Flight Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.read_excel('Data_Train.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            object\n",
       "Date_of_Journey    object\n",
       "Source             object\n",
       "Destination        object\n",
       "Route              object\n",
       "Dep_Time           object\n",
       "Arrival_Time       object\n",
       "Duration           object\n",
       "Total_Stops        object\n",
       "Additional_Info    object\n",
       "Price               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From description we can see that Date_of_Journey is a object data type,\n",
    "     Therefore, we have to convert this datatype into timestamp so as to use this column properly for prediction,bcz our \n",
    "     model will not be able to understand Theses string values,it just understand Time-stamp\n",
    "        For this we require pandas to_datetime to convert object data type to datetime dtype.\n",
    "\n",
    "\n",
    "         dt.day method will extract only day of that date\n",
    "            dt.month method will extract only month of that date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_into_datetime(col):\n",
    "    train_data[col]=pd.to_datetime(train_data[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
       "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops',\n",
       "       'Additional_Info', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['Date_of_Journey','Dep_Time','Arrival_Time']:\n",
    "    change_into_datetime(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                    object\n",
       "Date_of_Journey    datetime64[ns]\n",
       "Source                     object\n",
       "Destination                object\n",
       "Route                      object\n",
       "Dep_Time           datetime64[ns]\n",
       "Arrival_Time       datetime64[ns]\n",
       "Duration                   object\n",
       "Total_Stops                object\n",
       "Additional_Info            object\n",
       "Price                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Journey_day']=train_data['Date_of_Journey'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Journey_month']=train_data['Date_of_Journey'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>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>2019-03-24</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2021-09-02 22:20:00</td>\n",
       "      <td>2021-03-22 01:10:00</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>2019-01-05</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2021-09-02 05:50:00</td>\n",
       "      <td>2021-09-02 13:15:00</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2021-09-02 09:25:00</td>\n",
       "      <td>2021-06-10 04:25:00</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>2021-09-02 18:05:00</td>\n",
       "      <td>2021-09-02 23:30:00</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>2021-09-02 16:50:00</td>\n",
       "      <td>2021-09-02 21:35:00</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India      2019-01-05   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways      2019-09-06     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      2019-12-05   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      2019-01-03  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "             Dep_Time        Arrival_Time Duration Total_Stops  \\\n",
       "0 2021-09-02 22:20:00 2021-03-22 01:10:00   2h 50m    non-stop   \n",
       "1 2021-09-02 05:50:00 2021-09-02 13:15:00   7h 25m     2 stops   \n",
       "2 2021-09-02 09:25:00 2021-06-10 04:25:00      19h     2 stops   \n",
       "3 2021-09-02 18:05:00 2021-09-02 23:30:00   5h 25m      1 stop   \n",
       "4 2021-09-02 16:50:00 2021-09-02 21:35:00   4h 45m      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  \n",
       "0         No info   3897           24              3  \n",
       "1         No info   7662            5              1  \n",
       "2         No info  13882            6              9  \n",
       "3         No info   6218            5             12  \n",
       "4         No info  13302            3              1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Since we have converted Date_of_Journey column into integers, Now we can drop as it is of no use.\n",
    "train_data.drop('Date_of_Journey', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hour(df,col):\n",
    "    df[col+\"_hour\"]=df[col].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_min(df,col):\n",
    "    df[col+\"_minute\"]=df[col].dt.minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_column(df,col):\n",
    "    df.drop(col,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
    "extract_hour(train_data,'Dep_Time')\n",
    "# Extracting Minutes\n",
    "extract_min(train_data,'Dep_Time')\n",
    "# Now we can drop Dep_Time as it is of no use\n",
    "drop_column(train_data,'Dep_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
    "extract_hour(train_data,'Arrival_Time')\n",
    "\n",
    "# Extracting minutes\n",
    "extract_min(train_data,'Arrival_Time')\n",
    "\n",
    "# Now we can drop Arrival_Time as it is of no use\n",
    "drop_column(train_data,'Arrival_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>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_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</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>5</td>\n",
       "      <td>1</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>6</td>\n",
       "      <td>9</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>5</td>\n",
       "      <td>12</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>3</td>\n",
       "      <td>1</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  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            5              1   \n",
       "2     2 stops         No info  13882            6              9   \n",
       "3      1 stop         No info   6218            5             12   \n",
       "4      1 stop         No info  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \n",
       "0             22               20                  1                   10  \n",
       "1              5               50                 13                   15  \n",
       "2              9               25                  4                   25  \n",
       "3             18                5                 23                   30  \n",
       "4             16               50                 21                   35  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets Apply pre-processing on duration column,Separate Duration hours and minute from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "duration=list(train_data['Duration'])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split(' '))==2:\n",
    "        pass\n",
    "    else:\n",
    "        if 'h' in duration[i]:                   # Check if duration contains only hour\n",
    "            duration[i]=duration[i] + ' 0m'      # Adds 0 minute\n",
    "        else:\n",
    "            duration[i]='0h '+ duration[i]       # if duration contains only second, Adds 0 hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Duration']=duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
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       "      <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>5</td>\n",
       "      <td>1</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 0m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</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>5</td>\n",
       "      <td>12</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>3</td>\n",
       "      <td>1</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 0m   \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  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            5              1   \n",
       "2     2 stops         No info  13882            6              9   \n",
       "3      1 stop         No info   6218            5             12   \n",
       "4      1 stop         No info  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \n",
       "0             22               20                  1                   10  \n",
       "1              5               50                 13                   15  \n",
       "2              9               25                  4                   25  \n",
       "3             18                5                 23                   30  \n",
       "4             16               50                 21                   35  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hour(x):\n",
    "    return x.split(' ')[0][0:-1]\n",
    "def min(x):\n",
    "    return x.split(' ')[1][0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Duration_hours']=train_data['Duration'].apply(hour)\n",
    "train_data['Duration_mins']=train_data['Duration'].apply(min)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>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_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</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>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",
       "      <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>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</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>19h 0m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</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>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</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>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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 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 0m   \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  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            5              1   \n",
       "2     2 stops         No info  13882            6              9   \n",
       "3      1 stop         No info   6218            5             12   \n",
       "4      1 stop         No info  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \\\n",
       "0             22               20                  1                   10   \n",
       "1              5               50                 13                   15   \n",
       "2              9               25                  4                   25   \n",
       "3             18                5                 23                   30   \n",
       "4             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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop('Duration',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>5</td>\n",
       "      <td>1</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>6</td>\n",
       "      <td>9</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>5</td>\n",
       "      <td>12</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>3</td>\n",
       "      <td>1</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_Time_hour  \\\n",
       "0         No info   3897           24              3             22   \n",
       "1         No info   7662            5              1              5   \n",
       "2         No info  13882            6              9              9   \n",
       "3         No info   6218            5             12             18   \n",
       "4         No info  13302            3              1             16   \n",
       "\n",
       "   Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute Duration_hours  \\\n",
       "0               20                  1                   10              2   \n",
       "1               50                 13                   15              7   \n",
       "2               25                  4                   25             19   \n",
       "3                5                 23                   30              5   \n",
       "4               50                 21                   35              4   \n",
       "\n",
       "  Duration_mins  \n",
       "0            50  \n",
       "1            25  \n",
       "2             0  \n",
       "3            25  \n",
       "4            45  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                object\n",
       "Source                 object\n",
       "Destination            object\n",
       "Route                  object\n",
       "Total_Stops            object\n",
       "Additional_Info        object\n",
       "Price                   int64\n",
       "Journey_day             int64\n",
       "Journey_month           int64\n",
       "Dep_Time_hour           int64\n",
       "Dep_Time_minute         int64\n",
       "Arrival_Time_hour       int64\n",
       "Arrival_Time_minute     int64\n",
       "Duration_hours         object\n",
       "Duration_mins          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Duration_hours']=train_data['Duration_hours'].astype(int)\n",
    "train_data['Duration_mins']=train_data['Duration_mins'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                object\n",
       "Source                 object\n",
       "Destination            object\n",
       "Route                  object\n",
       "Total_Stops            object\n",
       "Additional_Info        object\n",
       "Price                   int64\n",
       "Journey_day             int64\n",
       "Journey_month           int64\n",
       "Dep_Time_hour           int64\n",
       "Dep_Time_minute         int64\n",
       "Arrival_Time_hour       int64\n",
       "Arrival_Time_minute     int64\n",
       "Duration_hours          int32\n",
       "Duration_mins           int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Airline', 'Source', 'Destination', 'Route', 'Total_Stops', 'Additional_Info']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Categorical Columns having object as data type\n",
    "cat_col=[col for col in train_data.columns if train_data[col].dtype=='O']\n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Price',\n",
       " 'Journey_day',\n",
       " 'Journey_month',\n",
       " 'Dep_Time_hour',\n",
       " 'Dep_Time_minute',\n",
       " 'Arrival_Time_hour',\n",
       " 'Arrival_Time_minute',\n",
       " 'Duration_hours',\n",
       " 'Duration_mins']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#contiguous columns having int as data type\n",
    "cont_col=[col for col in train_data.columns if train_data[col].dtype!='O']\n",
    "cont_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We are using 2 main Encoding Techniques to convert Categorical data into some numerical format\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": 36,
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0         No info  \n",
       "1         No info  \n",
       "2         No info  \n",
       "3         No info  \n",
       "4         No info  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical=train_data[cat_col]\n",
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Airline'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Airline', ylabel='Price'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.boxplot(x='Airline',y='Price',data=train_data.sort_values('Price',ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion--> From graph we can see that Jet Airways Business have the highest Price., Apart from the first Airline almost all are having similar median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Total_Stops vs Price Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total_Stops', ylabel='Price'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.boxplot(y='Price',x='Total_Stops',data=train_data.sort_values('Price',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(categorical['Airline'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>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>Trujet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>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": [
       "   Air India  GoAir  IndiGo  Jet Airways  Jet Airways Business  \\\n",
       "0          0      0       1            0                     0   \n",
       "1          1      0       0            0                     0   \n",
       "2          0      0       0            1                     0   \n",
       "3          0      0       1            0                     0   \n",
       "4          0      0       1            0                     0   \n",
       "\n",
       "   Multiple carriers  Multiple carriers Premium economy  SpiceJet  Trujet  \\\n",
       "0                  0                                  0         0       0   \n",
       "1                  0                                  0         0       0   \n",
       "2                  0                                  0         0       0   \n",
       "3                  0                                  0         0       0   \n",
       "4                  0                                  0         0       0   \n",
       "\n",
       "   Vistara  Vistara Premium economy  \n",
       "0        0                        0  \n",
       "1        0                        0  \n",
       "2        0                        0  \n",
       "3        0                        0  \n",
       "4        0                        0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "Airline=pd.get_dummies(categorical['Airline'], drop_first=True)\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x175e3ae6d60>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source vs Price\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.catplot(y='Price',x='Source',data=train_data.sort_values('Price',ascending=False),kind='boxen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>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": [
       "   Chennai  Delhi  Kolkata  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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "\n",
    "Source=pd.get_dummies(categorical['Source'], drop_first=True)\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>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",
       "      <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": [
       "   Cochin  Delhi  Hyderabad  Kolkata  New Delhi\n",
       "0       0      0          0        0          1\n",
       "1       0      0          0        0          0\n",
       "2       1      0          0        0          0\n",
       "3       0      0          0        0          0\n",
       "4       0      0          0        0          1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination=pd.get_dummies(categorical['Destination'], drop_first=True)\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Route']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-48-103bd018a128>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_1']=categorical['Route'].str.split('→').str[0]\n",
      "<ipython-input-48-103bd018a128>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_2']=categorical['Route'].str.split('→').str[1]\n",
      "<ipython-input-48-103bd018a128>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_3']=categorical['Route'].str.split('→').str[2]\n",
      "<ipython-input-48-103bd018a128>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_4']=categorical['Route'].str.split('→').str[3]\n",
      "<ipython-input-48-103bd018a128>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  categorical['Route_5']=categorical['Route'].str.split('→').str[4]\n"
     ]
    }
   ],
   "source": [
    "categorical['Route_1']=categorical['Route'].str.split('→').str[0]\n",
    "categorical['Route_2']=categorical['Route'].str.split('→').str[1]\n",
    "categorical['Route_3']=categorical['Route'].str.split('→').str[2]\n",
    "categorical['Route_4']=categorical['Route'].str.split('→').str[3]\n",
    "categorical['Route_5']=categorical['Route'].str.split('→').str[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</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>BLR</td>\n",
       "      <td>DEL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>CCU</td>\n",
       "      <td>IXR</td>\n",
       "      <td>BBI</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NaN</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>DEL</td>\n",
       "      <td>LKO</td>\n",
       "      <td>BOM</td>\n",
       "      <td>COK</td>\n",
       "      <td>NaN</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>CCU</td>\n",
       "      <td>NAG</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>BLR</td>\n",
       "      <td>NAG</td>\n",
       "      <td>DEL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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 Route_1 Route_2 Route_3 Route_4 Route_5  \n",
       "0         No info    BLR      DEL     NaN     NaN     NaN  \n",
       "1         No info    CCU     IXR     BBI      BLR     NaN  \n",
       "2         No info    DEL     LKO     BOM      COK     NaN  \n",
       "3         No info    CCU     NAG      BLR     NaN     NaN  \n",
       "4         No info    BLR     NAG      DEL     NaN     NaN  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_column(categorical,'Route')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical['Route_1'].fillna('None',inplace=True)\n",
    "categorical['Route_2'].fillna('None',inplace=True)\n",
    "categorical['Route_3'].fillna('None',inplace=True)\n",
    "categorical['Route_4'].fillna('None',inplace=True)\n",
    "categorical['Route_5'].fillna('None',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Source             0\n",
       "Destination        0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Route_1            0\n",
       "Route_2            0\n",
       "Route_3            0\n",
       "Route_4            0\n",
       "Route_5            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airline has total 12 categories \n",
      "\n",
      "Source has total 5 categories \n",
      "\n",
      "Destination has total 6 categories \n",
      "\n",
      "Total_Stops has total 5 categories \n",
      "\n",
      "Additional_Info has total 10 categories \n",
      "\n",
      "Route_1 has total 5 categories \n",
      "\n",
      "Route_2 has total 45 categories \n",
      "\n",
      "Route_3 has total 30 categories \n",
      "\n",
      "Route_4 has total 14 categories \n",
      "\n",
      "Route_5 has total 6 categories \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#now extract how many categories in each cat_feature\n",
    "for feature in categorical.columns:\n",
    "    print('{} has total {} categories \\n'.format(feature,len(categorical[feature].value_counts())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### as we will see we have lots of features in Route , one hot encoding will not be a better option lets appply Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Source', 'Destination', 'Total_Stops', 'Additional_Info',\n",
       "       'Route_1', 'Route_2', 'Route_3', 'Route_4', 'Route_5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['Route_1', 'Route_2', 'Route_3', 'Route_4','Route_5']:\n",
    "    categorical[i]=encoder.fit_transform(categorical[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info,so we can drop this column\n",
    "# we can drop Route as well as we have pre-process that column\n",
    "    \n",
    "drop_column(categorical,'Additional_Info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Total_Stops'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "# Here Values are assigned with corresponding key\n",
    "\n",
    "dict={'non-stop':0, '2 stops':2, '1 stop':1, '3 stops':3, '4 stops':4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical['Total_Stops']=categorical['Total_Stops'].map(dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</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>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</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>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</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>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Route_1  Route_2  Route_3  \\\n",
       "0       IndiGo  Banglore   New Delhi            0        0       13       29   \n",
       "1    Air India   Kolkata    Banglore            2        2       25        1   \n",
       "2  Jet Airways     Delhi      Cochin            2        3       32        4   \n",
       "3       IndiGo   Kolkata    Banglore            1        2       34        3   \n",
       "4       IndiGo  Banglore   New Delhi            1        0       34        8   \n",
       "\n",
       "   Route_4  Route_5  \n",
       "0       13        5  \n",
       "1        3        5  \n",
       "2        5        5  \n",
       "3       13        5  \n",
       "4       13        5  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</th>\n",
       "      <th>Air India</th>\n",
       "      <th>...</th>\n",
       "      <th>New Delhi</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</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>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</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>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</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>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</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>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Route_1  Route_2  Route_3  \\\n",
       "0       IndiGo  Banglore   New Delhi            0        0       13       29   \n",
       "1    Air India   Kolkata    Banglore            2        2       25        1   \n",
       "2  Jet Airways     Delhi      Cochin            2        3       32        4   \n",
       "3       IndiGo   Kolkata    Banglore            1        2       34        3   \n",
       "4       IndiGo  Banglore   New Delhi            1        0       34        8   \n",
       "\n",
       "   Route_4  Route_5  Air India  ...  New Delhi  Price  Journey_day  \\\n",
       "0       13        5          0  ...          1   3897           24   \n",
       "1        3        5          1  ...          0   7662            5   \n",
       "2        5        5          0  ...          0  13882            6   \n",
       "3       13        5          0  ...          0   6218            5   \n",
       "4       13        5          0  ...          1  13302            3   \n",
       "\n",
       "   Journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0              3             22               20                  1   \n",
       "1              1              5               50                 13   \n",
       "2              9              9               25                  4   \n",
       "3             12             18                5                 23   \n",
       "4              1             16               50                 21   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_mins  \n",
       "0                   10               2             50  \n",
       "1                   15               7             25  \n",
       "2                   25              19              0  \n",
       "3                   30               5             25  \n",
       "4                   35               4             45  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate dataframe --> categorical + Airline + Source + Destination\n",
    "\n",
    "data_train=pd.concat([categorical,Airline,Source,Destination,train_data[cont_col]],axis=1)\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_column(data_train,'Airline')\n",
    "drop_column(data_train,'Source')\n",
    "drop_column(data_train,'Destination')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Route_1  Route_2  Route_3  Route_4  Route_5  Air India  GoAir  \\\n",
       "0            0        0       13       29       13        5          0      0   \n",
       "1            2        2       25        1        3        5          1      0   \n",
       "2            2        3       32        4        5        5          0      0   \n",
       "3            1        2       34        3       13        5          0      0   \n",
       "4            1        0       34        8       13        5          0      0   \n",
       "\n",
       "   IndiGo  Jet Airways  ...  New Delhi  Price  Journey_day  Journey_month  \\\n",
       "0       1            0  ...          1   3897           24              3   \n",
       "1       0            0  ...          0   7662            5              1   \n",
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       "4       1            0  ...          1  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \\\n",
       "0             22               20                  1                   10   \n",
       "1              5               50                 13                   15   \n",
       "2              9               25                  4                   25   \n",
       "3             18                5                 23                   30   \n",
       "4             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  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Total_Stops  Route_1  Route_2  Route_3  Route_4  Route_5  Air India  GoAir  \\\n",
       "0            0        0       13       29       13        5          0      0   \n",
       "1            2        2       25        1        3        5          1      0   \n",
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       "\n",
       "   IndiGo  Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0       1            0                     0                  0   \n",
       "1       0            0                     0                  0   \n",
       "2       0            1                     0                  0   \n",
       "3       1            0                     0                  0   \n",
       "4       1            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Trujet  Vistara  \\\n",
       "0                                  0         0       0        0   \n",
       "1                                  0         0       0        0   \n",
       "2                                  0         0       0        0   \n",
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       "4                                  0         0       0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      0        0       0       0      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      0        1       0       0      0   \n",
       "4                        0        0      0        0       0       0      0   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  Price  Journey_day  Journey_month  \\\n",
       "0          0        0          1   3897           24              3   \n",
       "1          0        0          0   7662            5              1   \n",
       "2          0        0          0  13882            6              9   \n",
       "3          0        0          0   6218            5             12   \n",
       "4          0        0          1  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \\\n",
       "0             22               20                  1                   10   \n",
       "1              5               50                 13                   15   \n",
       "2              9               25                  4                   25   \n",
       "3             18                5                 23                   30   \n",
       "4             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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns',35)\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(df,col):\n",
    "    fig,(ax1,ax2)=plt.subplots(2,1)\n",
    "    sns.distplot(df[col],ax=ax1)\n",
    "    sns.boxplot(df[col],ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2160x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,20))\n",
    "plot(data_train,'Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['Price']=np.where(data_train['Price']>=40000,data_train['Price'].median(),data_train['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2160x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,20))\n",
    "plot(data_train,'Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### separate your independent & dependent data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Route_1</th>\n",
       "      <th>Route_2</th>\n",
       "      <th>Route_3</th>\n",
       "      <th>Route_4</th>\n",
       "      <th>Route_5</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>Trujet</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",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</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",
       "      <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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</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",
       "      <td>5</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</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",
       "      <td>6</td>\n",
       "      <td>9</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>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</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",
       "      <td>5</td>\n",
       "      <td>12</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</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",
       "      <td>3</td>\n",
       "      <td>1</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": [
       "   Total_Stops  Route_1  Route_2  Route_3  Route_4  Route_5  Air India  GoAir  \\\n",
       "0            0        0       13       29       13        5          0      0   \n",
       "1            2        2       25        1        3        5          1      0   \n",
       "2            2        3       32        4        5        5          0      0   \n",
       "3            1        2       34        3       13        5          0      0   \n",
       "4            1        0       34        8       13        5          0      0   \n",
       "\n",
       "   IndiGo  Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0       1            0                     0                  0   \n",
       "1       0            0                     0                  0   \n",
       "2       0            1                     0                  0   \n",
       "3       1            0                     0                  0   \n",
       "4       1            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Trujet  Vistara  \\\n",
       "0                                  0         0       0        0   \n",
       "1                                  0         0       0        0   \n",
       "2                                  0         0       0        0   \n",
       "3                                  0         0       0        0   \n",
       "4                                  0         0       0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      0        0       0       0      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      0        1       0       0      0   \n",
       "4                        0        0      0        0       0       0      0   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  Journey_day  Journey_month  Dep_Time_hour  \\\n",
       "0          0        0          1           24              3             22   \n",
       "1          0        0          0            5              1              5   \n",
       "2          0        0          0            6              9              9   \n",
       "3          0        0          0            5             12             18   \n",
       "4          0        0          1            3              1             16   \n",
       "\n",
       "   Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  Duration_hours  \\\n",
       "0               20                  1                   10               2   \n",
       "1               50                 13                   15               7   \n",
       "2               25                  4                   25              19   \n",
       "3                5                 23                   30               5   \n",
       "4               50                 21                   35               4   \n",
       "\n",
       "   Duration_mins  \n",
       "0             50  \n",
       "1             25  \n",
       "2              0  \n",
       "3             25  \n",
       "4             45  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=data_train.drop('Price',axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3897.0\n",
       "1         7662.0\n",
       "2        13882.0\n",
       "3         6218.0\n",
       "4        13302.0\n",
       "          ...   \n",
       "10678     4107.0\n",
       "10679     4145.0\n",
       "10680     7229.0\n",
       "10681    12648.0\n",
       "10682    11753.0\n",
       "Name: Price, Length: 10682, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data_train['Price']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "##type(X)\n",
    "##type(y)\n",
    "##X.isnull().sum()\n",
    "##y.isnull().sum()\n",
    "#### as now we dont have any missing value in data, we can definitely go ahead with Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "    Finding out the best feature which will contribute and have good relation with target variable. \n",
    "    \n",
    "### Why to apply Feature Selection?\n",
    "    To select important features to get rid of curse of dimensionality ie..to get rid of duplicate features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0, 13, ..., 10,  2, 50],\n",
       "       [ 2,  2, 25, ..., 15,  7, 25],\n",
       "       [ 2,  3, 32, ..., 25, 19,  0],\n",
       "       ...,\n",
       "       [ 0,  0, 13, ..., 20,  3,  0],\n",
       "       [ 0,  0, 13, ..., 10,  2, 40],\n",
       "       [ 2,  3, 16, ..., 15,  8, 20]], dtype=int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3897.,  7662., 13882., ...,  7229., 12648., 11753.])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I wanted to find mutual information scores or matrix to get to know about the relationship between all features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection using Information Gain,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "mutual_info_classif() missing 2 required positional arguments: 'X' and 'y'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-81-b23b8e994ae7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmutual_info_classif\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: mutual_info_classif() missing 2 required positional arguments: 'X' and 'y'"
     ]
    }
   ],
   "source": [
    "mutual_info_classif()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "###mutual_info_classif(np.array(X),np.array(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total_Stops                          int64\n",
       "Route_1                              int32\n",
       "Route_2                              int32\n",
       "Route_3                              int32\n",
       "Route_4                              int32\n",
       "Route_5                              int32\n",
       "Air India                            uint8\n",
       "GoAir                                uint8\n",
       "IndiGo                               uint8\n",
       "Jet Airways                          uint8\n",
       "Jet Airways Business                 uint8\n",
       "Multiple carriers                    uint8\n",
       "Multiple carriers Premium economy    uint8\n",
       "SpiceJet                             uint8\n",
       "Trujet                               uint8\n",
       "Vistara                              uint8\n",
       "Vistara Premium economy              uint8\n",
       "Chennai                              uint8\n",
       "Delhi                                uint8\n",
       "Kolkata                              uint8\n",
       "Mumbai                               uint8\n",
       "Cochin                               uint8\n",
       "Delhi                                uint8\n",
       "Hyderabad                            uint8\n",
       "Kolkata                              uint8\n",
       "New Delhi                            uint8\n",
       "Journey_day                          int64\n",
       "Journey_month                        int64\n",
       "Dep_Time_hour                        int64\n",
       "Dep_Time_minute                      int64\n",
       "Arrival_Time_hour                    int64\n",
       "Arrival_Time_minute                  int64\n",
       "Duration_hours                       int32\n",
       "Duration_mins                        int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.14214181e+00, 2.05413295e+00, 2.82324134e+00, 2.28197102e+00,\n",
       "       1.46418888e+00, 6.87715952e-01, 7.72063144e-01, 9.33648613e-02,\n",
       "       6.89843629e-01, 9.36730961e-01, 1.08053270e-02, 5.60694021e-01,\n",
       "       2.79114796e-04, 3.40668881e-01, 3.04594151e-03, 2.32386359e-01,\n",
       "       0.00000000e+00, 1.75682852e-01, 1.52575327e+00, 8.62224505e-01,\n",
       "       3.03703629e-01, 1.53997633e+00, 4.02077785e-01, 2.93716452e-01,\n",
       "       1.66891866e-01, 3.78384375e-01, 1.09193031e+00, 9.11104452e-01,\n",
       "       1.42032685e+00, 1.21132703e+00, 1.86357183e+00, 1.51278948e+00,\n",
       "       1.78398237e+00, 1.05421215e+00])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info_classif(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>2.135591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_1</th>\n",
       "      <td>2.037867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_2</th>\n",
       "      <td>2.804413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_3</th>\n",
       "      <td>2.337900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_4</th>\n",
       "      <td>1.420441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_5</th>\n",
       "      <td>0.747298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air India</th>\n",
       "      <td>0.753300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoAir</th>\n",
       "      <td>0.096451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndiGo</th>\n",
       "      <td>0.674557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways</th>\n",
       "      <td>0.934092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <td>0.000067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers</th>\n",
       "      <td>0.564401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <td>0.014913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpiceJet</th>\n",
       "      <td>0.313838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trujet</th>\n",
       "      <td>0.009068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara</th>\n",
       "      <td>0.228985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <td>0.001372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>0.174327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1.552177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.891843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.288535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cochin</th>\n",
       "      <td>1.554028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.386398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>0.292606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.181647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi</th>\n",
       "      <td>0.393233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey_day</th>\n",
       "      <td>1.102636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey_month</th>\n",
       "      <td>0.856643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>1.426938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <td>1.208160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>1.808098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <td>1.519529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_hours</th>\n",
       "      <td>1.801242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_mins</th>\n",
       "      <td>1.082376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          0\n",
       "Total_Stops                        2.135591\n",
       "Route_1                            2.037867\n",
       "Route_2                            2.804413\n",
       "Route_3                            2.337900\n",
       "Route_4                            1.420441\n",
       "Route_5                            0.747298\n",
       "Air India                          0.753300\n",
       "GoAir                              0.096451\n",
       "IndiGo                             0.674557\n",
       "Jet Airways                        0.934092\n",
       "Jet Airways Business               0.000067\n",
       "Multiple carriers                  0.564401\n",
       "Multiple carriers Premium economy  0.014913\n",
       "SpiceJet                           0.313838\n",
       "Trujet                             0.009068\n",
       "Vistara                            0.228985\n",
       "Vistara Premium economy            0.001372\n",
       "Chennai                            0.174327\n",
       "Delhi                              1.552177\n",
       "Kolkata                            0.891843\n",
       "Mumbai                             0.288535\n",
       "Cochin                             1.554028\n",
       "Delhi                              0.386398\n",
       "Hyderabad                          0.292606\n",
       "Kolkata                            0.181647\n",
       "New Delhi                          0.393233\n",
       "Journey_day                        1.102636\n",
       "Journey_month                      0.856643\n",
       "Dep_Time_hour                      1.426938\n",
       "Dep_Time_minute                    1.208160\n",
       "Arrival_Time_hour                  1.808098\n",
       "Arrival_Time_minute                1.519529\n",
       "Duration_hours                     1.801242\n",
       "Duration_mins                      1.082376"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp=pd.DataFrame(mutual_info_classif(X,y),index=X.columns)\n",
    "imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Route_2</th>\n",
       "      <td>2.804413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_3</th>\n",
       "      <td>2.337900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>2.135591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_1</th>\n",
       "      <td>2.037867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>1.808098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_hours</th>\n",
       "      <td>1.801242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cochin</th>\n",
       "      <td>1.554028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1.552177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <td>1.519529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>1.426938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_4</th>\n",
       "      <td>1.420441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <td>1.208160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey_day</th>\n",
       "      <td>1.102636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_mins</th>\n",
       "      <td>1.082376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways</th>\n",
       "      <td>0.934092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.891843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey_month</th>\n",
       "      <td>0.856643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air India</th>\n",
       "      <td>0.753300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_5</th>\n",
       "      <td>0.747298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndiGo</th>\n",
       "      <td>0.674557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers</th>\n",
       "      <td>0.564401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi</th>\n",
       "      <td>0.393233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.386398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpiceJet</th>\n",
       "      <td>0.313838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>0.292606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.288535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara</th>\n",
       "      <td>0.228985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.181647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>0.174327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoAir</th>\n",
       "      <td>0.096451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <td>0.014913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trujet</th>\n",
       "      <td>0.009068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <td>0.001372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <td>0.000067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   importance\n",
       "Route_2                              2.804413\n",
       "Route_3                              2.337900\n",
       "Total_Stops                          2.135591\n",
       "Route_1                              2.037867\n",
       "Arrival_Time_hour                    1.808098\n",
       "Duration_hours                       1.801242\n",
       "Cochin                               1.554028\n",
       "Delhi                                1.552177\n",
       "Arrival_Time_minute                  1.519529\n",
       "Dep_Time_hour                        1.426938\n",
       "Route_4                              1.420441\n",
       "Dep_Time_minute                      1.208160\n",
       "Journey_day                          1.102636\n",
       "Duration_mins                        1.082376\n",
       "Jet Airways                          0.934092\n",
       "Kolkata                              0.891843\n",
       "Journey_month                        0.856643\n",
       "Air India                            0.753300\n",
       "Route_5                              0.747298\n",
       "IndiGo                               0.674557\n",
       "Multiple carriers                    0.564401\n",
       "New Delhi                            0.393233\n",
       "Delhi                                0.386398\n",
       "SpiceJet                             0.313838\n",
       "Hyderabad                            0.292606\n",
       "Mumbai                               0.288535\n",
       "Vistara                              0.228985\n",
       "Kolkata                              0.181647\n",
       "Chennai                              0.174327\n",
       "GoAir                                0.096451\n",
       "Multiple carriers Premium economy    0.014913\n",
       "Trujet                               0.009068\n",
       "Vistara Premium economy              0.001372\n",
       "Jet Airways Business                 0.000067"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.columns=['importance']\n",
    "imp.sort_values(by='importance',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### split dataset into train & test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "##dump your model using pickle so that we will re-use\n",
    "import pickle\n",
    "def predict(ml_model,dump):\n",
    "    model=ml_model.fit(X_train,y_train)\n",
    "    print('Training score : {}'.format(model.score(X_train,y_train)))\n",
    "    y_prediction=model.predict(X_test)\n",
    "    print('predictions are: \\n {}'.format(y_prediction))\n",
    "    print('\\n')\n",
    "    r2_score=metrics.r2_score(y_test,y_prediction)\n",
    "    print('r2 score: {}'.format(r2_score))\n",
    "    print('MAE:',metrics.mean_absolute_error(y_test,y_prediction))\n",
    "    print('MSE:',metrics.mean_squared_error(y_test,y_prediction))\n",
    "    print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test,y_prediction)))\n",
    "    sns.distplot(y_test-y_prediction)\n",
    "    \n",
    "    if dump==1:\n",
    "        ##dump your model using pickle so that we will re-use\n",
    "        file=open('E:\\End-2-end Projects\\Flight_Price/model.pkl','wb')\n",
    "        pickle.dump(model,file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### import randomforest class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score : 0.9555049413464213\n",
      "predictions are: \n",
      " [12141.27666667  3224.75        7409.735      ...  8355.4\n",
      " 13117.20566667 15443.53      ]\n",
      "\n",
      "\n",
      "r2 score: 0.799239257274158\n",
      "MAE: 1176.7873872179916\n",
      "MSE: 3434124.2773402412\n",
      "RMSE: 1853.1390334619368\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'E:\\\\End-2-end Projects\\\\Flight_Price/model.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-94-3a4a7070f541>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRandomForestRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-91-63d13f328a4a>\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(ml_model, dump)\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdump\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[1;31m##dump your model using pickle so that we will re-use\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m         \u001b[0mfile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'E:\\End-2-end Projects\\Flight_Price/model.pkl'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m         \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'E:\\\\End-2-end Projects\\\\Flight_Price/model.pkl'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict(RandomForestRegressor(),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### play with multiple Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score : 0.9691355575055074\n",
      "predictions are: \n",
      " [12609.  2754.  8033. ...  7893. 12898. 15078.]\n",
      "\n",
      "\n",
      "r2 score: 0.6757998084927397\n",
      "MAE: 1360.401762595539\n",
      "MSE: 5545624.773334374\n",
      "RMSE: 2354.9150246525614\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict(DecisionTreeRegressor(),0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score : 0.6156172724973314\n",
      "predictions are: \n",
      " [12749.47402851  4366.73332628  6646.27021622 ... 10049.04842374\n",
      " 11445.36470031 12191.55892708]\n",
      "\n",
      "\n",
      "r2 score: 0.6141516003905015\n",
      "MAE: 1899.0345697858165\n",
      "MSE: 6600151.695400639\n",
      "RMSE: 2569.0760392406914\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict(LinearRegression(),0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning\n",
    "    1.Choose following method for hyperparameter tuning\n",
    "        a.RandomizedSearchCV --> Fast way to Hypertune model\n",
    "        b.GridSearchCV--> Slow way to hypertune my model\n",
    "    \n",
    "    2.Assign hyperparameters in form of dictionary\n",
    "    3.Fit the model\n",
    "    4.Check best paramters and best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators=[int(x) for x in np.linspace(start=100,stop=1200,num=6)]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features=['auto','sqrt']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth=[int(x) for x in np.linspace(5,30,num=4)]\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split=[5,10,15,100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "\n",
    "random_grid={\n",
    "    'n_estimators':n_estimators,\n",
    "    'max_features':max_features,\n",
    "'max_depth':max_depth,\n",
    "    'min_samples_split':min_samples_split\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "### initialise your estimator\n",
    "reg_rf=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation\n",
    "\n",
    "rf_random=RandomizedSearchCV(estimator=reg_rf,param_distributions=random_grid,cv=3,verbose=2,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [5, 13, 21, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_split': [5, 10, 15, 100],\n",
       "                                        'n_estimators': [100, 320, 540, 760,\n",
       "                                                         980, 1200]},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 980,\n",
       " 'min_samples_split': 10,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 13}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8322929154837541"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 1141.5001171812412\n",
      "MSE 2868723.051127956\n",
      "RMSE 1693.7305131360054\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Save the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not find a version that satisfies the requirement pickle (from versions: none)\n",
      "ERROR: No matching distribution found for pickle\n"
     ]
    }
   ],
   "source": [
    "!pip install pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open a file, where you want to store the data\n",
    "file=open('rf_random.pkl','wb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dump information to that file\n",
    "pickle.dump(rf_random,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=open('rf_random.pkl','rb')\n",
    "forest=pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction=forest.predict(X_test)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12128.41310149,  3308.87212862,  6626.28236302, ...,\n",
       "        8527.53881573, 13076.24329212, 15832.33139253])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8322929154837541"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "metrics.r2_score(y_test,y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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