flight_price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flight Price Prediction\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing dataset\n",
    "\n",
    "1. Since data is in form of excel file we have to use pandas read_excel to load the data\n",
    "2. After loading it is important to check the complete information of data as it can indication many of the hidden infomation such as null values in a column or a row\n",
    "3. Check whether any null values are there or not. if it is present then following can be done,\n",
    "    1. Imputing data using Imputation method in sklearn\n",
    "    2. Filling NaN values with mean, median and mode using fillna() method\n",
    "4. Describe data --> which can give statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_excel(r\"E:\\MachineLearning\\EDA\\Flight_Price\\Data_Train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2h 50m     550\n",
       "1h 30m     386\n",
       "2h 45m     337\n",
       "2h 55m     337\n",
       "2h 35m     329\n",
       "          ... \n",
       "42h 5m       1\n",
       "28h 30m      1\n",
       "36h 25m      1\n",
       "40h 20m      1\n",
       "30h 25m      1\n",
       "Name: Duration, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From description we can see that Date_of_Journey is a object data type,\\\n",
    "Therefore, we have to convert this datatype into timestamp so as to use this column properly for prediction\n",
    "\n",
    "For this we require pandas **to_datetime** to convert object data type to datetime dtype.\n",
    "\n",
    "<span style=\"color: red;\">**.dt.day method will extract only day of that date**</span>\\\n",
    "<span style=\"color: red;\">**.dt.month method will extract only month of that date**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_day\"] = pd.to_datetime(train_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_month\"] = pd.to_datetime(train_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we have converted Date_of_Journey column into integers, Now we can drop as it is of no use.\n",
    "\n",
    "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Departure time is when a plane leaves the gate. \n",
    "# Similar to Date_of_Journey we can extract values from Dep_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_data[\"Dep_hour\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_data[\"Dep_min\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.minute\n",
    "\n",
    "# Now we can drop Dep_Time as it is of no use\n",
    "train_data.drop([\"Dep_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrival time is when the plane pulls up to the gate.\n",
    "# Similar to Date_of_Journey we can extract values from Arrival_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_data[\"Arrival_hour\"] = pd.to_datetime(train_data.Arrival_Time).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_data[\"Arrival_min\"] = pd.to_datetime(train_data.Arrival_Time).dt.minute\n",
    "\n",
    "# Now we can drop Arrival_Time as it is of no use\n",
    "train_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time taken by plane to reach destination is called Duration\n",
    "# It is the differnce betwwen Departure Time and Arrival time\n",
    "\n",
    "\n",
    "# Assigning and converting Duration column into list\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding duration_hours and duration_mins list to train_data dataframe\n",
    "\n",
    "train_data[\"Duration_hours\"] = duration_hours\n",
    "train_data[\"Duration_mins\"] = duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Duration\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Categorical Data\n",
    "\n",
    "One can find many ways to handle categorical data. Some of them categorical data are,\n",
    "1. <span style=\"color: blue;\">**Nominal data**</span> --> data are not in any order --> <span style=\"color: green;\">**OneHotEncoder**</span> is used in this case\n",
    "2. <span style=\"color: blue;\">**Ordinal data**</span> --> data are in order --> <span style=\"color: green;\">**LabelEncoder**</span> is used in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1751\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              818\n",
       "Vistara                               479\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Airline\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Airline = train_data[[\"Airline\"]]\n",
    "\n",
    "Airline = pd.get_dummies(Airline, drop_first= True)\n",
    "\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4536\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source vs Price\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Source\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Source = train_data[[\"Source\"]]\n",
    "\n",
    "Source = pd.get_dummies(Source, drop_first= True)\n",
    "\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination = train_data[[\"Destination\"]]\n",
    "\n",
    "Destination = pd.get_dummies(Destination, drop_first = True)\n",
    "\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    BLR → DEL\n",
       "1        CCU → IXR → BBI → BLR\n",
       "2        DEL → LKO → BOM → COK\n",
       "3              CCU → NAG → BLR\n",
       "4              BLR → NAG → DEL\n",
       "                 ...          \n",
       "10678                CCU → BLR\n",
       "10679                CCU → BLR\n",
       "10680                BLR → DEL\n",
       "10681                BLR → DEL\n",
       "10682    DEL → GOI → BOM → COK\n",
       "Name: Route, Length: 10682, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Route\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "train_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      5625\n",
       "non-stop    3491\n",
       "2 stops     1520\n",
       "3 stops       45\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Total_Stops\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "# Here Values are assigned with corresponding keys\n",
    "\n",
    "train_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate dataframe --> train_data + Airline + Source + Destination\n",
    "\n",
    "data_train = pd.concat([train_data, Airline, Source, Destination], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <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",
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       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
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       "      <th>Destination_Cochin</th>\n",
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       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
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       "      <td>30</td>\n",
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       "      <td>25</td>\n",
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       "      <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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       "      <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  Airline_Air India  Airline_GoAir  \\\n",
       "0               2             50                  0              0   \n",
       "1               7             25                  1              0   \n",
       "2              19              0                  0              0   \n",
       "3               5             25                  0              0   \n",
       "4               4             45                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0               1                    0                             0   \n",
       "1               0                    0                             0   \n",
       "2               0                    1                             0   \n",
       "3               1                    0                             0   \n",
       "4               1                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Trujet  Airline_Vistara  \\\n",
       "0                 0               0                0   \n",
       "1                 0               0                0   \n",
       "2                 0               0                0   \n",
       "3                 0               0                0   \n",
       "4                 0               0                0   \n",
       "\n",
       "   Airline_Vistara Premium economy  Source_Chennai  Source_Delhi  \\\n",
       "0                                0               0             0   \n",
       "1                                0               0             0   \n",
       "2                                0               0             1   \n",
       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "\n",
       "   Source_Kolkata  Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0               0              0                   0                  0   \n",
       "1               1              0                   0                  0   \n",
       "2               0              0                   1                  0   \n",
       "3               1              0                   0                  0   \n",
       "4               0              0                   0                  0   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  Destination_New Delhi  \n",
       "0                      0                    0                      1  \n",
       "1                      0                    0                      0  \n",
       "2                      0                    0                      0  \n",
       "3                      0                    0                      0  \n",
       "4                      0                    0                      1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897           24              3        22       20   \n",
       "1            2   7662            1              5         5       50   \n",
       "2            2  13882            9              6         9       25   \n",
       "3            1   6218           12              5        18        5   \n",
       "4            1  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0             1           10               2             50   \n",
       "1            13           15               7             25   \n",
       "2             4           25              19              0   \n",
       "3            23           30               5             25   \n",
       "4            21           35               4             45   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(r\"E:\\MachineLearning\\EDA\\Flight_Price\\Test_set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Info\n",
      "---------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2671 entries, 0 to 2670\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          2671 non-null   object\n",
      " 1   Date_of_Journey  2671 non-null   object\n",
      " 2   Source           2671 non-null   object\n",
      " 3   Destination      2671 non-null   object\n",
      " 4   Route            2671 non-null   object\n",
      " 5   Dep_Time         2671 non-null   object\n",
      " 6   Arrival_Time     2671 non-null   object\n",
      " 7   Duration         2671 non-null   object\n",
      " 8   Total_Stops      2671 non-null   object\n",
      " 9   Additional_Info  2671 non-null   object\n",
      "dtypes: object(10)\n",
      "memory usage: 208.8+ KB\n",
      "None\n",
      "\n",
      "\n",
      "Null values :\n",
      "---------------------------------------------------------------------------\n",
      "Airline            0\n",
      "Date_of_Journey    0\n",
      "Source             0\n",
      "Destination        0\n",
      "Route              0\n",
      "Dep_Time           0\n",
      "Arrival_Time       0\n",
      "Duration           0\n",
      "Total_Stops        0\n",
      "Additional_Info    0\n",
      "dtype: int64\n",
      "Airline\n",
      "---------------------------------------------------------------------------\n",
      "Jet Airways                          897\n",
      "IndiGo                               511\n",
      "Air India                            440\n",
      "Multiple carriers                    347\n",
      "SpiceJet                             208\n",
      "Vistara                              129\n",
      "Air Asia                              86\n",
      "GoAir                                 46\n",
      "Multiple carriers Premium economy      3\n",
      "Jet Airways Business                   2\n",
      "Vistara Premium economy                2\n",
      "Name: Airline, dtype: int64\n",
      "\n",
      "Source\n",
      "---------------------------------------------------------------------------\n",
      "Delhi       1145\n",
      "Kolkata      710\n",
      "Banglore     555\n",
      "Mumbai       186\n",
      "Chennai       75\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "Destination\n",
      "---------------------------------------------------------------------------\n",
      "Cochin       1145\n",
      "Banglore      710\n",
      "Delhi         317\n",
      "New Delhi     238\n",
      "Hyderabad     186\n",
      "Kolkata        75\n",
      "Name: Destination, dtype: int64\n",
      "\n",
      "\n",
      "Shape of test data :  (2671, 28)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "\n",
    "print(\"Test data Info\")\n",
    "print(\"-\"*75)\n",
    "print(test_data.info())\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Null values :\")\n",
    "print(\"-\"*75)\n",
    "test_data.dropna(inplace = True)\n",
    "print(test_data.isnull().sum())\n",
    "\n",
    "# EDA\n",
    "\n",
    "# Date_of_Journey\n",
    "test_data[\"Journey_day\"] = pd.to_datetime(test_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day\n",
    "test_data[\"Journey_month\"] = pd.to_datetime(test_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month\n",
    "test_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)\n",
    "\n",
    "# Dep_Time\n",
    "test_data[\"Dep_hour\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.hour\n",
    "test_data[\"Dep_min\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.minute\n",
    "test_data.drop([\"Dep_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Arrival_Time\n",
    "test_data[\"Arrival_hour\"] = pd.to_datetime(test_data.Arrival_Time).dt.hour\n",
    "test_data[\"Arrival_min\"] = pd.to_datetime(test_data.Arrival_Time).dt.minute\n",
    "test_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Duration\n",
    "duration = list(test_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration\n",
    "\n",
    "# Adding Duration column to test set\n",
    "test_data[\"Duration_hours\"] = duration_hours\n",
    "test_data[\"Duration_mins\"] = duration_mins\n",
    "test_data.drop([\"Duration\"], axis = 1, inplace = True)\n",
    "\n",
    "\n",
    "# Categorical data\n",
    "\n",
    "print(\"Airline\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Airline\"].value_counts())\n",
    "Airline = pd.get_dummies(test_data[\"Airline\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Source\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Source\"].value_counts())\n",
    "Source = pd.get_dummies(test_data[\"Source\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Destination\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Destination\"].value_counts())\n",
    "Destination = pd.get_dummies(test_data[\"Destination\"], drop_first = True)\n",
    "\n",
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "test_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)\n",
    "\n",
    "# Replacing Total_Stops\n",
    "test_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)\n",
    "\n",
    "# Concatenate dataframe --> test_data + Airline + Source + Destination\n",
    "data_test = pd.concat([test_data, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "data_test.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Shape of test data : \", data_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
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       "      <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",
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       "      <td>45</td>\n",
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       "    </tr>\n",
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       "      <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",
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       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\n",
       "0                                  0         0        0   \n",
       "1                                  0         0        0   \n",
       "2                                  0         0        0   \n",
       "3                                  0         0        0   \n",
       "4                                  0         0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "\n",
    "Finding out the best feature which will contribute and have good relation with target variable.\n",
    "Following are some of the feature selection methods,\n",
    "\n",
    "\n",
    "1. <span style=\"color: purple;\">**heatmap**</span>\n",
    "2. <span style=\"color: purple;\">**feature_importance_**</span>\n",
    "3. <span style=\"color: purple;\">**SelectKBest**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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,
   "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",
       "        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",
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       "      <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",
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       "      <th>Source_Chennai</th>\n",
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       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
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       "      <th>1</th>\n",
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       "      <td>50</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  \\\n",
       "0           10               2             50                  0   \n",
       "1           15               7             25                  1   \n",
       "2           25              19              0                  0   \n",
       "3           30               5             25                  0   \n",
       "4           35               4             45                  0   \n",
       "\n",
       "   Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0              0               1                    0   \n",
       "1              0               0                    0   \n",
       "2              0               0                    1   \n",
       "3              0               1                    0   \n",
       "4              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_train.loc[:, ['Total_Stops', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
    "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
    "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
    "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
    "       'Airline_Multiple carriers',\n",
    "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
    "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
    "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
    "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
    "       'Destination_Kolkata', 'Destination_New Delhi']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finds correlation between Independent and dependent attributes\n",
    "\n",
    "plt.figure(figsize = (18,18))\n",
    "sns.heatmap(train_data.corr(), annot = True, cmap = \"RdYlGn\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
       "                    max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                    max_samples=None, min_impurity_decrease=0.0,\n",
       "                    min_impurity_split=None, min_samples_leaf=1,\n",
       "                    min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                    n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                    random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "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,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.95434120e-01 1.44021586e-01 5.38555703e-02 2.40539410e-02\n",
      " 2.17430940e-02 2.73470116e-02 1.96095029e-02 1.27500039e-01\n",
      " 1.74337150e-02 1.06741541e-02 1.87128973e-03 1.65718507e-02\n",
      " 1.51364596e-01 6.81637760e-02 1.89902192e-02 8.47326514e-04\n",
      " 3.10615675e-03 1.08644888e-04 5.29446496e-03 9.35369428e-05\n",
      " 6.27777871e-04 1.23137157e-02 3.24867396e-03 8.91085147e-03\n",
      " 1.41174133e-02 1.95895945e-02 7.68279226e-03 4.01014841e-04\n",
      " 2.50235698e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting model using Random Forest\n",
    "\n",
    "1. Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "2. If needed do scaling of data\n",
    "    * Scaling is not done in Random forest\n",
    "3. Import model\n",
    "4. Fit the data\n",
    "5. Predict w.r.t X_test\n",
    "6. In regression check **RSME** Score\n",
    "7. Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 52,
     "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": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9539164511170628"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.798383043987616"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1172.5455945373583\n",
      "MSE: 4347276.1614450775\n",
      "RMSE: 2085.0122688955757\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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026887077025966846"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE/(max(DV)-min(DV))\n",
    "\n",
    "2090.5509/(max(y)-min(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7983830439876158"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "\n",
    "\n",
    "* Choose following method for hyperparameter tuning\n",
    "    1. **RandomizedSearchCV** --> Fast\n",
    "    2. **GridSearchCV**\n",
    "* Assign hyperparameters in form of dictionery\n",
    "* Fit the model\n",
    "* Check best paramters and best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 5 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = reg_rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=  13.0s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   12.9s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=  11.5s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=  11.8s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=  12.4s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=  10.9s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=  18.6s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=  17.9s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=  18.5s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=  16.6s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=  16.6s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=  10.8s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   8.9s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   8.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   9.0s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   8.6s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=  16.6s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=  16.0s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=  15.3s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=  16.0s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=  15.8s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=  25.0s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=  25.6s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=  25.2s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=  24.2s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=  25.5s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=  24.9s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=  24.8s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=  24.7s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=  25.4s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=  25.5s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   9.4s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   8.9s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   9.3s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   9.4s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   9.1s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   4.7s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   4.6s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   4.1s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   4.3s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   4.5s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   5.8s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   5.9s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   5.9s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   6.0s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   5.8s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  30.0s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  29.0s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  28.8s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  29.1s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=  30.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed: 12.8min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   ccp_alpha=0.0,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   max_samples=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators=100,\n",
       "                                                   n_jobs=None, oob_score=Fals...\n",
       "                   iid='deprecated', n_iter=10, 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",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 15,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'prediction' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-6564e5e324aa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'prediction' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
    "sns.distplot(y_test-prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "plt.scatter(y_test, prediction, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1165.606162629916\n",
      "MSE: 4062650.6911608884\n",
      "RMSE: 2015.6018186042818\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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(reg_rf, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_price_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8117443234361064"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)"
   ]
  },
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   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
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