Flight_fare.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flight Price Prediction\n",
    "---"
   ]
  },
  {
   "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\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing dataset\n",
    "\n",
    "1. Since data is in form of excel file we have to use pandas read_excel to load the data\n",
    "2. After loading it is important to check the complete information of data as it can indication many of the hidden infomation such as null values in a column or a row\n",
    "3. Check whether any null values are there or not. if it is present then following can be done,\n",
    "    1. Imputing data using Imputation method in sklearn\n",
    "    2. Filling NaN values with mean, median and mode using fillna() method\n",
    "4. Describe data --> which can give statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_excel(r\"E:\\Data_Train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2h 50m     550\n",
       "1h 30m     386\n",
       "2h 45m     337\n",
       "2h 55m     337\n",
       "2h 35m     329\n",
       "          ... \n",
       "47h          1\n",
       "30h 25m      1\n",
       "35h 35m      1\n",
       "35h 20m      1\n",
       "32h 20m      1\n",
       "Name: Duration, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "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\n",
    "\n",
    "For this we require pandas **to_datetime** to convert object data type to datetime dtype.\n",
    "\n",
    "<span style=\"color: red;\">**.dt.day method will extract only day of that date**</span>\\\n",
    "<span style=\"color: red;\">**.dt.month method will extract only month of that date**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_day\"] = pd.to_datetime(train_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_month\"] = pd.to_datetime(train_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302   \n",
       "\n",
       "   Journey_day  Journey_month  \n",
       "0           24              3  \n",
       "1            1              5  \n",
       "2            9              6  \n",
       "3           12              5  \n",
       "4            1              3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we have converted Date_of_Journey column into integers, Now we can drop as it is of no use.\n",
    "\n",
    "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Departure time is when a plane leaves the gate. \n",
    "# Similar to Date_of_Journey we can extract values from Dep_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_data[\"Dep_hour\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_data[\"Dep_min\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.minute\n",
    "\n",
    "# Now we can drop Dep_Time as it is of no use\n",
    "train_data.drop([\"Dep_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route  Arrival_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL  01:10 22 Mar   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR         13:15   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK  04:25 10 Jun   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR         23:30   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL         21:35   \n",
       "\n",
       "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0   2h 50m    non-stop         No info   3897           24              3   \n",
       "1   7h 25m     2 stops         No info   7662            1              5   \n",
       "2      19h     2 stops         No info  13882            9              6   \n",
       "3   5h 25m      1 stop         No info   6218           12              5   \n",
       "4   4h 45m      1 stop         No info  13302            1              3   \n",
       "\n",
       "   Dep_hour  Dep_min  \n",
       "0        22       20  \n",
       "1         5       50  \n",
       "2         9       25  \n",
       "3        18        5  \n",
       "4        16       50  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrival time is when the plane pulls up to the gate.\n",
    "# Similar to Date_of_Journey we can extract values from Arrival_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_data[\"Arrival_hour\"] = pd.to_datetime(train_data.Arrival_Time).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_data[\"Arrival_min\"] = pd.to_datetime(train_data.Arrival_Time).dt.minute\n",
    "\n",
    "# Now we can drop Arrival_Time as it is of no use\n",
    "train_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time taken by plane to reach destination is called Duration\n",
    "# It is the differnce betwwen Departure Time and Arrival time\n",
    "\n",
    "\n",
    "# Assigning and converting Duration column into list\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding duration_hours and duration_mins list to train_data dataframe\n",
    "\n",
    "train_data[\"Duration_hours\"] = duration_hours\n",
    "train_data[\"Duration_mins\"] = duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Duration\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Categorical Data\n",
    "\n",
    "One can find many ways to handle categorical data. Some of them categorical data are,\n",
    "1. <span style=\"color: blue;\">**Nominal data**</span> --> data are not in any order --> <span style=\"color: green;\">**OneHotEncoder**</span> is used in this case\n",
    "2. <span style=\"color: blue;\">**Ordinal data**</span> --> data are in order --> <span style=\"color: green;\">**LabelEncoder**</span> is used in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1751\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              818\n",
       "Vistara                               479\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Airline\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graph we can see that Jet Airways Business have the highest Price.\n",
    "# Apart from the first Airline almost all are having similar median\n",
    "\n",
    "# Airline vs Price\n",
    "sns.catplot(y = \"Price\", x = \"Airline\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \n",
       "0               0                0                                0  \n",
       "1               0                0                                0  \n",
       "2               0                0                                0  \n",
       "3               0                0                                0  \n",
       "4               0                0                                0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Airline = train_data[[\"Airline\"]]\n",
    "\n",
    "Airline = pd.get_dummies(Airline, drop_first= True)\n",
    "\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4536\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source vs Price\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Source\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0               0             0               0              0\n",
       "1               0             0               1              0\n",
       "2               0             1               0              0\n",
       "3               0             0               1              0\n",
       "4               0             0               0              0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Source = train_data[[\"Source\"]]\n",
    "\n",
    "Source = pd.get_dummies(Source, drop_first= True)\n",
    "\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination = train_data[[\"Destination\"]]\n",
    "\n",
    "Destination = pd.get_dummies(Destination, drop_first = True)\n",
    "\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    BLR → DEL\n",
       "1        CCU → IXR → BBI → BLR\n",
       "2        DEL → LKO → BOM → COK\n",
       "3              CCU → NAG → BLR\n",
       "4              BLR → NAG → DEL\n",
       "                 ...          \n",
       "10678                CCU → BLR\n",
       "10679                CCU → BLR\n",
       "10680                BLR → DEL\n",
       "10681                BLR → DEL\n",
       "10682    DEL → GOI → BOM → COK\n",
       "Name: Route, Length: 10682, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Route\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "train_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      5625\n",
       "non-stop    3491\n",
       "2 stops     1520\n",
       "3 stops       45\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Total_Stops\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "# Here Values are assigned with corresponding keys\n",
    "\n",
    "train_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  \n",
       "0               2             50  \n",
       "1               7             25  \n",
       "2              19              0  \n",
       "3               5             25  \n",
       "4               4             45  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate dataframe --> train_data + Airline + Source + Destination\n",
    "\n",
    "data_train = pd.concat([train_data, Airline, Source, Destination], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  Airline_Air India  Airline_GoAir  \\\n",
       "0               2             50                  0              0   \n",
       "1               7             25                  1              0   \n",
       "2              19              0                  0              0   \n",
       "3               5             25                  0              0   \n",
       "4               4             45                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0               1                    0                             0   \n",
       "1               0                    0                             0   \n",
       "2               0                    1                             0   \n",
       "3               1                    0                             0   \n",
       "4               1                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Trujet  Airline_Vistara  \\\n",
       "0                 0               0                0   \n",
       "1                 0               0                0   \n",
       "2                 0               0                0   \n",
       "3                 0               0                0   \n",
       "4                 0               0                0   \n",
       "\n",
       "   Airline_Vistara Premium economy  Source_Chennai  Source_Delhi  \\\n",
       "0                                0               0             0   \n",
       "1                                0               0             0   \n",
       "2                                0               0             1   \n",
       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "\n",
       "   Source_Kolkata  Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0               0              0                   0                  0   \n",
       "1               1              0                   0                  0   \n",
       "2               0              0                   1                  0   \n",
       "3               1              0                   0                  0   \n",
       "4               0              0                   0                  0   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  Destination_New Delhi  \n",
       "0                      0                    0                      1  \n",
       "1                      0                    0                      0  \n",
       "2                      0                    0                      0  \n",
       "3                      0                    0                      0  \n",
       "4                      0                    0                      1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897           24              3        22       20   \n",
       "1            2   7662            1              5         5       50   \n",
       "2            2  13882            9              6         9       25   \n",
       "3            1   6218           12              5        18        5   \n",
       "4            1  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0             1           10               2             50   \n",
       "1            13           15               7             25   \n",
       "2             4           25              19              0   \n",
       "3            23           30               5             25   \n",
       "4            21           35               4             45   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(r\"E:\\Test_set.xlsx\")"
   ]
  },
  {
   "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>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Info\n",
      "---------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2671 entries, 0 to 2670\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          2671 non-null   object\n",
      " 1   Date_of_Journey  2671 non-null   object\n",
      " 2   Source           2671 non-null   object\n",
      " 3   Destination      2671 non-null   object\n",
      " 4   Route            2671 non-null   object\n",
      " 5   Dep_Time         2671 non-null   object\n",
      " 6   Arrival_Time     2671 non-null   object\n",
      " 7   Duration         2671 non-null   object\n",
      " 8   Total_Stops      2671 non-null   object\n",
      " 9   Additional_Info  2671 non-null   object\n",
      "dtypes: object(10)\n",
      "memory usage: 208.8+ KB\n",
      "None\n",
      "\n",
      "\n",
      "Null values :\n",
      "---------------------------------------------------------------------------\n",
      "Airline            0\n",
      "Date_of_Journey    0\n",
      "Source             0\n",
      "Destination        0\n",
      "Route              0\n",
      "Dep_Time           0\n",
      "Arrival_Time       0\n",
      "Duration           0\n",
      "Total_Stops        0\n",
      "Additional_Info    0\n",
      "dtype: int64\n",
      "Airline\n",
      "---------------------------------------------------------------------------\n",
      "Jet Airways                          897\n",
      "IndiGo                               511\n",
      "Air India                            440\n",
      "Multiple carriers                    347\n",
      "SpiceJet                             208\n",
      "Vistara                              129\n",
      "Air Asia                              86\n",
      "GoAir                                 46\n",
      "Multiple carriers Premium economy      3\n",
      "Jet Airways Business                   2\n",
      "Vistara Premium economy                2\n",
      "Name: Airline, dtype: int64\n",
      "\n",
      "Source\n",
      "---------------------------------------------------------------------------\n",
      "Delhi       1145\n",
      "Kolkata      710\n",
      "Banglore     555\n",
      "Mumbai       186\n",
      "Chennai       75\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "Destination\n",
      "---------------------------------------------------------------------------\n",
      "Cochin       1145\n",
      "Banglore      710\n",
      "Delhi         317\n",
      "New Delhi     238\n",
      "Hyderabad     186\n",
      "Kolkata        75\n",
      "Name: Destination, dtype: int64\n",
      "\n",
      "\n",
      "Shape of test data :  (2671, 28)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "\n",
    "print(\"Test data Info\")\n",
    "print(\"-\"*75)\n",
    "print(test_data.info())\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Null values :\")\n",
    "print(\"-\"*75)\n",
    "test_data.dropna(inplace = True)\n",
    "print(test_data.isnull().sum())\n",
    "\n",
    "# EDA\n",
    "\n",
    "# Date_of_Journey\n",
    "test_data[\"Journey_day\"] = pd.to_datetime(test_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day\n",
    "test_data[\"Journey_month\"] = pd.to_datetime(test_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month\n",
    "test_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)\n",
    "\n",
    "# Dep_Time\n",
    "test_data[\"Dep_hour\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.hour\n",
    "test_data[\"Dep_min\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.minute\n",
    "test_data.drop([\"Dep_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Arrival_Time\n",
    "test_data[\"Arrival_hour\"] = pd.to_datetime(test_data.Arrival_Time).dt.hour\n",
    "test_data[\"Arrival_min\"] = pd.to_datetime(test_data.Arrival_Time).dt.minute\n",
    "test_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Duration\n",
    "duration = list(test_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration\n",
    "\n",
    "# Adding Duration column to test set\n",
    "test_data[\"Duration_hours\"] = duration_hours\n",
    "test_data[\"Duration_mins\"] = duration_mins\n",
    "test_data.drop([\"Duration\"], axis = 1, inplace = True)\n",
    "\n",
    "\n",
    "# Categorical data\n",
    "\n",
    "print(\"Airline\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Airline\"].value_counts())\n",
    "Airline = pd.get_dummies(test_data[\"Airline\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Source\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Source\"].value_counts())\n",
    "Source = pd.get_dummies(test_data[\"Source\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Destination\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Destination\"].value_counts())\n",
    "Destination = pd.get_dummies(test_data[\"Destination\"], drop_first = True)\n",
    "\n",
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "test_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)\n",
    "\n",
    "# Replacing Total_Stops\n",
    "test_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)\n",
    "\n",
    "# Concatenate dataframe --> test_data + Airline + Source + Destination\n",
    "data_test = pd.concat([test_data, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "data_test.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Shape of test data : \", data_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
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      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\n",
       "0                                  0         0        0   \n",
       "1                                  0         0        0   \n",
       "2                                  0         0        0   \n",
       "3                                  0         0        0   \n",
       "4                                  0         0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "\n",
    "Finding out the best feature which will contribute and have good relation with target variable.\n",
    "Following are some of the feature selection methods,\n",
    "\n",
    "\n",
    "1. <span style=\"color: purple;\">**heatmap**</span>\n",
    "2. <span style=\"color: purple;\">**feature_importance_**</span>\n",
    "3. <span style=\"color: purple;\">**SelectKBest**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Price', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
       "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
       "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
       "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
       "       'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
       "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
       "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
       "       'Destination_Kolkata', 'Destination_New Delhi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  \\\n",
       "0           10               2             50                  0   \n",
       "1           15               7             25                  1   \n",
       "2           25              19              0                  0   \n",
       "3           30               5             25                  0   \n",
       "4           35               4             45                  0   \n",
       "\n",
       "   Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0              0               1                    0   \n",
       "1              0               0                    0   \n",
       "2              0               0                    1   \n",
       "3              0               1                    0   \n",
       "4              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_train.loc[:, ['Total_Stops', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
    "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
    "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
    "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
    "       'Airline_Multiple carriers',\n",
    "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
    "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
    "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
    "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
    "       'Destination_Kolkata', 'Destination_New Delhi']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3897\n",
       "1     7662\n",
       "2    13882\n",
       "3     6218\n",
       "4    13302\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:, 1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finds correlation between Independent and dependent attributes\n",
    "\n",
    "plt.figure(figsize = (18,18))\n",
    "sns.heatmap(train_data.corr(), annot = True, cmap = \"RdYlGn\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.11903710e-01 1.43028075e-01 5.47658745e-02 2.40969699e-02\n",
      " 2.10624211e-02 2.81020544e-02 1.89685848e-02 1.27912292e-01\n",
      " 1.76365321e-02 1.20269542e-02 1.94515130e-03 1.71728080e-02\n",
      " 1.41868090e-01 6.78927762e-02 1.83460824e-02 8.96181906e-04\n",
      " 2.92691042e-03 8.09292865e-05 5.74453384e-03 7.87295866e-05\n",
      " 4.94355891e-04 1.19345934e-02 3.37698607e-03 6.72986889e-03\n",
      " 1.16687043e-02 1.73004694e-02 6.76847676e-03 5.08378135e-04\n",
      " 2.47625069e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting model using Random Forest\n",
    "\n",
    "1. Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "2. If needed do scaling of data\n",
    "    * Scaling is not done in Random forest\n",
    "3. Import model\n",
    "4. Fit the data\n",
    "5. Predict w.r.t X_test\n",
    "6. In regression check **RSME** Score\n",
    "7. Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "reg_rf = RandomForestRegressor()\n",
    "reg_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9538160680730395"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7947471991770106"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/3PfQoUOYNWsWFi5ciNDQUNTW1vq/d/GTaz6fr8t9djt/oq27mptd8PlElzFVjYTTebZH+wkWF9awQ3Sb/yI9HwNA//kBzkG2a70xdGtJIS8vDy0tLZg8eTIef/xxnDp1Cq+++up1n1dVVYXnnnsOL7/8MiZOnIjo6Gg4nU7/951OJ4xG42XjTU1NMBqN3YlGRKQb1yxcj8eDV155Bbt27UJ+fj4qKysxatQohIaGIiLiyv+Ev+jEiROYM2cOCgoKYLFYAAD3338/jh49irq6OnR0dKCsrAwmkwkxMTEIDw9HVVUVAKC0tBQmk6mPpkhEFByuWbhFRUVwuVz46U9/6h9bunQpWlpa8NZbb11zx2vWrEFbWxvy8/ORnp6O9PR0bNq0Cfn5+Zg3bx5SU1Nx7733IiUlBQBQUFCAN954AykpKWhtbUVGRkYfTI+IKHgYROffCnmJtLQ0bNy4EbfcckuXcZfLhSeffBKffPKJ5gG7q7+t4b5X9jUEQjAjbWSgo/SKno8BoP/8AOcg2w2v4YaFhV1WtgAQERGBAQMG9D4ZEdFN5JqFGxISApfLddm4y+XiJ8GIiHromoWblpaGnJwctLa2+sdaW1uRk5MDs9mseTgiov7kmoX77LPPIjIyEo888gieeOIJTJo0CY888ghuu+02zJkzR1ZGIqJ+4ZoffAgJCcHSpUsxe/ZsfP311wgJCcGoUaN4jSwR0Q3o1ifNYmJiEBMTo3UWIqJ+rce/YoeIiG4MC5eISBIWLhGRJCxcIiJJWLhERJKwcImIJGHhEhFJwsIlIpKEhUtEJAkLl4hIEhYuEZEkLFwiIklYuEREkrBwiYgkYeESEUnCwiUikoSFS0QkCQuXiEgSFi4RkSQsXCIiSVi4RESSaFq4LpcLaWlpqK+vBwAsXrwYZrMZ6enpSE9Px2effQYAqKmpgc1mQ3JyMrKzs+H1erWMRUQUEJoV7v79+zFlyhTU1tb6x6qrq7F+/XqUlpaitLQUSUlJAIDMzEzk5uaioqICQggUFxdrFYuIKGA0K9zi4mLk5eXBaDQCAM6fP4/jx48jKysLVqsVRUVF8Pl8aGhogNvtRnx8PADAZrPBbrdrFYuIKGAUrXa8bNmyLo+bmpowduxY5OXlITIyErNmzcLGjRsxbNgwqKrq305VVTgcjh6/XlRUxBXHVTWyx/sKBoqioN3r023+zvQ+B73nBziHYKFZ4V4qNjYWb7/9tv/x1KlTUVJSgqFDh8JgMPjHhRBdHndXc7MLPp/oMqaqkXA6z9546AC6sI4dotv8F+n5GAD6zw9wDrJd641B2lUKBw8eREVFhf+xEAKKoiA6OhpOp9M/3tTU5F+GICLqT6QVrhACy5cvx5kzZ9De3o4NGzYgKSkJMTExCA8PR1VVFQCgtLQUJpNJViwiImmkLSmMGDECM2fOxJQpU+D1emE2m5GWlgYAKCgoQE5ODlwuF+Li4pCRkSErFhGRNAYhhLj+ZsGvv63hvlf2NQRCMCNtZKCj9IqejwGg//wA5yBbUKzhEhHd7Fi4RESSsHCJiCRh4RIRScLCJSKShIVLRCQJC5eISBIWLhGRJCxcIiJJWLhERJKwcImIJGHhEhFJwsINQu9XfBPoCESkARZuEPK0dwQ6AhFpgIVLRCQJCzeIhSkhWPPJvwIdg4j6CAs3yHF5gaj/YOESEUnCwiUikoSFS0QkCQuXiEgSFi4RkSQsXCIiSVi4RESSsHCJiCRh4RIRScLCJSKSRNPCdblcSEtLQ319PQCgsrISVqsVZrMZhYWF/u1qampgs9mQnJyM7OxseL1eLWMREQWEZoW7f/9+TJkyBbW1tQAAt9uNrKwsrF69GuXl5aiursbOnTsBAJmZmcjNzUVFRQWEECguLtYqFhFRwGhWuMXFxcjLy4PRaAQAHDhwAEOGDEFsbCwURYHVaoXdbkdDQwPcbjfi4+MBADabDXa7XatYREQBo2i142XLlnV53NjYCFVV/Y+NRiMcDsdl46qqwuFw9Pj1oqIirjiuqpE93legKYrS5Ws9zqEz5g88ziE4aFa4l/L5fDAYDP7HQggYDIarjvdUc7MLPp/oMqaqkXA6z9546AC5uIatKAq8Xq8u53CRXo/BRXrPD3AOsl3rjUHaVQrR0dFwOp3+x06nE0aj8bLxpqYm/zIEEVF/Iq1w77//fhw9ehR1dXXo6OhAWVkZTCYTYmJiEB4ejqqqKgBAaWkpTCaTrFhBh79Akqj/krakEB4ejvz8fMybNw9tbW1ITExESkoKAKCgoAA5OTlwuVyIi4tDRkaGrFhBh7/hgaj/0rxw//KXv/i/TkhIwJYtWy7bZsSIEdi4caPWUYiIAoqfNCMikoSFS0QkCQuXiEgSFi4RkSQsXCIiSVi4RESSsHCJiCRh4RIRScLCJSKShIVLRCQJC5eISBIWLhGRJCzcIMJbMxL1byzcIMJbMxL1byzcIBemhGDNJ/8KdAwi6gMsXB0QQrB0ifoBFq5OcLmBSP9YuEREkrBwiYgkYeESEUnCwiUikoSFS0QkCQuXiEgSFi4RkSQsXCIiSVi4RESSsHCJiCRRAvGiU6dOxcmTJ6EoF17+tddew7lz5/DGG2+gra0N48ePx4IFCwIRLeit+eRf+N+WHwc6BhHdAOmFK4RAbW0t/vrXv/oL1+12IyUlBevWrcPgwYMxa9Ys7Ny5E4mJibLjBUx374XLeyoQ6Zf0wj1y5AgAYPr06Th9+jSeeOIJDB8+HEOGDEFsbCwAwGq1wm6331SFyyIl6v+kF25LSwsSEhLw6quvor29HRkZGXj++eehqqp/G6PRCIfD0aP9RkVFXHFcVSN7lVeWi2f7VxtXFODD7d9CURSoaiQK1lfh/zzzoMyIN0wvx+Bq9J4f4ByChfTCfeCBB/DAAw/4H0+aNAlFRUV48MH/lIcQAgaDoUf7bW52wecTXcZUNRJO59neBZbE6/VeNqYoSpdxr9cLr9cHp/MsWs+36WJuejoGV6L3/ADnINu13hikX6Wwd+9e7Nq1y/9YCIGYmBg4nU7/mNPphNFolB2NiEhT0gv37NmzWLFiBdra2uByubB582a89NJLOHr0KOrq6tDR0YGysjKYTCbZ0YiINCV9SWHcuHHYv38/JkyYAJ/Ph6eeegoPPPAA8vPzMW/ePLS1tSExMREpKSmyoxERaSog1+G++OKLePHFF7uMJSQkYMuWLYGIQ0QkBT9pFgS6ew0uEekbCzcI9OQa3DAlhAVNpFMBWVKgC260OPkhCSJ9YuEGEIuT6ObCJYUA4bIA0c2HhRsgPLsluvmwcAPg/23/d6/3EaaEYM0n/+qDNEQkCws3APrq7JZnyUT6wsIlIpKEhUtEJAkLl4hIEhYuEZEkLFwd45UKRPrCwpWor8sxTAnhlQpEOsLClUgI0eefMLv0LJdnvETBi4WrsYsFeLFotTgj7bxPnvESBS8WrsYuFiCLkIhYuBrj/WuJ6CIWrgSyzm5Z7ETBjYXbj3DZgii4sXD7UOcfkMm8WqDzsgWvzSUKXizcPtT5B2SyzzY7v54QgqVLFIRYuBoJ9A/LLhaw7LNtIro6/k6zPnRpyQZyTfViFk97B9q9voDlIKL/4BluHwumH1wFUxYiYuHeMD39M50f/yUKDizcG3Txvgh6Ka/OP0jjD9WIAiOoCnfr1q1ITU2F2WzGBx98EOg41+Vp79BVeXW+eQ6XG4jkC5rCdTgcKCwsxIcffoiSkhJs2LAB3377baBj+V16E5rO9FReespK1N8EzVUKlZWVGDt2LG6//XYAQHJyMux2O+bOndut54eEGHo03h2bvziCif/rXgBA1H/dgk921+G2/zEAISEG/M/bB/q383b4Lhu7FkUJgbcbVw6EKgo6vN5e7+dqSr866p/flXSef3dcbfveHINgoPf8AOcQLAxCCBHoEADwzjvvoLW1FQsWLAAAfPTRRzhw4ACWLl0a4GRERH0jaJYUfD4fDIb/vIMJIbo8JiLSu6Ap3OjoaDidTv9jp9MJo9EYwERERH0raAr34Ycfxq5du3Dy5EmcP38en376KUwmU6BjERH1maD5odmdd96JBQsWICMjA+3t7Zg0aRJGjRoV6FhERH0maH5oRkTU3wXNkgIRUX/HwiUikoSFS0QkCQuXiEgSXRbu7373O7z11lv+xy0tLZg5cybGjx+Pp59+2n89r8fjQWZmJsaPH4+JEyfi8OHDAC58qOLNN99ESkoKUlNTUVVV5d/Xn/70J6SkpCA5ORmffvqp3Il1Eow38nG5XEhLS0N9fT2ACx/HtlqtMJvNKCws9G9XU1MDm82G5ORkZGdnw/vfH08+fvw4nn76aaSkpOCFF17AuXPnAFz9+PWlVatWwWKxwGKxYMWKFbrLDwArV65EamoqLBYL1q5dq8s5AMCbb76JRYsW6TZ/rwgdaWlpEYsXLxajRo0SRUVF/vElS5aId955RwghxObNm8Wvf/1rIYQQ7733nnj11VeFEELs2bNHPP7440IIIbZt2yZmzJghOjo6xJEjR0RSUpJob28X+/fvF+np6cLtdoumpibxy1/+Upw6dUrqHIUQ4vvvvxfjxo0Tp06dEufOnRNWq1UcOnRIeo7O/vGPf4i0tDQRFxcnjh07Js6fPy8SExPFd999J9rb28X06dPFjh07hBBCWCwWsW/fPiGEEIsXLxYffPCBEEKImTNnirKyMiGEEKtWrRIrVqwQQlz9+PWVr776Sjz55JOira1NeDwekZGRIbZu3aqb/EII8be//U1MnjxZtLe3i/Pnz4tx48aJmpoaXc1BCCEqKyvFz372M/HKK6/o6u9QX9HVGe727dtx9913Y9q0aV3Gd+zYAavVCgBIS0vD559/jvb2duzYsQOPPvooAOChhx7CyZMncfz4cezcuROpqakICQnBPffcg8GDB2Pfvn34/PPPkZSUhPDwcERFRWHMmDHYsWOH7Gl2uZHPrbfe6r+RTyAVFxcjLy/P/+m/AwcOYMiQIYiNjYWiKLBarbDb7WhoaIDb7UZ8fDwAwGazwW63o729HX//+9+RnJzcZRy4+vHrK6qqYtGiRRgwYADCwsIwdOhQ1NbW6iY/AIwZMwbvv/8+FEVBc3MzOjo60NLSoqs5nD59GoWFhZg9ezYAff0d6iu6KtwJEyZg5syZCA0N7TLe2NgIVVUBAIqiICIiAidPnuwyDlz4H+/7779HY2Njl48NX29ctktzG41GOBwO6Tk6W7ZsGUaPHu1/fLWMV/ozdzgcOHXqFCIiIqAoSpfxS/fV+fj1lWHDhvn/562trcW2bdtgMBh0k/+isLAwFBUVwWKxICEhQVfHAAByc3OxYMEC3HbbbZe9ph7y94WgLNxt27bBZDJ1+e+5557r9vOFEAgJCbnsBjgXx690o5yL45cKCZH/R6SHG/lcLePVxq80h6vN6eLx6GuHDh3C9OnTsXDhQsTGxuouPwDMnz8fu3btwokTJ1BbW6ubOXz00UcYPHgwEhIS/GN6/DvUW0Hz0d7Oxo8fj/Hjx3d7e6PRiKamJkRHR8Pr9eLcuXO4/fbbceedd6KxsRE//OEPAQBNTU0wGo2Ijo5GY2Oj//mdxy+9gc4999zTdxPrpujoaOzdu7dLjmC7kc/VbjZ06fjFP9s77rgDZ8+eRUdHB0JDQ7vM6WrHry9VVVVh/vz5yMrKgsViwZ49e3SV//Dhw/B4PBg5ciQGDhwIs9kMu93e5V97wTyH8vJyOJ1OpKen48yZM2htbUVDQ4Nu8veV4HsLuAGJiYkoKSkBcOHAjh49GmFhYUhMTERpaSkAYO/evQgPD8ddd90Fk8mErVu3oqOjA3V1daitrcV9990Hk8mETz/9FOfPn8fJkyexe/fuLu/IsujhRj73338/jh49irq6OnR0dKCsrAwmkwkxMTEIDw/3X/lRWloKk8mEsLAwjB49GuXl5QCAkpIS/5yudvz6yokTJzBnzhwUFBTAYrHoLj8A1NfXIycnBx6PBx6PB9u3b8fkyZN1M4e1a9eirKwMpaWlmD9/Pn7xi1/gvffe003+PiP1R3R9pKioqMtVCqdOnRKzZs0Sqamp4sknnxTHjh0TQgjhdrvFwoULRWpqqpgwYYKorq4WQgjh8/lEfn6+SE1NFampqeKLL77w72vNmjUiNTVVmM1msXnzZqnz6mzLli3CYrEIs9ks/vjHPwYsx6XGjRvn//OtrKwUVqtVmM1msWzZMuHz+YQQQtTU1IjHHntMJCcni5deekm0tbUJIYSor68XzzzzjBg/fryYPn26OH36tBDi6sevryxdulTEx8eLRx991P/fhx9+qJv8FxUVFYnx48eLtLQ0/99/vc1BCCE+/vhj8corr+g2f2/w5jVERJL0iyUFIiI9YOESEUnCwiUikoSFS0QkCQuXiEiSoPzgA1Fv1dfXIykpCcOHD/ePCSGQkZGBSZMmddl2+/bt2LVrF3JycmTHpJsMLwujfqm+vh5WqxX79u3zjzkcDqSlpWHdunUYMWJEANPRzYpnuHTTuPPOOzFkyBB89dVXeO2113D+/HlERERg4sSJqKiowDvvvAOn04m8vDwcOXIEISEhmDx5MjIyMnD27FksW7YM//73v9He3o6EhAQsXLjQfyMVou7gGi7dNPbt24fvvvsObrcb3377LdatW4d169Z12WbJkiW4++67YbfbsWHDBhQXF6Ourg7Lly9HXFwcNm3ahJKSEpw6dcp/E3Ci7uLbM/Vbbrcb6enpAICOjg4MGjQIv/nNb9Dc3Iwf/ehHiIiIuOw5lZWVyMzMBABERkairKwMwIX7rf7zn//Exo0b/fsm6ikWLvVbt9xyi//mRZ1t2rQJt9566xWfoyhKl1v+HTt2DIMGDYLP58PKlSsxdOhQABd+pUuw3TKTgh+XFIg6SUhIwMcffwwAOHv2LJ599lnU1tbi5z//Of785z9DCAGPx4MXXngB69evD3Ba0hsWLlEnubm5OHLkCKxWK6ZMmYJZs2bhJz/5CbKzs9Ha2gqr1Qqr1Yrhw4fj+eefD3Rc0hleFkZEJAnPcImIJGHhEhFJwsIlIpKEhUtEJAkLl4hIEhYuEZEkLFwiIklYuEREkvx/Mtghjcus1loAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(y_test-y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1184.323506791498\n",
      "MSE: 4425672.452037256\n",
      "RMSE: 2103.7282267529845\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026942908955281467"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE/(max(DV)-min(DV))\n",
    "\n",
    "2103.7282/(max(y)-min(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7947471991770106"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "\n",
    "\n",
    "* Choose following method for hyperparameter tuning\n",
    "    1. **RandomizedSearchCV** --> Fast\n",
    "    2. **GridSearchCV**\n",
    "* Assign hyperparameters in form of dictionery\n",
    "* Fit the model\n",
    "* Check best paramters and best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 5 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = reg_rf, param_distributions = random_grid, scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=0, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 700, 'min_samples_split': 15, 'min_samples_leaf': 1, 'max_features': 'auto', 'max_depth': 20}\n"
     ]
    }
   ],
   "source": [
    "print(rf_random.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sns.distplot(y_test-prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "plt.scatter(y_test, prediction, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1165.0975277586867\n",
      "MSE: 4049198.4856210076\n",
      "RMSE: 2012.2620320477668\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('flight_rf.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8122072206497486"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)"
   ]
  },
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   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
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