Flight Price EDa And FE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67fc4ad4-89ca-466a-b3a3-5112de2f7013",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing basics libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "599e2c52-1326-4974-aab3-02a2e38259d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel('flight_price.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b079ef3-e7db-45e3-896a-5aeb28fb30dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25b34546-b17d-43ea-920b-66b2fe670e69",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Airline'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa9d2550-5046-4ae4-b0c3-fb8ab99c4f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35a57307-03af-4721-8f4b-862c2a56dd19",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date_of_Journey']=pd.to_datetime(df['Date_of_Journey'],infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3252c5af-bbc2-44f9-923f-04be74b92e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   Airline          10683 non-null  object        \n",
      " 1   Date_of_Journey  10683 non-null  datetime64[ns]\n",
      " 2   Source           10683 non-null  object        \n",
      " 3   Destination      10683 non-null  object        \n",
      " 4   Route            10682 non-null  object        \n",
      " 5   Dep_Time         10683 non-null  object        \n",
      " 6   Arrival_Time     10683 non-null  object        \n",
      " 7   Duration         10683 non-null  object        \n",
      " 8   Total_Stops      10682 non-null  object        \n",
      " 9   Additional_Info  10683 non-null  object        \n",
      " 10  Price            10683 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(9)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c75157f-df8e-41cf-9e68-dc58f28fcd74",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day']=df['Date_of_Journey'].dt.day\n",
    "df['Month']=df['Date_of_Journey'].dt.month\n",
    "df['Year']=df['Date_of_Journey'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "34910ab0-befd-46e0-8e78-75b26598a4bb",
   "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",
       "      <th>Day</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>2019-03-24</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",
       "      <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>2019-05-01</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",
       "      <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>2019-06-09</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",
       "      <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>2019-05-12</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",
       "      <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>2019-03-01</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",
       "      <td>1</td>\n",
       "      <td>3</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      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways      2019-06-09     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      2019-05-12   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      2019-03-01  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  Day  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897   24   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662    1   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882    9   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218   12   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302    1   \n",
       "\n",
       "   Month  Year  \n",
       "0      3  2019  \n",
       "1      5  2019  \n",
       "2      6  2019  \n",
       "3      5  2019  \n",
       "4      3  2019  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a8a7566-012f-4966-be64-47e8edd0f8eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Date_of_Journey',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f020f555-47ae-4990-954c-f7d2659d4327",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
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       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Day</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</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</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</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</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</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  Day  Month  Year  \n",
       "0  01:10 22 Mar   2h 50m    non-stop         No info   3897   24      3  2019  \n",
       "1         13:15   7h 25m     2 stops         No info   7662    1      5  2019  \n",
       "2  04:25 10 Jun      19h     2 stops         No info  13882    9      6  2019  \n",
       "3         23:30   5h 25m      1 stop         No info   6218   12      5  2019  \n",
       "4         21:35   4h 45m      1 stop         No info  13302    1      3  2019  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33f2de4c-f1b7-4541-9d27-fd083eb297bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Extract day,month and year from the string\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": null,
   "id": "25589a50-8e2c-45e2-9f7a-2a113dead537",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Fetaure Engineering Process\n",
    "df['Date']=df['Date_of_Journey'].str.split('/').str[0]\n",
    "final_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": 18,
   "id": "61608367-5e71-4cf0-85dd-27c39b3e4c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>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>Day</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</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</td>\n",
       "      <td>1</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",
       "\n",
       "   Arrival_Time Duration Total_Stops Additional_Info  Price  Day  Month  Year  \n",
       "0  01:10 22 Mar   2h 50m    non-stop         No info   3897   24      3  2019  \n",
       "1         13:15   7h 25m     2 stops         No info   7662    1      5  2019  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44897142-ac5a-4ddb-a88b-f852bbe33d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Fetaure Engineering Process\n",
    "df['Dept_Hour']=df['Dep_Time'].str.split(':').str[0]\n",
    "df['Dept_Min']=df['Dep_Time'].str.split(':').str[1]\n",
    "df.drop('Dep_Time',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8fb1b7eb-04cd-4135-9d15-c7a3b81b05d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Source           10683 non-null  object\n",
      " 2   Destination      10683 non-null  object\n",
      " 3   Route            10682 non-null  object\n",
      " 4   Arrival_Time     10683 non-null  object\n",
      " 5   Duration         10683 non-null  object\n",
      " 6   Total_Stops      10682 non-null  object\n",
      " 7   Additional_Info  10683 non-null  object\n",
      " 8   Price            10683 non-null  int64 \n",
      " 9   Day              10683 non-null  int64 \n",
      " 10  Month            10683 non-null  int64 \n",
      " 11  Year             10683 non-null  int64 \n",
      " 12  Dept_Hour        10683 non-null  object\n",
      " 13  Dept_Min         10683 non-null  object\n",
      "dtypes: int64(4), object(10)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fa8f6a11-6b68-4062-b9fa-50413114e8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Dept_Hour']=df['Dept_Hour'].astype(int)\n",
    "df['Dept_Min']=df['Dept_Min'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb6ae857-6fb3-4d75-908f-1c0b70b80444",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Arrival_Time']=df['Arrival_Time'].apply(lambda x: x.split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2b9dd5c7-e596-4cc7-8fa9-753cb087ac83",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Arrival_hour']=df['Arrival_Time'].str.split(':').str[0]\n",
    "df['Arrival_min']=df['Arrival_Time'].str.split(':').str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7b7e9a81-fd90-4012-920d-888bd67706fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Arrival_hour']=df['Arrival_hour'].astype(int)\n",
    "df['Arrival_min']=df['Arrival_min'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5a202652-3bb1-4ce3-ae34-dfc430f5ddbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Arrival_Time',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a0155cea-55f0-4e5f-b6b0-42ce228edc26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Source           10683 non-null  object\n",
      " 2   Destination      10683 non-null  object\n",
      " 3   Route            10682 non-null  object\n",
      " 4   Duration         10683 non-null  object\n",
      " 5   Total_Stops      10682 non-null  object\n",
      " 6   Additional_Info  10683 non-null  object\n",
      " 7   Price            10683 non-null  int64 \n",
      " 8   Day              10683 non-null  int64 \n",
      " 9   Month            10683 non-null  int64 \n",
      " 10  Year             10683 non-null  int64 \n",
      " 11  Dept_Hour        10683 non-null  int64 \n",
      " 12  Dept_Min         10683 non-null  int64 \n",
      " 13  Arrival_hour     10683 non-null  int64 \n",
      " 14  Arrival_min      10683 non-null  int64 \n",
      "dtypes: int64(8), object(7)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b494651-64c6-4da8-a823-6ae15fdfbb4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', nan, '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "73c97a67-43cd-44e0-a232-6c0205222137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      5625\n",
       "non-stop    3491\n",
       "2 stops     1520\n",
       "3 stops       45\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Stops'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7dc84513-70a6-44ca-a5a7-85738f735bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Dept_Min</th>\n",
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       "      <th>Arrival_min</th>\n",
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       "      <th>9039</th>\n",
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       "      <td>Cochin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23h 40m</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "        Airline Source Destination Route Duration Total_Stops Additional_Info  \\\n",
       "9039  Air India  Delhi      Cochin   NaN  23h 40m         NaN         No info   \n",
       "\n",
       "      Price  Day  Month  Year  Dept_Hour  Dept_Min  Arrival_hour  Arrival_min  \n",
       "9039   7480    6      5  2019          9        45             9           25  "
      ]
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     "execution_count": 31,
     "metadata": {},
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   "source": [
    "df[df['Total_Stops'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6949a594-d64d-4a41-ac69-1f7f94917fe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total_Stops']=df['Total_Stops'].map({'non-stop':0,'1 stop':1,'2 stops':2,'3 stops':3,'4 stops':4,'nan':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "35bbfb61-99f7-4cd6-b3af-67e4156c0af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
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       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
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       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "      <td>5</td>\n",
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       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
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       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "   Total_Stops Additional_Info  Price  Day  Month  Year  Dept_Hour  Dept_Min  \\\n",
       "0          0.0         No info   3897   24      3  2019         22        20   \n",
       "1          2.0         No info   7662    1      5  2019          5        50   \n",
       "2          2.0         No info  13882    9      6  2019          9        25   \n",
       "3          1.0         No info   6218   12      5  2019         18         5   \n",
       "4          1.0         No info  13302    1      3  2019         16        50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  \n",
       "0             1           10  \n",
       "1            13           15  \n",
       "2             4           25  \n",
       "3            23           30  \n",
       "4            21           35  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6cab25b8-8039-45eb-bb79-549b42286e7a",
   "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>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Dept_Hour</th>\n",
       "      <th>Dept_Min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</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>0.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>20</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>7h 25m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "   Total_Stops Additional_Info  Price  Day  Month  Year  Dept_Hour  Dept_Min  \\\n",
       "0          0.0         No info   3897   24      3  2019         22        20   \n",
       "1          2.0         No info   7662    1      5  2019          5        50   \n",
       "2          2.0         No info  13882    9      6  2019          9        25   \n",
       "3          1.0         No info   6218   12      5  2019         18         5   \n",
       "4          1.0         No info  13302    1      3  2019         16        50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  \n",
       "0             1           10  \n",
       "1            13           15  \n",
       "2             4           25  \n",
       "3            23           30  \n",
       "4            21           35  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0aa5e2ab-ac52-43a6-a652-f3e70f2107b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['duration_hour']=df['Duration'].str.split(' ').str[0].str.split('h').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0518ff86-c3d3-498f-9730-239726ebfae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
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       "      <th>Source</th>\n",
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       "      <th>Arrival_min</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Airline, Source, Destination, Route, Duration, Total_Stops, Additional_Info, Price, Day, Month, Year, Dept_Hour, Dept_Min, Arrival_hour, Arrival_min, duration_hour]\n",
       "Index: []"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['duration_hour']=='5m']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c8d5a845-08da-4217-89c3-cf7e1bb0855e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(6474,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ebe74019-6a21-40e6-9547-243e0f75aa3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['duration_min']=df['Duration'].str.split(' ').str[1].str.split('m').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "34c7a99a-fbe1-4dc3-873d-f148d7af0e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['duration_min']=df['duration_min'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "929ccb18-efb6-4a90-a69e-b80f3b458221",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Duration\n",
    "df.drop('Duration',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cf5e4245-a9a4-41b2-8f0e-e976cbfc48ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['duration_hour']=df['duration_hour'].astype(int)\n",
    "df['duration_min']=df['duration_min'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8d6a0154-dbfb-4fcf-a647-2f88fd04e2eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Krish'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Krish Naik\".split(\" \")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "478497db-2f18-45be-a359-70084a53e525",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10682 entries, 0 to 10682\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Airline          10682 non-null  object \n",
      " 1   Source           10682 non-null  object \n",
      " 2   Destination      10682 non-null  object \n",
      " 3   Route            10681 non-null  object \n",
      " 4   Total_Stops      10681 non-null  float64\n",
      " 5   Additional_Info  10682 non-null  object \n",
      " 6   Price            10682 non-null  int64  \n",
      " 7   Day              10682 non-null  int64  \n",
      " 8   Month            10682 non-null  int64  \n",
      " 9   Year             10682 non-null  int64  \n",
      " 10  Dept_Hour        10682 non-null  int64  \n",
      " 11  Dept_Min         10682 non-null  int64  \n",
      " 12  Arrival_hour     10682 non-null  int64  \n",
      " 13  Arrival_min      10682 non-null  int64  \n",
      " 14  duration_hour    10682 non-null  int64  \n",
      " 15  duration_min     10682 non-null  int64  \n",
      "dtypes: float64(1), int64(10), object(5)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fdaca6a2-5f78-42c7-929a-509ad908d7e2",
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Airline.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "31e211b9-3ca1-4346-ae29-c8fb5a3e2bb8",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Dept_Hour</th>\n",
       "      <th>Dept_Min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>duration_hour</th>\n",
       "      <th>duration_min</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>0.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route  Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL          0.0   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR          2.0   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK          2.0   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR          1.0   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL          1.0   \n",
       "\n",
       "  Additional_Info  Price  Day  Month  Year  Dept_Hour  Dept_Min  Arrival_hour  \\\n",
       "0         No info   3897   24      3  2019         22        20             1   \n",
       "1         No info   7662    1      5  2019          5        50            13   \n",
       "2         No info  13882    9      6  2019          9        25             4   \n",
       "3         No info   6218   12      5  2019         18         5            23   \n",
       "4         No info  13302    1      3  2019         16        50            21   \n",
       "\n",
       "   Arrival_min  duration_hour  duration_min  \n",
       "0           10              2            50  \n",
       "1           15              7            25  \n",
       "2           25             19             0  \n",
       "3           30              5            25  \n",
       "4           35              4            45  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "62040403-2c76-403c-9453-003f10822262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline\n",
       "Trujet                                4140.000000\n",
       "SpiceJet                              4338.284841\n",
       "Air Asia                              5590.260188\n",
       "IndiGo                                5673.682903\n",
       "GoAir                                 5861.056701\n",
       "Vistara                               7796.348643\n",
       "Vistara Premium economy               8962.333333\n",
       "Air India                             9606.804112\n",
       "Multiple carriers                    10902.678094\n",
       "Multiple carriers Premium economy    11418.846154\n",
       "Jet Airways                          11643.923357\n",
       "Jet Airways Business                 58358.666667\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## traget guided ordinal\n",
    "\n",
    "df.groupby('Airline')['Price'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "635a632f-befc-4dd1-809e-9c74dcee1b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "770ee280-378a-43c5-8fe1-f7772a2bfbf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe=OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9746a25d-ec0e-4bb1-ab58-9571931b6aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<10682x12 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 10682 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe.fit_transform(df[['Airline']]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec5483b8-933c-471a-bdd8-8f8ad8d79d63",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Replacing target guided ordinal encoding\n",
    "def replace_airline_with_mean(df):\n",
    "    mean_prices = df.groupby('Airline')['Price'].mean().sort_values()\n",
    "    df['Airline'] = df['Airline'].apply(lambda x: mean_prices[x])\n",
    "    return df\n",
    "\n",
    "df = replace_airline_with_mean(df)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "08cc70e7-56e1-475e-83ea-fca98c7d47da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
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       "\n",
       "[10682 rows x 12 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(ohe.fit_transform(df[['Airline']]).toarray(),columns=ohe.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "52c5612e-9723-488c-ae4c-7adfe43899f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe.transform([['Air Asia']]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9076f870-73f3-4e98-a303-b8beab1abe6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97381fbb-72fd-4eb5-bcb0-cf547b659a6c",
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   "source": []
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  {
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   "execution_count": null,
   "id": "1e98ca40-d391-46bf-8f50-e9f9b0bb5109",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
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   "execution_count": null,
   "id": "aa782ef9-ff47-4925-88ef-95c9fa870774",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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