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(\"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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "31h 30m      1\n",
       "30h 25m      1\n",
       "42h 5m       1\n",
       "4h 10m       1\n",
       "47h 40m      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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",
      "text/plain": [
       "<Figure size 1800x600 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 Asia</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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
       "      <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>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 Asia  Airline_Air India  Airline_GoAir  Airline_IndiGo  \\\n",
       "0                 0                  0              0               1   \n",
       "1                 0                  1              0               0   \n",
       "2                 0                  0              0               0   \n",
       "3                 0                  0              0               1   \n",
       "4                 0                  0              0               1   \n",
       "\n",
       "   Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0                    0                             0   \n",
       "1                    0                             0   \n",
       "2                    1                             0   \n",
       "3                    0                             0   \n",
       "4                    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  \n",
       "0                                0  \n",
       "1                                0  \n",
       "2                                0  \n",
       "3                                0  \n",
       "4                                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)\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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",
      "text/plain": [
       "<Figure size 1200x400 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_Banglore</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</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": [
       "   Source_Banglore  Source_Chennai  Source_Delhi  Source_Kolkata  \\\n",
       "0                1               0             0               0   \n",
       "1                0               0             0               1   \n",
       "2                0               0             1               0   \n",
       "3                0               0             0               1   \n",
       "4                1               0             0               0   \n",
       "\n",
       "   Source_Mumbai  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              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)\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_Banglore</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>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Banglore  Destination_Cochin  Destination_Delhi  \\\n",
       "0                     0                   0                  0   \n",
       "1                     1                   0                  0   \n",
       "2                     0                   1                  0   \n",
       "3                     1                   0                  0   \n",
       "4                     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": 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)\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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air Asia</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Banglore</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Banglore</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</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>1</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",
       "    </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>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>1</td>\n",
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       "      <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>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  Airline_Air Asia  Airline_Air India  \\\n",
       "0               2             50                 0                  0   \n",
       "1               7             25                 0                  1   \n",
       "2              19              0                 0                  0   \n",
       "3               5             25                 0                  0   \n",
       "4               4             45                 0                  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_Banglore  Source_Chennai  Source_Delhi  Source_Kolkata  \\\n",
       "0                1               0             0               0   \n",
       "1                0               0             0               1   \n",
       "2                0               0             1               0   \n",
       "3                0               0             0               1   \n",
       "4                1               0             0               0   \n",
       "\n",
       "   Source_Mumbai  Destination_Banglore  Destination_Cochin  Destination_Delhi  \\\n",
       "0              0                     0                   0                  0   \n",
       "1              0                     1                   0                  0   \n",
       "2              0                     0                   1                  0   \n",
       "3              0                     1                   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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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
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       "      <td>7</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </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  Airline_Air Asia  \\\n",
       "0             1           10               2             50                 0   \n",
       "1            13           15               7             25                 0   \n",
       "2             4           25              19              0                 0   \n",
       "3            23           30               5             25                 0   \n",
       "4            21           35               4             45                 0   \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_Banglore  Source_Chennai  Source_Delhi  Source_Kolkata  \\\n",
       "0                1               0             0               0   \n",
       "1                0               0             0               1   \n",
       "2                0               0             1               0   \n",
       "3                0               0             0               1   \n",
       "4                1               0             0               0   \n",
       "\n",
       "   Source_Mumbai  Destination_Banglore  Destination_Cochin  Destination_Delhi  \\\n",
       "0              0                     0                   0                  0   \n",
       "1              0                     1                   0                  0   \n",
       "2              0                     0                   1                  0   \n",
       "3              0                     1                   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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 33)"
      ]
     },
     "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(\"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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
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       "      <th>Total_Stops</th>\n",
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       "  <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",
      "Vistara Premium economy                2\n",
      "Jet Airways Business                   2\n",
      "Name: Airline, dtype: int64\n",
      "\n",
      "Source\n",
      "---------------------------------------------------------------------------\n",
      "Delhi       1145\n",
      "Kolkata      710\n",
      "Banglore     555\n",
      "Mumbai       186\n",
      "Chennai       75\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "Destination\n",
      "---------------------------------------------------------------------------\n",
      "Cochin       1145\n",
      "Banglore      710\n",
      "Delhi         317\n",
      "New Delhi     238\n",
      "Hyderabad     186\n",
      "Kolkata        75\n",
      "Name: Destination, dtype: int64\n",
      "\n",
      "\n",
      "Shape of test data :  (2671, 31)\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\"])\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\"])\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\"])\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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
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      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air Asia  Air India  GoAir  \\\n",
       "0           25              10             55         0          0      0   \n",
       "1           20               4              0         0          0      0   \n",
       "2            0              23             45         0          0      0   \n",
       "3            0              13              0         0          0      0   \n",
       "4           45               2             50         1          0      0   \n",
       "\n",
       "   IndiGo  Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0       0            1                     0                  0   \n",
       "1       1            0                     0                  0   \n",
       "2       0            1                     0                  0   \n",
       "3       0            0                     0                  1   \n",
       "4       0            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  Banglore  Chennai  Delhi  Kolkata  Mumbai  \\\n",
       "0                        0         0        0      1        0       0   \n",
       "1                        0         0        0      0        1       0   \n",
       "2                        0         0        0      1        0       0   \n",
       "3                        0         0        0      1        0       0   \n",
       "4                        0         1        0      0        0       0   \n",
       "\n",
       "   Banglore  Cochin  Delhi  Hyderabad  Kolkata  New Delhi  \n",
       "0         0       1      0          0        0          0  \n",
       "1         1       0      0          0        0          0  \n",
       "2         0       1      0          0        0          0  \n",
       "3         0       1      0          0        0          0  \n",
       "4         0       0      1          0        0          0  "
      ]
     },
     "execution_count": 44,
     "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 33)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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 Asia', 'Airline_Air India',\n",
       "       'Airline_GoAir', 'Airline_IndiGo', 'Airline_Jet Airways',\n",
       "       'Airline_Jet Airways Business', 'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
       "       'Source_Banglore', 'Source_Chennai', 'Source_Delhi', 'Source_Kolkata',\n",
       "       'Source_Mumbai', 'Destination_Banglore', 'Destination_Cochin',\n",
       "       'Destination_Delhi', 'Destination_Hyderabad', 'Destination_Kolkata',\n",
       "       'Destination_New Delhi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air Asia  \\\n",
       "0           10               2             50                 0   \n",
       "1           15               7             25                 0   \n",
       "2           25              19              0                 0   \n",
       "3           30               5             25                 0   \n",
       "4           35               4             45                 0   \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_Banglore  Source_Chennai  Source_Delhi  Source_Kolkata  \\\n",
       "0                1               0             0               0   \n",
       "1                0               0             0               1   \n",
       "2                0               0             1               0   \n",
       "3                0               0             0               1   \n",
       "4                1               0             0               0   \n",
       "\n",
       "   Source_Mumbai  Destination_Banglore  Destination_Cochin  Destination_Delhi  \\\n",
       "0              0                     0                   0                  0   \n",
       "1              0                     1                   0                  0   \n",
       "2              0                     0                   1                  0   \n",
       "3              0                     1                   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": 44,
     "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 Asia', 'Airline_Air India',\n",
    "       'Airline_GoAir', 'Airline_IndiGo', 'Airline_Jet Airways',\n",
    "       'Airline_Jet Airways Business', 'Airline_Multiple carriers',\n",
    "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
    "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
    "       'Source_Banglore', 'Source_Chennai', 'Source_Delhi', 'Source_Kolkata',\n",
    "       'Source_Mumbai', 'Destination_Banglore', 'Destination_Cochin',\n",
    "       'Destination_Delhi', 'Destination_Hyderabad', 'Destination_Kolkata',\n",
    "       'Destination_New Delhi']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:, 1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/z3/0dz3d5ld6cq3k16qb86ymjlm0000gn/T/ipykernel_21493/3228867913.py:4: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(train_data.corr(), annot = True, cmap = \"RdYlGn\")\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x1800 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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 47,
     "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": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.28756671e-01 1.44594816e-01 5.27947412e-02 2.36455139e-02\n",
      " 2.06099112e-02 2.72995005e-02 1.88854060e-02 1.20535072e-01\n",
      " 1.83090810e-02 1.94846184e-03 8.98729865e-03 1.66977518e-03\n",
      " 1.86494377e-02 1.39598206e-01 6.68246754e-02 1.76767500e-02\n",
      " 8.19691506e-04 3.04456988e-03 1.14924619e-04 4.65083070e-03\n",
      " 9.63673243e-05 8.72272327e-03 4.91034388e-04 8.52138980e-03\n",
      " 1.97288945e-03 6.13421769e-03 1.57530736e-03 1.37319606e-02\n",
      " 1.46707965e-02 7.03653712e-03 4.50226924e-04 1.71812157e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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msLAwpaen65NPPtE777yjbdu2ZfmaAOB+bGwee7AuCrF77zvvPywZeQCQByiYsl0YSk9P18aNG9WqVSvZ29997HCLFi20YcMGjRgxwrzt7wYPHqxKlSpJknmUyIOkpaVp0qRJqlWrliRp4MCBGjJkiC5fvqzk5GR98803WrdunerWrStJ6tOnj06ePKnQ0NAcKQyFhoaqSZMmGjx4sCSpatWq5pE4hw4dkqenp4oVKyY7OzuVLVv2vn3s3r1bly9fVseOHSVJ7dq104cffqjVq1drypQpDzx3rVq11LlzZ/PPrq6u2rNnj+rXr699+/apRYsW2rt3r44dO6aGDRtq165dat26tSSpbt26+te//qXGjRtLkipUqCAvLy/zSJcWLVqoTJky2rhxowYOHCij0ajIyEj1799fkvT777/L0dFRlStXlpOTk9577z15eHg8cg0kd3d3SXdHNKWkpEiSZs6cma01gX7//Xe5urqqcuXKKlq0qMaPH6/OnTs/cO2p9PR0zZgxQyVLlpR0d7RVcHCwJOnAgQM6evSo9u/fLxcXF0lSYGCgjhw5ouXLl2v69OmPvNbff/9dzZo1U8WKFWVra6uPPvpIZ8+eldFolLU1v8QBPD5n5/vfJ2EZeP8B8gCQyAMULNkuDO3atStDwUOSOnbsqG3btmnTpk16+eWX73tc1apVs3We6tWrm7+/t/5QWlqafvnlF0nKNCUqLS1Nzs7O2TrHg/zyyy+KjY01Fzz+KiYmJktPEAsLC1PJkiXVpEkTSVKpUqXUpEkTbdy4UaNHj5ajo+N9j6tSpUqGn729vbVv3z4NGTJE+/fvV5s2bZSUlKQDBw7I1dVVR44c0ahRoyRJXbt21f79+/Xpp5/qt99+U0xMjM6ePSsPDw9Jkq2trbp06aINGzZo4MCBOnDggK5cuaJOnTpJkgYMGKCAgAA1adJE7u7uatasmXx8fB65/tP69esl3S0MJSUlaceOHRo1apRMJlOGItfDvPPOOxo1apS2bdsmDw8PeXl5qWPHjg8sLpUpU8ZcFJLufkbS0tIkSSdOnJAkc8HsntTUVN25cydL1zp8+HB99NFHWrVqlf75z3+qefPm6tChA0UhAE8sKSlZBoMxv8NAHrOxsZazsz3vPywaeQCQB8g7zs72WR6Zlu3CUHh4uCRp6NChmfatXr36gYWhvz6NKyvutyCzyWQyL/q8YsWKTIsRZ/WP9uvXrysuLk516tTJ0Pe90SlGo1GdO3e+73pI90agPMy1a9cUFRWltLQ01atXz7zdaDTKZDJpw4YN6tGjx32P/fvr1KpVK4WGhuratWs6fPiwJkyYoKtXr+rgwYN67rnnVLp0afPIqUmTJmnz5s3y9fVVy5YtNWjQIIWGhurixYvm/vz9/RUaGqrjx48rMjJSrVu3NhdY3N3dtWvXLu3du1f79+/XunXrFBISomXLlpkLXPfz92JWvXr19NNPP+mLL77IUBj664Ld6enpGY5p27atvv/+e33//ffat2+fli1bprlz5+rrr79WzZo1M53zYQt2G41GOTo6mj+r9zvuUdfao0cPtW/fXrt27TIX20JCQrR+/XqVKVPmgecGgEcxGIxKT+d/BC0V7z9AHgASeYCCJVvDH65du6Zdu3bJz89P69evz/D18ssv6+effzaP1sgt94oEly5dUpUqVcxf4eHhCgsLy1If33zzjXr27JmhUJGUlGQu+tSsWVOnT5/O0L/BYNDHH3+sCxcuSJKsrKwe2H9kZKTS0tK0YMGCTK9T6dKlMz3F7WHq16+vEiVKaNGiRSpRooSqVaumpk2b6n//+5+2bNkib29vWVlZKTExUatWrdKkSZM0btw4+fn5qVatWjp79myG66xevbrc3d21efNm7dy5U35+fuZ98+bN0+HDh9W6dWu9//772rJliypVqqQtW7ZkOd6/undeOzs7STIv3i1JsbGx5u9TU1P18ccfKy4uTh07dtSHH36obdu2ydraWlFRUdk+r6urq27evKnU1NQM7+HSpUu1Y8eOR17rlStXNGXKFKWlpcnPz0/BwcGKjIzU5cuXMyyIDQAAAABAYZetwtCGDRuUnp6u/v37y9XVNcNXQECAbGxsMj15K6fVrFlTrVq10sSJE7Vjxw7FxcUpNDRUixcvNq9h9Cienp66c+eOlixZonPnzmnmzJm6c+eOebHkvn376tdff9UHH3ygM2fO6KefftLIkSN17tw585Q4BwcHXbp0SXFxcZn6DwsLk7u7u9q0aZPhNXr++efVvXt3RUdH6/Dhw1mK1crKSi1atDBPaZLuFotsbGy0detWtWnTRtLdqVROTk7asWOHYmNjderUKU2YMEEnTpxQampqhj79/f21YsUKFSlSRF5eXubtsbGxmjhxovbv36+EhAR99913On/+/H2n1P3V5cuXzV9xcXFaunSpDhw4oC5duki6W6gpXry4Fi5cqNjYWP3www+aPXu2ubhWpEgR/fTTT5owYYL+97//KT4+XuHh4bp169Yjz30/zZs3V61atTRs2DDt379fsbGx+uSTTxQWFmaeoviway1ZsqSioqL0/vvv69dff1VcXJxWrlwpOzu7DKPMAAAAAAAo7LJVGAoPD1fTpk0zrP9zT6VKldS2bVtt2rQpw8iQ3DB79my1a9dOEydOVMeOHRUWFqapU6fK398/S8fXqFFDn3zyiSIiItSlSxdFRUVpzpw5qly5siTpxRdf1LJlyxQdHS0/Pz+9/fbbqlSpkv7973+bpyL5+voqOTlZnTp10qVLl8x9Hz9+XNHR0Q+cKta9e3cVK1YsW6OGvL29lZqaai4M2dnZycPDQw4ODuaFpm1tbTV37lxFR0erc+fO6t+/v5KTkxUYGKjTp0/r9u3b5v46dOhgvoa/Lu48efJkNWnSRKNGjVK7du00b948jRw5Ul27dn1ofF5eXuYvHx8fRUZGasyYMXrrrbckSY6Ojpo5c6ZiYmLk4+OjKVOmaPTo0Rmm/s2dO1eVKlXSoEGD1L59e61Zs0azZs0yr4+UHTY2Nvr8889Vr149DR8+XF26dNHBgwcVEhJinhL3sGu1tbXV0qVLZW1trd69e8vHx0cHDhzQkiVLzJ8RAAAAAACeBlamv84zgkWIj49X27Zt9e2332Z7UXA8vmGfRikm4Xp+hwEgn1SvUEJzAlsqMfEWawpYIFtba5UqVZz3HxaNPADIA+QdF5fiubf4NAqvCxcu6NixY1q5cqWaN29OUSiPVSz38Ke7AXi68TsAAAAABdFTVxiaMmWKIiIiHtpm7ty5eumll/IoooIjMTFRQUFBqlq1qubPn5/f4VgUk8mkkT0a5ncYAPKZwWCU0chAXQAAABQcT91UsmvXrunGjRsPbfOPf/xD9vb2eRQRcFdSUrIMBoaLwjLZ2FjL2dne4vPAaDRRGLJQTB0AyANAIg+Qdyx6KpmLi4v5sfNAQWIwGPnlD4tHHgAAAAAFS7aeSgYAAAAAAICnB4UhAAAAAAAAC0VhCAAAAAAAwEJRGAIAAAAAALBQFIYAAAAAAAAsFIUhAAAAAAAAC0VhCAAAAAAAwEJRGAIAAAAAALBQFIYAAAAAAAAsFIUhAAAAAAAAC0VhCAAAAAAAwELZ5ncAgKWwsaEOC8t17/NPHsBSkQNPzmg0yWg05XcYAAA8dSgMAXnAZDLJ2dk+v8MA8h15AEtHDjw+g8GoP/+8TXEIAIAcRmEIyANWVlaaueKw4i/eyO9QAAAodCqWc9LIHg1lbW1FYQgAgBxGYQjII/EXbygm4Xp+hwEAAAAAgBkT3Qsgb29vubm5mb/q1Kmjli1basqUKUpMTMz189++fVsrVqww/xwUFKSePXvm+nn/qmfPngoKCsrTcwIAAAAAYGkYMVRA9e3bV3379pUkpaSkKDo6WsHBwfrhhx+0atUqOTo65tq5P//8c4WHh6tHjx6SpPHjx8tgMOTa+QAAAAAAQP6gMFRAOTg4qGzZsuafK1WqpFq1asnHx0ehoaF67733cu3cJlPGuftOTk65di4AAAAAAJB/mEpWiDz77LNq27atvvnmG0mSm5ubwsPDM7Tx9vZWSEiIJCk8PFze3t6aNm2aPDw8FBAQIEn673//q9dff13u7u6qW7euXn75Ze3bt0+SFBISovnz5yshIUFubm6Kj4/PNJUsJiZGAQEB8vT0VMOGDTV06FCdP3/evL9nz5765JNPNG7cOHl4eKhBgwYaM2aMbt26la3rvXXrlrmPhg0bKigoSLdv385WHH+fjvbXa4mPj5ebm5s+++wzNWvWTN7e3kpKStKuXbvk5+en+vXrq0mTJgoKCtL166wNBAAAAAB4+jBiqJBxdXXVhg0bslxkSUhI0MWLFxUREaGUlBQdP35cQ4YM0ahRoxQcHKxbt25p9uzZGjlypKKiotS3b1/dvn1bmzdv1rp16+Ti4pKpv9dee01NmzbVl19+qdTUVH3yySd68803FRkZaZ7i9tVXX6lv375au3atfv31V40ZM0aVK1fWkCFDsnytW7du1cCBAxUeHq7Tp09r+PDhKl++vN57770sx5EVkZGR+vLLL5WcnKz09HS98847CgoKUsuWLfXHH39o9OjRmjFjhqZNm5blPgEAQM6zseHfNAuze+8f7yMsGXmAgojCUCHj7OwsSbp582aWjxk8eLAqVaokSfr111/1/vvvm9cPkqRevXqpb9++unr1qsqXLy8HBwfZ2NhkmMp2z8qVK+Xg4KCZM2eqSJEikqR58+bJ29tbkZGR6t69uySpevXqCgwMlCQ999xz2rRpk44cOZKta61bt665j8qVK6tZs2Y6fvx4tuLIiu7du6tGjRrm1yc1NVXPPvusKlSooAoVKmjRokWssQQAQAHg7Gyf3yEgB/A+AuQBChYKQ4XMjRs3JClbI2KqVq1q/r5WrVoqUaKEli5dqnPnzum3337Tr7/+KklZKn5ER0erTp065mKMJJUuXVrPPfecTp06Zd5WvXr1DMc5OTkpKSkpyzFLdwtKf1WiRAklJCRkK46sqFKlivn7WrVqqVOnTgoICFD58uXVtGlTtWzZUt7e3tnqEwAA5LykpGQZDMb8DgOPycbGWs7O9ryPsGjkAfKKs7N9lkemURgqZE6cOKGqVauqePHikjIvFJ2WlpbpmGLFipm//+GHH9S3b1+1aNFCHh4e8vHxUXJycpaneJlMJllZWWXabjAYZGdnZ/75rwWbx2VjY/PEcWT39ZGkWbNmaciQIdq9e7f27dunwMBANWjQQMuXL8/uJQAAgBxkMBiVns4fUoUd7yNAHqBgYWJjIfLHH39ox44d6ty5syTJzs7OPIJIuju97Nq1aw/tIzQ0VJ6enpo/f7569+6tZs2a6cKFC5L+r4hyv4LLPa6urjp27JhSU1PN265cuaLY2NhMo4RyU1bi+PvrI0m///77Q/v93//+p48++kjVqlVT7969tWTJEn300Uc6ePCgrl69mvMXAgAAAABAPqIwVEDdvn1bly9f1uXLlxUXF6ft27erf//+qlixovr06SNJcnd315o1a3TixAlFR0dr9OjRsrV9+CCw8uXL69SpU/rxxx8VHx+vsLAwzZ07V5LMRRYHBwddv35d586dyzTC5o033tDNmzc1cuRInTx5UseOHdN7772nUqVKycfHJxdeifvLShwNGjTQvn379N///ldxcXGaN2+eoqOjH9qvo6OjVq5cqeDgYMXGxurUqVPatGmTqlatqlKlSuXFpQEAAAAAkGcoDBVQn3/+uby8vOTl5aUuXbpo1qxZat26tVauXGmeRjZp0iSVLVtWr7/+ugYMGKDGjRvL3d39of0OHTpUL774ogICAuTr66u1a9fqo48+UrFixXTs2DFJ0r/+9S+VLVtWXbp00S+//JLh+EqVKumrr75SUlKSXnvtNfXr109ly5bVqlWrzAtj54WsxNG7d2+1a9dOo0aNUrdu3XTlyhX17t37of3WqFFDISEhOnDggHx9fdW9e3fZ2tpq6dKlsrYmXQAAAAAATxcr098XYQGQK4Z9GqWYhOv5HQYAAIVO9QolNCewpRITb7EmRyFma2utUqWK8z7CopEHyCsuLsVZfBooaCqWc8rvEAAAKJS4hwIAkHsoDCFPLV26VJ999tlD2wQFBem1117Lo4jyhslk0sgeDfM7DAAACi2DwSijkYHuAADkNKaSIU9dv35df/7550PbuLi4yMnp6fuXwaSkZBkMDBeFZbKxsZazsz15AItFDjw5o9FEYaiQYwoNQB4g7zCVDAVWiRIlVKJEifwOI18YDEZ++cPikQewdOQAAAAoaHjMEgAAAAAAgIWiMAQAAAAAAGChKAwBAAAAAABYKApDAAAAAAAAForCEAAAAAAAgIWiMAQAAAAAAGChKAwBAAAAAABYKApDAAAAAAAAForCEAAAAAAAgIWiMAQAAAAAAGChKAwBAAAAAABYKNv8DgCwFDY21GFhue59/skDWCpyAHnFaDTJaDTldxgAgEKEwhCQB0wmk5yd7fM7DCDfkQewdOQAcpvBYNSff96mOAQAyDIKQ0AesLKy0swVhxV/8UZ+hwIAAJ5SFcs5aWSPhrK2tqIwBADIMgpDQB6Jv3hDMQnX8zsMAAAAAADMmOieA27evKn69euradOmSk1NzbCvZ8+eCgoKeuCxBw8elJubm+Lj47PUPi94e3srJCQky+0TExO1du3aR7b7+uuv5ebmpo8++ijTvvj4eLm5uengwYPZihUAAAAAADw+RgzlgE2bNql06dK6cuWKtm3bJh8fH/O+kJAQ2djYZLmv7LYvCGbMmKH4+Hi98sorD20XHh6u5557TuvXr1dgYKCKFStm3le+fHnt2bNHJUqUyO1wAQAAAADA/8eIoRwQFhYmLy8vNWnSRKtXr86wr2TJknJycspyX9ltXxCYTI+ewx4TE6OjR49q5MiRunHjhjZv3pxhv42NjcqWLasiRYrkVpgAAAAAAOBvKAw9oZiYGP30009q1qyZ2rdvr0OHDikmJsa8/69Tw8LDw+Xt7a1p06bJw8NDAQEBmfq7X/uIiAi1bdtWderUkb+/v44ePWpun5qaquDgYDVv3lzu7u569dVXtWfPnhy9xiNHjqhHjx6qV6+eWrZsqcmTJ+vmzZuSpKCgIEVEROjQoUNyc3N7YB/h4eFydnZWy5Yt5eHhoVWrVmXY//epZD179tS4ceP0yiuvyMPDQ/Pnz1fjxo1lMBgk3S1GeXp6ql+/fuY+zpw5Izc3N/3+++9KTU3VrFmz1KZNG9WpU0eenp4KDAxUYmKiJGnw4MHq1atXhhjOnj0rNzc3nTx5UsnJyRo/fryaNWumunXrytfXV1u3bn3yFxMAAAAAgAKEqWRPaN26dXJwcNBLL72k9PR0FSlSRKtWrdL7779/3/YJCQm6ePGiIiIilJKSomvXrj20/0uXLmn16tUKDg6WnZ2dJk2apDFjxmjLli2ysrLS2LFjdfr0aQUHB+uZZ57Rzp07FRAQoPnz56tly5ZPfH0nT55U7969FRAQoGnTpunKlSuaMWOG+vbtqzVr1mj8+PFKSUnRH3/88cB1iQwGgzZs2KA2bdrI1tZWPj4+mjhxon755RfVrl37gecODw9XcHCwnn/+ed25c0chISE6fvy46tevrxMnTuj69es6cuSI0tLSZGdnp6ioKNWsWVOVK1fWhx9+qB07dmj69OmqWLGiTp8+rTFjxmjhwoUaN26c/P39NWTIEJ0/f17PPvusJGn9+vV64YUX9Pzzz2v69Ok6deqUlixZImdnZ61du1bDhw/Xli1bVLFixSd+XQEAAHKLjU3B/Lffe3EV1PiAvEAeoCCiMPQE0tPTtXHjRrVq1Ur29vaSpBYtWmjDhg0aMWKEedvfDR48WJUqVZKkRy62nJaWpkmTJqlWrVqSpIEDB2rIkCG6fPmykpOT9c0332jdunWqW7euJKlPnz46efKkQkNDc6QwFBoaqiZNmmjw4MGSpKpVq5pH4hw6dEienp4qVqyY7OzsVLZs2fv2sXv3bl2+fFkdO3aUJLVr104ffvihVq9erSlTpjzw3LVq1VLnzp3NP7u6umrPnj2qX7++9u3bpxYtWmjv3r06duyYGjZsqF27dql169aSpLp16+pf//qXGjduLEmqUKGCvLy8dOrUKUl336cyZcpo48aNGjhwoIxGoyIjI9W/f39J0u+//y5HR0dVrlxZTk5Oeu+99+Th4cEaSAAAoMBzdr7//4MWFAU9PiAvkAcoSCgMPYFdu3ZlKHhIUseOHbVt2zZt2rRJL7/88n2Pq1q1arbOU716dfP399YfSktL0y+//CJJmaZEpaWlydnZOVvneJBffvlFsbGxcnd3z7QvJiZGnp6ej+wjLCxMJUuWVJMmTSRJpUqVUpMmTbRx40aNHj1ajo6O9z2uSpUqGX729vbWvn37NGTIEO3fv19t2rRRUlKSDhw4IFdXVx05ckSjRo2SJHXt2lX79+/Xp59+qt9++00xMTE6e/asPDw8JEm2trbq0qWLNmzYoIEDB+rAgQO6cuWKOnXqJEkaMGCAAgIC1KRJE7m7u6tZs2by8fEpdOs/AQAAy5OUlCyDwZjfYWRiY2MtZ2f7AhsfkBfIA+QVZ2f7LI9MozD0BMLDwyVJQ4cOzbRv9erVDywM/fVpXFlxvwWZTSaTedHnFStWqHjx4hn2W1tn7QNw/fp1xcXFqU6dOhn6vvdkNKPRqM6dO993PSQXF5dH9n/t2jVFRUUpLS1N9erVM283Go0ymUzasGGDevTocd9j//46tWrVSqGhobp27ZoOHz6sCRMm6OrVqzp48KCee+45lS5d2jxyatKkSdq8ebN8fX3VsmVLDRo0SKGhobp48aK5P39/f4WGhur48eOKjIxU69atVbJkSUmSu7u7du3apb1792r//v1at26dQkJCtGzZMnOBCwAAoCAyGIxKTy+4f3AW9PiAvEAeoCChMPSYrl27pl27dsnPz099+vTJsO/LL7/UunXrdOLEiVyNoWbNmpLurkP012ljs2fPlpWVlYYNG/bIPr755hvNnDlTR44ckZWVlSQpKSnJXPSpWbOmTp8+nWH0ztmzZzVjxgwFBgbKycnJfNz9REZGKi0tTQsWLFDlypXN241Go/r27avVq1c/sDD0d/Xr11eJEiW0aNEilShRQtWqVVPTpk21bNkylSpVSt7e3rKyslJiYqJWrVql2bNnZxjNdfbsWTk4OJh/rl69utzd3bV582bt3LlTM2bMMO+bN2+eGjZsqNatW6t169YaO3asfHx8tGXLFgpDAAAAAICnBitePaYNGzYoPT1d/fv3l6ura4avgIAA2djYZHryVk6rWbOmWrVqpYkTJ2rHjh2Ki4tTaGioFi9ebF7D6FE8PT11584dLVmyROfOndPMmTN1584dNW3aVJLUt29f/frrr/rggw905swZ/fTTTxo5cqTOnTtnnhLn4OCgS5cuKS4uLlP/YWFhcnd3V5s2bTK8Rs8//7y6d++u6OhoHT58OEuxWllZqUWLFlq1apX++c9/SrpbLLKxsdHWrVvVpk0bSXen2zk5OWnHjh2KjY3VqVOnNGHCBJ04cUKpqakZ+vT399eKFStUpEgReXl5mbfHxsZq4sSJ2r9/vxISEvTdd9/p/Pnz951SBwAAAABAYUVh6DGFh4eradOmGdb/uadSpUpq27atNm3aZH6se26ZPXu22rVrp4kTJ6pjx44KCwvT1KlT5e/vn6Xja9SooU8++UQRERHq0qWLoqKiNGfOHPPonhdffFHLli1TdHS0/Pz89Pbbb6tSpUr697//bZ7i5uvrq+TkZHXq1EmXLl0y9338+HFFR0c/cERQ9+7dVaxYMa1evTrL1+vt7a3U1FRzYcjOzk4eHh5ycHAwLzRta2uruXPnKjo6Wp07d1b//v2VnJyswMBAnT59Wrdv3zb316FDB/M13Js+J0mTJ09WkyZNNGrUKLVr107z5s3TyJEj1bVr1yzHCgAAAABAQWdlurdQDWCB4uPj1bZtW3377bfZXhQ8u4Z9GqWYhOu5eg4AAGC5qlcooTmBLZWYeKtArl1ia2utUqWKF9j4gLxAHiCvuLgUZ/Fp4GEuXLigY8eOaeXKlWrevHmuF4UkqWI5nmgGAAByD/+vAQB4HBSGnmJTpkxRRETEQ9vMnTtXL730Uh5FVHAkJiYqKChIVatW1fz583P9fCaTSSN7NMz18wAAAMtmMBhlNDIhAACQdUwle4pdu3ZNN27ceGibf/zjH7K3t8+jiCxbUlKyDAaGi8Iy2dhYy9nZnjyAxSIHkFeMRlOBLQwxhQYgD5B3mEoGSZKLi4v5sfPIfwaDkV/+sHjkASwdOQAAAAoankoGAAAAAABgoSgMAQAAAAAAWCgKQwAAAAAAABaKwhAAAAAAAICFojAEAAAAAABgoSgMAQAAAAAAWCgKQwAAAAAAABaKwhAAAAAAAICFojAEAAAAAABgoSgMAQAAAAAAWCgKQwAAAAAAABbKNr8DACyFjQ11WFiue59/8gCWihxAYWQ0mmQ0mvI7DABALqMwBOQBk8kkZ2f7/A4DyHfkASwdOYDCxGAw6s8/b1McAoCnHIUhIA9YWVlp5orDir94I79DAQAAeKSK5Zw0skdDWVtbURgCgKcchSEL4ubmpo8//lh+fn75HUqeio+PV+vWrbV8+XJ5enrmXxwXbygm4Xq+nR8AAAAAgL9jojsAAAAAAICFojAEAAAAAABgoSgMWaioqCi9+uqrcnd3l5eXl6ZPn647d+6Y97u5uSk8PDzDMd7e3goJCZEkhYeHy9vbW9OmTZOHh4cCAgJ08OBBubm5adeuXerUqZPq1KkjHx8f7dy509yHyWTS0qVL1bp1a9WvX19du3ZVZGSkeb+vr6/Gjh2b4by7d+9WnTp1dO3atSxdW3R0tHr16qUXX3xR7dq104EDBzLsT01N1axZs9SmTRvVqVNHnp6eCgwMVGJioiRp8ODB6tWrV4Zjzp49Kzc3N508eTJLMQAAAAAAUBhQGLJA27dv16BBg9SiRQuFhYVp6tSp+vbbbzVy5Mhs9ZOQkKCLFy8qIiJCI0aMMG8PDg7W+PHjFR4erkqVKmnkyJG6deuWJGn27NlauXKl3n//fW3cuFG9evXSpEmTtGLFCkmSn5+ftmzZopSUFHN/GzZsUKtWreTi4vLImG7cuKHevXvL0dFRa9eu1QcffKDPPvssQ5sZM2bom2++0bRp07RlyxZ98skn2rt3rxYuXChJ8vf316FDh3T+/HnzMevXr9cLL7yg559/PluvEQAAAAAABRmLT1ugxYsXq23bthoyZIgkqVq1ajKZTBo0aJBiYmJUvXr1LPc1ePBgVapUSZJ08OBBSdKwYcPUpEkT8/ddu3ZVdHS03Nzc9MUXX2jGjBlq1aqVJKly5cpKSEhQaGioevTooS5duig4OFjbt29Xp06ddPPmTW3fvl1z5szJUjybNm1ScnKyPvnkEzk5OalmzZoaN26c+VolqW7duvrXv/6lxo0bS5IqVKggLy8vnTp1SpLUokULlSlTRhs3btTAgQNlNBoVGRmp/v37Z/l1AQAAeBrY2OTcvyPf6ysn+wQKG/IABRGFIQsUHR0tHx+fDNsaNWokSTp16lS2CkNVq1bNtK1atWrm7x0dHSVJaWlpOnPmjO7cuaMxY8ZkmC6Wnp6u1NRUpaSkqGTJkvL29tb69evVqVMnffvtt3JyclLz5s2zfG1Vq1aVk5OTeZu7u3uGNl27dtX+/fv16aef6rffflNMTIzOnj0rDw8PSZKtra26dOmiDRs2aODAgTpw4ICuXLmiTp06Zfl1AQAAeBo4O9sXij6BwoY8QEFCYegpdeXKFV29elVubm6S7q7tI0k2NjYymUyysrLK0N5gMEi6WxS5594x96SlpWU6T7FixTJtK1KkSKZtJpPJ3N+cOXMyFI/+fpy/v78CAgJ05coVRUZGqkuXLhniepS/x/33YydNmqTNmzfL19dXLVu21KBBgxQaGqqLFy+a2/j7+ys0NFTHjx9XZGSkWrdurZIlS2Y5BgAAgKdBUlKyDAZjjvRlY2MtZ2f7HO0TKGzIA+QVZ2f7LI9MozD0lAoNDdXu3bu1adMmSVJSUpIkycXFRa6urjp8+LDeeustc/sff/xRksyjhezs7HTjxg3z/ps3b2Z58ecHqVatmmxtbXX+/HnzVDJJWr58uc6cOaMpU6ZIkry8vFS2bFmtXbtWhw8f1sSJE7N8jlq1aiksLEzXrl0zr0n0888/m/cnJiZq1apVmj17tjp27GjefvbsWTk4OJh/rl69utzd3bV582bt3LlTM2bMeOzrBgAAKKwMBqPS03P2j9fc6BMobMgDFCRMbHxKNW3aVGfOnFFERIRiYmL08ccfy9nZWe7u7urXr5+2bt2qBQsW6Ny5c9q5c6emTp2qVq1amQtD7u7uWrNmjU6cOKHo6GiNHj06W6N27sfJyUmvv/665syZo/Xr1ysuLk4REREKDg5WmTJlzO2sra3l6+urRYsWqU6dOqpRo0aWz+Hj46PSpUtrxIgROnnypA4dOqSPPvooQwxOTk7asWOHYmNjderUKU2YMEEnTpxQampqhr78/f21YsUKFSlSRF5eXk907QAAAAAAFEQUhp5SzZs3V1BQkEJCQtStWzedPn1aCxculKOjozp06KCZM2fqu+++U+fOnTVx4kT5+PhkWOB50qRJKlu2rF5//XUNGDBAjRs3zrRWz+MYO3asevfurXnz5qlDhw5asGCB3nnnHb377rsZ2vn5+SklJUV+fn7Z6t/BwUHLly+XnZ2d3njjDY0ePVoDBgww77e1tdXcuXMVHR2tzp07q3///kpOTlZgYKBOnz6t27dvm9t26NBBkuTr6ysbG5snuGoAAAAAAAomK9PfF2QBCoAffvhBAwYM0Pfff59hIem8FB8fr7Zt2+rbb7+97yLb2TXs0yjFJFx/8sAAAAByWfUKJTQnsKUSE2/l2HQXW1trlSpVPEf7BAob8gB5xcWlOGsMoXCKiYlRdHS0Fi1apG7duuVLUejChQs6duyYVq5cqebNm+dIUUiSKpbLnwIXAABAdvH/LQBgOSgMoUD57bffNHbsWNWrV0/Dhw/PsK9Lly6Ki4t76PF79+7NsIj040hMTFRQUJCqVq2q+fPnP1Ff95hMJo3s0TBH+gIAAMgLBoNRRiOTCwDgacdUMhQa58+fV1pa2kPbVK5cWVZWVnkUUfbwSEpYMh7NCktHDqAwMhpNOVoYYgoNQB4g7zCVDE+lZ599Nr9DeCI8khIgDwByAAAAFDQ8lQwAAAAAAMBCURgCAAAAAACwUBSGAAAAAAAALBSFIQAAAAAAAAtFYQgAAAAAAMBCURgCAAAAAACwUBSGAAAAAAAALBSFIQAAAAAAAAtFYQgAAAAAAMBCURgCAAAAAACwUBSGAAAAAAAALBSFIQAAAAAAAAtlm98BAJbCxoY6LCzXvc8/eQBLRQ4A5AEgkQeFndFoktFoyu8wcpyVyWR6+q4KKGBMJpOsrKzyOwwAAAAAwGMyGIz688/bhaI45OJSPMsFSEYMAXnAyspKM1ccVvzFG/kdCgAAAAAgmyqWc9LIHg1lbW1VKApD2UFhyEIFBQUpIiLioW1OnTr1wH3nz5/X0aNH5ePjk6XzhYeHa+zYsQ/t8+9u3Lihzz77TFu3btXFixfl6OgoDw8PDR48WLVr1za3O3z4sEwmkzw8PLLcd36Iv3hDMQnX8zsMAAAAAADMmNhoocaPH689e/aYvyRp3LhxmbY9yJgxY/T999/naoyDBg3Sjz/+qA8//FBbtmzRkiVLZGNjox49eujMmTPmdt27d9fvv/+eq7EAAAAAAPA0YsSQhXJycpKTk1OmbWXLls2niDKKjo7WDz/8oPDwcL3wwguSpAoVKmjWrFlq06aN1q5dq7Fjx+ZzlAAAAAAAFG4UhnBfUVFR+uyzz3T69GkVL15cnTp10vDhw1W0aFH17NlThw4dkiQdOnRI//3vf/XHH39o5syZ2rdvn65fv64yZcrI19dX7733nqytsz8w7d4xu3btUu3atc0LN9va2uqrr76Sg4ODJMnNzU2SNHbsWB06dEjTp0/XhQsXNGvWLO3fv1+3bt1Sw4YNNXr0aHPboKAgpaamqkyZMgoLC1ORIkXUtWtXBQYGqkiRIpKk9evXa+nSpfr9999VsmRJtW/fXqNGjTLvBwAAAADgaUBhCJls375d7777rt555x1Nnz5dsbGxmjRpkhISEhQSEqKQkBAFBATomWee0QcffCBJGjhwoEqXLq3Q0FA5OjoqKipKH374oerWras2bdpkO4YaNWrI29tbc+fO1Zo1a9SsWTN5eHioWbNmqlSpkrndnj175OXlpXHjxsnPz083b97UG2+8oUqVKmnhwoUqUqSIFixYoDfffFMbNmzQs88+K0naunWrWrZsqVWrVikuLk7jx49XcnKyJk+erJMnT+r999/XzJkzVa9ePcXExGjEiBEqVaqUBg8enDMvMgAAAACg0Mnqk74KEwpDyGTx4sVq27athgwZIkmqVq2aTCaTBg0apJiYGFWvXl12dnYqVqyYXFxclJKSoq5du6pdu3aqUKGCJKlnz55asmSJTp069ViFIUmaP3++wsLCtHHjRkVGRiosLExWVlbq0KGDpk6dKkdHR/PUt3tT41auXKnExESFh4fLxcVFkjRz5ky1adNGK1as0KhRoyRJJUqUUHBwsOzt7eXq6qpLly5p2rRpGjVqlOLj42VlZaWKFSvq2Wef1bPPPmsueAEAAAAALJezs31+h5DjKAwhk+jo6ExPG2vUqJGku08qq169eoZ9xYoV05tvvqnvvvtOX375pWJjY3Xy5EldunRJRqPxseOwsbHRq6++qldffVW3b9/W4cOH9e233yoiIkImk0lz5sy5b+xVq1Y1F4UkqWjRoqpXr16GJ6LVrVt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      "text/plain": [
       "<Figure size 1200x800 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": 50,
   "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9531920901997483"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.795976548144611"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/z3/0dz3d5ld6cq3k16qb86ymjlm0000gn/T/ipykernel_21493/3453123835.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_test-y_pred)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 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": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1173.9248956742836\n",
      "MSE: 4385687.129359326\n",
      "RMSE: 2094.2032206448653\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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026887077025966846"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE/(max(DV)-min(DV))\n",
    "\n",
    "2090.5509/(max(y)-min(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.795976548144611"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 62,
   "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": 63,
   "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": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.8s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   4.3s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   4.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   4.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   4.3s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   4.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/debapratim/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n",
      "20 fits failed out of a total of 50.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "20 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/debapratim/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/debapratim/anaconda3/lib/python3.11/site-packages/sklearn/base.py\", line 1144, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"/Users/debapratim/anaconda3/lib/python3.11/site-packages/sklearn/base.py\", line 637, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"/Users/debapratim/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/Users/debapratim/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_search.py:976: UserWarning: One or more of the test scores are non-finite: [-6326080.2152357  -4866100.01257413               nan               nan\n",
      "               nan -4569475.15962203 -9029040.78346667 -4589728.36801062\n",
      " -8570726.56537702               nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/z3/0dz3d5ld6cq3k16qb86ymjlm0000gn/T/ipykernel_21493/375150797.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_test-prediction)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 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": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1265.3825938360176\n",
      "MSE: 3886713.0798505885\n",
      "RMSE: 1971.4748489013468\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": 70,
   "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": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.795976548144611"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}