flight-price-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1ee93fee",
   "metadata": {},
   "source": [
    "# Flight Price Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1965aaf3",
   "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",
   "id": "799a1282",
   "metadata": {},
   "source": [
    "## Importing the dataset \n",
    "1. Since Data is in the form of excel file, we use pandas read_excel to load the data \n",
    "2. After loading, it's important that we check the complete information of the data as it is an indication of many hidden information, such as null values in a column or a row\n",
    "3. Check whether any null values are there or not. If there, following can be done,\n",
    "    - Inputing data using inputation method in sklearn\n",
    "    - Filling NaN values with mean, median and mode using the fillna() method \n",
    "4. Describe Data --> This can give us a statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2718cb1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-44ee5a8ceefa>:1: FutureWarning: Your version of xlrd is 1.2.0. In xlrd >= 2.0, only the xls format is supported. As a result, the openpyxl engine will be used if it is installed and the engine argument is not specified. Install openpyxl instead.\n",
      "  train_data = pd.read_excel(r\"Data_train.xlsx\")\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_excel(r\"Data_train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cdd584a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None) # display all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f2f9b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cce11585",
   "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() # object data type = string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a093f4f0",
   "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",
       "3h 25m       1\n",
       "29h 30m      1\n",
       "31h 50m      1\n",
       "31h 30m      1\n",
       "30h 25m      1\n",
       "Name: Duration, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2759ef73",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73d01ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5b968b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08bb05d",
   "metadata": {},
   "source": [
    "## EDA \n",
    "### Date and Month of Journey\n",
    "From the description, we can see that the Date_of_Journey is a object data type. Therefore, to convert this datatype to 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",
    "- .dt.day method will extract only day of that date \n",
    "- .dt.month method will extract only month of that date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d40fd06",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_day\"] = pd.to_datetime(train_data.Date_of_Journey, format = \"%d/%m/%Y\").dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "60481972",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_month\"] = pd.to_datetime(train_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7757a401",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302   \n",
       "\n",
       "   Journey_day  Journey_month  \n",
       "0           24              3  \n",
       "1            1              5  \n",
       "2            9              6  \n",
       "3           12              5  \n",
       "4            1              3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d714f9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we have converted Date_of_Journey column into integers,\n",
    "# We can drop it as of now, it's of no use\n",
    "\n",
    "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6f72d7",
   "metadata": {},
   "source": [
    "### Departure Time\n",
    "This is the time when the flight leaves the gate. Similar to Date_of_Journey we can extract values of Dep_Time\n",
    "We thereby extract the hours, the minutes from the given \"Dep_Time\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "806c339c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting hours \n",
    "train_data[\"Dep_hour\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.hour\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 that is of no use\n",
    "train_data.drop([\"Dep_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "867d447b",
   "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": "markdown",
   "id": "d6dafd24",
   "metadata": {},
   "source": [
    "### Arrival Time\n",
    "This indicates when the flight pulls up to the gate \n",
    "Similar to Date_of_Journey we can extract values from Arrival_time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f299b7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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 the Arrival_Time as it is of no use \n",
    "train_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b1156ac",
   "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>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",
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       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>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": "markdown",
   "id": "0e09311b",
   "metadata": {},
   "source": [
    "### Time Taken\n",
    "Indicates the actual time required by a flight to reach from source to destination.\n",
    "It is the differnce between departure time and arrival time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4fde4509",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time taken by plane to reach destination is called Duration\n",
    "# It is the differnce betwwen Departure Time and Arrival time\n",
    "\n",
    "\n",
    "# Assigning and converting Duration column into list\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "551d668a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding duration_hours and duration_mins lists to the train_data dataframe\n",
    "train_data[\"Duration_hours\"] = duration_hours\n",
    "train_data[\"Duration_mins\"]  = duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b82fa84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>IndiGo</td>\n",
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       "      <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",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</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",
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       "      <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",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>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 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  Duration_hours  Duration_mins  \n",
       "0       20             1           10               2             50  \n",
       "1       50            13           15               7             25  \n",
       "2       25             4           25              19              0  \n",
       "3        5            23           30               5             25  \n",
       "4       50            21           35               4             45  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e046c849",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Duration\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01470662",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaec28fb",
   "metadata": {},
   "source": [
    "### Handling Categorical Data \n",
    "There can be many ways to handle categorical data. Some of the categorical data are,\n",
    "1. **Nominal Data** : Data is not in any order --> OneHotEncoder is used in this case\n",
    "2. **Ordinal Data** : Data is in order --> LabelEncoder is used in this case"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6dead0b",
   "metadata": {},
   "source": [
    "### The Airlines\n",
    "This is a categorical data, we can use OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b7fb4ec6",
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.Airline.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a0e9c280",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graph, we can see tht Jet Airways Business has the highest price.\n",
    "# Apart from the first Airline almost all are having similar median\n",
    "\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": 25,
   "id": "d18cabda",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Airline details are unordered categorical data, so we settle on using One Hot Encoding \n",
    "# If the number of airlines to encode was too high, we could have used Cardinality Encoding \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": 26,
   "id": "00c379c0",
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Source Location is also a categorical data \n",
    "\n",
    "train_data['Source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5eb94270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We analyse source location vs price \n",
    "sns.catplot(y = 'Price', x = 'Source', data = train_data.sort_values('Price', ascending = False), kind = \"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ac83e8",
   "metadata": {},
   "source": [
    "### There are quite a few outliers in Bangalore, but for the rest, we have pretty regular data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c569aa5",
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The source location is unordered Categorical Data, we perform One Hot Encoding \n",
    "\n",
    "Source = train_data[['Source']]\n",
    "\n",
    "Source = pd.get_dummies(Source, drop_first = True)\n",
    "\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77634abb",
   "metadata": {},
   "source": [
    "### Similar things apply for destination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "efb6e13c",
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Destination Location is also a categorical data \n",
    "\n",
    "train_data['Destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e1ab3ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We analyse source location vs price \n",
    "sns.catplot(y = 'Price', x = 'Destination', data = train_data.sort_values('Price', ascending = False), kind = \"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "32b23200",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The destionation location is unordered Categorical Data, we perform One Hot Encoding \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": 32,
   "id": "ee255ec0",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Route']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0ac48874",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Route and total stops are related to each other \n",
    "# And we care way more about the number of layovers than the actual routes so, dropping it \n",
    "# Additional_info is also not required for prediction info and moreover contains 80% no-data\n",
    "\n",
    "train_data.drop(['Route', 'Additional_Info'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ad6d3c05",
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Total_Stops'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f35cd878",
   "metadata": {},
   "outputs": [],
   "source": [
    "# More the total stops, more the price, from our observation in the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9f4c73cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# It's a case of ordered categorical data so we perform LabelHotEncoder -ing \n",
    "# Values are assigned to 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": 37,
   "id": "b94ce79c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Total_Stops</th>\n",
       "      <th>Price</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
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       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
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       "      <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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ff1bf298",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = pd.concat([train_data, Airline, Source, Destination], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b0e7d357",
   "metadata": {},
   "outputs": [
    {
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      ],
      "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bc30bf14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10682 entries, 0 to 10682\n",
      "Data columns (total 30 columns):\n",
      " #   Column                                     Non-Null Count  Dtype\n",
      "---  ------                                     --------------  -----\n",
      " 0   Total_Stops                                10682 non-null  int64\n",
      " 1   Price                                      10682 non-null  int64\n",
      " 2   Journey_day                                10682 non-null  int64\n",
      " 3   Journey_month                              10682 non-null  int64\n",
      " 4   Dep_hour                                   10682 non-null  int64\n",
      " 5   Dep_min                                    10682 non-null  int64\n",
      " 6   Arrival_hour                               10682 non-null  int64\n",
      " 7   Arrival_min                                10682 non-null  int64\n",
      " 8   Duration_hours                             10682 non-null  int64\n",
      " 9   Duration_mins                              10682 non-null  int64\n",
      " 10  Airline_Air India                          10682 non-null  uint8\n",
      " 11  Airline_GoAir                              10682 non-null  uint8\n",
      " 12  Airline_IndiGo                             10682 non-null  uint8\n",
      " 13  Airline_Jet Airways                        10682 non-null  uint8\n",
      " 14  Airline_Jet Airways Business               10682 non-null  uint8\n",
      " 15  Airline_Multiple carriers                  10682 non-null  uint8\n",
      " 16  Airline_Multiple carriers Premium economy  10682 non-null  uint8\n",
      " 17  Airline_SpiceJet                           10682 non-null  uint8\n",
      " 18  Airline_Trujet                             10682 non-null  uint8\n",
      " 19  Airline_Vistara                            10682 non-null  uint8\n",
      " 20  Airline_Vistara Premium economy            10682 non-null  uint8\n",
      " 21  Source_Chennai                             10682 non-null  uint8\n",
      " 22  Source_Delhi                               10682 non-null  uint8\n",
      " 23  Source_Kolkata                             10682 non-null  uint8\n",
      " 24  Source_Mumbai                              10682 non-null  uint8\n",
      " 25  Destination_Cochin                         10682 non-null  uint8\n",
      " 26  Destination_Delhi                          10682 non-null  uint8\n",
      " 27  Destination_Hyderabad                      10682 non-null  uint8\n",
      " 28  Destination_Kolkata                        10682 non-null  uint8\n",
      " 29  Destination_New Delhi                      10682 non-null  uint8\n",
      "dtypes: int64(10), uint8(20)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "data_train.drop(['Airline', 'Source', 'Destination'], axis = 1, inplace = True)\n",
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e7a9f325",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
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       "      <td>13</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9032c822",
   "metadata": {},
   "source": [
    "# Training on the Cleaned and Optimised Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "edf82286",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-42-bd1b4446b079>:1: FutureWarning: Your version of xlrd is 1.2.0. In xlrd >= 2.0, only the xls format is supported. As a result, the openpyxl engine will be used if it is installed and the engine argument is not specified. Install openpyxl instead.\n",
      "  test_data = pd.read_excel(r'Test_set.xlsx')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_excel(r'Test_set.xlsx')\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1c46497f",
   "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",
      "Vistara Premium economy                2\n",
      "Jet Airways Business                   2\n",
      "Name: Airline, dtype: int64\n",
      "\n",
      "Source\n",
      "---------------------------------------------------------------------------\n",
      "Delhi       1145\n",
      "Kolkata      710\n",
      "Banglore     555\n",
      "Mumbai       186\n",
      "Chennai       75\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "Destination\n",
      "---------------------------------------------------------------------------\n",
      "Cochin       1145\n",
      "Banglore      710\n",
      "Delhi         317\n",
      "New Delhi     238\n",
      "Hyderabad     186\n",
      "Kolkata        75\n",
      "Name: Destination, dtype: int64\n",
      "\n",
      "\n",
      "Shape of test data :  (2671, 28)\n"
     ]
    }
   ],
   "source": [
    "# We could have combined train and test data together so as to get optimized features for both at the same time\n",
    "# rather than writing the steps for the training steps again, HOWEVER, We can't because that might lead to Data Leakage\n",
    "\n",
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "045d3b4b",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Total_Stops</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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9df2f3",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d19f7ab9",
   "metadata": {},
   "source": [
    "#### Finding out the best feature which will contribute and have good relation with target variable. Following are some of the feature selection methods,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2bd2a65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2de15d89",
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "96548744",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  \\\n",
       "0           10               2             50                  0   \n",
       "1           15               7             25                  1   \n",
       "2           25              19              0                  0   \n",
       "3           30               5             25                  0   \n",
       "4           35               4             45                  0   \n",
       "\n",
       "   Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0              0               1                    0   \n",
       "1              0               0                    0   \n",
       "2              0               0                    1   \n",
       "3              0               1                    0   \n",
       "4              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 47,
     "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": 48,
   "id": "f468fb80",
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:, 1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a498cb61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding 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": 50,
   "id": "79cd6b4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 50,
     "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": 51,
   "id": "d5d7a5fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.04190815e-01 1.43965782e-01 5.31452555e-02 2.47156789e-02\n",
      " 2.07247725e-02 2.78299714e-02 1.93720725e-02 1.61014168e-01\n",
      " 1.76740652e-02 9.58287755e-03 1.79995479e-03 1.69400577e-02\n",
      " 1.30421490e-01 6.71604236e-02 2.04803400e-02 8.37076419e-04\n",
      " 2.53469424e-03 8.51940129e-05 5.00443330e-03 7.39279087e-05\n",
      " 4.14754206e-04 7.51617123e-03 3.49306659e-03 6.42159766e-03\n",
      " 1.12893611e-02 1.24508102e-02 5.59295604e-03 4.56843112e-04\n",
      " 2.48113898e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d97d28f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ccf80c",
   "metadata": {},
   "source": [
    "## Fitting model using Random Forest\n",
    "Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "If needed do scaling of data\n",
    "Scaling is not done in Random forest\n",
    "Import model\n",
    "Fit the data\n",
    "Predict w.r.t X_test\n",
    "In regression check RSME Score\n",
    "Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0939fb0f",
   "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": 54,
   "id": "779c0068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 54,
     "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": 55,
   "id": "47190620",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "de0b1e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9531172230277272"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1bccacb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7962626417370747"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c885a7d8",
   "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,
   "id": "6e5c6d43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1181.617572636587\n",
      "MSE: 4392996.394201123\n",
      "RMSE: 2095.9476124658086\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "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": 60,
   "id": "f0086701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7962626417370747"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d050f23f",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "Choose following method for hyperparameter tuning\n",
    "- RandomizedSearchCV --> Fast\n",
    "- GridSearchCV\n",
    "- Assign hyperparameters in form of dictionery\n",
    "- Fit the model\n",
    "- Check best paramters and best score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "79baf388",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f3e515c4",
   "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": 64,
   "id": "18b78736",
   "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": 65,
   "id": "280ee93a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 5 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = reg_rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fc36f7a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   3.1s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   3.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   2.8s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   3.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   2.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   4.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   4.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   5.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   5.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   4.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   2.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   2.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   2.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   2.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   2.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   5.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   5.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   5.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   5.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   5.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   8.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   8.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   8.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   8.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   8.2s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   7.3s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   7.5s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   7.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   7.5s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   7.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  10.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  10.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   9.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  10.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  10.6s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b496f9b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 15,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f2bf477c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d00ee610",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python3\\envs\\maskdetection\\lib\\site-packages\\seaborn\\distributions.py:2557: 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": 70,
   "id": "89e9a296",
   "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": "markdown",
   "id": "e1d206ed",
   "metadata": {},
   "source": [
    "# We save the model, to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "baae1494",
   "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": 78,
   "id": "0ff73c00",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d9c2e6fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9ec9c821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8114838986322325"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f30e3e72",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69516518",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d58c404",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bbc637f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2804a31",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36cbcf1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd60e985",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0682ece2",
   "metadata": {},
   "outputs": [],
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