Flight_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "22c6dda0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816d8ebe",
   "metadata": {},
   "source": [
    "# Importing Dataset\n",
    "\n",
    "1. Since the data is in the form of excelfile we have to pandas read_excel to load the data \n",
    "2. After loading it is important to check the complete information of data as it indicate many of the hidden information \n",
    "   such as null values in a column or row .\n",
    "3. Check wether any null values are there or not . If it is present then following can be done,\n",
    "                A. Imputing data using imputation method in  sklearn\n",
    "                B. Filling NAN values with mean , median and mode using filna() method\n",
    "4. Describe data --> which can give statistical analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d900746f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_excel(r\"Data_Train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b41762c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4a29682",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c858772",
   "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": 7,
   "id": "e4e4ca4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99223c58",
   "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",
       "4h 10m       1\n",
       "33h 20m      1\n",
       "40h 20m      1\n",
       "35h 20m      1\n",
       "32h 20m      1\n",
       "Name: Duration, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05edd5fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4bbd4942",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1099f1",
   "metadata": {},
   "source": [
    "## EDA\n",
    "From description we can see that Date_of_Journey is a object data type ,\n",
    "therefore we have to convert this data type in timestamp  so as to use this column properly for prediction\n",
    "\n",
    "for this require pandas **to_datetime** to convert object data type to determine type\n",
    "\n",
    "   **.dt.day method will extract only the day of that date**\\ **.dt.month method will extract only month of that date**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7009a5a5",
   "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": 12,
   "id": "0faf1c08",
   "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": 13,
   "id": "a576c3c8",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302   \n",
       "\n",
       "   Journey_day  Journey_month  \n",
       "0           24              3  \n",
       "1            1              5  \n",
       "2            9              6  \n",
       "3           12              5  \n",
       "4            1              3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6bf9dbfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5dcac57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Departure time is when the planepull up to the gate\n",
    "# Similar to Date_of_Journeywe extract the values from Dept_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": 16,
   "id": "29fc18d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Arrival_Time</th>\n",
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       "      <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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0046c31",
   "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": 18,
   "id": "49cf9dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
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       "      <th>Dep_hour</th>\n",
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       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
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       "      <td>10</td>\n",
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       "    <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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bb8cf93b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"2h 50m\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7a0960dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time taken by plane to reach destination is called duration \n",
    "# It is the difference between departure and the arrival time\n",
    "\n",
    "# Assigning and converting duration column into list\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split())  != 2:       # check if duration contains only hours or minute  \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]))\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "edcbf61c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Duration_hours\"]= duration_hours\n",
    "train_data[\"Duration_mins\"]= duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9364e22a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
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       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
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       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
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       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0       20             1           10               2             50  \n",
       "1       50            13           15               7             25  \n",
       "2       25             4           25              19              0  \n",
       "3        5            23           30               5             25  \n",
       "4       50            21           35               4             45  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0c05be06",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Duration\"], axis=1 , inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "098f271f",
   "metadata": {},
   "outputs": [
    {
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       "    <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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca71c546",
   "metadata": {},
   "source": [
    " ## Handling Categorical Data\n",
    " One can find many ways to find categorical data. Some of the categorical data are,\n",
    " \n",
    "    1. **Nominal Data** --> Data are not in any order --> **OneHotEncoder** is used in this case\n",
    "    2. **Ordinal data** --> data are in order --> **LableEncoder** is used in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "70f5c5c7",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Airline\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e395a5d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graphwe can see that jet Airways business have highest price\n",
    "# Apart from the first airline almost all have the same 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": 27,
   "id": "9fc452be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \n",
       "0               0                0                                0  \n",
       "1               0                0                                0  \n",
       "2               0                0                                0  \n",
       "3               0                0                                0  \n",
       "4               0                0                                0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal categorical data . We will perform OneHotEncoding\n",
    "\n",
    "Airline = train_data[[\"Airline\"]]\\\n",
    "\n",
    "Airline = pd.get_dummies(Airline, drop_first =True)\n",
    "\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3890555b",
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "813c7389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OW1VVJb16RRtdhoiIiIhIh6b7iEm72bw5n+eff4bNm/ONLkVEREREJGgpiMl5sdsL2LNnt5atFxERERG5AApiIiIiIiIiAaYgJiJBpaqq0ugSRERERC6YgpiIBI2iokLuvfcuiooKjS5FRERE5IIoiIlIUPB4PKxatRKAVatW4vF4DK5IREREpO0UxEQkKOTnb8LhqAHA4aghP/9dgysSERERaTsFMRHp8Gpqqlm/fi1OpxMAp9PJ+vVrqKmpMbgyERERkbZREBORDs9uL8Dj8TZr83q9uo2CiIiIBC0FMRHp8BISrsVsbv5xFRISQkLCSIMqEhEREbkwCmIi0uFFRkYyceIkwsLCAAgNDWPixJuJjIw0uDIRERGRtvFrELv11ltJTU1lwoQJTJgwgc8++4yCggLS0tIYM2YMy5cvb9p29+7dpKenk5yczNy5c3G73QAcOnSIKVOmMHbsWGbMmEFdXR0ADoeDadOmkZKSwpQpUygrK/NnV0TEYKNHJ9OzZ2PwioyMZPToMQZXJCIiItJ2fgtiPp+Pffv2kZOT0/Tf4MGDmTNnDllZWeTm5rJr1y62bNkCwOzZs5k/fz6bNm3C5/OxZs0aABYuXMjkyZPJy8tj2LBhZGVlAbBixQri4+PZuHEjkyZNYtGiRf7qioh0AGazmalTpwMwdep0zGazwRWJiIiItJ3fgtjXX38NwP/7f/+PG264gb/+9a/s3LmTgQMHEhcXh8ViIS0tjby8PA4ePEh9fT3Dhw8HID09nby8PFwuF9u3byc5OblZO8DmzZtJS0sDYPz48WzduhWXy+Wv7ohIBzBo0BCeeOJpBg0aYnQpIiIiIhfE4q8DOxwORowYwYMPPojL5eK2227jjjvuwGazNW0TExNDaWkpR44cadZus9koLS2lqqqK8PBwLBZLs3ag2T4Wi4Xw8HAqKyvp27dvq+rr3Tu8vbrapVit5qafNluEwdVIV6T3nYiIiHQGfgtiV1xxBVdccUXT45tuuok//vGPXHXVVU1tPp8Pk8mE1+vFZDKd1n7i58lOfXzyPiEhrR/gq6ioxev1tXp7aeRyeZp+lpUdNbgaEREREZGO62wnkP02NfHTTz/l448/bnrs8/no379/s0U1ysrKiImJITY2tll7eXk5MTExREdHc/ToUTweT7PtoXE0rby8HAC3201dXR1RUVH+6o6IiIiIiEi78VsQO3r0KI899hhOp5Pa2lrWr1/P73//e/bu3UtxcTEej4cNGzaQmJhI//79CQsLY8eOHQDk5OSQmJiI1WolPj6e3NxcALKzs0lMTAQgKSmJ7OxsAHJzc4mPj8dqtfqrOyIiIiIiIu3Gb1MTr7/+ej777DNuvPFGvF4vkydP5oorrmDJkiXMnDkTp9NJUlISY8eOBSAzM5N58+ZRW1vL0KFDue222wBYsGABGRkZPPvss/Tr149ly5YBMGvWLDIyMkhNTSUiIoLMzEx/dUVERERERKRdmXw+X5e8UErXiLXN0qWPsGfPbgYPvpT773/Q6HKkC6qqqqRXr2ijyxARERE5J0OuERMRaW9FRYXce+9dFBUVGl2KiIiIyAVREBORoODxeFi1aiUAq1atbFrER0RERCQYKYiJSFDIz9+Ew1EDgMNRQ37+uwZXJCIiItJ2CmIi0uHV1FSzfv1anE4nAE6nk/Xr11BTU2NwZSIiIiJtoyAmIh2e3V6Ax+Nt1ub1erHbCwyqSEREROTCKIiJSIeXkHAtZnPzj6uQkBASEkYaVJGIiIjIhVEQE5EOLzIykokTJxEWFgZAaGgYEyfeTGRkpMGViYiIiLSNgpiIBIXRo5Pp2bMxeEVGRjJ69BiDKxIRERFpOwUxEQkKZrOZqVOnAzB16nTMZrPBFYmIiIi0ncXoAkREWmvQoCE88cTT9OoVbXQpIiIiIhekVSNiXq+XF154gfvvv5/a2lqee+453UxVRAyhECYiIiKdQauC2GOPPUZRURE7d+4E4MMPP2Tx4sV+LUxERERERKSzalUQ+/jjj1myZAlhYWGEh4fz4osv8tFHH/m7NhERERERkU6pVUHMYrEQEvLvTUNDQ7FYdHmZiIiIiIhIW7QqTQ0aNIhXXnkFj8fD119/zZ///GeGDBni79pEREREREQ6pVaNiM2dO5fPP/+ciooKJk+ezLFjx5gzZ46/axMREREREemUWjUiFh4ezowZM3j00Uepra2lpKSEXr16+bs2ERERERGRTqlVI2KrV6/mN7/5DQBVVVXMnDmTtWvX+rUwERERERGRzqpVQeyNN97gtddeAyAuLo7s7GxefvllvxYmIiIiIiLSWbUqiHk8HsLDw5seR0REYDKZWvUCS5cuJSMjA4CCggLS0tIYM2YMy5cvb9pm9+7dpKenk5yczNy5c3G73QAcOnSIKVOmMHbsWGbMmEFdXR0ADoeDadOmkZKSwpQpUygrK2tdb0VERERERDqAVgWx7373u2RmZrJ//37279/Pk08+ybe//e1z7vfxxx+zfv16AOrr65kzZw5ZWVnk5uaya9cutmzZAsDs2bOZP38+mzZtwufzsWbNGgAWLlzI5MmTycvLY9iwYWRlZQGwYsUK4uPj2bhxI5MmTWLRokVt6buIiIiIiIghWhXEFi5cyL59+7jxxhu56aab2LdvHw899NBZ96murmb58uVMnz4dgJ07dzJw4EDi4uKwWCykpaWRl5fHwYMHqa+vZ/jw4QCkp6eTl5eHy+Vi+/btJCcnN2sH2Lx5M2lpaQCMHz+erVu34nK52tJ/ERERERGRgGvVqol9+vTh6aefPq8Dz58/n3vuuYfDhw8DcOTIEWw2W9PzMTExlJaWntZus9koLS2lqqqK8PDwphtHn2g/9VgWi4Xw8HAqKyvp27fvedUoIiIiIiJihLMGsUWLFjF37tymUa1TrVy5ssX2tWvX0q9fP0aMGMG6desA8Hq9za4r8/l8mEymM7af+HmyM12X5vP5CAlp1eBek969w8+9kZzGajU3/bTZIgyuRkREREQkOJ01iI0YMQKgaXpga+Xm5lJWVsaECROoqanh2LFjHDx4ELPZ3LRNWVkZMTExxMbGNltso7y8nJiYGKKjozl69Cgejwez2dy0PTSOppWXlxMbG4vb7aauro6oqKjzqrGiohav13de+wi4XJ6mn2VlRw2uRkRERESk4zrbwMVZg9ioUaMAyM7O5i9/+UurX/Cll15q+v9169axbds2Fi5cyJgxYyguLmbAgAFs2LCBn/3sZ/Tv35+wsDB27NjBVVddRU5ODomJiVitVuLj48nNzSUtLY3s7GwSExMBSEpKIjs7m+nTp5Obm0t8fDxWq7XV9YmIiIiIiBipVdeIHT16lGPHjtGjR482v1BYWBhLlixh5syZOJ1OkpKSGDt2LACZmZnMmzeP2tpahg4dym233QbAggULyMjI4Nlnn6Vfv34sW7YMgFmzZpGRkUFqaioRERFkZma2uS4RCS5VVZX06hVtdBkiIiIiF8Tk8/nOOT9vypQpfPnllwwePLhZGDvTNWLBQFMT22bp0kfYs2c3gwdfyv33P2h0OdLFFBUVsmTJw2RkzGfQoCFGlyMiIiJyVm2emghQVFTE6NGj+dGPfkRsbGy7FiYi0loej4dVqxpP/qxatZJHH32i2XWnIiIiIsHkrEHsrbfeYunSpQwcOJCSkhIyMzO57rrrAlWbiEiT/PxNOBw1ADgcNeTnv8uYMSkGVyUiIiLSNmdd83316tW88847rF27lpUrV/KnP/0pUHWJiDSpqalm/fq1OJ1OAJxOJ+vXr6GmpsbgykRERETa5pw33zpxk+QrrriCqqoqvxckInIqu70Aj8fbrM3r9WK3FxhUkYiIiMiFOWsQO/UGyroeQ0SMkJBwLWZz84+rkJAQEhJGGlSRiIiIyIU554jYyU4NZiIigRAZGcnEiZMICwsDIDQ0jIkTbyYyMtLgykRERETa5qyLdezZs4crr7yy6XF9fT1XXnklPp8Pk8nEP/7xD78XKB3H5s35lJQUA1BdrWmqElijRyfzzjvZOJ1OQkOtjB49xuiSRERERNrsrEHsvffeC1QdEgTs9gJ8Ph/dunXH4XAYXY50MR6Ph2PH6gA4duwYHo9H06VFREQkaJ01iPXv3z9QdUiQ6Nc/DoBvDh0wuBLpalaufIoT95/3er0899zTzJz5e4OrEhEREWmb87pGTETECPv3F/M//7OjWds///kpBw7sN6giERERkQujICYiHV529psttq9fvzbAlYiIiIi0DwUxEenwJk68+QztkwJciYiIiEj7UBATkQ5vwIA4LroovFnbFVfEM2BAnEEViYiIiFwYBTERCQoXX/zvxYNCQkL49a/vMrAa6YqqqiqNLkFERDoRBTERCQohIf/+uLrlltsIDQ01sBrpaoqKCrn33rsoKio0uhQREekkFMREJKgMHPht3cxZAsrj8bBq1UoAVq1aicfjMbgiERHpDBTERCSodOvW3egSpIvJz9+Ew1EDgMNRQ37+uwZXJCIinYGCmIiIyBnU1FSzfv1anE4nAE6nk/Xr11BTU2NwZSIiEuwUxERERM7Abi/A4/E2a/N6vdjtBQZVJCIinYWCmIiIyBkkJFyL2dz8V2VISAgJCSMNqkhERDoLvwaxJ598knHjxpGamspLL70EQEFBAWlpaYwZM4bly5c3bbt7927S09NJTk5m7ty5uN1uAA4dOsSUKVMYO3YsM2bMoK6uDgCHw8G0adNISUlhypQplJWV+bMrIiLSBUVGRjJx4iTCwsIACA0NY+LEm4mMjDS4MhERCXZ+C2Lbtm3jk08+4e233+att95i9erVFBYWMmfOHLKyssjNzWXXrl1s2bIFgNmzZzN//nw2bdqEz+djzZo1ACxcuJDJkyeTl5fHsGHDyMrKAmDFihXEx8ezceNGJk2axKJFi/zVFRER6cJGj06mZ8/G4BUZGalVO0VEpF34LYhdffXVvPzyy1gsFioqKvB4PDgcDgYOHEhcXBwWi4W0tDTy8vI4ePAg9fX1DB8+HID09HTy8vJwuVxs376d5OTkZu0AmzdvJi0tDYDx48ezdetWXC6Xv7ojIiJdlNlsZurU6QBMnTods9lscEUiItIZWPx5cKvVyh//+EdefPFFxo4dy5EjR7DZbE3Px8TEUFpaelq7zWajtLSUqqoqwsPDsVgszdqBZvtYLBbCw8OprKykb9++raqtd+/w9upml2G1mmlwN94/x2QCmy3C4IqkK7FazU0/9d6TQLPZfsif//xnevfubXQpIiLSSfg1iAHcfffd3HnnnUyfPp19+/ZhMpmanvP5fJhMJrxeb4vtJ36e7NTHJ+8TEtL6Ab6Kilq8Xt959qZrc7n+fRNTnw/Kyo4aWI10NSfefy6XR+89MUio3nsiInJeznby2G9TE7/66it2794NQPfu3RkzZgx2u73ZohplZWXExMQQGxvbrL28vJyYmBiio6M5evQoHo+n2fbQOJpWXl4OgNvtpq6ujqioKH91R0REREREpN34LYgdOHCAefPm0dDQQENDA/n5+fziF79g7969FBcX4/F42LBhA4mJifTv35+wsDB27NgBQE5ODomJiVitVuLj48nNzQUgOzubxMREAJKSksjOzgYgNzeX+Ph4rFarv7ojIiIiIiLSbvw2NTEpKYmdO3dy4403YjabGTNmDKmpqURHRzNz5kycTidJSUmMHTsWgMzMTObNm0dtbS1Dhw7ltttuA2DBggVkZGTw7LPP0q9fP5YtWwbArFmzyMjIIDU1lYiICDIzM/3VFRERERERkXZl8vl8XfJCKV0jdn42b85n7drXOPF2aWhw8p//eTs//vFogyuTrmLp0kfYs2c3gwdfyv33P2h0OSIiIiLnZMg1YtK52O0FeL1e+vS9mD59L8ZqDcVuLzC6LBERERGRoOT3VROl87DF9mfSbXcBsPblpw2uRrqa6uqqZj9FREREgpmCmJzV5s352O0FlJQU4/F6mwLYkW8OYg4JYenSR0hIGKkpiuJ3Doej2U8RERGRYKYgJmdltxfw9d69RPeJBf59L6devRsff713L4CCmIiIiB9VVVXSq1e00WWISDtSEJNziu4Ty5j0O1p87t11LwS4GhERka6lqKiQJUseJiNjPoMGDTG6HBFpJ1qsQ0RERKSD8ng8rFq1EoBVq1bi8XgMrkhE2otGxKSZE9eEnVBSUozb4znjyFdl+WEcVWaWLn2kqU3XjImIiLSP/PxNOBw1ADgcNeTnv8uYMSkGVyUi7UFBTJqx2wv46uuv6dmrLwDdI/oA4HJ7W9w+Iqpxu7KqOgAcVaWArhkTERG5UDU11axfvxan0wmA0+lk/fo1JCSMJDIy0uDqRORCKYjJaXr26kvCT6e0aV/7e6+0czUiIiJdk91egMvlbtbmdrux2ws0KibSCegaMREREZEOKCHhWrze5teEeTweEhJGGlSRiLQnBTERERGRDsjhqG6x/ehR3U9RpDNQEBMRERHpgNaufa3F9jVrXg1wJSLiDwpiIiIiIh2QzRZzXu0iElwUxEREREQ6oNDQbi22h4WFBbgSEfEHBTERERGRDuibbw612N6tW8sBTUSCi4KYiAQFt7txCWens57Nm/MNrkZExP9qa4/So3t3Lh00iEsHDaJH9+4AJCXpXp0inYGCmIgEBY/HTY9uYXQLtWK3FxhdjoiI31ksFgbGxbFg9mwWzJ7NwLg4bLYY3cxZpJPQDZ1FJGh86+K+APgMrkNExCi9ekUbXYKItBONiImIiIgECZPJZHQJItJO/BrEnn76aVJTU0lNTeWxxx4DoKCggLS0NMaMGcPy5cubtt29ezfp6ekkJyczd+7cputBDh06xJQpUxg7diwzZsygrq4OAIfDwbRp00hJSWHKlCmUlZX5sysiIiIiIl1OVVWl0SV0Wn4LYgUFBfz9739n/fr1ZGdn8/nnn7NhwwbmzJlDVlYWubm57Nq1iy1btgAwe/Zs5s+fz6ZNm/D5fKxZswaAhQsXMnnyZPLy8hg2bBhZWVkArFixgvj4eDZu3MikSZNYtGiRv7oiIiIiItLlFBUVcu+9d1FUVGh0KZ2S34KYzWYjIyOD0NBQrFYr3/ve99i3bx8DBw4kLi4Oi8VCWloaeXl5HDx4kPr6eoYPHw5Aeno6eXl5uFwutm/fTnJycrN2gM2bN5OWlgbA+PHj2bp1Ky6Xy1/dEREDbd6cj9frpfhQKcWHSikpKdbKiSIiIn7k8XhYtWolAKtWrcTj8RhcUefjtyB2ySWXNAWrffv2sXHjRkwmEzabrWmbmJgYSktLOXLkSLN2m81GaWkpVVVVhIeHY7FYmrUDzfaxWCyEh4dTWamhU5HOyG4voHtYKHExvYmL6Q1et1ZOFBER8aP8/E04HDUAOBw15Oe/a3BFnY/fV0384osv+PWvf81//dd/YTab2bdvX9NzPp8Pk8mE1+ttdvHpifYTP092potUfT4fISGtz5W9e4efX0e6CKvV3C7HsNki2qEakUZWq5m4vn2YfduNADz+crbeZyLSaeXl5bFlyxb27y8Gn4+Fjz8OQPH+/WAysWzZoyQlJTF27FiDK5XOqqqqiuzsN3E6nQA4nU6ys9cybtxP6dWrl8HVdR5+DWI7duzg7rvvZs6cOaSmprJt27Zmi2qUlZURExNDbGxss/by8nJiYmKIjo7m6NGjeDwezGZz0/bQOJpWXl5ObGwsbreburo6oqKiWl1bRUUtXq8WwT6Vy3Xhw84ul4eysqPtUI1Io5bel3qfSaBVVVVq6XAJiP/+7/cp2fc1cbGxAHj+9WV4wL++A331xRe4XB6uuupaw2qUzu3dd9/F7W7+u9fj8ZCb+x5jxqQYVFVwOttJY79NTTx8+DC//e1vyczMJDU1FYDLL7+cvXv3UlxcjMfjYcOGDSQmJtK/f3/CwsLYsWMHADk5OSQmJmK1WomPjyc3NxeA7OxsEhMTAUhKSiI7OxuA3Nxc4uPjsVqt/uqOiIh0YbpgXQItLjaW2b+6vcX/TgQ0EX9JSLgWs7l5TAgJCSEhYaRBFXVOfhsRW7VqFU6nkyVLljS1/eIXv2DJkiXMnDkTp9PZbFg9MzOTefPmUVtby9ChQ7ntttsAWLBgARkZGTz77LP069ePZcuWATBr1iwyMjJITU0lIiKCzMxMf3VFRAyyeXM+dnsBJSXF4HXz+MvZAOwvLYeQapYufYSEhJH8+MejjS1UOrVTL1h/9NEnMJsvfBq3iEhHFRkZycSJk1i/fi1Op5PQ0DAmTryZyMhIo0vrVPwWxObNm8e8efNafO7tt98+rW3IkCG8+eabp7X379+f1atXn9YeFRXFypUrL7xQEemw7PYCSvZ+yQBb43x0n6fx/oID+kQBULL3SwAFMfGr/PxNVFZWAFBZWUF+/ruamiMind7o0cnk579LWdkRIiMjGT16jNEldTp+X6xDRORCDLD14p6bftLic8vf/O8AVyNdTU1NNW+99UbTss0ej4e33nqdhISROjMsIp2a2Wxm6tTpLFnyMFOnTtdMAD9QEOtiTkz1OpOSkmJcbg/2915p0/EdVaUcP2pm6dJHzriNppKJSLCw2wtOu0ely+XCbi/QqJiIdHqDBg3hiSee1kJFfqIg1sXY7QV8+dVX9Ohpa/F5S/deWACXu22rJ3aP6APAoTJHi88fczSujqkgJi059URBSUkxeFxnHPk6UFYFlbXNgr+CvrSnAQMGttj+rW+13C4i0tkohPmPglgX1KOnjSEjfmbIaxd+/JYhryvBwW4voHjvF/SP7gnAxZHdgG54/3Vt2Kkujm5cEtZd03ij94OVjScAFMSkveTn57XY/t57eQwZ8h8BrkY6qxZPQnk9PP7nl1rcfv8330CIWSehRIKcgpiIdCj9o3syMzWhTfs+9Td7O1cjXd0111zHP/+547T2kSN/ZEA10lk1noT6iv7RvQG4+F/XH3qcDS1uf/G/RijcNdUAHPzXYjIKYiLBRUFMRAKmNdco+twNbQ5UByscmGrqdY2itJtPPvmwxfaCgr9z1VVXB7ga6cz6R/fmrvHj27Tv0xs2tHM1IhIICmIiEjB2ewHFXxdxcVSPFp/vF24GuuPzuFp8/lwujuoOgKvyQIvPH6o+BuissbTexIk3889/7qBb924A1B+v/1f7JCPLEhGRTkBBTEQC6uKoHsy4fqghr/3sB58b8roSvL78soiQkJBmbXFxAxkwIM6gikREpLNQEBORVjvX1MJzaZx66DQsEB2qPoaptvisUxfPRVMbO6dT39vV1VU4HA6cznq8Xi/OemfTcwcP7ue3v72Dnj17EhXVq6ld7w0RETkfCmIi0mp2ewHFX+2hX8+wNu0f2x0gFN8ZVkH0t34RoYCPhrJ9bdr/sKPxy7i+bAef1lyfeLz+OJbQ5r8WQ6xmQjj9JqYur4uK6goqqhsXSXA3uCkpKT7rayioiYjIyRTEROS89OsZxh3XdM17KL3wSbHRJXRZ7TEae7z+GGbrmX/tma1mfD5fm45vtppp8DTw5d4vWnze4zp3UDsXBTkRkc5FQUxERDo8u72AL74uIiy6e9sO0DOE7j3D27eoNiip3t+m/ZyVxwGNxoqIdCYKYkGmPc4KN7jcht1Y+ZijjJLjVbpGp40u9O//QpWUFONzObvsyNBhhxPT8Qu7xuxCdeX3f1h0d+KSBxtdhiH2b9pjdAkiItLOFMSCjN1eQNGXXxF6UXTbDmDticUKDW5P+xbWSpYe0XiBfYer2rR/Q10l0HXPCtvtBez7spDYcGP+6caEAqFmvAZd42W0vheZARf133xpyOt/U9v4595V3/8iIiKdiYJYEAq9KJp+w1KNLsMQh3f9zegSDBcbbuH2H7QxiEtQe2lnpWGv3RFGY53u+i47MuSsPEaJQ6OxIiKdiYKYiIick91eQNHXezBHhhpTwEUQQiiuLjoaGxIZigsvX1XsNeT1PTUNgEZj26o1q3b63G6e3rChTcc/WFGBqabmrEFdQVqk41EQExGRVjFHhhKZeLHRZYgBarYeMrqEoNZ4648viI3o2eLzfbt1A8DjdLb4/LnEhjcuROM8Utri898cdQAK0tKyk08UnLiH4tnoHortR0FMRERExM9iI3ry/xKuMeS1X7R/YsjrSnCw2wsoLt5Hv9gB9OgeQY/uEefcp8HZODvh8DcHAIX8tlIQExERERHpwvrFDmDa1N+d937Pr1rR7rV0JSH+PHhtbS3jx4/nwIHGtFxQUEBaWhpjxoxh+fLlTdvt3r2b9PR0kpOTmTt3Lm53Y8o+dOgQU6ZMYezYscyYMYO6ujoAHA4H06ZNIyUlhSlTplBWVubPboiIiIiIiLQrvwWxzz77jFtuuYV9+/YBUF9fz5w5c8jKyiI3N5ddu3axZcsWAGbPns38+fPZtGkTPp+PNWvWALBw4UImT55MXl4ew4YNIysrC4AVK1YQHx/Pxo0bmTRpEosWLfJXN0RERERERNqd36YmrlmzhgULFvBf//VfAOzcuZOBAwcSFxcHQFpaGnl5eXz/+9+nvr6e4cOHA5Cens4f//hHJk2axPbt23nmmWea2v/zP/+T2bNns3nzZl555RUAxo8fz8MPP4zL5cJqtfqrOyIdxjcVNXz8tTH3gRNjfVNRy7djja5CRERE2oPfgtipo1RHjhzBZrM1PY6JiaG0tPS0dpvNRmlpKVVVVYSHh2OxWJq1n3osi8VCeHg4lZWV9O3b11/dERERERERaTcBW6zD6/ViMpmaHvt8Pkwm0xnbT/w82amPT94nJOT8Zln27h1+Xtt3FFar2egSDGe1mrHZzr2iT2dktZqJ7R3JiO/qhs5dUWGt2bD3vz57pCt/9l4oq9VM2xamb98a9PcnLbFazU2rILZ1f7232iZgQSw2NrbZohplZWXExMSc1l5eXk5MTAzR0dEcPXoUj8eD2Wxu2h4aR9PKy8uJjY3F7XZTV1dHVFTUedVTUVGL1+trl74FksulKWkul4eysqNGl2EI/f2LUe9/l8tDTXk1rt0tnxCTzu1YeTWu3l33s/dCdYTP7q78u1PO7kLfn3pvnd3ZQqpfV0082eWXX87evXspLi7G4/GwYcMGEhMT6d+/P2FhYezYsQOAnJwcEhMTsVqtxMfHk5ubC0B2djaJiYkAJCUlkZ2dDUBubi7x8fG6PkxERERERIJGwEbEwsLCWLJkCTNnzsTpdJKUlMTYsWMByMzMZN68edTW1jJ06FBuu+02ABYsWEBGRgbPPvss/fr1Y9myZQDMmjWLjIwMUlNTiYiIIDMzM1DdaHb3cSOUlBTT0ODi8K6/GVaDkRrqKigpcbB06SOG1aA7yEtXFdknishL+xldhhigpiz4ZpCIiHR0fg9i77//ftP/jxgxgrfffvu0bYYMGcKbb755Wnv//v1ZvXr1ae1RUVGsXLmyfQttJbu9gD1ffElItyhDXh/TRZjCwOU2fpqDEUxhUTiBL/aXG/L63vpqQHeQFxHpSi70JGxJSTFeVwMv2j9px6pa7/BRByH19Rd0ElMnIUXaX8BGxDqTkG5R9BioD6Ou6FhxvtEliIhIgNntBez7Yg8x3bu3af8+ISYIC8PTYMySHTFhYQAcO1DSpv2PHD8O6CRksDrXiYSSkmJ8Xh/Pr1px3sc+fPgAphDTWUO+QvyZKYiJiIiInENM9+7c8t3vG12GIV77+kujS5ALYLcXsHfvXmy9W74RZXRU42J4DU7XeR+7d3TjraNqHcdbfL6s4htAIf5MFMREgsw3tW5e2llpdBligG9q3Xzb6CJERCSg2mNqLBh1naePkpJiTYs9AwUxkSCSkDDS0NcvKSnG56qnX88wQ+swymGHE5O1G9/61kBDXv/bGP8eEBGRwLLbC/j6q6+J7Glr0/4XdW+896jzAu4V1lY9wxtrrmjj8vY1jsZbXCmIiYjhfvzj0YZ+GC1d+ggNZfu44xpjgojRXvikmFDbQO6//0GjSxERkS4ksqeNpGsmGV1GwG35ZK3RJfiVglgbOKrKqLd8bnQZYoCGqjKI62N0GYY67HDywifFRpdhiMMOJwPbdkJSREREpBkFMRFptQudFldSUkx9/THCLOZ2quj8ON0eunXr0eaphQNtmhooYoSOcA9Pr7O+yy5aceT4cUIu8DqfC9WZrxOSrktBrA169rLRo/9Qo8sQAxxzf2N0CYa60KmRrVpC191A/94923T8gxUOTJbQswYt/TIXCT52ewF7iwrpYzbmJE4vgJAQPE5jlp83Wu+QEHA1cPSrLwx5/XJP471Tjfrs7ggnAlwud6efpteSascR6o5XdtqTAApiIhIw5wpyS5c+QvHeC/hFb4JvfUvXcPmLp6aBmq2HjC5DDOCpaYDextbQx2xmQkSUsUWIIXKOVhv6+nZ7AV9+8WXToheBZg2JwBrWtuXlg12PsF4AHD5gzGrRdccbX1dBrAPx1lfrxr5dlLe+Guja14j506nT/qqrq3A4HDid9Xi9XkJMJgC8Ph8mUwjdunWjZ8+eREU1flAPjOyrqYN+YvSfa0lJMU53PWHRPQytwyjOymOEWYxbsZPexr8HRIx0Ufdohn5vrNFlSIB9/lWeX4+vIHaejP5FVFJSTH2Di9CLDD41aZCGugq6hVqN+zJCH8PfA53ZmUbMNm/O5+WXV+H1/fs+KGZzCM8880Igy+vSOsKKnV98XWTY6xvPpNFeEZFORkHsPHWELyNFX35l2OsbT19GuqJ/DYQ143a7+fDDD7juuusDX5AEXHssFHO8/hhmqzG/9jwuN90vYKEYoow/ESjSlVVWlbF3/06jy5AAq6wqo98A/01JVRALMu3yZeT4ccwWaztVdH48bhfdu3e/gBGtXvoy0gW98cYrLba/9tpqBbEuwt8LxZyYBnuC2+3C4/Hg9Xpb3N5kMmGxWLCc9Fl68jTZlmihGJHg5Wyo5UDpZ0aXIQHmbKj16/EVxIJMIFatO15/HIsltE3Hd7sb6N7t7EFLX0bkfN1yy6289NKfWmwXaY22fnZOm3Ybbrf7tHaz2cxzz/2lPUqTVjpSU8M/G07/u5DO78jxOiIMfH2jTwCfeqLofJ04sXQmZzrhdD5CQkLO+JzZbG520up8neskl7/58+9fQayLOdeXkVOD2ol//G63C7fbje+ka3SsVisWi/W0fyAKWtLerrvuel599WWcJy0dHRbWTaNh4nc33fRzXn/99BHZm276uQHViIgRjL4sxd9O/u538vc+l+v0VRqtVivR0b31va+dmHwnf7PuQioqavF6u2TXRYJSTU0N99wzo+nx8uXPEhkZaWBF0lXceeetzc4mm80W/vSnlw2sqOtZuvQRQ+8jJsYq93j4zqAhuj48gGpqasjI+N0pJ0DDWLJkhX73nieb7czjuRoRE5GgEBkZyeDBl7Jnz24GD/4P/SKQgFm69Enuu++ukx6vMK6YLsroqWElJcXUHz9G6FmmX3VmDV4v3bpfwGIzFygC498DXU1kZCQTJ05i/fq1OJ1OQkPDmDjxZv3ubWcKYiISNO6//0Hee28jP/1pitGlSBcSHR1NXNxA9u8vJi7u20RHG3NT167M6Klh57q++lyagpzFmK9dDW73BQcpTT/rekaPTiY//13Kyo4QGRnJ6NFjjC6p09HURBERkVbIyXmLCRN+ZnQZEoTae9VO+Pd12ido1U7xh6KiQpYseZiMjPkMGjTE6HKC0tmmJiqIiYiIiHRAH374QYsrxt5++51arEgCpqqqkl69NBOgrc4WxIJ6svM777zDuHHjGDNmDK+80vJ9hkRERESC0XXXXU9YWFizNq0YK4GmEOY/QRvESktLWb58Oa+++irZ2dm88cYbfPnll0aXJSIiItJulixZccrj5cYUIiLtLmiDWEFBAddccw1RUVH06NGD5ORk8vLyjC5LREREpN2cWDEW0IqxIp1M0K6aeOTIEWw2W9PjmJgYdu7c2er9e/cO90dZIiIiIu0qM/Mx3n77bW644QajSxGRdhS0Qczr9WIymZoe+3y+Zo/PRYt1iIiISLAYMeJ6ysqOGl2GiJynTrlYR2xsLGVlZU2Py8rKiImJMbAiERERERGR1gnaIDZy5Eg+/vhjKisrOX78OO+++y6JiYlGlyUiIiIiInJOQTs1sW/fvtxzzz3cdtttuFwubrrpJn7wgx8YXZaIiIiIiMg5ddkbOldV1ekaMRERERER8ZuzLRDYZYOYiIiIiIiIUYL2GjEREREREZFgpSAmIiIiIiISYApiIiIiIiIiAaYgJiIiIiIiEmAKYiIiIiIiIgGmICYiIiIiIhJgCmIiIiIiIiIBpiAmIiIiIiISYApiIiIiIiIiAaYg1snZ7XauuOIKJkyYwA033EBKSgp/+ctf2vU1nnrqKZ566ql2PaZ0HQcOHGDYsGFMmDCBCRMmkJyczAMPPEB5eflZ9xk1ahQAGRkZrFu37rRtXnvtNV577TW/1S3BxW63c+uttzY9rq2t5eabb2bJkiVn3GfUqFEcOHCAdevWkZGR0erXWrNmDRs2bLigeqVzOHDgAIMHD2b+/PnN2nfv3s3gwYNb/Ow6H2f6/DuT//3f/2Xu3LkX9JoSHGpra1m4cCHjx49nwoQJ3HrrrXz++eenfRYG0pNPPkl+fr4hr91RWYwuQPxv2LBhrF69Gmj8h5mamsq1117L97//fYMrE2kUExNDTk4OAD6fj2XLlnH33Xfz6quvtvmYt9xyS3uVJ51MXV0dd9xxB1dffTX33Xdfux//H//4B1dffXW7H1eCU1RUFB9++CEejwez2QxAbm4u0dHRAa/lsssu47LLLgv460pgeb1e7rzzThISEsjOzsZisfDJJ59w5513smDBAsPqmjVrlmGv3VEpiHUxTqcTs9lMREQEGzdu5KWXXqK+vp6GhgYeffRRrrzySm699VYuu+wyduzYQWVlJfPmzSMpKYlvvvmG++67j5qaGgYNGsT27dvZunVrs+N/8MEHrFixAq/XS1xcHA8//DB9+vRh1KhR/OAHP2D37t28+uqrfPjhh/zlL3/B6/UydOhQFixYQFhYmEF/KtKRmEwmZs6cybXXXkthYSFbt25l48aNeDwefvSjHzF79uzT9tm8eTOvvvoqFRUVTJ8+nZ///OdNo7QzZ84MdBekAzt27BjTpk3jmmuu4Xe/+x1w5s+tlixatIiKigoef/xx3n333dM+Q+vr63n//ff55JNPsNls9O3bl0ceeYRjx45RWVnJtGnTdJKgi7nooosYMmQI27dv55prrgHgo48+YuTIkQAMHjyYPXv2ALBu3Tq2bdvGkiVLGDVqFKmpqXz00UdYLBZ+85vf8OKLL1JcXMz999/PuHHjgMbPv7/+9a+4XC5mzJjBuHHjqK2tZc6cOZSWlnLkyBFGjBjBokWL2LZtG08//XTTyVnpnOx2O4cPH+buu+8mJKRx8ts111zD4sWLqauro7KykjvvvJOSkhK+853v8Mc//pHQ0FCys7Nb/G72ox/9iOTkZHbs2IHZbGbFihXExcUxatQobrjhBv7+979z/Phxli5dyrBhw9i2bRvLly+nvr4eh8PBAw88wE9+8hMyMjK4+uqrSU9PN/hPqOPQ1MQuYNeuXUyYMIG0tDRGjRrF1VdfTZ8+fXj99ddZuXIlb7/9NnfccQfPP/980z4ul4s33niDBx54gCeffBJo/AKSkpLCO++8w9ixYyktLW32OhUVFcyfP59nnnmGd955hyuvvJKHH3646fnExEQ2bdpEZWUla9as4fXXXycnJ4fevXuzatWqwPxhSFAIDQ1l4MCBFBYWsmvXLt58802ys7MpLS3l7bffPm37hoYG1q5dy3PPPcfy5csNqFiCwfHjx/n1r39NUVERv/rVr4Bzf26d7KmnnqK0tJTHHnsMk8nU4mfoyJEjGTVqFHfffTfXXXcda9eu5Te/+Q1vvfUWL7/8Mo899lgAeywdRUpKCps2bQJg586dDB48GKvVes79+vTpw7p16/je977H888/z4svvsjjjz/e7Pf18ePHWbNmDS+88AKPPvooZWVlbN68mUsvvZQ33niDTZs2sX37dj7//HO/9U86lv/7v/9jyJAhTSHshKSkJHr37s2hQ4eYP38+GzdupLy8nIKCAr744oszfjcrKytjxIgRZGdn88Mf/pBXXnml6ZhRUVG8+eab/OIXv+C5554D4K9//St/+MMfWL9+PX/4wx+avkfK6TQi1gWcOjXxjjvu4IUXXuCZZ57h/fffZ+/evWzbtq3ZP9jrrrsOgEsuuYTq6mqg8Qze4sWLAfjpT39Kz549m73Ozp07+cEPfsCAAQMA+PnPf97sl8Xll18ONJ6pKS4u5uabbwYaQ99//Md/+KHnEsxMJhMvv/wylZWVTWfP6uvrufjii7nqqquabTt69GhMJhOXXHIJVVVVRpQrQeB///d/mTVrFt/97neZN28eTz/99Dk/t07YunUrlZWVvPnmm1gsjb86z/YZekJGRgYffvghzz33HEVFRRw7dsy/nZQOadSoUU2jrhs3biQlJYXc3Nxz7peYmAjAxRdfTExMDBaLhYsvvhiHw9G0zcSJE7FYLPTt25fhw4fz2WefMX78eHbu3Mmf//xnvv76a6qrq/Xe60JCQkLOOstoyJAhxMXFAfC9732PqqoqDhw4cNbvZid/L/z0009bbH/33XcBePzxx/nggw/Iy8vjs88+o66urn072IkoiHUx4eHhpKSk8MEHH7B+/XpuuOEGfvjDHzJ48OBmZzhO/AM2mUxNbWazGZ/Pd8Zje73eZo99Ph9ut/u0Y3o8HlJSUpg3bx7QeL2Gx+O58M5Jp9HQ0MDevXtJSEggLS2N22+/HQCHw4HZbD4tbJ247uLk96vIqa644gp+85vfcPz4cW688UZef/11bDZbs21O/dw6oX///txzzz08/PDDvP766xw/fpybbrrpjJ+hJ/zud7+jZ8+eXH/99YwbN06LeHRRJ6Yn7tixg08++YR77723WRDz+XyYTKbT3nsnj5qdOAFwqhOff9D4e9hqtbJ69Wo2bdrEzTffzMiRIykqKjrr72/pXIYNG8arr77a9L46YdmyZYwcObLZe8lkMuHz+c753ezk74Unv5da+r44efJkEhISSEhIYMSIEX65Frez0NTELsbj8bBt2za6deuGyWRi+vTpJCQk8N57750zDI0YMYJ33nkHgC1btjQ7IweNI16fffYZBw4cAOCNN94gISHhtOOceL2Kigp8Ph8PPfRQu6/kKMHL6/Xy1FNPcfnll/Ozn/2MnJwc6urqcLvd/Pa3v22a3iNyvk58qe3evTuPPfYYjz32GBEREa363Pre977HpEmT6N69O6+88gr79u0742eo2Wxu+v+PPvqIu+++m5/85CdN19TqxFPXlJKSwhNPPMGwYcOafRHu1asXX3zxBT6fj/fff/+8j/u3v/0Nn8/HwYMH2bVrF5dddhkfffQRP//5z7nhhhtwOp0UFhaedrJUOq/4+Hh69+7N008/3fR58+GHH7Ju3ToqKytb3Ke9vptVV1ezb98+Zs2aRWJiIvn5+frMOwuNiHUBJ64RO3G2bfDgwTzyyCPMmzePlJQUTCYTP/rRj9ixY8dZjzN37lzuv/9+1qxZw5AhQ06bmtinTx8efvhh7rrrLlwuFxdffDGLFi067ThDhgzhrrvu4pe//CVer5dLL72UadOmtWufJbgcOXKECRMmADS9J5YtW0ZkZCSFhYXcfPPNeDwerrvuOiZOnMjBgwcNrliC3eWXX86vfvUrHnnkkVZ9bp3w0EMPccstt/DWW29x6aWXtvgZOnLkSJYtW0ZERAQzZ85k8uTJhIWFMWTIEPr378+BAwcYOHBgoLoqHcT111/P3LlzT1s57t5772X69On06dOHq6666rynV/fo0YP09HTcbjcPP/ww0dHR/PKXv+Shhx7i+eefJzw8nCuuuIIDBw7wrW99qz27JB2UyWQiKyuLxYsXM378eCwWC7169eL555/n6NGjLe7TXt/NoqKiuOmmm0hNTcVisXDNNddQX1+vqbFnYPJprFpa6eWXX2bkyJF8//vf5/PPP+fBBx+84HugiIiIiIh0RRoRk1YbOHAgv//975suAn3kkUeMLklEREREJChpRExERERERCTAtFiHiIiIiIhIgCmIiYiIiIiIBJiCmIiIiIiISIBpsQ4REen0/ud//ocnnniC6upqfD4fsbGx3H///VxyySVGlyYiIl2UFusQEZFOraGhgeuuu44XX3yRoUOHApCTk8Py5cvJz8/HbDYbXKGIiHRFGhETEZFO7fjx4xw9erTZDUVvuOEGwsPD8Xg8vPnmm6xevZqQkBD69OnDgw8+yHe+8x0yMjK45JJLmDp1KkCzx6NGjeIHP/gBe/bs4fe//z3f//73mT9/PpWVlYSEhDBjxgzGjRtHaWkpDz/8MIcPH8blcpGamsr06dON+qMQEZEOREFMREQ6tcjISGbPns0dd9xBnz59uPLKK0lISCA1NZUdO3bwwgsv8MYbbxAdHc26dev47W9/y9/+9rdzHveSSy5hxYoVAEycOJGbbrqJKVOmcPjwYW699VYSExOZPXs2v/rVrxg1ahROp5M777yTb33rW4wbN87PvRYRkY5Oi3WIiEind/vtt/PRRx8xb948bDYbf/rTn7jxxhv54IMPGDduHNHR0QCkp6dTWlrKgQMHznnM+Ph4AKqrqyksLGTSpEkA9OvXj//+7/8mJCSE7du38+STTzJhwgRuvvlmDh8+TGFhof86KiIiQUMjYiIi0qnt2LGDf/7zn9xxxx1cf/31XH/99fz+979n/PjxvP/++6Smpjbb3ufz4Xa7MZlMnHwZtcvlarZdjx49ALBYGn+Vmkympue+/vprbDYbPp+P119/ne7duwNQWVlJWFiYX/opIiLBRSNiIiLSqUVHR/Pss8/y6aefNrWVlZVRW1vLHXfcQW5uLpWVlQC89dZbREVFMXDgQHr16sWuXbsAKC0tZdu2bS0ePzw8nKFDh5KdnQ3A4cOHueWWW6ivr2f48OG89NJLADgcDm655Rby8/P92FsREQkWGhETEZFO7Tvf+Q7PPPMMy5cv55tvviEsLIyIiAgeffRREhMT8Xg8/PKXv8Tr9RIdHc1zzz1HSEgIt956K/fddx/JyckMGDCAa6655oyv8cQTT7Bw4UJWr16NyWRi0aJF2Gw2MjMzeeSRR0hLS6OhoYHx48dzww03BLD3IiLSUWn5ehERERERkQDT1EQREREREZEAUxATEREREREJMAUxERERERGRAFMQExERERERCTAFMRERERERkQBTEBMREREREQkwBTEREREREZEAUxATEREREREJsP8PSIuJXWpdA8UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Source vs Price\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": 30,
   "id": "05b62ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0               0             0               0              0\n",
       "1               0             0               1              0\n",
       "2               0             1               0              0\n",
       "3               0             0               1              0\n",
       "4               0             0               0              0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal categorical data we willperform OneHotEncoding\n",
    "Source = train_data[[\"Source\"]]\n",
    "\n",
    "Source = pd.get_dummies(Source ,drop_first= True)\n",
    "\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2aec38d",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1dd13af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination = train_data[[\"Destination\"]]\n",
    "\n",
    "Destination = pd.get_dummies(Destination, drop_first = True)\n",
    "\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4773b49a",
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Route\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d5ab091b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_ Info contains almost 80% of 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": 35,
   "id": "3476bda2",
   "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",
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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>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>non-stop</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 stops</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 stops</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 stop</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 stop</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    non-stop   3897           24   \n",
       "1    Air India   Kolkata    Banglore     2 stops   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin     2 stops  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore      1 stop   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi      1 stop  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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "02748eb8",
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Total_Stops\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cccc2212",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is a case of Ordinal Categorical type we will 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": 38,
   "id": "7d84eb2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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",
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       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
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       "      <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",
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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>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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "331266a3",
   "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": 40,
   "id": "f3a08ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>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",
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       "      <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",
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       "      <td>25</td>\n",
       "      <td>19</td>\n",
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       "      <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",
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       "    </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",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  Airline_Air India  Airline_GoAir  \\\n",
       "0               2             50                  0              0   \n",
       "1               7             25                  1              0   \n",
       "2              19              0                  0              0   \n",
       "3               5             25                  0              0   \n",
       "4               4             45                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0               1                    0                             0   \n",
       "1               0                    0                             0   \n",
       "2               0                    1                             0   \n",
       "3               1                    0                             0   \n",
       "4               1                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Trujet  Airline_Vistara  \\\n",
       "0                 0               0                0   \n",
       "1                 0               0                0   \n",
       "2                 0               0                0   \n",
       "3                 0               0                0   \n",
       "4                 0               0                0   \n",
       "\n",
       "   Airline_Vistara Premium economy  Source_Chennai  Source_Delhi  \\\n",
       "0                                0               0             0   \n",
       "1                                0               0             0   \n",
       "2                                0               0             1   \n",
       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "\n",
       "   Source_Kolkata  Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0               0              0                   0                  0   \n",
       "1               1              0                   0                  0   \n",
       "2               0              0                   1                  0   \n",
       "3               1              0                   0                  0   \n",
       "4               0              0                   0                  0   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  Destination_New Delhi  \n",
       "0                      0                    0                      1  \n",
       "1                      0                    0                      0  \n",
       "2                      0                    0                      0  \n",
       "3                      0                    0                      0  \n",
       "4                      0                    0                      1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8d20bfb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop([\"Airline\", \"Source\",\"Destination\",],axis =1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "56195e51",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897           24              3        22       20   \n",
       "1            2   7662            1              5         5       50   \n",
       "2            2  13882            9              6         9       25   \n",
       "3            1   6218           12              5        18        5   \n",
       "4            1  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0             1           10               2             50   \n",
       "1            13           15               7             25   \n",
       "2             4           25              19              0   \n",
       "3            23           30               5             25   \n",
       "4            21           35               4             45   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
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       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c37b41e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cae4a0",
   "metadata": {},
   "source": [
    "## Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2603ec21",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(r\"Test_set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a2a99527",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5653af4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Info\n",
      "---------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2671 entries, 0 to 2670\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          2671 non-null   object\n",
      " 1   Date_of_Journey  2671 non-null   object\n",
      " 2   Source           2671 non-null   object\n",
      " 3   Destination      2671 non-null   object\n",
      " 4   Route            2671 non-null   object\n",
      " 5   Dep_Time         2671 non-null   object\n",
      " 6   Arrival_Time     2671 non-null   object\n",
      " 7   Duration         2671 non-null   object\n",
      " 8   Total_Stops      2671 non-null   object\n",
      " 9   Additional_Info  2671 non-null   object\n",
      "dtypes: object(10)\n",
      "memory usage: 208.8+ KB\n",
      "None\n",
      "\n",
      "\n",
      "Null Values : \n",
      "---------------------------------------------------------------------------\n",
      "Airline            0\n",
      "Date_of_Journey    0\n",
      "Source             0\n",
      "Destination        0\n",
      "Route              0\n",
      "Dep_Time           0\n",
      "Arrival_Time       0\n",
      "Duration           0\n",
      "Total_Stops        0\n",
      "Additional_Info    0\n",
      "dtype: int64\n",
      "Airline\n",
      "---------------------------------------------------------------------------\n",
      "Jet Airways                          897\n",
      "IndiGo                               511\n",
      "Air India                            440\n",
      "Multiple carriers                    347\n",
      "SpiceJet                             208\n",
      "Vistara                              129\n",
      "Air Asia                              86\n",
      "GoAir                                 46\n",
      "Multiple carriers Premium economy      3\n",
      "Vistara Premium economy                2\n",
      "Jet Airways Business                   2\n",
      "Name: Airline, dtype: int64\n",
      "\n",
      "Source\n",
      "---------------------------------------------------------------------------\n",
      "Delhi       1145\n",
      "Kolkata      710\n",
      "Banglore     555\n",
      "Mumbai       186\n",
      "Chennai       75\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "Destination\n",
      "---------------------------------------------------------------------------\n",
      "Cochin       1145\n",
      "Banglore      710\n",
      "Delhi         317\n",
      "New Delhi     238\n",
      "Hyderabad     186\n",
      "Kolkata        75\n",
      "Name: Destination, dtype: int64\n",
      "\n",
      "\n",
      "Shape of test data :  (2671, 28)\n"
     ]
    }
   ],
   "source": [
    "# Preproccessing\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",
    "\n",
    "# Duration\n",
    "duration = list(test_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration\n",
    "\n",
    "# Adding Duration column to test set\n",
    "test_data[\"Duration_hours\"] = duration_hours\n",
    "test_data[\"Duration_mins\"] = duration_mins\n",
    "test_data.drop([\"Duration\"], axis = 1, inplace = True)\n",
    "\n",
    "\n",
    "# Categorical data\n",
    "\n",
    "print(\"Airline\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Airline\"].value_counts())\n",
    "Airline = pd.get_dummies(test_data[\"Airline\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Source\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Source\"].value_counts())\n",
    "Source = pd.get_dummies(test_data[\"Source\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Destination\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Destination\"].value_counts())\n",
    "Destination = pd.get_dummies(test_data[\"Destination\"], drop_first = True)\n",
    "\n",
    "\n",
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "test_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)\n",
    "\n",
    "# Replacing Total_Stops\n",
    "test_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)\n",
    "\n",
    "# Concatenate dataframe --> test_data + Airline + Source + Destination\n",
    "data_test = pd.concat([test_data, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "data_test.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Shape of test data : \", data_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7dcc1f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\n",
       "0                                  0         0        0   \n",
       "1                                  0         0        0   \n",
       "2                                  0         0        0   \n",
       "3                                  0         0        0   \n",
       "4                                  0         0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec020fe",
   "metadata": {},
   "source": [
    "## Feature Selection \n",
    "\n",
    "Finding out the best feature which will contribute and have a good relation with target variable.\n",
    "Following are some of the feature selection methods,\n",
    "\n",
    " 1. **Heatmap**\n",
    " 2. **feature_importance_**\n",
    " 3. **SelectKBest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7fc3972a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1df514c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Price', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
       "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
       "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
       "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
       "       'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
       "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
       "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
       "       'Destination_Kolkata', 'Destination_New Delhi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "639d73d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  \\\n",
       "0           10               2             50                  0   \n",
       "1           15               7             25                  1   \n",
       "2           25              19              0                  0   \n",
       "3           30               5             25                  0   \n",
       "4           35               4             45                  0   \n",
       "\n",
       "   Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0              0               1                    0   \n",
       "1              0               0                    0   \n",
       "2              0               0                    1   \n",
       "3              0               1                    0   \n",
       "4              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_train.loc[:, ['Total_Stops', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
    "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
    "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
    "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
    "       'Airline_Multiple carriers',\n",
    "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
    "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
    "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
    "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
    "       'Destination_Kolkata', 'Destination_New Delhi']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e444ecac",
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:, 1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "95778e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    " # 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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "37aa2897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 53,
     "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9ed7ce8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.20513615e-01 1.44050632e-01 5.33386520e-02 2.45803724e-02\n",
      " 2.11910430e-02 2.72763539e-02 1.97746119e-02 1.30998339e-01\n",
      " 1.75306442e-02 9.13215658e-03 1.94961912e-03 1.73911712e-02\n",
      " 1.39886262e-01 6.74699791e-02 1.81767141e-02 8.76086705e-04\n",
      " 3.09997255e-03 1.08112817e-04 4.82076162e-03 8.32567913e-05\n",
      " 5.45322259e-04 1.13020381e-02 3.56040927e-03 6.53297780e-03\n",
      " 8.90235703e-03 1.50488662e-02 6.68950193e-03 4.73015519e-04\n",
      " 2.46971560e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "555ae370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1025f4dc",
   "metadata": {},
   "source": [
    "## Fitting model using Random Forest\n",
    "\n",
    "\n",
    " 1.Split dataset into train and test set in order to prediction w.r.t X_test\n",
    " \n",
    " 2.If needed do scaling of data\n",
    "   - Scaling is not done in Random forest\n",
    " \n",
    " 3.Import model\n",
    " \n",
    " 4.Fit the data \n",
    " \n",
    " 5.Predict w.r.t X_test\n",
    " \n",
    " 6.In regression check **RSME** Score\n",
    "\n",
    "7.Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "54939f55",
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bfb87826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 57,
     "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": 58,
   "id": "f6664545",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "68f44aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9532282378878109"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "65f8a8b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7972943034483153"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "51cf59c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1ad748fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2e06ca2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "13c1208c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1176.696477674252\n",
      "MSE: 4370751.646275872\n",
      "RMSE: 2090.6342688944596\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": 66,
   "id": "7e8c55fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026887077025966846"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE/(max(DV)-min(DV))\n",
    "\n",
    "2090.5509/(max(y)-min(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "187af9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7972943034483153"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6370b2f3",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\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": 68,
   "id": "e1ce149d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e39838fb",
   "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": 70,
   "id": "8e255556",
   "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": 72,
   "id": "1aa2c400",
   "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": 74,
   "id": "02dd10c7",
   "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=   5.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.1s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   6.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   6.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.6s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  10.8s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  10.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  11.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  10.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  10.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   4.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   4.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   2.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  14.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  14.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  13.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  14.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  13.8s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2a30181d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 15,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5b2e7835",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "78354d8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sns.distplot(y_test-prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "47f6a1d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "plt.scatter(y_test, prediction, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ce90740f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1165.8803729330511\n",
      "MSE: 4054249.090882626\n",
      "RMSE: 2013.5165981145092\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66159027",
   "metadata": {},
   "source": [
    "## Save the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6467ac2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('flight_rf.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "94a8fdcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "89b30a90",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "35b72ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8119729848613948"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c6124ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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