{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_excel('Flight Dataset/Data_Train.xlsx')\n",
    "train_data.dropna(inplace=True)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Destination\n",
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def newd(x):\n",
    "    if x=='New Delhi':\n",
    "        return 'Delhi'\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "train_data['Destination'] = train_data['Destination'].apply(newd)\n",
    "train_data['Source'] = train_data['Source'].apply(newd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10682 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10682 non-null  object\n",
      " 1   Date_of_Journey  10682 non-null  object\n",
      " 2   Source           10682 non-null  object\n",
      " 3   Destination      10682 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10682 non-null  object\n",
      " 6   Arrival_Time     10682 non-null  object\n",
      " 7   Duration         10682 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10682 non-null  object\n",
      " 10  Price            10682 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 1001.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
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       "      <th>Additional_Info</th>\n",
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       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time  \\\n",
       "0       IndiGo  Banglore       Delhi              BLR → DEL    22:20   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   \n",
       "4       IndiGo  Banglore       Delhi        BLR → NAG → DEL    16:50   \n",
       "\n",
       "   Arrival_Time Duration Total_Stops Additional_Info  Price  Journey_day  \\\n",
       "0  01:10 22 Mar   2h 50m    non-stop         No info   3897           24   \n",
       "1         13:15   7h 25m     2 stops         No info   7662            1   \n",
       "2  04:25 10 Jun      19h     2 stops         No info  13882            9   \n",
       "3         23:30   5h 25m      1 stop         No info   6218           12   \n",
       "4         21:35   4h 45m      1 stop         No info  13302            1   \n",
       "\n",
       "   Journey_month  \n",
       "0              3  \n",
       "1              5  \n",
       "2              6  \n",
       "3              5  \n",
       "4              3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Journey_day'] = pd.to_datetime(train_data['Date_of_Journey'],format='%d/%m/%Y').dt.day\n",
    "train_data['Journey_month'] = pd.to_datetime(train_data['Date_of_Journey'],format='%d/%m/%Y').dt.month\n",
    "\n",
    "train_data.drop('Date_of_Journey',inplace=True,axis=1)\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_27332\\4113259681.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  train_data['Dep_hour'] = pd.to_datetime(train_data['Dep_Time']).dt.hour\n",
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_27332\\4113259681.py:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  train_data['Dep_min'] = pd.to_datetime(train_data['Dep_Time']).dt.minute\n",
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_27332\\4113259681.py:5: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  train_data['Arrival_hour'] = pd.to_datetime(train_data['Arrival_Time']).dt.hour\n",
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_27332\\4113259681.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  train_data['Arrival_min'] = pd.to_datetime(train_data['Arrival_Time']).dt.minute\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Airline</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
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       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore       Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore       Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Dep_hour'] = pd.to_datetime(train_data['Dep_Time']).dt.hour\n",
    "train_data['Dep_min'] = pd.to_datetime(train_data['Dep_Time']).dt.minute\n",
    "train_data.drop('Dep_Time',axis=1,inplace=True)\n",
    "\n",
    "train_data['Arrival_hour'] = pd.to_datetime(train_data['Arrival_Time']).dt.hour\n",
    "train_data['Arrival_min'] = pd.to_datetime(train_data['Arrival_Time']).dt.minute\n",
    "train_data.drop('Arrival_Time',axis=1,inplace=True)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore       Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore       Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration = list(train_data['Duration'])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:\n",
    "        if 'h' in duration[i]:\n",
    "            duration[i] = duration[i] + ' 0m'\n",
    "        else:\n",
    "            duration[i] = '0h ' + duration[i]\n",
    "\n",
    "duration_hour = []\n",
    "duration_min = []\n",
    "\n",
    "for i in duration:\n",
    "    h,m = i.split()\n",
    "    duration_hour.append(int(h[:-1]))\n",
    "    duration_min.append(int(m[:-1]))\n",
    "\n",
    "    \n",
    "train_data['Duration_hours'] = duration_hour\n",
    "train_data['Duration_mins'] = duration_min\n",
    "train_data.drop('Duration',axis=1,inplace=True)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c0aa34ce00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 1811.11x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Airline',y='Price',data=train_data.sort_values('Price',ascending=False),kind='boxen',aspect=3,height=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "airline = train_data[['Airline']]\n",
    "airline = pd.get_dummies(airline,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c0ab165ca0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1211.11x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Source',y='Price',data=train_data.sort_values('Price',ascending=False),kind='boxen',aspect=3,height=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0           False         False           False          False\n",
       "1           False         False            True          False\n",
       "2           False          True           False          False\n",
       "3           False         False            True          False\n",
       "4           False         False           False          False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source = train_data[['Source']]\n",
    "source = pd.get_dummies(source,drop_first=True)\n",
    "source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c0ab166930>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1211.11x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Destination',y='Price',data=train_data.sort_values('Price',ascending=False),kind='boxen',aspect=3,height=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0               False               True                  False   \n",
       "1               False              False                  False   \n",
       "2                True              False                  False   \n",
       "3               False              False                  False   \n",
       "4               False               True                  False   \n",
       "\n",
       "   Destination_Kolkata  \n",
       "0                False  \n",
       "1                False  \n",
       "2                False  \n",
       "3                False  \n",
       "4                False  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "destination = train_data[['Destination']]\n",
    "destination = pd.get_dummies(destination,drop_first=True)\n",
    "destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(['Route','Additional_Info'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total_Stops\n",
       "1 stop      5625\n",
       "non-stop    3491\n",
       "2 stops     1520\n",
       "3 stops       45\n",
       "4 stops        1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Total_Stops'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>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>Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore       Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore       Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  \n",
       "0               2             50  \n",
       "1               7             25  \n",
       "2              19              0  \n",
       "3               5             25  \n",
       "4               4             45  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# acc to the data, price is directly prop to the no. of stops\n",
    "train_data['Total_Stops'].replace({'non-stop':0,'1 stop':1,'2 stops':2,'3 stops':3,'4 stops':4},inplace=True)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10682, 11)\n",
      "(10682, 4)\n",
      "(10682, 4)\n",
      "(10682, 13)\n"
     ]
    }
   ],
   "source": [
    "print(airline.shape)\n",
    "print(source.shape)\n",
    "print(destination.shape)\n",
    "print(train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>...</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>6218</td>\n",
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       "      <td>5</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897           24              3        22       20   \n",
       "1            2   7662            1              5         5       50   \n",
       "2            2  13882            9              6         9       25   \n",
       "3            1   6218           12              5        18        5   \n",
       "4            1  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  ...  \\\n",
       "0             1           10               2             50  ...   \n",
       "1            13           15               7             25  ...   \n",
       "2             4           25              19              0  ...   \n",
       "3            23           30               5             25  ...   \n",
       "4            21           35               4             45  ...   \n",
       "\n",
       "   Airline_Vistara  Airline_Vistara Premium economy  Source_Chennai  \\\n",
       "0            False                            False           False   \n",
       "1            False                            False           False   \n",
       "2            False                            False           False   \n",
       "3            False                            False           False   \n",
       "4            False                            False           False   \n",
       "\n",
       "   Source_Delhi  Source_Kolkata  Source_Mumbai  Destination_Cochin  \\\n",
       "0         False           False          False               False   \n",
       "1         False            True          False               False   \n",
       "2          True           False          False                True   \n",
       "3         False            True          False               False   \n",
       "4         False           False          False               False   \n",
       "\n",
       "   Destination_Delhi  Destination_Hyderabad  Destination_Kolkata  \n",
       "0               True                  False                False  \n",
       "1              False                  False                False  \n",
       "2              False                  False                False  \n",
       "3              False                  False                False  \n",
       "4               True                  False                False  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.concat([train_data,airline,source,destination],axis=1)\n",
    "data_train.drop(['Airline','Source','Destination'],axis=1,inplace=True)\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Price', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
       "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
       "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
       "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
       "       'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
       "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
       "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
       "       'Destination_Kolkata'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 29)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Airline_Vistara Premium economy</th>\n",
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       "      <td>50</td>\n",
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       "      <td>35</td>\n",
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       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  ...  \\\n",
       "0           10               2             50              False  ...   \n",
       "1           15               7             25               True  ...   \n",
       "2           25              19              0              False  ...   \n",
       "3           30               5             25              False  ...   \n",
       "4           35               4             45              False  ...   \n",
       "\n",
       "   Airline_Vistara  Airline_Vistara Premium economy  Source_Chennai  \\\n",
       "0            False                            False           False   \n",
       "1            False                            False           False   \n",
       "2            False                            False           False   \n",
       "3            False                            False           False   \n",
       "4            False                            False           False   \n",
       "\n",
       "   Source_Delhi  Source_Kolkata  Source_Mumbai  Destination_Cochin  \\\n",
       "0         False           False          False               False   \n",
       "1         False            True          False               False   \n",
       "2          True           False          False                True   \n",
       "3         False            True          False               False   \n",
       "4         False           False          False               False   \n",
       "\n",
       "   Destination_Delhi  Destination_Hyderabad  Destination_Kolkata  \n",
       "0               True                  False                False  \n",
       "1              False                  False                False  \n",
       "2              False                  False                False  \n",
       "3              False                  False                False  \n",
       "4               True                  False                False  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_train.drop('Price',axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train['Price']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exclude non-numeric columns before calculating correlation\n",
    "numeric_train_data = train_data.select_dtypes(include=['int64', 'float64'])\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(numeric_train_data.corr(), cmap='viridis', annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.39672221e-01 1.43225496e-01 5.78276330e-02 2.46011748e-02\n",
      " 2.11173149e-02 2.72539397e-02 1.93071265e-02 1.26528731e-01\n",
      " 1.79452283e-02 9.48235812e-03 1.91459243e-03 2.20293783e-02\n",
      " 1.30647366e-01 6.71652406e-02 1.90663986e-02 8.21145825e-04\n",
      " 5.56214050e-03 9.24557228e-05 4.15751006e-03 6.39203993e-05\n",
      " 7.81226854e-04 1.13017168e-02 4.99738926e-03 7.28477892e-03\n",
      " 9.28399498e-03 1.92601873e-02 7.92637726e-03 6.82956436e-04]\n"
     ]
    }
   ],
   "source": [
    "reg = ExtraTreesRegressor()\n",
    "reg.fit(X,y)\n",
    "\n",
    "print(reg.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(reg.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hp\\anaconda3\\envs\\resparser\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "20 fits failed out of a total of 50.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "20 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hp\\anaconda3\\envs\\resparser\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hp\\anaconda3\\envs\\resparser\\Lib\\site-packages\\sklearn\\base.py\", line 1467, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\hp\\anaconda3\\envs\\resparser\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\hp\\anaconda3\\envs\\resparser\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hp\\anaconda3\\envs\\resparser\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [-6621619.98833738 -5131062.17444438               nan               nan\n",
      "               nan -4664545.07850963 -9440049.70961768 -4886155.1706001\n",
      " -8861386.05872784               nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" 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=1)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><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=1)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><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=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]\n",
    "\n",
    "# Create the random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "\n",
    "# Random search of parameters, using 5 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = RandomForestRegressor(), param_distributions = random_grid,\n",
    "                               scoring='neg_mean_squared_error', n_iter = 10, cv = 5, \n",
    "                               verbose=1, random_state=42, n_jobs = 1)\n",
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_27332\\375150797.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_test-prediction)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 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": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score:  0.8158850137423834\n"
     ]
    }
   ],
   "source": [
    "print('r2 score: ', metrics.r2_score(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('flight_rf.pkl', 'wb')\n",
    "pickle.dump(rf_random, file)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}