flight price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_excel('Flight Dataset/Data_Train.xlsx')\n",
    "train_data.dropna(inplace=True)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def newd(x):\n",
    "    if x=='New Delhi':\n",
    "        return 'Delhi'\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "train_data['Destination'] = train_data['Destination'].apply(newd)\n",
    "train_data['Source'] = train_data['Source'].apply(newd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10682 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10682 non-null  object\n",
      " 1   Date_of_Journey  10682 non-null  object\n",
      " 2   Source           10682 non-null  object\n",
      " 3   Destination      10682 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10682 non-null  object\n",
      " 6   Arrival_Time     10682 non-null  object\n",
      " 7   Duration         10682 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10682 non-null  object\n",
      " 10  Price            10682 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 1001.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time  \\\n",
       "0       IndiGo  Banglore       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       Delhi        BLR → NAG → DEL    16:50   \n",
       "\n",
       "   Arrival_Time Duration Total_Stops Additional_Info  Price  Journey_day  \\\n",
       "0  01:10 22 Mar   2h 50m    non-stop         No info   3897           24   \n",
       "1         13:15   7h 25m     2 stops         No info   7662            1   \n",
       "2  04:25 10 Jun      19h     2 stops         No info  13882            9   \n",
       "3         23:30   5h 25m      1 stop         No info   6218           12   \n",
       "4         21:35   4h 45m      1 stop         No info  13302            1   \n",
       "\n",
       "   Journey_month  \n",
       "0              3  \n",
       "1              5  \n",
       "2              6  \n",
       "3              5  \n",
       "4              3  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Journey_day'] = pd.to_datetime(train_data['Date_of_Journey'],format='%d/%m/%Y').dt.day\n",
    "train_data['Journey_month'] = pd.to_datetime(train_data['Date_of_Journey'],format='%d/%m/%Y').dt.month\n",
    "\n",
    "train_data.drop('Date_of_Journey',inplace=True,axis=1)\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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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>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_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>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</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>Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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       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       Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Dep_hour'] = pd.to_datetime(train_data['Dep_Time']).dt.hour\n",
    "train_data['Dep_min'] = pd.to_datetime(train_data['Dep_Time']).dt.minute\n",
    "train_data.drop('Dep_Time',axis=1,inplace=True)\n",
    "\n",
    "train_data['Arrival_hour'] = pd.to_datetime(train_data['Arrival_Time']).dt.hour\n",
    "train_data['Arrival_min'] = pd.to_datetime(train_data['Arrival_Time']).dt.minute\n",
    "train_data.drop('Arrival_Time',axis=1,inplace=True)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>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>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</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 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</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 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</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>Delhi</td>\n",
       "      <td>BLR → NAG → DEL</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>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       Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore       Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration = list(train_data['Duration'])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:\n",
    "        if 'h' in duration[i]:\n",
    "            duration[i] = duration[i] + ' 0m'\n",
    "        else:\n",
    "            duration[i] = '0h ' + duration[i]\n",
    "\n",
    "duration_hour = []\n",
    "duration_min = []\n",
    "\n",
    "for i in duration:\n",
    "    h,m = i.split()\n",
    "    duration_hour.append(int(h[:-1]))\n",
    "    duration_min.append(int(m[:-1]))\n",
    "\n",
    "    \n",
    "train_data['Duration_hours'] = duration_hour\n",
    "train_data['Duration_mins'] = duration_min\n",
    "train_data.drop('Duration',axis=1,inplace=True)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21219d1a080>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can observe that jet airways bussiness is he costliest airways\n",
    "sns.catplot(x='Airline',y='Price',data=train_data.sort_values('Price',ascending=False),kind='boxen',aspect=3,height=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "airline = train_data[['Airline']]\n",
    "airline = pd.get_dummies(airline,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x212187365f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If we are going from Banglore the prices are slightly higher as compared to other cities\n",
    "sns.catplot(x='Source',y='Price',data=train_data.sort_values('Price',ascending=False),kind='boxen',aspect=3,height=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0               0             0               0              0\n",
       "1               0             0               1              0\n",
       "2               0             1               0              0\n",
       "3               0             0               1              0\n",
       "4               0             0               0              0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source = train_data[['Source']]\n",
    "source = pd.get_dummies(source,drop_first=True)\n",
    "source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2121a04cc10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If we are going to New Delhi the prices are slightly higher as compared to other cities\n",
    "sns.catplot(x='Destination',y='Price',data=train_data.sort_values('Price',ascending=False),kind='boxen',aspect=3,height=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  1                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  1                      0   \n",
       "\n",
       "   Destination_Kolkata  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "destination = train_data[['Destination']]\n",
    "destination = pd.get_dummies(destination,drop_first=True)\n",
    "destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(['Route','Additional_Info'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Total_Stops'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
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       "      <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>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</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>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</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>Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore       Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore       Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  \n",
       "0               2             50  \n",
       "1               7             25  \n",
       "2              19              0  \n",
       "3               5             25  \n",
       "4               4             45  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# acc to the data, price is directly prop to the no. of stops\n",
    "train_data['Total_Stops'].replace({'non-stop':0,'1 stop':1,'2 stops':2,'3 stops':3,'4 stops':4},inplace=True)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10682, 11)\n",
      "(10682, 4)\n",
      "(10682, 4)\n",
      "(10682, 13)\n"
     ]
    }
   ],
   "source": [
    "print(airline.shape)\n",
    "print(source.shape)\n",
    "print(destination.shape)\n",
    "print(train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>...</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
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       "      <td>5</td>\n",
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       "      <td>...</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</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",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897           24              3        22       20   \n",
       "1            2   7662            1              5         5       50   \n",
       "2            2  13882            9              6         9       25   \n",
       "3            1   6218           12              5        18        5   \n",
       "4            1  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  ...  \\\n",
       "0             1           10               2             50  ...   \n",
       "1            13           15               7             25  ...   \n",
       "2             4           25              19              0  ...   \n",
       "3            23           30               5             25  ...   \n",
       "4            21           35               4             45  ...   \n",
       "\n",
       "   Airline_Vistara  Airline_Vistara Premium economy  Source_Chennai  \\\n",
       "0                0                                0               0   \n",
       "1                0                                0               0   \n",
       "2                0                                0               0   \n",
       "3                0                                0               0   \n",
       "4                0                                0               0   \n",
       "\n",
       "   Source_Delhi  Source_Kolkata  Source_Mumbai  Destination_Cochin  \\\n",
       "0             0               0              0                   0   \n",
       "1             0               1              0                   0   \n",
       "2             1               0              0                   1   \n",
       "3             0               1              0                   0   \n",
       "4             0               0              0                   0   \n",
       "\n",
       "   Destination_Delhi  Destination_Hyderabad  Destination_Kolkata  \n",
       "0                  1                      0                    0  \n",
       "1                  0                      0                    0  \n",
       "2                  0                      0                    0  \n",
       "3                  0                      0                    0  \n",
       "4                  1                      0                    0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.concat([train_data,airline,source,destination],axis=1)\n",
    "data_train.drop(['Airline','Source','Destination'],axis=1,inplace=True)\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding out the best feature which will contribute and have good relation with target variable. Following are some of the feature selection methods,\n",
    "\n",
    "1. **heatmap** \n",
    "2. **feature_importance_** \n",
    "3. **SelectKBest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Price', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
       "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
       "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
       "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
       "       'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
       "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
       "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
       "       'Destination_Kolkata'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</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",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  ...  \\\n",
       "0           10               2             50                  0  ...   \n",
       "1           15               7             25                  1  ...   \n",
       "2           25              19              0                  0  ...   \n",
       "3           30               5             25                  0  ...   \n",
       "4           35               4             45                  0  ...   \n",
       "\n",
       "   Airline_Vistara  Airline_Vistara Premium economy  Source_Chennai  \\\n",
       "0                0                                0               0   \n",
       "1                0                                0               0   \n",
       "2                0                                0               0   \n",
       "3                0                                0               0   \n",
       "4                0                                0               0   \n",
       "\n",
       "   Source_Delhi  Source_Kolkata  Source_Mumbai  Destination_Cochin  \\\n",
       "0             0               0              0                   0   \n",
       "1             0               1              0                   0   \n",
       "2             1               0              0                   1   \n",
       "3             0               1              0                   0   \n",
       "4             0               0              0                   0   \n",
       "\n",
       "   Destination_Delhi  Destination_Hyderabad  Destination_Kolkata  \n",
       "0                  1                      0                    0  \n",
       "1                  0                      0                    0  \n",
       "2                  0                      0                    0  \n",
       "3                  0                      0                    0  \n",
       "4                  1                      0                    0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_train.drop('Price',axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3897\n",
       "1     7662\n",
       "2    13882\n",
       "3     6218\n",
       "4    13302\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train['Price']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(train_data.corr(),cmap='viridis',annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.48260109e-01 1.42969051e-01 5.84608708e-02 2.44600018e-02\n",
      " 2.08046311e-02 2.88481085e-02 1.89712047e-02 9.98734045e-02\n",
      " 1.75907576e-02 9.98855737e-03 1.90852299e-03 1.77283653e-02\n",
      " 1.46213508e-01 6.77077357e-02 1.86863512e-02 8.79227182e-04\n",
      " 4.77035134e-03 1.01358251e-04 4.88554441e-03 7.30559279e-05\n",
      " 6.40696788e-04 8.73633118e-03 4.66577105e-03 8.06904102e-03\n",
      " 1.54149631e-02 1.87081756e-02 9.78598134e-03 7.98323103e-04]\n"
     ]
    }
   ],
   "source": [
    "reg = ExtraTreesRegressor()\n",
    "reg.fit(X,y)\n",
    "\n",
    "print(reg.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(reg.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting model using Random Forest\n",
    "1. Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "2. If needed do scaling of data(Scaling is not done in Random forest)\n",
    "3. Import model\n",
    "4. Fit the data\n",
    "5. Predict w.r.t X_test\n",
    "6. In regression check RSME Score\n",
    "7. Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]\n",
    "\n",
    "# Create the random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "\n",
    "# Random search of parameters, using 5 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = RandomForestRegressor(), param_distributions = random_grid,\n",
    "                               scoring='neg_mean_squared_error', n_iter = 10, cv = 5, \n",
    "                               verbose=1, random_state=42, n_jobs = 1)\n",
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 15,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Projects\\Python\\Virtual Environment\\AirFare\\Environment\\AirFare\\venv\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sns.distplot(y_test-prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "plt.scatter(y_test, prediction, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score:  0.811732127524842\n"
     ]
    }
   ],
   "source": [
    "print('r2 score: ', metrics.r2_score(y_test,prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('flight_rf.pkl', 'wb')\n",
    "pickle.dump(rf_random, file)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}