flight fare prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46d5b7de",
   "metadata": {},
   "source": [
    "# Flight Fare Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90fd3de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae4e0dea",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Loading the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7f891598",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_excel(\"flight_train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f8e5892",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fcff7d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2662f99",
   "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": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dd53c89d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2h 50m     550\n",
       "1h 30m     386\n",
       "2h 45m     337\n",
       "2h 55m     337\n",
       "2h 35m     329\n",
       "          ... \n",
       "31h 30m      1\n",
       "30h 25m      1\n",
       "42h 5m       1\n",
       "4h 10m       1\n",
       "47h 40m      1\n",
       "Name: Duration, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50e7243a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aafd90b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb4ec175",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "806191ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_day\"] = pd.to_datetime(train_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15b979a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_month\"] = pd.to_datetime(train_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "196ddc6c",
   "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>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>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",
       "      <td>24</td>\n",
       "      <td>3</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",
       "      <td>1</td>\n",
       "      <td>5</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",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 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>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",
       "      <td>1</td>\n",
       "      <td>3</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   \n",
       "\n",
       "   Journey_day  Journey_month  \n",
       "0           24              3  \n",
       "1            1              5  \n",
       "2            9              6  \n",
       "3           12              5  \n",
       "4            1              3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f132028",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we have converted Date_of_Journey column into integers, Now we can drop as it is of no use.\n",
    "\n",
    "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef6759a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Departure time is when a plane leaves the gate. \n",
    "# Similar to Date_of_Journey we can extract values from Dep_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_data[\"Dep_hour\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_data[\"Dep_min\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.minute\n",
    "\n",
    "# Now we can drop Dep_Time as it is of no use\n",
    "train_data.drop([\"Dep_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f90da161",
   "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>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",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_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>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>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>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>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>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>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>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>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>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>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route  Arrival_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL  01:10 22 Mar   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR         13:15   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK  04:25 10 Jun   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR         23:30   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL         21:35   \n",
       "\n",
       "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0   2h 50m    non-stop         No info   3897           24              3   \n",
       "1   7h 25m     2 stops         No info   7662            1              5   \n",
       "2      19h     2 stops         No info  13882            9              6   \n",
       "3   5h 25m      1 stop         No info   6218           12              5   \n",
       "4   4h 45m      1 stop         No info  13302            1              3   \n",
       "\n",
       "   Dep_hour  Dep_min  \n",
       "0        22       20  \n",
       "1         5       50  \n",
       "2         9       25  \n",
       "3        18        5  \n",
       "4        16       50  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4fb3135e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrival time is when the plane pulls up to the gate.\n",
    "# Similar to Date_of_Journey we can extract values from Arrival_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_data[\"Arrival_hour\"] = pd.to_datetime(train_data.Arrival_Time).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_data[\"Arrival_min\"] = pd.to_datetime(train_data.Arrival_Time).dt.minute\n",
    "\n",
    "# Now we can drop Arrival_Time as it is of no use\n",
    "train_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "26e43b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>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>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</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>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</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   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  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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d97642a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time taken by plane to reach destination is called Duration\n",
    "# It is the differnce betwwen Departure Time and Arrival time\n",
    "\n",
    "\n",
    "# Assigning and converting Duration column into list\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c7e7cd43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding duration_hours and duration_mins list to train_data dataframe\n",
    "\n",
    "train_data[\"Duration_hours\"] = duration_hours\n",
    "train_data[\"Duration_mins\"] = duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a6f6d851",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Duration\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b71e43ec",
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>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>New 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>New 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   New 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   New 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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3cb9dd",
   "metadata": {},
   "source": [
    "## Handling Categorical Data"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5442ea65",
   "metadata": {},
   "source": [
    "One can find many ways to handle categorical data. Some of them categorical data are,\n",
    "\n",
    "**Nominal data** --> data are not in any order --> **OneHotEncoder** is used in this case\n",
    "**Ordinal data** --> data are in order --> **LabelEncoder** is used in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2c904220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1751\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              818\n",
       "Vistara                               479\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Airline\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "152473ef",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graph we can see that Jet Airways Business have the highest Price.\n",
    "# Apart from the first Airline almost all are having similar median\n",
    "\n",
    "# Airline vs Price\n",
    "sns.catplot(y = \"Price\", x = \"Airline\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "152aaad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <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>2</th>\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",
       "      <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",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \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  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Airline = train_data[[\"Airline\"]]\n",
    "\n",
    "Airline = pd.get_dummies(Airline, drop_first= True)\n",
    "\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ca5c0ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4536\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c6e47563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source vs Price\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Source\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b06c3a39",
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Source = train_data[[\"Source\"]]\n",
    "\n",
    "Source = pd.get_dummies(Source, drop_first= True)\n",
    "\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "69c27f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3d895928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</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",
       "      <td>1</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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination = train_data[[\"Destination\"]]\n",
    "\n",
    "Destination = pd.get_dummies(Destination, drop_first = True)\n",
    "\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "50c02485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    BLR → DEL\n",
       "1        CCU → IXR → BBI → BLR\n",
       "2        DEL → LKO → BOM → COK\n",
       "3              CCU → NAG → BLR\n",
       "4              BLR → NAG → DEL\n",
       "                 ...          \n",
       "10678                CCU → BLR\n",
       "10679                CCU → BLR\n",
       "10680                BLR → DEL\n",
       "10681                BLR → DEL\n",
       "10682    DEL → GOI → BOM → COK\n",
       "Name: Route, Length: 10682, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Route\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dd96845a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "train_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "515584eb",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Total_Stops\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "beaf6c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "# Here Values are assigned with corresponding keys\n",
    "\n",
    "train_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4defe526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>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>New Delhi</td>\n",
       "      <td>0</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>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>New 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   New 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   New 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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d8c6acfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate dataframe --> train_data + Airline + Source + Destination\n",
    "\n",
    "data_train = pd.concat([train_data, Airline, Source, Destination], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "44d116d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th></th>\n",
       "      <th>Airline</th>\n",
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       "      <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>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</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",
       "      <th>Destination_New Delhi</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>0</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",
       "      <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",
       "      <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</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>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",
       "      <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>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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <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",
       "      <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",
       "      <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>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New 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",
       "      <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",
       "      <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New 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   New 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  Airline_Air India  Airline_GoAir  \\\n",
       "0               2             50                  0              0   \n",
       "1               7             25                  1              0   \n",
       "2              19              0                  0              0   \n",
       "3               5             25                  0              0   \n",
       "4               4             45                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0               1                    0                             0   \n",
       "1               0                    0                             0   \n",
       "2               0                    1                             0   \n",
       "3               1                    0                             0   \n",
       "4               1                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Trujet  Airline_Vistara  \\\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",
       "   Airline_Vistara Premium economy  Source_Chennai  Source_Delhi  \\\n",
       "0                                0               0             0   \n",
       "1                                0               0             0   \n",
       "2                                0               0             1   \n",
       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "\n",
       "   Source_Kolkata  Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0               0              0                   0                  0   \n",
       "1               1              0                   0                  0   \n",
       "2               0              0                   1                  0   \n",
       "3               1              0                   0                  0   \n",
       "4               0              0                   0                  0   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  Destination_New Delhi  \n",
       "0                      0                    0                      1  \n",
       "1                      0                    0                      0  \n",
       "2                      0                    0                      0  \n",
       "3                      0                    0                      0  \n",
       "4                      0                    0                      1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "45623574",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "349b248b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>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>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</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",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</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",
       "      <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",
       "      <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>1</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>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",
       "      <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>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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</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>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",
       "      <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",
       "      <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>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",
       "      <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\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_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   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "83f97e20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de65e95",
   "metadata": {},
   "source": [
    "## Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9e868a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(\"filght_test.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7cde8da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c054fbd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Info\n",
      "---------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2671 entries, 0 to 2670\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          2671 non-null   object\n",
      " 1   Date_of_Journey  2671 non-null   object\n",
      " 2   Source           2671 non-null   object\n",
      " 3   Destination      2671 non-null   object\n",
      " 4   Route            2671 non-null   object\n",
      " 5   Dep_Time         2671 non-null   object\n",
      " 6   Arrival_Time     2671 non-null   object\n",
      " 7   Duration         2671 non-null   object\n",
      " 8   Total_Stops      2671 non-null   object\n",
      " 9   Additional_Info  2671 non-null   object\n",
      "dtypes: object(10)\n",
      "memory usage: 208.8+ KB\n",
      "None\n",
      "\n",
      "\n",
      "Null values :\n",
      "---------------------------------------------------------------------------\n",
      "Airline            0\n",
      "Date_of_Journey    0\n",
      "Source             0\n",
      "Destination        0\n",
      "Route              0\n",
      "Dep_Time           0\n",
      "Arrival_Time       0\n",
      "Duration           0\n",
      "Total_Stops        0\n",
      "Additional_Info    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "\n",
    "print(\"Test data Info\")\n",
    "print(\"-\"*75)\n",
    "print(test_data.info())\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Null values :\")\n",
    "print(\"-\"*75)\n",
    "test_data.dropna(inplace = True)\n",
    "print(test_data.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e4988256",
   "metadata": {},
   "outputs": [],
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ea4682ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airline\n",
      "---------------------------------------------------------------------------\n",
      "Jet Airways                          897\n",
      "IndiGo                               511\n",
      "Air India                            440\n",
      "Multiple carriers                    347\n",
      "SpiceJet                             208\n",
      "Vistara                              129\n",
      "Air Asia                              86\n",
      "GoAir                                 46\n",
      "Multiple carriers Premium economy      3\n",
      "Vistara Premium economy                2\n",
      "Jet Airways Business                   2\n",
      "Name: Airline, dtype: int64\n",
      "\n",
      "Source\n",
      "---------------------------------------------------------------------------\n",
      "Delhi       1145\n",
      "Kolkata      710\n",
      "Banglore     555\n",
      "Mumbai       186\n",
      "Chennai       75\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "Destination\n",
      "---------------------------------------------------------------------------\n",
      "Cochin       1145\n",
      "Banglore      710\n",
      "Delhi         317\n",
      "New Delhi     238\n",
      "Hyderabad     186\n",
      "Kolkata        75\n",
      "Name: Destination, dtype: int64\n",
      "\n",
      "\n",
      "Shape of test data :  (2671, 28)\n"
     ]
    }
   ],
   "source": [
    "# Date_of_Journey\n",
    "test_data[\"Journey_day\"] = pd.to_datetime(test_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day\n",
    "test_data[\"Journey_month\"] = pd.to_datetime(test_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month\n",
    "test_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)\n",
    "\n",
    "# Dep_Time\n",
    "test_data[\"Dep_hour\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.hour\n",
    "test_data[\"Dep_min\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.minute\n",
    "test_data.drop([\"Dep_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Arrival_Time\n",
    "test_data[\"Arrival_hour\"] = pd.to_datetime(test_data.Arrival_Time).dt.hour\n",
    "test_data[\"Arrival_min\"] = pd.to_datetime(test_data.Arrival_Time).dt.minute\n",
    "test_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Duration\n",
    "duration = list(test_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration\n",
    "# Adding Duration column to test set\n",
    "test_data[\"Duration_hours\"] = duration_hours\n",
    "test_data[\"Duration_mins\"] = duration_mins\n",
    "test_data.drop([\"Duration\"], axis = 1, inplace = True)\n",
    "\n",
    "\n",
    "# Categorical data\n",
    "\n",
    "print(\"Airline\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Airline\"].value_counts())\n",
    "Airline = pd.get_dummies(test_data[\"Airline\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Source\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Source\"].value_counts())\n",
    "Source = pd.get_dummies(test_data[\"Source\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "print(\"Destination\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Destination\"].value_counts())\n",
    "Destination = pd.get_dummies(test_data[\"Destination\"], drop_first = True)\n",
    "\n",
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "test_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)\n",
    "\n",
    "# Replacing Total_Stops\n",
    "test_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)\n",
    "\n",
    "# Concatenate dataframe --> test_data + Airline + Source + Destination\n",
    "data_test = pd.concat([test_data, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "data_test.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Shape of test data : \", data_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "32dd6640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>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>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <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>21</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>45</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>13</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",
       "      <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>1</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>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>50</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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\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",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \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  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5cf7c15",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f9da422b",
   "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",
    "** heatmap**\n",
    "**feature_importance_**\n",
    "**SelectKBest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "55c1238e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8726759a",
   "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', 'Destination_New Delhi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "41e10b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>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>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</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",
       "      <th>Destination_New Delhi</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
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       "      <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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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",
       "      <td>1</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>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",
       "      <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>2</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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</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>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",
       "      <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",
       "      <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>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",
       "      <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0              0               1                    0   \n",
       "1              0               0                    0   \n",
       "2              0               0                    1   \n",
       "3              0               1                    0   \n",
       "4              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\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_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   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_train.loc[:, ['Total_Stops', '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', 'Destination_New Delhi']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4ff48207",
   "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:, 1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1fb82e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finds correlation between Independent and dependent attributes\n",
    "\n",
    "plt.figure(figsize = (18,18))\n",
    "sns.heatmap(train_data.corr(), annot = True, cmap = \"RdYlGn\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "90cdd698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8746ee2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.34862327e-01 1.44178131e-01 5.28223145e-02 2.34629679e-02\n",
      " 2.12169727e-02 2.83246986e-02 1.91577954e-02 1.24908593e-01\n",
      " 1.84298739e-02 9.27193990e-03 1.75639727e-03 1.80652169e-02\n",
      " 1.36547788e-01 6.69470991e-02 2.02120772e-02 8.40900512e-04\n",
      " 3.48159522e-03 1.20395253e-04 4.91367187e-03 8.17609758e-05\n",
      " 5.58550760e-04 6.93185327e-03 3.01500021e-03 6.18284109e-03\n",
      " 1.00440155e-02 1.13210251e-02 6.65511021e-03 5.23034677e-04\n",
      " 2.51660530e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "777c4541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0223363a",
   "metadata": {},
   "source": [
    "## Fitting model using Random Forest\n",
    "* Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "* If needed do scaling of data\n",
    "* Import model Fit the data\n",
    "* Predict w.r.t X_test\n",
    "* Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ee6f8299",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e89f6f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "reg_rf = RandomForestRegressor()\n",
    "reg_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2d33562f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c09f4c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9532501234796201"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f48a7026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8002084671479188"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e6cad692",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fcb2a9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "591b7df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "aa9f8588",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1161.645115795515\n",
      "MSE: 4118308.1448879647\n",
      "RMSE: 2029.3615116306814\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "700e25a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026887077025966846"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE/(max(DV)-min(DV))\n",
    "\n",
    "2090.5509/(max(y)-min(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "aac1dafd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8002084671479188"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "992afdb7",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "Choose following method for hyperparameter tuning\n",
    "RandomizedSearchCV --> Fast\n",
    "GridSearchCV\n",
    "Assign hyperparameters in form of dictionery\n",
    "Fit the model\n",
    "Check best paramters and best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5985f55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ef4ba6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# 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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6d57a56c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bdfd93bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 5 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = reg_rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "93e66350",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.7s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.7s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.7s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   8.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   8.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  12.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  12.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  12.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  12.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  14.2s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  12.5s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  12.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  12.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  13.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  13.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   4.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   4.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   2.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   2.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  14.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  15.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  15.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  15.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  15.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "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=2)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e1d2c4f8",
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9892304c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b25e4477",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\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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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sns.distplot(y_test-prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a45a2c3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 74,
   "id": "8fb8771e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1152.9964589464637\n",
      "MSE: 3861861.780069002\n",
      "RMSE: 1965.1620238720782\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "520365c8",
   "metadata": {},
   "source": [
    "## Save the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4770dbf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you want to store the data\n",
    "file = open('flight_rf.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(reg_rf, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b29f483f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cca8eed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "779baf1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8002084671479188"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95317397",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00b9daf3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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