0     9      6   \n",
       "3         23:30   5h 25m      1 stop         No info   6218.0    12      5   \n",
       "4         21:35   4h 45m      1 stop         No info  13302.0     1      3   \n",
       "\n",
       "   year  \n",
       "0  2019  \n",
       "1  2019  \n",
       "2  2019  \n",
       "3  2019  \n",
       "4  2019  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41870ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Arrival_Time'] = final_df['Arrival_Time'].str.split(\" \").str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "255e2a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>IndiGo</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>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>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>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>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>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>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>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    22:20   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL    16:50   \n",
       "\n",
       "  Arrival_Time Duration Total_Stops Additional_Info    Price  date  month  \\\n",
       "0        01:10   2h 50m    non-stop         No info   3897.0    24      3   \n",
       "1        13:15   7h 25m     2 stops         No info   7662.0     1      5   \n",
       "2        04:25      19h     2 stops         No info  13882.0     9      6   \n",
       "3        23:30   5h 25m      1 stop         No info   6218.0    12      5   \n",
       "4        21:35   4h 45m      1 stop         No info  13302.0     1      3   \n",
       "\n",
       "   year  \n",
       "0  2019  \n",
       "1  2019  \n",
       "2  2019  \n",
       "3  2019  \n",
       "4  2019  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b37e4677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline               0\n",
       "Source                0\n",
       "Destination           0\n",
       "Route                 1\n",
       "Dep_Time              0\n",
       "Arrival_Time          0\n",
       "Duration              0\n",
       "Total_Stops           1\n",
       "Additional_Info       0\n",
       "Price              2671\n",
       "date                  0\n",
       "month                 0\n",
       "year                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "642fda06",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Arrival_hour'] = final_df['Arrival_Time'].apply(lambda x: x.split(':')[0])\n",
    "final_df['Arrival_minute'] = final_df['Arrival_Time'].apply(lambda x: x.split(':')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "20930921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
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       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>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>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>04</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>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>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>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>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    22:20   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL    16:50   \n",
       "\n",
       "  Arrival_Time Duration Total_Stops Additional_Info    Price  date  month  \\\n",
       "0        01:10   2h 50m    non-stop         No info   3897.0    24      3   \n",
       "1        13:15   7h 25m     2 stops         No info   7662.0     1      5   \n",
       "2        04:25      19h     2 stops         No info  13882.0     9      6   \n",
       "3        23:30   5h 25m      1 stop         No info   6218.0    12      5   \n",
       "4        21:35   4h 45m      1 stop         No info  13302.0     1      3   \n",
       "\n",
       "   year Arrival_hour Arrival_minute  \n",
       "0  2019           01             10  \n",
       "1  2019           13             15  \n",
       "2  2019           04             25  \n",
       "3  2019           23             30  \n",
       "4  2019           21             35  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fd8abac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Arrival_hour']=final_df['Arrival_hour'].astype(int)\n",
    "final_df['Arrival_minute']=final_df['Arrival_minute'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf3962f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13354 entries, 0 to 2670\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Airline          13354 non-null  object \n",
      " 1   Source           13354 non-null  object \n",
      " 2   Destination      13354 non-null  object \n",
      " 3   Route            13353 non-null  object \n",
      " 4   Dep_Time         13354 non-null  object \n",
      " 5   Arrival_Time     13354 non-null  object \n",
      " 6   Duration         13354 non-null  object \n",
      " 7   Total_Stops      13353 non-null  object \n",
      " 8   Additional_Info  13354 non-null  object \n",
      " 9   Price            10683 non-null  float64\n",
      " 10  date             13354 non-null  int64  \n",
      " 11  month            13354 non-null  int64  \n",
      " 12  year             13354 non-null  int64  \n",
      " 13  Arrival_hour     13354 non-null  int64  \n",
      " 14  Arrival_minute   13354 non-null  int64  \n",
      "dtypes: float64(1), int64(5), object(9)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0aa7b80a",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop(\"Arrival_Time\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8cf10bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
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       "  <tbody>\n",
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       "      <td>IndiGo</td>\n",
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       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
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       "      <td>3897.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    22:20   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL    16:50   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info    Price  date  month  year  Arrival_hour  \\\n",
       "0    non-stop         No info   3897.0    24      3  2019             1   \n",
       "1     2 stops         No info   7662.0     1      5  2019            13   \n",
       "2     2 stops         No info  13882.0     9      6  2019             4   \n",
       "3      1 stop         No info   6218.0    12      5  2019            23   \n",
       "4      1 stop         No info  13302.0     1      3  2019            21   \n",
       "\n",
       "   Arrival_minute  \n",
       "0              10  \n",
       "1              15  \n",
       "2              25  \n",
       "3              30  \n",
       "4              35  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "76fb66cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Dep_hour'] = final_df['Dep_Time'].apply(lambda x: x.split(':')[0])\n",
    "final_df['Dep_minute'] = final_df['Dep_Time'].apply(lambda x: x.split(':')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f7d8cde9",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>05</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    22:20   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL    16:50   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info    Price  date  month  year  Arrival_hour  \\\n",
       "0    non-stop         No info   3897.0    24      3  2019             1   \n",
       "1     2 stops         No info   7662.0     1      5  2019            13   \n",
       "2     2 stops         No info  13882.0     9      6  2019             4   \n",
       "3      1 stop         No info   6218.0    12      5  2019            23   \n",
       "4      1 stop         No info  13302.0     1      3  2019            21   \n",
       "\n",
       "   Arrival_minute Dep_hour Dep_minute  \n",
       "0              10       22         20  \n",
       "1              15       05         50  \n",
       "2              25       09         25  \n",
       "3              30       18         05  \n",
       "4              35       16         50  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d4a47e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Dep_hour']=final_df['Dep_hour'].astype(int)\n",
    "final_df['Dep_minute']=final_df['Dep_minute'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "94858eda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13354 entries, 0 to 2670\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Airline          13354 non-null  object \n",
      " 1   Source           13354 non-null  object \n",
      " 2   Destination      13354 non-null  object \n",
      " 3   Route            13353 non-null  object \n",
      " 4   Dep_Time         13354 non-null  object \n",
      " 5   Duration         13354 non-null  object \n",
      " 6   Total_Stops      13353 non-null  object \n",
      " 7   Additional_Info  13354 non-null  object \n",
      " 8   Price            10683 non-null  float64\n",
      " 9   date             13354 non-null  int64  \n",
      " 10  month            13354 non-null  int64  \n",
      " 11  year             13354 non-null  int64  \n",
      " 12  Arrival_hour     13354 non-null  int64  \n",
      " 13  Arrival_minute   13354 non-null  int64  \n",
      " 14  Dep_hour         13354 non-null  int64  \n",
      " 15  Dep_minute       13354 non-null  int64  \n",
      "dtypes: float64(1), int64(7), object(8)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "64c802b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop(\"Dep_Time\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b491798a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info    Price  date  month  year  Arrival_hour  \\\n",
       "0    non-stop         No info   3897.0    24      3  2019             1   \n",
       "1     2 stops         No info   7662.0     1      5  2019            13   \n",
       "2     2 stops         No info  13882.0     9      6  2019             4   \n",
       "3      1 stop         No info   6218.0    12      5  2019            23   \n",
       "4      1 stop         No info  13302.0     1      3  2019            21   \n",
       "\n",
       "   Arrival_minute  Dep_hour  Dep_minute  \n",
       "0              10        22          20  \n",
       "1              15         5          50  \n",
       "2              25         9          25  \n",
       "3              30        18           5  \n",
       "4              35        16          50  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c23ee551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', nan, '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a82bf2a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9039</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23h 40m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No info</td>\n",
       "      <td>7480.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline Source Destination Route Duration Total_Stops Additional_Info  \\\n",
       "9039  Air India  Delhi      Cochin   NaN  23h 40m         NaN         No info   \n",
       "\n",
       "       Price  date  month  year  Arrival_hour  Arrival_minute  Dep_hour  \\\n",
       "9039  7480.0     6      5  2019             9              25         9   \n",
       "\n",
       "      Dep_minute  \n",
       "9039          45  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df[\"Total_Stops\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "90e2b17f",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Total_Stops'] = final_df['Total_Stops'].map({'non-stop':0, '1 stop':1, '2 stops':2, '3 stops':3, '4 stops':4, 'nan':1  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a6bf11c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
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       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No info</td>\n",
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       "      <td>24</td>\n",
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       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "   Total_Stops Additional_Info    Price  date  month  year  Arrival_hour  \\\n",
       "0          0.0         No info   3897.0    24      3  2019             1   \n",
       "1          2.0         No info   7662.0     1      5  2019            13   \n",
       "2          2.0         No info  13882.0     9      6  2019             4   \n",
       "3          1.0         No info   6218.0    12      5  2019            23   \n",
       "4          1.0         No info  13302.0     1      3  2019            21   \n",
       "\n",
       "   Arrival_minute  Dep_hour  Dep_minute  \n",
       "0              10        22          20  \n",
       "1              15         5          50  \n",
       "2              25         9          25  \n",
       "3              30        18           5  \n",
       "4              35        16          50  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "445fad75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13354 entries, 0 to 2670\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Airline          13354 non-null  object \n",
      " 1   Source           13354 non-null  object \n",
      " 2   Destination      13354 non-null  object \n",
      " 3   Route            13353 non-null  object \n",
      " 4   Duration         13354 non-null  object \n",
      " 5   Total_Stops      13353 non-null  float64\n",
      " 6   Additional_Info  13354 non-null  object \n",
      " 7   Price            10683 non-null  float64\n",
      " 8   date             13354 non-null  int64  \n",
      " 9   month            13354 non-null  int64  \n",
      " 10  year             13354 non-null  int64  \n",
      " 11  Arrival_hour     13354 non-null  int64  \n",
      " 12  Arrival_minute   13354 non-null  int64  \n",
      " 13  Dep_hour         13354 non-null  int64  \n",
      " 14  Dep_minute       13354 non-null  int64  \n",
      "dtypes: float64(2), int64(7), object(6)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c45dd5db",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop(\"Route\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "89f13a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>19h</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Duration  Total_Stops Additional_Info  \\\n",
       "0       IndiGo  Banglore   New Delhi   2h 50m          0.0         No info   \n",
       "1    Air India   Kolkata    Banglore   7h 25m          2.0         No info   \n",
       "2  Jet Airways     Delhi      Cochin      19h          2.0         No info   \n",
       "3       IndiGo   Kolkata    Banglore   5h 25m          1.0         No info   \n",
       "4       IndiGo  Banglore   New Delhi   4h 45m          1.0         No info   \n",
       "\n",
       "     Price  date  month  year  Arrival_hour  Arrival_minute  Dep_hour  \\\n",
       "0   3897.0    24      3  2019             1              10        22   \n",
       "1   7662.0     1      5  2019            13              15         5   \n",
       "2  13882.0     9      6  2019             4              25         9   \n",
       "3   6218.0    12      5  2019            23              30        18   \n",
       "4  13302.0     1      3  2019            21              35        16   \n",
       "\n",
       "   Dep_minute  \n",
       "0          20  \n",
       "1          50  \n",
       "2          25  \n",
       "3           5  \n",
       "4          50  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c456d2e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No info', 'In-flight meal not included',\n",
       "       'No check-in baggage included', '1 Short layover', 'No Info',\n",
       "       '1 Long layover', 'Change airports', 'Business class',\n",
       "       'Red-eye flight', '2 Long layover'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Additional_Info'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4e0b8898",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Duration'] = final_df['Duration'].str.replace(\"h\", '*60').str.replace('m','*1').str.replace(' ','+').apply(eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c2733a61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_minute</th>\n",
       "      <th>dur_hr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>2h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>445</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>7h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>1140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>19h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>325</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>285</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>4h</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Duration  Total_Stops Additional_Info  \\\n",
       "0       IndiGo  Banglore   New Delhi       170          0.0         No info   \n",
       "1    Air India   Kolkata    Banglore       445          2.0         No info   \n",
       "2  Jet Airways     Delhi      Cochin      1140          2.0         No info   \n",
       "3       IndiGo   Kolkata    Banglore       325          1.0         No info   \n",
       "4       IndiGo  Banglore   New Delhi       285          1.0         No info   \n",
       "\n",
       "     Price  date  month  year  Arrival_hour  Arrival_minute  Dep_hour  \\\n",
       "0   3897.0    24      3  2019             1              10        22   \n",
       "1   7662.0     1      5  2019            13              15         5   \n",
       "2  13882.0     9      6  2019             4              25         9   \n",
       "3   6218.0    12      5  2019            23              30        18   \n",
       "4  13302.0     1      3  2019            21              35        16   \n",
       "\n",
       "   Dep_minute dur_hr  \n",
       "0          20     2h  \n",
       "1          50     7h  \n",
       "2          25    19h  \n",
       "3           5     5h  \n",
       "4          50     4h  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9f15ed6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13354 entries, 0 to 2670\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Airline          13354 non-null  object \n",
      " 1   Source           13354 non-null  object \n",
      " 2   Destination      13354 non-null  object \n",
      " 3   Duration         13354 non-null  int64  \n",
      " 4   Total_Stops      13353 non-null  float64\n",
      " 5   Additional_Info  13354 non-null  object \n",
      " 6   Price            10683 non-null  float64\n",
      " 7   date             13354 non-null  int64  \n",
      " 8   month            13354 non-null  int64  \n",
      " 9   year             13354 non-null  int64  \n",
      " 10  Arrival_hour     13354 non-null  int64  \n",
      " 11  Arrival_minute   13354 non-null  int64  \n",
      " 12  Dep_hour         13354 non-null  int64  \n",
      " 13  Dep_minute       13354 non-null  int64  \n",
      " 14  dur_hr           13354 non-null  object \n",
      "dtypes: float64(2), int64(8), object(5)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6074430e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IndiGo', 'Air India', 'Jet Airways', 'SpiceJet',\n",
       "       'Multiple carriers', 'GoAir', 'Vistara', 'Air Asia',\n",
       "       'Vistara Premium economy', 'Jet Airways Business',\n",
       "       'Multiple carriers Premium economy', 'Trujet'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Airline'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "14481992",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "800d23fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Airline'] = labelencoder.fit_transform(final_df['Airline'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "37be590c",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Source'] = labelencoder.fit_transform(final_df['Source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b253dd51",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Destination'] = labelencoder.fit_transform(final_df['Destination'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "572fb0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Additional_Info'] = labelencoder.fit_transform(final_df['Additional_Info'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "02790b61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 50,
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  {
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       "   Airline  Source  Destination  Duration  Total_Stops  Additional_Info  \\\n",
       "0        3       0            5       170          0.0                8   \n",
       "1        1       3            0       445          2.0                8   \n",
       "2        4       2            1      1140          2.0                8   \n",
       "3        3       3            0       325          1.0                8   \n",
       "4        3       0            5       285          1.0                8   \n",
       "\n",
       "     Price  date  month  year  Arrival_hour  Arrival_minute  Dep_hour  \\\n",
       "0   3897.0    24      3  2019             1              10        22   \n",
       "1   7662.0     1      5  2019            13              15         5   \n",
       "2  13882.0     9      6  2019             4              25         9   \n",
       "3   6218.0    12      5  2019            23              30        18   \n",
       "4  13302.0     1      3  2019            21              35        16   \n",
       "\n",
       "   Dep_minute dur_hr  \n",
       "0          20     2h  \n",
       "1          50     7h  \n",
       "2          25    19h  \n",
       "3           5     5h  \n",
       "4          50     4h  "
      ]
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5ff5ae4a",
   "metadata": {},
   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13354 entries, 0 to 2670\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Airline          13354 non-null  int64  \n",
      " 1   Source           13354 non-null  int64  \n",
      " 2   Destination      13354 non-null  int64  \n",
      " 3   Duration         13354 non-null  int64  \n",
      " 4   Total_Stops      13353 non-null  float64\n",
      " 5   Additional_Info  13354 non-null  int64  \n",
      " 6   Price            10683 non-null  float64\n",
      " 7   date             13354 non-null  int64  \n",
      " 8   month            13354 non-null  int64  \n",
      " 9   year             13354 non-null  int64  \n",
      " 10  Arrival_hour     13354 non-null  int64  \n",
      " 11  Arrival_minute   13354 non-null  int64  \n",
      " 12  Dep_hour         13354 non-null  int64  \n",
      " 13  Dep_minute       13354 non-null  int64  \n",
      " 14  dur_hr           13354 non-null  object \n",
      "dtypes: float64(2), int64(12), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
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   ]
  },
  {
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       "<p>13354 rows × 40 columns</p>\n",
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      ],
      "text/plain": [
       "      Duration  Total_Stops    Price  date  month  year  Arrival_hour  \\\n",
       "0          170          0.0   3897.0    24      3  2019             1   \n",
       "1          445          2.0   7662.0     1      5  2019            13   \n",
       "2         1140          2.0  13882.0     9      6  2019             4   \n",
       "3          325          1.0   6218.0    12      5  2019            23   \n",
       "4          285          1.0  13302.0     1      3  2019            21   \n",
       "...        ...          ...      ...   ...    ...   ...           ...   \n",
       "2666      1435          1.0      NaN     6      6  2019            20   \n",
       "2667       155          0.0      NaN    27      3  2019            16   \n",
       "2668       395          1.0      NaN     6      3  2019             4   \n",
       "2669       915          1.0      NaN     6      3  2019            19   \n",
       "2670       860          1.0      NaN    15      6  2019            19   \n",
       "\n",
       "      Arrival_minute  Dep_hour  Dep_minute  ... Destination_5  \\\n",
       "0                 10        22          20  ...             1   \n",
       "1                 15         5          50  ...             0   \n",
       "2                 25         9          25  ...             0   \n",
       "3                 30        18           5  ...             0   \n",
       "4                 35        16          50  ...             1   \n",
       "...              ...       ...         ...  ...           ...   \n",
       "2666              25        20          30  ...             0   \n",
       "2667              55        14          20  ...             0   \n",
       "2668              25        21          50  ...             0   \n",
       "2669              15         4           0  ...             0   \n",
       "2670              15         4          55  ...             0   \n",
       "\n",
       "      Additional_Info_1  Additional_Info_2  Additional_Info_3  \\\n",
       "0                     0                  0                  0   \n",
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       "...                 ...                ...                ...   \n",
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       "\n",
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       "2                     0                  0                  0   \n",
       "3                     0                  0                  0   \n",
       "4                     0                  0                  0   \n",
       "...                 ...                ...                ...   \n",
       "2666                  0                  0                  0   \n",
       "2667                  0                  0                  0   \n",
       "2668                  0                  0                  0   \n",
       "2669                  0                  0                  0   \n",
       "2670                  0                  0                  0   \n",
       "\n",
       "      Additional_Info_7  Additional_Info_8  Additional_Info_9  \n",
       "0                     0                  1                  0  \n",
       "1                     0                  1                  0  \n",
       "2                     0                  1                  0  \n",
       "3                     0                  1                  0  \n",
       "4                     0                  1                  0  \n",
       "...                 ...                ...                ...  \n",
       "2666                  0                  1                  0  \n",
       "2667                  0                  1                  0  \n",
       "2668                  0                  1                  0  \n",
       "2669                  0                  1                  0  \n",
       "2670                  0                  1                  0  \n",
       "\n",
       "[13354 rows x 40 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(final_df,columns = ['Airline','Source', 'Destination', 'Additional_Info'] , drop_first=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7926b8f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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