model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "instructional-plastic",
   "metadata": {},
   "source": [
    "# Flight Fare Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "orange-voice",
   "metadata": {},
   "source": [
    "### Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "intense-jerusalem",
   "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": "ethical-wheat",
   "metadata": {},
   "source": [
    "### Importing dataset\n",
    "\n",
    "1. Since data is in form of csv file we have to use pandas read_csv1. to load the data.\n",
    "2. After loading it is important to check the complete information of data as it can indicate many of the hidden infomation such as null values in a column or row.\n",
    "3. Check whether any null values are there or not. if it is present then following can be done,\n",
    "   A) Imputing data using Imputation method in sklearn\n",
    "   B) Filling NaN values with mean, median and mode using fillna() method\n",
    "4. Describe data --> which can give statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "blank-texture",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=pd.read_csv('Data_Train.csv')\n",
    "test_df=pd.read_csv('Test_set.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "artificial-white",
   "metadata": {},
   "source": [
    "## Train Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aboriginal-accuracy",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR ? DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU ? IXR ? BBI ? BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL ? LKO ? BOM ? COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU ? NAG ? BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR ? NAG ? DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR ? DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU ? IXR ? BBI ? BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL ? LKO ? BOM ? COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU ? NAG ? BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR ? NAG ? DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "congressional-butler",
   "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_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "guided-little",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regional-gender",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis (EDA) and Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "behavioral-brass",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Total_Stops']=train_df['Total_Stops'].fillna('1 stop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "selected-silver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "productive-yield",
   "metadata": {},
   "source": [
    "From description we can see that 'Date_of_Journey' is a object data type,therefore, we have to convert this datatype into timestamp so as to use this column properly for prediction.\n",
    "\n",
    "For this we require pandas to_datetime to convert object data type to datetime dtype.\n",
    "\n",
    "##### **.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": 8,
   "id": "digital-university",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[\"Journey_day\"] = pd.to_datetime(train_df.Date_of_Journey, format=\"%d/%m/%Y\").dt.day\n",
    "train_df[\"Journey_month\"] = pd.to_datetime(train_df[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "suburban-powder",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "criminal-champion",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we have converted Date_of_Journey column into integers, Now we can drop as it is of no use.\n",
    "\n",
    "train_df.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "valued-change",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Departure time is when a plane leaves the gate. \n",
    "# Similar to Date_of_Journey we can extract values from Dep_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_df[\"Dep_hour\"] = pd.to_datetime(train_df[\"Dep_Time\"]).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_df[\"Dep_min\"] = pd.to_datetime(train_df[\"Dep_Time\"]).dt.minute\n",
    "\n",
    "# Now we can drop Dep_Time as it is of no use\n",
    "train_df.drop([\"Dep_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "broken-sixth",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "clean-newport",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrival time is when the plane pulls up to the gate.\n",
    "# Similar to Date_of_Journey we can extract values from Arrival_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_df[\"Arrival_hour\"] = pd.to_datetime(train_df.Arrival_Time).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_df[\"Arrival_min\"] = pd.to_datetime(train_df.Arrival_Time).dt.minute\n",
    "\n",
    "# Now we can drop Arrival_Time as it is of no use\n",
    "train_df.drop([\"Arrival_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "naughty-proportion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR ? DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU ? IXR ? BBI ? BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL ? LKO ? BOM ? COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU ? NAG ? BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR ? NAG ? DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR ? DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU ? IXR ? BBI ? BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL ? LKO ? BOM ? COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU ? NAG ? BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR ? NAG ? DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "diagnostic-david",
   "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_df[\"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": 16,
   "id": "aquatic-house",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding duration_hours and duration_mins list to train_data dataframe\n",
    "\n",
    "train_df[\"Duration_hours\"] = duration_hours\n",
    "train_df[\"Duration_mins\"] = duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "whole-irrigation",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop([\"Duration\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "tight-minority",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR ? DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
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       "      <td>22</td>\n",
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       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU ? IXR ? BBI ? BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL ? LKO ? BOM ? COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU ? NAG ? BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR ? NAG ? DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR ? DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU ? IXR ? BBI ? BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL ? LKO ? BOM ? COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU ? NAG ? BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR ? NAG ? DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "joint-theater",
   "metadata": {},
   "source": [
    "### Handling Categorical Data\n",
    "One can find many ways to handle categorical data. Some of them are:\n",
    "\n",
    "##### 1. **Nominal data** --> data are not in any order --> **OneHotEncoder** is used in this case\n",
    "##### 2. **Ordinal data** --> data are in order --> **LabelEncoder** is used in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "usual-knife",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1752\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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"Airline\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "returning-rwanda",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graph we can see that Jet Airways Business have the highest Price.\n",
    "# Apart from the first Airline almost all are having similar median\n",
    "\n",
    "# Airline vs Price\n",
    "sns.catplot(y = \"Price\", x = \"Airline\", data = train_df.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "widespread-astrology",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Airline = train_df[[\"Airline\"]]\n",
    "\n",
    "Airline = pd.get_dummies(Airline, drop_first= True)\n",
    "\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "funded-interim",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4537\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "experimental-gothic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source vs Price\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Source\", data = train_df.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "compressed-following",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Source = train_df[[\"Source\"]]\n",
    "\n",
    "Source = pd.get_dummies(Source, drop_first= True)\n",
    "\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "finished-metro",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4537\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "accessible-routine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Destination vs Price\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Destination\", data = train_df.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "controlled-brother",
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination = train_df[[\"Destination\"]]\n",
    "\n",
    "Destination = pd.get_dummies(Destination, drop_first = True)\n",
    "\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "metallic-radius",
   "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: 10683, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"Route\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "increased-detroit",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "train_df.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "arranged-lawsuit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      5626\n",
       "non-stop    3491\n",
       "2 stops     1520\n",
       "3 stops       45\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"Total_Stops\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "matched-canon",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "# Here Values are assigned with corresponding keys\n",
    "\n",
    "\n",
    "train_df.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "needed-geneva",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  \n",
       "0               2             50  \n",
       "1               7             25  \n",
       "2              19              0  \n",
       "3               5             25  \n",
       "4               4             45  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "breathing-happiness",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate dataframe --> train_data + Airline + Source + Destination\n",
    "\n",
    "data_train = pd.concat([train_df, Airline, Source, Destination], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "consecutive-consumer",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>...</th>\n",
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       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "<p>5 rows × 33 columns</p>\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  ...  \\\n",
       "0              3        22       20             1  ...   \n",
       "1              5         5       50            13  ...   \n",
       "2              6         9       25             4  ...   \n",
       "3              5        18        5            23  ...   \n",
       "4              3        16       50            21  ...   \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  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "speaking-turkish",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "expressed-rolling",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>...</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",
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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",
       "      <td>45</td>\n",
       "      <td>...</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",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897           24              3        22       20   \n",
       "1            2   7662            1              5         5       50   \n",
       "2            2  13882            9              6         9       25   \n",
       "3            1   6218           12              5        18        5   \n",
       "4            1  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  ...  \\\n",
       "0             1           10               2             50  ...   \n",
       "1            13           15               7             25  ...   \n",
       "2             4           25              19              0  ...   \n",
       "3            23           30               5             25  ...   \n",
       "4            21           35               4             45  ...   \n",
       "\n",
       "   Airline_Vistara 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  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "photographic-naples",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 30)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alive-funds",
   "metadata": {},
   "source": [
    "## Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "powerful-disposal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
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       "      <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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "inside-consent",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<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"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "broad-march",
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "primary-sandwich",
   "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": [
    "print(\"Test data Info\")\n",
    "print(\"-\"*75)\n",
    "print(test_df.info())\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Null values :\")\n",
    "print(\"-\"*75)\n",
    "print(test_df.isnull().sum())\n",
    "\n",
    "\n",
    "# Exploratory Data Analysis (EDA) and Feature Engineering\n",
    "\n",
    "\n",
    "\n",
    "## Date_of_Journey\n",
    "test_df[\"Journey_day\"] = pd.to_datetime(test_df.Date_of_Journey, format=\"%d/%m/%Y\").dt.day\n",
    "test_df[\"Journey_month\"] = pd.to_datetime(test_df[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month\n",
    "test_df.drop([\"Date_of_Journey\"], axis = 1, inplace = True)\n",
    "\n",
    "## Dep_Time\n",
    "test_df[\"Dep_hour\"] = pd.to_datetime(test_df[\"Dep_Time\"]).dt.hour\n",
    "test_df[\"Dep_min\"] = pd.to_datetime(test_df[\"Dep_Time\"]).dt.minute\n",
    "test_df.drop([\"Dep_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Arrival_Time\n",
    "test_df[\"Arrival_hour\"] = pd.to_datetime(test_df.Arrival_Time).dt.hour\n",
    "test_df[\"Arrival_min\"] = pd.to_datetime(test_df.Arrival_Time).dt.minute\n",
    "test_df.drop([\"Arrival_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "## Duration\n",
    "duration = list(test_df[\"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_df[\"Duration_hours\"] = duration_hours\n",
    "test_df[\"Duration_mins\"] = duration_mins\n",
    "test_df.drop([\"Duration\"], axis = 1, inplace = True)\n",
    "\n",
    "## Categorical data\n",
    "\n",
    "print(\"Airline\")\n",
    "print(\"-\"*75)\n",
    "print(test_df[\"Airline\"].value_counts())\n",
    "Airline = pd.get_dummies(test_df[\"Airline\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Source\")\n",
    "print(\"-\"*75)\n",
    "print(test_df[\"Source\"].value_counts())\n",
    "Source = pd.get_dummies(test_df[\"Source\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Destination\")\n",
    "print(\"-\"*75)\n",
    "print(test_df[\"Destination\"].value_counts())\n",
    "Destination = pd.get_dummies(test_df[\"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_df.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)\n",
    "\n",
    "### Replacing Total_Stops\n",
    "test_df.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_df, Airline, Source, Destination], axis = 1)\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": 42,
   "id": "together-revelation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Air India</th>\n",
       "      <th>...</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  ...  \\\n",
       "0           25              10             55          0  ...   \n",
       "1           20               4              0          0  ...   \n",
       "2            0              23             45          0  ...   \n",
       "3            0              13              0          0  ...   \n",
       "4           45               2             50          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  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "absolute-worker",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "Finding out the best feature which will contribute and have good relation with target variable. Following are some of the feature selection methods,\n",
    "\n",
    "#### 1. **heatmap**\n",
    "#### 2. **feature_importance_**\n",
    "#### 3. **SelectKBest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "incorporate-looking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 30)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "roman-germany",
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "everyday-vietnamese",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>...</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>...</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>...</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>...</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  ...  \\\n",
       "0           10               2             50                  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_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  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Removing the Price column,since it is the dependent feature\n",
    "\n",
    "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": 46,
   "id": "compressed-partnership",
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:, 1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "civil-matter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finds correlation between Independent and dependent attributes\n",
    "\n",
    "plt.figure(figsize = (18,18))\n",
    "sns.heatmap(train_df.corr(), annot = True, cmap = \"RdYlGn\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "purple-korea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 48,
     "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": 49,
   "id": "right-click",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.48128971e-01 1.43360423e-01 5.32961467e-02 2.40415203e-02\n",
      " 2.10375434e-02 2.80009367e-02 1.93152417e-02 1.20406941e-01\n",
      " 1.76179874e-02 9.77770701e-03 1.83339934e-03 1.70217757e-02\n",
      " 1.31812752e-01 6.70715243e-02 1.93594454e-02 8.06201605e-04\n",
      " 3.35819146e-03 1.15348530e-04 5.31878931e-03 7.70901775e-05\n",
      " 5.04763945e-04 7.78941318e-03 3.29232692e-03 5.57132322e-03\n",
      " 5.76048332e-03 1.32666843e-02 6.87128712e-03 5.82955108e-04\n",
      " 2.46028271e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "embedded-inside",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interstate-grounds",
   "metadata": {},
   "source": [
    "### Fitting model using Random Forest\n",
    "\n",
    "##### 1. Split dataset into train and test set in order to prediction w.r.t X_test.\n",
    "##### 2. If needed do scaling of data--Scaling is not done in Random forest\n",
    "##### 3. Import model\n",
    "##### 4. Fit the data\n",
    "##### 5. Predict w.r.t X_test\n",
    "##### 6. In regression check RSME Score\n",
    "##### 7. Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "general-sodium",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "precious-teddy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "reg_rf = RandomForestRegressor()\n",
    "reg_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "caroline-introduction",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "clean-yield",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9547517654521749"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "retired-treasure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.785110137771236"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "public-sector",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\swarnava\\appdata\\local\\programs\\python\\python37\\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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "measured-repository",
   "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "pregnant-spine",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "visible-blackberry",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1192.8230449337618\n",
      "MSE: 4547921.749919326\n",
      "RMSE: 2132.585695797317\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "democratic-method",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026887077025966846"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE/(max(DV)-min(DV))\n",
    "\n",
    "2090.5509/(max(y)-min(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "loving-denmark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.785110137771236"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attractive-bidding",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning\n",
    "\n",
    "#### 1. Choose following method for hyperparameter tuning:\n",
    "##### a) RandomizedSearchCV --> Fast\n",
    "##### b) GridSearchCV\n",
    "#### 3. Assign hyperparameters in form of dictionery\n",
    "#### 4. Fit the model\n",
    "#### 5. Check best paramters and best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "diverse-extension",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "charming-batch",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "unnecessary-interpretation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "proprietary-companion",
   "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": 69,
   "id": "hazardous-lightning",
   "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=  11.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=  12.1s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=  13.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=  12.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=  13.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  19.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  19.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  18.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  18.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  18.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   9.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   9.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   9.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   8.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   9.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=  15.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=  15.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=  15.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=  15.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=  15.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  25.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  27.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  27.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  27.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  32.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  50.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  34.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  34.2s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  29.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  28.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=  10.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=  10.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=  11.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=  10.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=  10.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   5.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   4.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   4.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   4.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   5.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   7.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   6.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   9.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   7.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   8.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  43.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  38.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  37.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  29.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  29.3s\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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "sought-tobacco",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 15,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "selective-berry",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "indian-barrel",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\swarnava\\appdata\\local\\programs\\python\\python37\\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": 73,
   "id": "trained-blackjack",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "plt.scatter(y_test, prediction, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "physical-suite",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1181.0323018354006\n",
      "MSE: 4167591.639386191\n",
      "RMSE: 2041.4680108652674\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "agreed-buying",
   "metadata": {},
   "source": [
    "### Save the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "legislative-supplier",
   "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": 77,
   "id": "thermal-louis",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "vulnerable-fight",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "chubby-croatia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.803080782287133"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}