FE_Time_Series_Flight_Price_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "745c918b",
   "metadata": {},
   "source": [
    "# Flight Price Prediction(EDA + Feature Engineering)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ec5645",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af53b9b6",
   "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\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22168f4",
   "metadata": {},
   "source": [
    "### Importing Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1b4d1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df=pd.read_excel('Flight_Data_Train.xlsx')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08018dc1",
   "metadata": {},
   "source": [
    "### Importing Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "208d2edf",
   "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",
       "    </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df=pd.read_excel('Flight_Data_Test.xlsx')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d4980d9",
   "metadata": {},
   "source": [
    "### Checking for number of Rows and Columns in the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b998fce4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10683, 11) (2671, 10)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape,test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9c3c62f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13354, 11)\n"
     ]
    }
   ],
   "source": [
    "final_df=train_df.append(test_df)\n",
    "final_df.head()\n",
    "print(final_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e3de9de",
   "metadata": {},
   "source": [
    "### Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5ca20c42",
   "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>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",
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      "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce92bd42",
   "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": "markdown",
   "id": "80ad57a2",
   "metadata": {},
   "source": [
    "### Checking For Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "849728dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline               0\n",
       "Date_of_Journey       0\n",
       "Source                0\n",
       "Destination           0\n",
       "Route                 1\n",
       "Dep_Time              0\n",
       "Arrival_Time          0\n",
       "Duration              0\n",
       "Total_Stops           1\n",
       "Additional_Info       0\n",
       "Price              2671\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f9e0aa",
   "metadata": {},
   "source": [
    "### Checking for Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10fcfb2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <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>683</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>1/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → NAG → BOM → COK</td>\n",
       "      <td>14:35</td>\n",
       "      <td>04:25 02 Jun</td>\n",
       "      <td>13h 50m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>Air India</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → GOI → BOM → COK</td>\n",
       "      <td>22:00</td>\n",
       "      <td>19:15 22 May</td>\n",
       "      <td>21h 15m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>10231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>Air India</td>\n",
       "      <td>18/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → HYD → BOM → COK</td>\n",
       "      <td>17:15</td>\n",
       "      <td>19:15 19 May</td>\n",
       "      <td>26h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>12392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → JAI → BOM → COK</td>\n",
       "      <td>05:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>22h 55m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>10368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</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>18:25</td>\n",
       "      <td>21:20</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2536</th>\n",
       "      <td>Air India</td>\n",
       "      <td>3/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → GOI → BOM → COK</td>\n",
       "      <td>22:00</td>\n",
       "      <td>19:15 04 Jun</td>\n",
       "      <td>21h 15m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>15/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → JAI → BOM → COK</td>\n",
       "      <td>05:30</td>\n",
       "      <td>19:00</td>\n",
       "      <td>13h 30m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>3/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>13:00</td>\n",
       "      <td>01:30 04 Jun</td>\n",
       "      <td>12h 30m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2582</th>\n",
       "      <td>Air India</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BOM → GOI → BLR</td>\n",
       "      <td>16:50</td>\n",
       "      <td>05:35 13 May</td>\n",
       "      <td>12h 45m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2583</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>18:55</td>\n",
       "      <td>21:45</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Airline Date_of_Journey    Source Destination  \\\n",
       "683         Jet Airways       1/06/2019     Delhi      Cochin   \n",
       "1061          Air India      21/05/2019     Delhi      Cochin   \n",
       "1348          Air India      18/05/2019     Delhi      Cochin   \n",
       "1418        Jet Airways       6/06/2019     Delhi      Cochin   \n",
       "1674             IndiGo      24/03/2019  Banglore   New Delhi   \n",
       "...                 ...             ...       ...         ...   \n",
       "2536          Air India       3/06/2019     Delhi      Cochin   \n",
       "2553        Jet Airways      15/06/2019     Delhi      Cochin   \n",
       "2560  Multiple carriers       3/06/2019     Delhi      Cochin   \n",
       "2582          Air India      12/05/2019   Kolkata    Banglore   \n",
       "2583             IndiGo      24/03/2019  Banglore   New Delhi   \n",
       "\n",
       "                      Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
       "683   DEL → NAG → BOM → COK    14:35  04:25 02 Jun  13h 50m     2 stops   \n",
       "1061  DEL → GOI → BOM → COK    22:00  19:15 22 May  21h 15m     2 stops   \n",
       "1348  DEL → HYD → BOM → COK    17:15  19:15 19 May      26h     2 stops   \n",
       "1418  DEL → JAI → BOM → COK    05:30  04:25 07 Jun  22h 55m     2 stops   \n",
       "1674              BLR → DEL    18:25         21:20   2h 55m    non-stop   \n",
       "...                     ...      ...           ...      ...         ...   \n",
       "2536  DEL → GOI → BOM → COK    22:00  19:15 04 Jun  21h 15m     2 stops   \n",
       "2553  DEL → JAI → BOM → COK    05:30         19:00  13h 30m     2 stops   \n",
       "2560        DEL → BOM → COK    13:00  01:30 04 Jun  12h 30m      1 stop   \n",
       "2582  CCU → BOM → GOI → BLR    16:50  05:35 13 May  12h 45m     2 stops   \n",
       "2583              BLR → DEL    18:55         21:45   2h 50m    non-stop   \n",
       "\n",
       "                  Additional_Info    Price  \n",
       "683                       No info  13376.0  \n",
       "1061                      No info  10231.0  \n",
       "1348                      No info  12392.0  \n",
       "1418  In-flight meal not included  10368.0  \n",
       "1674                      No info   7303.0  \n",
       "...                           ...      ...  \n",
       "2536                      No info      NaN  \n",
       "2553                      No info      NaN  \n",
       "2560                      No info      NaN  \n",
       "2582                      No info      NaN  \n",
       "2583                      No info      NaN  \n",
       "\n",
       "[246 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a01b2aa6",
   "metadata": {},
   "source": [
    "### Feature Engineering Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d2c3219d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       24\n",
      "1        1\n",
      "2        9\n",
      "3       12\n",
      "4       01\n",
      "        ..\n",
      "2666     6\n",
      "2667    27\n",
      "2668     6\n",
      "2669     6\n",
      "2670    15\n",
      "Name: Date_of_Journey, Length: 13354, dtype: object \n",
      " 0       03\n",
      "1       05\n",
      "2       06\n",
      "3       05\n",
      "4       03\n",
      "        ..\n",
      "2666    06\n",
      "2667    03\n",
      "2668    03\n",
      "2669    03\n",
      "2670    06\n",
      "Name: Date_of_Journey, Length: 13354, dtype: object \n",
      " 0       2019\n",
      "1       2019\n",
      "2       2019\n",
      "3       2019\n",
      "4       2019\n",
      "        ... \n",
      "2666    2019\n",
      "2667    2019\n",
      "2668    2019\n",
      "2669    2019\n",
      "2670    2019\n",
      "Name: Date_of_Journey, Length: 13354, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(final_df['Date_of_Journey'].str.split('/').str[0],'\\n',\n",
    "      final_df['Date_of_Journey'].str.split('/').str[1],'\\n',\n",
    "      final_df['Date_of_Journey'].str.split('/').str[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649c1e51",
   "metadata": {},
   "source": [
    "### Spliting Date_of_Journey into Three seperate Columns (Year,Month,Day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b175ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=final_df.copy()\n",
    "df['Date']=df['Date_of_Journey'].str.split('/').str[0]\n",
    "df['Month']=df['Date_of_Journey'].str.split('/').str[1]\n",
    "df['Year']=df['Date_of_Journey'].str.split('/').str[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "525ee759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61226802",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another Smart Method\n",
    "df['Date']=df['Date_of_Journey'].apply(lambda x:x.split('/')[0])\n",
    "df['Month']=df['Date_of_Journey'].apply(lambda x:x.split('/')[1])\n",
    "df['Year']=df['Date_of_Journey'].apply(lambda x:x.split('/')[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fe353f57",
   "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": [
    "df[['Date','Month','Year']]=df[['Date','Month','Year']].astype('int64')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd0a609",
   "metadata": {},
   "source": [
    "### Dropping the Original Columns after splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37624a00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Date_of_Journey',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "59b8d570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>09:00</td>\n",
       "      <td>11:25</td>\n",
       "      <td>2h 25m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>18:55</td>\n",
       "      <td>10:25 13 Mar</td>\n",
       "      <td>15h 30m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11087.0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>08:00</td>\n",
       "      <td>05:05 02 Mar</td>\n",
       "      <td>21h 5m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>22270.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>08:55</td>\n",
       "      <td>10:25 13 Mar</td>\n",
       "      <td>25h 30m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11087.0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>11:25</td>\n",
       "      <td>19:15</td>\n",
       "      <td>7h 50m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>8625.0</td>\n",
       "      <td>27</td>\n",
       "      <td>5</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",
       "5           SpiceJet   Kolkata    Banglore              CCU → BLR    09:00   \n",
       "6        Jet Airways  Banglore   New Delhi        BLR → BOM → DEL    18:55   \n",
       "7        Jet Airways  Banglore   New Delhi        BLR → BOM → DEL    08:00   \n",
       "8        Jet Airways  Banglore   New Delhi        BLR → BOM → DEL    08:55   \n",
       "9  Multiple carriers     Delhi      Cochin        DEL → BOM → COK    11:25   \n",
       "\n",
       "   Arrival_Time Duration Total_Stops              Additional_Info    Price  \\\n",
       "0  01:10 22 Mar   2h 50m    non-stop                      No info   3897.0   \n",
       "1         13:15   7h 25m     2 stops                      No info   7662.0   \n",
       "2  04:25 10 Jun      19h     2 stops                      No info  13882.0   \n",
       "3         23:30   5h 25m      1 stop                      No info   6218.0   \n",
       "4         21:35   4h 45m      1 stop                      No info  13302.0   \n",
       "5         11:25   2h 25m    non-stop                      No info   3873.0   \n",
       "6  10:25 13 Mar  15h 30m      1 stop  In-flight meal not included  11087.0   \n",
       "7  05:05 02 Mar   21h 5m      1 stop                      No info  22270.0   \n",
       "8  10:25 13 Mar  25h 30m      1 stop  In-flight meal not included  11087.0   \n",
       "9         19:15   7h 50m      1 stop                      No info   8625.0   \n",
       "\n",
       "   Date  Month  Year  \n",
       "0    24      3  2019  \n",
       "1     1      5  2019  \n",
       "2     9      6  2019  \n",
       "3    12      5  2019  \n",
       "4     1      3  2019  \n",
       "5    24      6  2019  \n",
       "6    12      3  2019  \n",
       "7     1      3  2019  \n",
       "8    12      3  2019  \n",
       "9    27      5  2019  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a5bfe54",
   "metadata": {},
   "source": [
    "### Splitting Arrival Time into two seperate Hour and Minute Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "338e4003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       01:10\n",
       "1       13:15\n",
       "2       04:25\n",
       "3       23:30\n",
       "4       21:35\n",
       "        ...  \n",
       "2666    20:25\n",
       "2667    16:55\n",
       "2668    04:25\n",
       "2669    19:15\n",
       "2670    19:15\n",
       "Name: Arrival_Time, Length: 13354, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Arrival_Time'].str.split(' ').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e5075b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Arrival_Time']=df['Arrival_Time'].str.split(' ').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7263fed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>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</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": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f7059dab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       01\n",
      "1       13\n",
      "2       04\n",
      "3       23\n",
      "4       21\n",
      "        ..\n",
      "2666    20\n",
      "2667    16\n",
      "2668    04\n",
      "2669    19\n",
      "2670    19\n",
      "Name: Arrival_Time, Length: 13354, dtype: object \n",
      " 0       10\n",
      "1       15\n",
      "2       25\n",
      "3       30\n",
      "4       35\n",
      "        ..\n",
      "2666    25\n",
      "2667    55\n",
      "2668    25\n",
      "2669    15\n",
      "2670    15\n",
      "Name: Arrival_Time, Length: 13354, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Arrival_Time\"].str.split(':').str[0],'\\n',\n",
    "      df[\"Arrival_Time\"].str.split(':').str[1])      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4365d93",
   "metadata": {
    "scrolled": false
   },
   "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   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",
      "dtypes: float64(1), int64(5), object(8)\n",
      "memory usage: 1.5+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>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",
       "    </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",
       "    </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",
       "    </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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Arrival_Hour\"]=df[\"Arrival_Time\"].str.split(':').str[0]\n",
    "df[\"Arrival_Minute\"]=df[\"Arrival_Time\"].str.split(':').str[1]\n",
    "df.drop('Arrival_Time',axis=1,inplace=True)\n",
    "df[['Arrival_Hour','Arrival_Minute']]=df[['Arrival_Hour','Arrival_Minute']].astype('int64')\n",
    "display(df.info(),df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8504c586",
   "metadata": {},
   "source": [
    "### Splitting Departure Time into two seperate columns Hour and Minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f0b2ecb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       22\n",
      "1       05\n",
      "2       09\n",
      "3       18\n",
      "4       16\n",
      "        ..\n",
      "2666    20\n",
      "2667    14\n",
      "2668    21\n",
      "2669    04\n",
      "2670    04\n",
      "Name: Dep_Time, Length: 13354, dtype: object \n",
      " 0       20\n",
      "1       50\n",
      "2       25\n",
      "3       05\n",
      "4       50\n",
      "        ..\n",
      "2666    30\n",
      "2667    20\n",
      "2668    50\n",
      "2669    00\n",
      "2670    55\n",
      "Name: Dep_Time, Length: 13354, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Dep_Time\"].str.split(':').str[0],'\\n',\n",
    "      df[\"Dep_Time\"].str.split(':').str[1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294ccab8",
   "metadata": {},
   "source": [
    "### Convert the New columns Data type into Integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "52230994",
   "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  object \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  Departure_Hour    13354 non-null  int64  \n",
      " 14  Departure_Minute  13354 non-null  int64  \n",
      "dtypes: float64(1), int64(7), object(7)\n",
      "memory usage: 1.6+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Arrival_Hour</th>\n",
       "      <th>Arrival_Minute</th>\n",
       "      <th>Departure_Hour</th>\n",
       "      <th>Departure_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  Departure_Hour  Departure_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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Departure_Hour\"]=df[\"Dep_Time\"].str.split(':').str[0]\n",
    "df[\"Departure_Minute\"]=df[\"Dep_Time\"].str.split(':').str[1]\n",
    "df.drop('Dep_Time',axis=1,inplace=True)\n",
    "df[['Departure_Hour','Departure_Minute']]=df[['Departure_Hour','Departure_Minute']].astype('int64')\n",
    "display(df.info(),df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28196f3",
   "metadata": {},
   "source": [
    "### Checking for Unique distinct values in the Total Stops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e36a8020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', nan, '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5f600752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Stops'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ecd4ab84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1 stop'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Stops'].mode()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02bd1b8a",
   "metadata": {},
   "source": [
    "### Mode Imputation as it is a Discrete Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2aa6a700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Arrival_Hour</th>\n",
       "      <th>Arrival_Minute</th>\n",
       "      <th>Departure_Hour</th>\n",
       "      <th>Departure_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  Departure_Hour  Departure_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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Stops']=df['Total_Stops'].fillna(df['Total_Stops'].mode()[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "82479ce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Stops'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f46f274",
   "metadata": {},
   "source": [
    "### Both Route and Total Stops are sharing the same information we can drop Route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "403fb626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Arrival_Hour</th>\n",
       "      <th>Arrival_Minute</th>\n",
       "      <th>Departure_Hour</th>\n",
       "      <th>Departure_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>1 stop</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      1 stop         No info   \n",
       "\n",
       "       Price  Date  Month  Year  Arrival_Hour  Arrival_Minute  Departure_Hour  \\\n",
       "9039  7480.0     6      5  2019             9              25               9   \n",
       "\n",
       "      Departure_Minute  \n",
       "9039                45  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Route'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2151f06",
   "metadata": {},
   "source": [
    "### Label encoding Total Stops into Numerical Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3edc3970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Arrival_Hour</th>\n",
       "      <th>Arrival_Minute</th>\n",
       "      <th>Departure_Hour</th>\n",
       "      <th>Departure_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>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>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2</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</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</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</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         No info   3897.0    24      3  2019             1   \n",
       "1            2         No info   7662.0     1      5  2019            13   \n",
       "2            2         No info  13882.0     9      6  2019             4   \n",
       "3            1         No info   6218.0    12      5  2019            23   \n",
       "4            1         No info  13302.0     1      3  2019            21   \n",
       "\n",
       "   Arrival_Minute  Departure_Hour  Departure_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": [
    "df['Total_Stops']=df['Total_Stops'].map({'non-stop':0,'1 stop':1,'2 stops':2,'3 stops':3,'4 stops':4})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "214e4eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Route',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cae5c58e",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Additional_Info'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd3e4e7",
   "metadata": {},
   "source": [
    "### Splitting Duration into either two seperate columns or either one columns saying Total Minutes of Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "303c69ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [2h, 50m]\n",
       "1        [7h, 25m]\n",
       "2            [19h]\n",
       "3        [5h, 25m]\n",
       "4        [4h, 45m]\n",
       "           ...    \n",
       "2666    [23h, 55m]\n",
       "2667     [2h, 35m]\n",
       "2668     [6h, 35m]\n",
       "2669    [15h, 15m]\n",
       "2670    [14h, 20m]\n",
       "Name: Duration, Length: 13354, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Duration'].str.split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9d328fe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2\n",
       "1        7\n",
       "2       19\n",
       "3        5\n",
       "4        4\n",
       "        ..\n",
       "2666    23\n",
       "2667     2\n",
       "2668     6\n",
       "2669    15\n",
       "2670    14\n",
       "Name: Duration, Length: 13354, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Duration'].str.split('h').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b8b4f9c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2h 50m', '7h 25m', '19h', '5h 25m', '4h 45m', '2h 25m', '15h 30m',\n",
       "       '21h 5m', '25h 30m', '7h 50m', '13h 15m', '2h 35m', '2h 15m',\n",
       "       '12h 10m', '26h 35m', '4h 30m', '22h 35m', '23h', '20h 35m',\n",
       "       '5h 10m', '15h 20m', '2h 55m', '13h 20m', '15h 10m', '5h 45m',\n",
       "       '5h 55m', '13h 25m', '22h', '5h 30m', '10h 25m', '5h 15m',\n",
       "       '2h 30m', '6h 15m', '11h 55m', '11h 5m', '8h 30m', '22h 5m',\n",
       "       '2h 45m', '12h', '16h 5m', '19h 55m', '3h 15m', '25h 20m', '3h',\n",
       "       '16h 15m', '15h 5m', '6h 30m', '25h 5m', '12h 25m', '27h 20m',\n",
       "       '10h 15m', '10h 30m', '1h 30m', '1h 25m', '26h 30m', '7h 20m',\n",
       "       '13h 30m', '5h', '19h 5m', '14h 50m', '2h 40m', '22h 10m',\n",
       "       '9h 35m', '10h', '21h 20m', '18h 45m', '12h 20m', '18h', '9h 15m',\n",
       "       '17h 30m', '16h 35m', '12h 15m', '7h 30m', '24h', '8h 55m',\n",
       "       '7h 10m', '14h 30m', '30h 20m', '15h', '12h 45m', '10h 10m',\n",
       "       '15h 25m', '14h 5m', '20h 15m', '23h 10m', '18h 10m', '16h',\n",
       "       '2h 20m', '8h', '16h 55m', '3h 10m', '14h', '23h 50m', '21h 40m',\n",
       "       '21h 15m', '10h 50m', '8h 15m', '8h 35m', '11h 50m', '27h 35m',\n",
       "       '8h 25m', '20h 55m', '4h 50m', '8h 10m', '24h 25m', '23h 35m',\n",
       "       '25h 45m', '26h 10m', '28h 50m', '25h 15m', '9h 20m', '9h 10m',\n",
       "       '3h 5m', '11h 30m', '9h 30m', '17h 35m', '5h 5m', '25h 50m', '20h',\n",
       "       '13h', '18h 25m', '24h 10m', '4h 55m', '25h 35m', '6h 20m',\n",
       "       '18h 40m', '19h 25m', '29h 20m', '9h 5m', '10h 45m', '11h 40m',\n",
       "       '22h 55m', '37h 25m', '25h 40m', '13h 55m', '8h 40m', '23h 30m',\n",
       "       '12h 35m', '24h 15m', '1h 20m', '11h', '11h 15m', '14h 35m',\n",
       "       '12h 55m', '9h', '7h 40m', '11h 45m', '24h 55m', '17h 5m',\n",
       "       '29h 55m', '22h 15m', '14h 40m', '7h 15m', '20h 10m', '20h 45m',\n",
       "       '27h', '24h 30m', '20h 25m', '5h 35m', '14h 45m', '5h 40m',\n",
       "       '4h 5m', '15h 55m', '7h 45m', '28h 20m', '4h 20m', '3h 40m',\n",
       "       '8h 50m', '23h 45m', '24h 45m', '21h 35m', '8h 5m', '6h 25m',\n",
       "       '15h 50m', '26h 25m', '24h 50m', '26h', '23h 5m', '7h 55m',\n",
       "       '26h 20m', '23h 15m', '5h 20m', '4h', '9h 45m', '8h 20m',\n",
       "       '17h 25m', '7h 5m', '34h 5m', '6h 5m', '5h 50m', '7h', '4h 25m',\n",
       "       '13h 45m', '19h 15m', '22h 30m', '16h 25m', '13h 50m', '27h 5m',\n",
       "       '28h 10m', '4h 40m', '15h 40m', '4h 35m', '18h 30m', '38h 15m',\n",
       "       '6h 35m', '12h 30m', '11h 20m', '7h 35m', '29h 35m', '26h 55m',\n",
       "       '23h 40m', '12h 50m', '9h 50m', '21h 55m', '10h 55m', '21h 10m',\n",
       "       '20h 40m', '30h', '13h 10m', '8h 45m', '6h 10m', '17h 45m',\n",
       "       '21h 45m', '3h 55m', '17h 20m', '30h 30m', '21h 25m', '12h 40m',\n",
       "       '24h 35m', '19h 10m', '22h 40m', '14h 55m', '21h', '6h 45m',\n",
       "       '28h 40m', '9h 40m', '16h 40m', '16h 20m', '16h 45m', '1h 15m',\n",
       "       '6h 55m', '11h 25m', '14h 20m', '12h 5m', '24h 5m', '28h 15m',\n",
       "       '17h 50m', '20h 20m', '28h 5m', '10h 20m', '14h 15m', '35h 15m',\n",
       "       '35h 35m', '26h 40m', '28h', '14h 25m', '13h 5m', '37h 20m',\n",
       "       '36h 10m', '25h 55m', '35h 5m', '19h 45m', '27h 55m', '47h',\n",
       "       '10h 35m', '1h 35m', '16h 10m', '38h 20m', '6h', '16h 50m',\n",
       "       '14h 10m', '23h 20m', '17h 40m', '11h 35m', '18h 20m', '6h 40m',\n",
       "       '30h 55m', '24h 40m', '29h 50m', '28h 25m', '17h 15m', '22h 45m',\n",
       "       '25h 25m', '21h 50m', '33h 15m', '30h 15m', '3h 35m', '27h 40m',\n",
       "       '30h 25m', '18h 50m', '27h 45m', '15h 15m', '10h 40m', '26h 15m',\n",
       "       '36h 25m', '26h 50m', '15h 45m', '19h 40m', '22h 25m', '19h 35m',\n",
       "       '25h', '26h 45m', '38h', '4h 15m', '25h 10m', '18h 15m', '6h 50m',\n",
       "       '23h 55m', '17h 55m', '23h 25m', '17h 10m', '24h 20m', '28h 30m',\n",
       "       '27h 10m', '19h 20m', '15h 35m', '9h 25m', '21h 30m', '34h 25m',\n",
       "       '18h 35m', '29h 40m', '26h 5m', '29h 5m', '27h 25m', '16h 30m',\n",
       "       '11h 10m', '28h 55m', '29h 10m', '34h', '30h 40m', '30h 45m',\n",
       "       '32h 55m', '10h 5m', '35h 20m', '32h 5m', '31h 40m', '19h 50m',\n",
       "       '33h 45m', '30h 10m', '13h 40m', '19h 30m', '31h 30m', '34h 30m',\n",
       "       '27h 50m', '38h 35m', '42h 5m', '4h 10m', '39h 5m', '3h 50m', '5m',\n",
       "       '32h 30m', '31h 55m', '33h 20m', '27h 30m', '18h 55m', '9h 55m',\n",
       "       '41h 20m', '20h 5m', '31h 50m', '42h 45m', '3h 25m', '37h 10m',\n",
       "       '29h 30m', '32h 20m', '20h 50m', '40h 20m', '13h 35m', '47h 40m',\n",
       "       '20h 30m', '3h 45m', '31h 10m', '40h 40m', '22h 50m', '32h 25m'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Duration'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4355f64f",
   "metadata": {},
   "source": [
    "### Found One Unique Value which does'nt make sense hence removing does rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b95f3aed",
   "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>Departure_Hour</th>\n",
       "      <th>Departure_Minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6474</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>5m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>17327.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2660</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>5m</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>55</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",
       "6474  Air India  Mumbai   Hyderabad       5m            2         No info   \n",
       "2660  Air India  Mumbai   Hyderabad       5m            2         No info   \n",
       "\n",
       "        Price  Date  Month  Year  Arrival_Hour  Arrival_Minute  \\\n",
       "6474  17327.0     6      3  2019            16              55   \n",
       "2660      NaN    12      3  2019            16              55   \n",
       "\n",
       "      Departure_Hour  Departure_Minute  \n",
       "6474              16                50  \n",
       "2660              16                50  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Duration']=='5m']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bb6ec8a2",
   "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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       "</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>Departure_Hour</th>\n",
       "      <th>Departure_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</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</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</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</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</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         No info   \n",
       "1    Air India   Kolkata    Banglore   7h 25m            2         No info   \n",
       "2  Jet Airways     Delhi      Cochin      19h            2         No info   \n",
       "3       IndiGo   Kolkata    Banglore   5h 25m            1         No info   \n",
       "4       IndiGo  Banglore   New Delhi   4h 45m            1         No info   \n",
       "\n",
       "     Price  Date  Month  Year  Arrival_Hour  Arrival_Minute  Departure_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",
       "   Departure_Minute  \n",
       "0                20  \n",
       "1                50  \n",
       "2                25  \n",
       "3                 5  \n",
       "4                50  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([6474,2660],axis=0,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a766b53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>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>Departure_Hour</th>\n",
       "      <th>Departure_Minute</th>\n",
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       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Airline, Source, Destination, Duration, Total_Stops, Additional_Info, Price, Date, Month, Year, Arrival_Hour, Arrival_Minute, Departure_Hour, Departure_Minute]\n",
       "Index: []"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Duration']=='5m']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ad6d96",
   "metadata": {},
   "source": [
    "### Splitting Duration into Hour and Minute Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "48fcc9d3",
   "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>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>Departure_Hour</th>\n",
       "      <th>Departure_Minute</th>\n",
       "      <th>Duration_Hours</th>\n",
       "      <th>Duration_Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>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>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2</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>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>19h</td>\n",
       "      <td>2</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>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1</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>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1</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>4</td>\n",
       "      <td>45</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         No info   \n",
       "1    Air India   Kolkata    Banglore   7h 25m            2         No info   \n",
       "2  Jet Airways     Delhi      Cochin      19h            2         No info   \n",
       "3       IndiGo   Kolkata    Banglore   5h 25m            1         No info   \n",
       "4       IndiGo  Banglore   New Delhi   4h 45m            1         No info   \n",
       "\n",
       "     Price  Date  Month  Year  Arrival_Hour  Arrival_Minute  Departure_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",
       "   Departure_Minute  Duration_Hours  Duration_Minutes  \n",
       "0                20               2                50  \n",
       "1                50               7                25  \n",
       "2                25              19                 0  \n",
       "3                 5               5                25  \n",
       "4                50               4                45  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.to_timedelta(df['Duration'])\n",
    "df['Duration_Hours']=s.dt.components['hours']\n",
    "df['Duration_Minutes']=s.dt.components['minutes']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef9490c",
   "metadata": {},
   "source": [
    "#### Dropping the original Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1c0083b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Duration',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fcb0b0ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13351 entries, 0 to 2670\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Airline           13351 non-null  object \n",
      " 1   Source            13351 non-null  object \n",
      " 2   Destination       13351 non-null  object \n",
      " 3   Total_Stops       13351 non-null  int64  \n",
      " 4   Additional_Info   13351 non-null  object \n",
      " 5   Price             10681 non-null  float64\n",
      " 6   Date              13351 non-null  int64  \n",
      " 7   Month             13351 non-null  int64  \n",
      " 8   Year              13351 non-null  int64  \n",
      " 9   Arrival_Hour      13351 non-null  int64  \n",
      " 10  Arrival_Minute    13351 non-null  int64  \n",
      " 11  Departure_Hour    13351 non-null  int64  \n",
      " 12  Departure_Minute  13351 non-null  int64  \n",
      " 13  Duration_Hours    13351 non-null  int64  \n",
      " 14  Duration_Minutes  13351 non-null  int64  \n",
      "dtypes: float64(1), int64(10), object(4)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6f0418",
   "metadata": {},
   "source": [
    "#### Another Method is to Convert the Duration into One Single clumns stating Total Minutes converting hours into Minutes as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9e518b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Duration_Hours</th>\n",
       "      <th>Duration_Minutes</th>\n",
       "      <th>Total_Duration</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</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>170</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</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>7</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>445</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</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>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1140</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</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>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>325</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</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>4</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>285</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         No info   \n",
       "1    Air India   Kolkata    Banglore   7h 25m            2         No info   \n",
       "2  Jet Airways     Delhi      Cochin      19h            2         No info   \n",
       "3       IndiGo   Kolkata    Banglore   5h 25m            1         No info   \n",
       "4       IndiGo  Banglore   New Delhi   4h 45m            1         No info   \n",
       "\n",
       "     Price  Date  Month  Year  Arrival_Hour  Arrival_Minute  Departure_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",
       "   Departure_Minute  Duration_Hour  Duration_Hours  Duration_Minutes  \\\n",
       "0                20              2               2                50   \n",
       "1                50              7               7                25   \n",
       "2                25             19              19                 0   \n",
       "3                 5              5               5                25   \n",
       "4                50              4               4                45   \n",
       "\n",
       "   Total_Duration  \n",
       "0             170  \n",
       "1             445  \n",
       "2            1140  \n",
       "3             325  \n",
       "4             285  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df1=df.copy()\n",
    "#df1['Duration_Time']=(df1['Duration_Hours'])*60+df1['Duration_Minutes']\n",
    "#df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7935ce5",
   "metadata": {},
   "source": [
    "### Checking Unique Values in other Feature Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "85aaf7b9",
   "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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Airline.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ae9280c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banglore', 'Kolkata', 'Delhi', 'Chennai', 'Mumbai'], dtype=object)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Source.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "51338407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Destination.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "05f603df",
   "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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Additional_Info.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91a82d2",
   "metadata": {},
   "source": [
    "### Converting All Categorical Feature into Numerical for the ease of computation for building a model for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6ece922b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "141282cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>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>Departure_Hour</th>\n",
       "      <th>Departure_Minute</th>\n",
       "      <th>Duration_Hours</th>\n",
       "      <th>Duration_Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</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>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</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>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</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>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</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>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</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>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline  Source  Destination  Total_Stops  Additional_Info    Price  Date  \\\n",
       "0        3       0            5            0                8   3897.0    24   \n",
       "1        1       3            0            2                8   7662.0     1   \n",
       "2        4       2            1            2                8  13882.0     9   \n",
       "3        3       3            0            1                8   6218.0    12   \n",
       "4        3       0            5            1                8  13302.0     1   \n",
       "\n",
       "   Month  Year  Arrival_Hour  Arrival_Minute  Departure_Hour  \\\n",
       "0      3  2019             1              10              22   \n",
       "1      5  2019            13              15               5   \n",
       "2      6  2019             4              25               9   \n",
       "3      5  2019            23              30              18   \n",
       "4      3  2019            21              35              16   \n",
       "\n",
       "   Departure_Minute  Duration_Hours  Duration_Minutes  \n",
       "0                20               2                50  \n",
       "1                50               7                25  \n",
       "2                25              19                 0  \n",
       "3                 5               5                25  \n",
       "4                50               4                45  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Airline']=labelencoder.fit_transform(df['Airline'])\n",
    "df['Source']=labelencoder.fit_transform(df['Source'])\n",
    "df['Destination']=labelencoder.fit_transform(df['Destination'])\n",
    "df['Additional_Info']=labelencoder.fit_transform(df['Additional_Info'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "76237e6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13351 entries, 0 to 2670\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Airline           13351 non-null  int32  \n",
      " 1   Source            13351 non-null  int32  \n",
      " 2   Destination       13351 non-null  int32  \n",
      " 3   Total_Stops       13351 non-null  int64  \n",
      " 4   Additional_Info   13351 non-null  int32  \n",
      " 5   Price             10681 non-null  float64\n",
      " 6   Date              13351 non-null  int64  \n",
      " 7   Month             13351 non-null  int64  \n",
      " 8   Year              13351 non-null  int64  \n",
      " 9   Arrival_Hour      13351 non-null  int64  \n",
      " 10  Arrival_Minute    13351 non-null  int64  \n",
      " 11  Departure_Hour    13351 non-null  int64  \n",
      " 12  Departure_Minute  13351 non-null  int64  \n",
      " 13  Duration_Hours    13351 non-null  int64  \n",
      " 14  Duration_Minutes  13351 non-null  int64  \n",
      "dtypes: float64(1), int32(4), int64(10)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "078f2f4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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