flight_price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flight Price Prediction\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing dataset\n",
    "\n",
    "1. Since data is in form of excel file we have to use pandas read_excel to load the data\n",
    "2. After loading it is important to check the complete information of data as it can indication many of the hidden infomation such as null values in a column or a row\n",
    "3. Check whether any null values are there or not. if it is present then following can be done,\n",
    "    1. Imputing data using Imputation method in sklearn\n",
    "    2. Filling NaN values with mean, median and mode using fillna() method\n",
    "4. Describe data --> which can give statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_excel(r'E:\\MachineLearning\\EDA\\Flight_Price\\Data_Train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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",
       "29h 30m      1\n",
       "47h 40m      1\n",
       "36h 25m      1\n",
       "32h 20m      1\n",
       "29h 40m      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,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From description we can see that Date_of_Journey is a object data type,\\\n",
    "Therefore, we have to convert this datatype into timestamp so as to use this column properly for prediction\n",
    "\n",
    "For this we require pandas **to_datetime** to convert object data type to datetime dtype.\n",
    "\n",
    "<span style=\"color: red;\">**.dt.day method will extract only day of that date**</span>\\\n",
    "<span style=\"color: red;\">**.dt.month method will extract only month of that date**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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,
   "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302   \n",
       "\n",
       "   Journey_day  Journey_month  \n",
       "0           24              3  \n",
       "1            1              5  \n",
       "2            9              6  \n",
       "3           12              5  \n",
       "4            1              3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we have converted Date_of_Journey column into integers, Now we can drop as it is of no use.\n",
    "\n",
    "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Departure time is when a plane leaves the gate. \n",
    "# Similar to Date_of_Journey we can extract values from Dep_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_data[\"Dep_hour\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_data[\"Dep_min\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.minute\n",
    "\n",
    "# Now we can drop Dep_Time as it is of no use\n",
    "train_data.drop([\"Dep_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route  Arrival_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL  01:10 22 Mar   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR         13:15   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK  04:25 10 Jun   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR         23:30   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL         21:35   \n",
       "\n",
       "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0   2h 50m    non-stop         No info   3897           24              3   \n",
       "1   7h 25m     2 stops         No info   7662            1              5   \n",
       "2      19h     2 stops         No info  13882            9              6   \n",
       "3   5h 25m      1 stop         No info   6218           12              5   \n",
       "4   4h 45m      1 stop         No info  13302            1              3   \n",
       "\n",
       "   Dep_hour  Dep_min  \n",
       "0        22       20  \n",
       "1         5       50  \n",
       "2         9       25  \n",
       "3        18        5  \n",
       "4        16       50  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time taken by plane to reach destination is called Duration\n",
    "# It is the differnce betwwen Departure Time and Arrival time\n",
    "\n",
    "\n",
    "# Assigning and converting Duration column into list\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding duration_hours and duration_mins list to train_data dataframe\n",
    "\n",
    "train_data[\"Duration_hours\"] = duration_hours\n",
    "train_data[\"Duration_mins\"] = duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Duration\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Categorical Data\n",
    "\n",
    "One can find many ways to handle categorical data. Some of them categorical data are,\n",
    "1. <span style=\"color: blue;\">**Nominal data**</span> --> data are not in any order --> <span style=\"color: green;\">**OneHotEncoder**</span> is used in this case\n",
    "2. <span style=\"color: blue;\">**Ordinal data**</span> --> data are in order --> <span style=\"color: green;\">**LabelEncoder**</span> is used in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1751\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              818\n",
       "Vistara                               479\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Airline\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graph we can see that Jet Airways Business have the highest Price.\n",
    "# Apart from the first Airline almost all are having similar median\n",
    "\n",
    "# Airline vs Price\n",
    "sns.catplot(y = \"Price\", x = \"Airline\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \n",
       "0               0                0                                0  \n",
       "1               0                0                                0  \n",
       "2               0                0                                0  \n",
       "3               0                0                                0  \n",
       "4               0                0                                0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4536\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source vs Price\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Source\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0               0             0               0              0\n",
       "1               0             0               1              0\n",
       "2               0             1               0              0\n",
       "3               0             0               1              0\n",
       "4               0             0               0              0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Source = train_data[[\"Source\"]]\n",
    "\n",
    "Source = pd.get_dummies(Source, drop_first= True)\n",
    "\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 30,
     "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": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Route\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "train_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Total_Stops\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "# Here Values are assigned with corresponding keys\n",
    "\n",
    "train_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  \n",
       "0               2             50  \n",
       "1               7             25  \n",
       "2              19              0  \n",
       "3               5             25  \n",
       "4               4             45  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air 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>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",
       "      <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>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
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       "      <td>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>1</td>\n",
       "      <td>13302</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897           24              3        22       20   \n",
       "1            2   7662            1              5         5       50   \n",
       "2            2  13882            9              6         9       25   \n",
       "3            1   6218           12              5        18        5   \n",
       "4            1  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0             1           10               2             50   \n",
       "1            13           15               7             25   \n",
       "2             4           25              19              0   \n",
       "3            23           30               5             25   \n",
       "4            21           35               4             45   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(r\"E:\\MachineLearning\\EDA\\Flight_Price\\Test_set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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       "    <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",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
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       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "# Preprocessing\n",
    "\n",
    "print(\"Test data Info\")\n",
    "print(\"-\"*75)\n",
    "print(test_data.info())\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Null values :\")\n",
    "print(\"-\"*75)\n",
    "test_data.dropna(inplace = True)\n",
    "print(test_data.isnull().sum())\n",
    "\n",
    "# EDA\n",
    "\n",
    "# Date_of_Journey\n",
    "test_data[\"Journey_day\"] = pd.to_datetime(test_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day\n",
    "test_data[\"Journey_month\"] = pd.to_datetime(test_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month\n",
    "test_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)\n",
    "\n",
    "# Dep_Time\n",
    "test_data[\"Dep_hour\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.hour\n",
    "test_data[\"Dep_min\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.minute\n",
    "test_data.drop([\"Dep_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Arrival_Time\n",
    "test_data[\"Arrival_hour\"] = pd.to_datetime(test_data.Arrival_Time).dt.hour\n",
    "test_data[\"Arrival_min\"] = pd.to_datetime(test_data.Arrival_Time).dt.minute\n",
    "test_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Duration\n",
    "duration = list(test_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration\n",
    "\n",
    "# Adding Duration column to test set\n",
    "test_data[\"Duration_hours\"] = duration_hours\n",
    "test_data[\"Duration_mins\"] = duration_mins\n",
    "test_data.drop([\"Duration\"], axis = 1, inplace = True)\n",
    "\n",
    "\n",
    "# Categorical data\n",
    "\n",
    "print(\"Airline\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Airline\"].value_counts())\n",
    "Airline = pd.get_dummies(test_data[\"Airline\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Source\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Source\"].value_counts())\n",
    "Source = pd.get_dummies(test_data[\"Source\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Destination\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Destination\"].value_counts())\n",
    "Destination = pd.get_dummies(test_data[\"Destination\"], drop_first = True)\n",
    "\n",
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "test_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)\n",
    "\n",
    "# Replacing Total_Stops\n",
    "test_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)\n",
    "\n",
    "# Concatenate dataframe --> test_data + Airline + Source + Destination\n",
    "data_test = pd.concat([test_data, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "data_test.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Shape of test data : \", data_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
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       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\n",
       "0                                  0         0        0   \n",
       "1                                  0         0        0   \n",
       "2                                  0         0        0   \n",
       "3                                  0         0        0   \n",
       "4                                  0         0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "\n",
    "Finding out the best feature which will contribute and have good relation with target variable.\n",
    "Following are some of the feature selection methods,\n",
    "\n",
    "\n",
    "1. <span style=\"color: purple;\">**heatmap**</span>\n",
    "2. <span style=\"color: purple;\">**feature_importance_**</span>\n",
    "3. <span style=\"color: purple;\">**SelectKBest**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Price', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
       "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
       "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
       "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
       "       'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
       "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
       "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
       "       'Destination_Kolkata', 'Destination_New Delhi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <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",
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       "      <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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_train.loc[:, ['Total_Stops', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
    "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
    "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
    "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
    "       'Airline_Multiple carriers',\n",
    "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
    "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
    "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
    "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
    "       'Destination_Kolkata', 'Destination_New Delhi']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3897\n",
       "1     7662\n",
       "2    13882\n",
       "3     6218\n",
       "4    13302\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:, 1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finds correlation between Independent and dependent attributes\n",
    "\n",
    "plt.figure(figsize = (18,18))\n",
    "sns.heatmap(train_data.corr(), annot = True, cmap = \"RdYlGn\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.27266589e-01 1.43441315e-01 5.39814369e-02 2.42040694e-02\n",
      " 2.10719256e-02 2.76652859e-02 1.96122759e-02 1.22097615e-01\n",
      " 1.75382457e-02 1.08941003e-02 1.99540808e-03 1.90361634e-02\n",
      " 1.36715024e-01 6.79986736e-02 1.77154000e-02 8.68327954e-04\n",
      " 3.18385528e-03 9.71709835e-05 5.28561307e-03 8.46855588e-05\n",
      " 5.04049195e-04 1.66437263e-02 3.38132154e-03 5.95844971e-03\n",
      " 1.02525689e-02 1.11917306e-02 5.90702603e-03 5.24922150e-04\n",
      " 2.48830250e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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58+cTGxtLeXk5jRs3JjExEbgciDp27Ei/fv1o0qQJLVu2pLCw8Krj33PPPSQnJzN37lwuXbqEi4sLcXFxdOrUibfffpuEhARSU1OxWCz4+PiwePFiu+pu0aKF7ZbCX3Jzc+ONN95g5MiRNG7c2HaL3o1o164d8+bN4/jx47YFIwDGjh3LpEmTSE9Pp3nz5rRv3/6G5wBIndrzpo4vKS2/qeNFRERE5M5lsP7yvi2RGu70aRMWi1pWrk8P54qj1DPiCPWLOEL9Urtca8EI3bYnIiIiIiJiB922V4udPn2a6OjoK7b36NGDcePGOaEiEREREZG6S+GpFvPx8bmhFfhERERERMRxum1PRERERETEDgpPIiIiIiIidlB4EhERERERsYPCk4iIiIiIiB0UnkREREREROyg8CQiIiIiImIHLVUutUpV3/Z8q5SUllN0ofi2zikiIiIiNZPCk9Qqo2ZvpeDs7QszmxZEUHTbZhMRERGRmky37YmIiIiIiNihzoen8PBwevfuzYABA+jRowdjx47liy++uOHx0tLSOH36tO316tWrSUtLu6kat23bRk5Oju11bm4uEydOvKkxq2K1Wvm///s/+vXrR79+/Rg4cCBTp07lwoULNzSe0Whk/PjxV31v+/btzJs372bKFRERERGpMe6I2/aWLl1KYGAgAFu3bmXMmDGkpqYSEhLi8Fh//etf6dSpEz4+PgA899xzN13ftm3baNOmDcHBwQC0bduWBQsW3PS4V7N48WI+//xz/u///o97770Xq9XKxx9/zPnz57nrrruqda6nnnqKp556qlrHFBERERFxljsiPP1Sz549ycnJITU1laSkJBYtWsTnn3+O2WwmKCiIGTNm4Onpyfvvv09aWhru7u5YLBYWL17M1q1bKSgoYPz48Xh4eLBgwQK2bNnCpUuXiIuLw2g0kpWVxV133cXRo0fx9vYmOTkZX19fDh8+zMyZMykuLqa0tJSoqChGjBjBZ599RnZ2Nrt27WLdunWMHDmSpk2bMm/ePIxGIwAbNmwgNTUVAH9/f2bNmoWPj88157uaixcv8pe//IUNGzZw7733AmAwGOjZs6dtn5SUFDIzM4HLIW7q1Kl4enpiNptZtGgRn332GS4uLvj5+fG///u/AJhMJiZMmHBFDUajkR07drB06VL27t1LQkICISEhfPnllxgMBhYtWkRAQMAt+12LiIiIiFSnOy48AYSEhJCdnc2KFSvw9vYmPT0dgMTERFJSUoiJiWH+/Pls2bKFJk2aYDabqaioYOzYsaxbt67Slaxfy83NJTMzk6ZNmzJ16lRWrlxJTEwMDz74oC2MXbx4kSFDhtClSxe6dOlCeHg4bdq0YdiwYQDs3bvXNt6RI0dISkrCaDTSpEkTFi9ezJtvvsnixYuvOd/V5OXl4e7uzsMPP3zV9z/99FMyMzNZs2YNnp6exMXFsXz5cl5//XVSUlI4efIkRqMRd3d3zpw5c93P/Gtff/01c+bMYdasWfzpT39i+fLlt+wKW3Xy9fV2dglyg/S7E0epZ8QR6hdxhPqlbrgjw5PVagUgOzsbk8nERx99BIDZbKZly5YAhIWFER8fT/fu3enWrRt+fn52jd2uXTuaNm0KXA5pu3btAqCkpIQZM2Zw+PBhDAYDBQUFHDp06LpXXvbu3UvXrl1p0qQJAEOHDiUiIuK6892I3bt307dvX7y8Li8HHhUVRUJCAgCffPIJ8fHxuLu7A9C4cWOHa2jevDmtW7cGIDQ0lE8++eSGa72dCgu13l5t5Ovrrd+dOEQ9I45Qv4gj1C+1i4uLocqvx7kjw1Nubi6PPPII+fn5TJ8+nY4dO16xz7Jly8jNzWXPnj28+OKLzJgxg65du153bA8PD9vPrq6uVFRUALBw4UJ8fX2ZO3cubm5uREdHU1paetOfpar5riYgIIDS0lKOHTtG8+bNb3puR2v4OXgBuLi4UF5eXm01iIiIiIjcanV+tb1f27ZtG6tXryY6Oprw8HDS0tIoKSkBLj+7k5eXR3l5OSdPniQ4OJgxY8bQuXNnDh48CICnpydFRY7/n4OioiLuv/9+3NzcOHLkCPv377e95+XlVeWYHTp04NNPP6WwsBCAtWvX0qlTJ4fn/7n2ESNGMG3aNNuKgVarlW3btnHy5Ek6duzIli1bMJlMWK1W0tPTbXN1796d//u//8NsNgNUum1PREREROROcEdceRo/fjzu7u4UFxcTEBBASkoKISEhtG7dmmXLljF48GAMBgMGg4Fx48bh5+dHfHw8RUVFGAwGmjZtals6/MUXX2TKlCnUr1/foed1xo4dy6RJk0hPT6d58+a0b9/e9t6AAQOYPHkyH374oW3BiJ8FBgYSGxtLdHQ0AH5+fsyaNeuGz8Vrr71GWloaw4cPBy6Hp8cee4zHH3+crl27cvjwYYYOHQpAmzZtGDt2LABjxoxhwYIFDBw4kHr16tGsWTOWLl16w3WIiIiIiNQ2BuvPDwCJyBVKSsspulDs7DLkBuj+cnGUekYcoX4RR6hfahc98yR1xunTJiwW5X0RERERuf0UnuqgdevWsXLlyiu2z507l1atWjmhIhERERGR2k/hqQ4aMmQIQ4YMcXYZIiIiIiJ1yh232p6IiIiIiMiNUHgSERERERGxg8KTiIiIiIiIHRSeRERERERE7KDwJCIiIiIiYgeFJxERERERETsoPImIiIiIiNhB3/MktYqPj5dT5i0pLafoQrFT5hYRERGRmkHhSWqVUbO3UnD29oeYTQsiKLrts4qIiIhITaLb9kREREREROyg8HQDzp8/T3BwMLNnz660ffXq1aSlpVV53PDhw/nkk08AWLJkCZs3b75lNebl5REUFHRFPY7Mu3fvXp555hmH587Pz6dDhw4AnDp1iuHDhzs8hoiIiIhITaPb9m5AVlYWISEhfPDBB0yaNAl3d3cAnnvuuSqPqaioqPT61VdfvaU1pqenExYWRkZGBiNGjLjuvBaLBYPBgMFgqNY67rvvPv72t79V65giIiIiIs6g8HQDMjIyeP3113nnnXfYvn07ffr0ASA5OZlLly4RFxeH0WgkMzMTT09Pjh8/TmJiYqUx4uPjadOmDcOGDSM5OZljx45RVFTEyZMn8ff3Z8mSJTRo0ACz2cyiRYv4/PPPMZvNBAUFMWPGDDw9Pausr7y8nE2bNvHee+8xevRocnJyCA4Ovuq8R48exWQy8f333/P+++9z9913X3XM/Px8Bg0axNChQ/n0008pLi7mrbfe4rHHHgPgvffeIy0tDS8vL7p27XrFcXv37gVg4sSJHDt2jLKyMvz9/UlISKhyThERERGRmkThyUGHDh3i3LlzhIWFUVhYSEZGhi08/dqBAwfYuHEj/v7+1x33q6++Ij09HW9vb0aNGsWmTZuIiopixYoVeHt7k56eDkBiYiIpKSnExMRUOdaOHTto1qwZzZo1IzIykoyMDFt4+rWcnByMRiONGze+bo3nzp0jNDSUmJgYMjMzSUpKYs2aNRw6dIg//elPbNiwgXvvvZcZM2ZUOcYbb7xhm2vRokW8++67xMbGXnfumsDX19vZJYiD9DsTR6lnxBHqF3GE+qVuUHhyUHp6OhERERgMBnr27Mns2bM5deoU99133xX7tmvXzq7gBPDEE09w1113ARAcHMyJEycAyM7OxmQy8dFHHwFgNptp2bLlNcfKyMggMjISgIEDBzJw4ECmTJmCh4fHFfs++eSTdgUngIYNG9K9e3cAQkNDmTdvHgD79u2jW7du3HvvvQA8++yzbNmy5apjbNy4kU2bNlFWVsalS5d46KGH7Jq7Jigs1Hp7tYmvr7d+Z+IQ9Yw4Qv0ijlC/1C4uLoYqvx5H4ckBZrOZrKws3N3d2bhxIwBlZWUYjUbGjh17xf7XurXu134ZbFxdXSktLQXAarUyffp0OnbsaNc4P/30E3//+985ePAgy5cvB6C4uJiPPvqIAQMG3FSNPz/bBeDi4kJ5ebndxwLs37+f1atXs2bNGho3bsymTZtYu3atQ2OIiIiIiDiLVttzwPbt22nevDk7d+4kOzub7Oxs/vznP7N+/fpbNmd4eDhpaWmUlJQAYDKZyMvLq3L/DRs20KtXL3bs2GGrMSEhgYyMjFtW4+OPP86nn37K6dOnAWy3GP7ahQsX8PLy4p577sFsNt/SmkREREREqpvCkwMyMjLo379/pW2PPvooFouFffv23ZI5x4wZQ8uWLRk8eDD9+/fn+eefv2Z4MhqNV9T41FNP8dVXX5Gfn39LamzZsiUvv/wyzz33HM888wze3le/p7dLly74+/vTq1cvhg0bRuvWrW9JPSIiIiIit4LBarVanV2ESE1XUlpO0YViZ5chDtD95eIo9Yw4Qv0ijlC/1C565knqjNOnTVgsyvsiIiIicvspPNVS06ZN48CBA5W2ubq6YjQanVSRiIiIiEjdpvBUS82aNcvZJYiIiIiI3FG0YISIiIiIiIgdFJ5ERERERETsoPAkIiIiIiJiB4UnEREREREROyg8iYiIiIiI2EHhSURERERExA4KTyIiIiIiInbQ9zxJreLj4+WUeUtKyym6UOyUuUVERESkZlB4klpl1OytFJy9/SFm04IIim77rCIiIiJSk+i2PRERERERETsoPAHh4eH07t2bAQMG0KNHD8aOHcsXX3xxw+OlpaVx+vRp2+vVq1eTlpZ2UzVu27aNnJwc2+vc3FwmTpx4U2NWZfjw4Tz11FNERETQo0cPRo4cyY4dO+w6Njk5mXnz5gFgNBoZP378Vffbvn27bT8RERERkdpAt+39P0uXLiUwMBCArVu3MmbMGFJTUwkJCXF4rL/+9a906tQJHx8fAJ577rmbrm/btm20adOG4OBgANq2bcuCBQtuetyqTJ06le7duwOwd+9eXnvtNaZPn07Pnj2rZfynnnqKp556qlrGEhERERG5HRSerqJnz57k5OSQmppKUlISixYt4vPPP8dsNhMUFMSMGTPw9PTk/fffJy0tDXd3dywWC4sXL2br1q0UFBQwfvx4PDw8WLBgAVu2bOHSpUvExcVhNBrJysrirrvu4ujRo3h7e5OcnIyvry+HDx9m5syZFBcXU1paSlRUFCNGjOCzzz4jOzubXbt2sW7dOkaOHEnTpk2ZN28eRqMRgA0bNpCamgqAv78/s2bNwsfH55rz2atDhw6MGzeOd955xxaeUlJS2Lp1KxUVFdx33328+eabVx3TZDIxYcKEK+Y2Go3s2LGDpUuXVsNv7Pbw9fV2dgniIP3OxFHqGXGE+kUcoX6pGxSeqhASEkJ2djYrVqzA29ub9PR0ABITE0lJSSEmJob58+ezZcsWmjRpgtlspqKigrFjx7Ju3bpKV7J+LTc3l8zMTJo2bcrUqVNZuXIlMTExPPjgg7YwdvHiRYYMGUKXLl3o0qUL4eHhtGnThmHDhgGXrwb97MiRIyQlJWE0GmnSpAmLFy/mzTffZPHixdecz9HzMXfuXAA2btzIyZMnWbt2LS4uLqxatYq5c+de9UpYdcxdUxQWasmI2sTX11u/M3GIekYcoX4RR6hfahcXF0OVKzwrPFXBarUCkJ2djclk4qOPPgLAbDbTsmVLAMLCwoiPj6d79+5069YNPz8/u8Zu164dTZs2BS6Hkl27dgFQUlLCjBkzOHz4MAaDgYKCAg4dOkRAQMA1x9u7dy9du3alSZMmAAwdOpSIiIjrzueIn88HXD4nX331FZGRkQBUVFTg5XX1BquOuUVEREREagKFpyrk5ubyyCOPkJ+fz/Tp0+nYseMV+yxbtozc3Fz27NnDiy++yIwZM+jatet1x/bw8LD97OrqSkVFBQALFy7E19eXuXPn4ubmRnR0NKWlpTf9WaqazxE/nw+4HKTGjh3L4MGDb8vcIiIiIiI1gVbbu4pt27axevVqoqOjCQ8PJy0tjZKSEuDyMzx5eXmUl5dz8uRJgoODGTNmDJ07d+bgwYMAeHp6UlTk+KXZoqIi7r//ftzc3Dhy5Aj79++3vefl5VXlmB06dODTTz+lsLAQgLVr19KpUyeH56/K/v37WbZsGaNHjwYur064atUqzp8/D1y+Gnfo0KFqm09EREREpCbSlaf/Z/z48bi7u1NcXExAQAApKSmEhITQunVrli1bxuDBgzEYDBgMBsaNG4efnx/x8fEUFRVhMBho2rSpbenwF198kSlTplC/fn2HVsQbO3YskyZNIj09nebNm9O+fXvbewMGDGDy5Ml8+OGHtgUjfhYYGEhsbCzR0dEA+Pn5MWvWrJs6H7Nnz2bx4sUUFxfzwAMP8Oabb9pW3xs4cCDnzp2zPX9ltVp57rnnbLczioiIiIjURQbrLx9mEZGrKiktp+hCsbPLEAfo4VxxlHpGHKF+EUeoX2oXLRghdcbp0yYsFuV9EREREbn9FJ7uUOvWrWPlypVXbJ87dy6tWrVyQkUiIiIiIjWbwtMdasiQIQwZMsTZZYiIiIiI1BpabU9ERERERMQOCk8iIiIiIiJ2UHgSERERERGxg8KTiIiIiIiIHRSeRERERERE7KDwJCIiIiIiYgeFJxERERERETvoe56kVvHx8XJ2CTekpLScogvFzi5DRERERG6CwpPUKqNmb6XgbO0LIZsWRFDk7CJERERE5Kbotj0RERERERE7KDzdRlu2bGHgwIFERETQu3dvJk6c6OySrspoNPLYY48xcOBA+vTpw4ABA1i2bBklJSXXPTY/P58OHTrYXgcFBXHx4sWr7hsREWHXmCIiIiIiNYFu27tNCgoKmDlzJuvXr6dp06ZYrVYOHjxYbeNXVFTg6upabeN16tSJpUuXAnD69GneeOMNJkyYwNtvv11tc2zcuLHaxhIRERERudUUnm6Tn376CTc3N+655x4ADAYDrVu3BmDnzp0sXLiQiooKGjduzKxZs2jWrBlGo5EdO3bYQswvXxuNRjIzM/H09OT48eMkJiZSUlLC/PnzbVd6Jk2axBNPPME333xDQkICZ8+epaysjJdeeolBgwbZXbuPjw/z5s3jySef5OjRozzyyCMcOHCApKQk21zjx4+nW7duVz3+b3/7Gx9//DHnzp1j0qRJ9OrVC7h8VeqLL77A09PzRk5prePr6+3sEu44OufiKPWMOEL9Io5Qv9QNCk+3ScuWLQkODqZbt2506NCBdu3aERERgcViYdKkSaxcuZIWLVqwbt06YmNjWbdu3XXHPHDgABs3bsTf359z587Rr18/kpOTadeuHRUVFZhMJsrLy4mNjSUxMZGAgABMJhODBg0iNDSUgIAAu+u/++67adasGUePHuW+++5j+vTppKSk0KRJEwoKChg8eDBZWVlXPdbLy4uMjAz++c9/MmHCBFt4utMUFmrJiNvJ19db51wcop4RR6hfxBHql9rFxcVQ5QrPCk+3iYuLC8uXL+fIkSN8/vnnbNu2jdTUVF577TVatmxJixYtABg0aBAzZ87EZDJdd8x27drh7+8PwL/+9S8CAgJo164dAK6urtx99918/fXX5OXl8dprr9mOKysr45tvvnEoPAFYrVYAvvzyS/Lz8xk9erTtPYPBwPHjx2nUqNEVx/Xt2xeA0NBQCgoKKC0txcPDw6G5RUREREScTeHpNgsMDCQwMJAXXnjBFiqq4urqisVisb0uLS2t9L49t7tZrVYaNWp0088XnT9/nhMnThAYGEh+fj5BQUG89957V+yXn59/xbafg9LPz2SVl5crPImIiIhIraPV9m6TU6dO8eWXX9pe//jjj5w5c4aAgAAOHTpEXl4eAOvXr6d169Z4eXnRrFkzDh8+jNlsxmw289FHH1U5fmhoKHl5ebY5KioqOH/+PM2bN6d+/fps2LDBtm9eXp5dV7Z+dubMGaZMmULHjh1p0aIFjz76KMePH2fPnj22fXJycmxXpkRERERE6iJdebpNysvLSU5O5rvvvqN+/fpYLBYmTJhAcHAw8+fPJzY2lvLycho3bkxiYiJwORB17NiRfv360aRJE1q2bElhYeFVx7/nnntITk5m7ty5XLp0CRcXF+Li4ujUqRNvv/02CQkJpKamYrFY8PHxYfHixdesd9euXQwcOJCSkhLc3d3p0aOH7Ta9u+++m+XLl5OYmEhCQgJlZWX4+flV60p8VUmd2vOWz3ErlJSWO7sEEREREblJBqsuF0gtcvq0CYtFLSvXp4dzxVHqGXGE+kUcoX6pXa61YIRu2xMREREREbGDbtu7Q50+fZro6Ogrtvfo0YNx48Y5oSIRERERkZpN4ekO5ePjc9Mr8ImIiIiI3El0256IiIiIiIgdFJ5ERERERETsoPAkIiIiIiJiB4UnEREREREROyg8iYiIiIiI2EHhSURERERExA5aqlxqlaq+7bm2KCktp+hCsbPLEBEREZEboPAktcqo2VspOFt7w8emBREUObsIEREREbkhum1PRERERETEDgpP1SQ8PJzevXszYMAAevTowdixY/niiy+qdY78/Hzef//9SttGjx7NiRMnqnUeR73xxhvs37/fqTWIiIiIiNxqum2vGi1dupTAwEAAtm7dypgxY0hNTSUkJMSu48vLy3Fzq/pX8t133/H+++/z7LPP2ra9++67N1d0NXjrrbecXYKIiIiIyC2n8HSL9OzZk5ycHFJTU2nYsCFt2rRh2LBhAMTHx9tex8fH4+rqyrFjx7h48SIbN25k4sSJHDt2jLKyMvz9/UlISODuu+9m1qxZ5OfnExERQbNmzVi6dCnh4eG8/fbbBAYGcvz4caZNm8aZM2dwc3MjJiaGJ598EoCgoCBiYmL4+OOPOXfuHJMmTaJXr15V1p+cnMw333yDyWTi22+/5be//S1jxoxh7ty5fP/99/To0YO4uDgAhg8fTnR0NN27dyc+Ph53d3e+/fZbfvzxR0JDQ5k3bx4Gg4H333+ftLQ03N3dsVgsLF68mICAgFv/yxARERERqQYKT7dQSEgI2dnZBAcHX3O/gwcPsnLlSho2bAhcvg2ucePGACxatIh3332X2NhYpk2bxrx58zAajVcdJzY2lqioKIYMGcLXX3/NCy+8wJYtW2xjeXl5kZGRwT//+U8mTJhwzfAE8O9//5uMjAwaNmxIZGQkCxYsYMWKFZSXl/PUU0/x7LPP8tBDD11x3NGjR0lLS8NgMBAZGcmuXbvo3Lkz8+fPZ8uWLTRp0gSz2UxFRcX1TmGd5Ovr7ewS7hg61+Io9Yw4Qv0ijlC/1A0KT7eQ1Wq1a7/evXvbghPAxo0b2bRpE2VlZVy6dOmqAeXXTCYTBw8eZNCgQQC0aNGCVq1a8a9//Yvw8HAA+vbtC0BoaCgFBQWUlpbi4eFR5ZhPPPEE3t6X/0MPCgqiZcuWuLu74+7uTvPmzTlx4sRVa3v66adt47Zu3ZoTJ07QuXNnwsLCiI+Pp3v37nTr1g0/Pz+7zk9dU1io9fZuB19fb51rcYh6RhyhfhFHqF9qFxcXQ5Vfj6MFI26h3NxcHnnkEVxdXbFYLLbtpaWllfb7ZXDav38/q1evZsWKFWzatIkJEyZgNpurpZ6fA42rqytw+Rkre/b/+Zhfv67qylFV+y1btowJEyZQXFzMiy++yKeffnpjH0RERERExAkUnm6Rbdu2sXr1aqKjo2nWrBm5ubkAFBQUsHfv3iqPu3DhAl5eXtxzzz2YzWYyMjJs73l5eWEyma56nJeXF61atWL9+vUA5OXlcejQIUJDQ6vvQ92E8vJyTp48SXBwMGPGjKFz584cPHjQ2WWJiIiIiNhNt+1Vo/Hjx+Pu7k5xcTEBAQGkpKQQEhKCv78/48ePp2/fvjz00EPXfAaqS5cuZGZm0qtXLxo1asRjjz1mC15BQUE0b96c3//+9zz88MMsXbq00rFJSUlMmzaNtLQ03NzcmD9/vu15J2ezWCzEx8dTVFSEwWCgadOmTJw40dlliYiIiIjYzWC198EcEblpJaXlFF0odnYZdwTdXy6OUs+II9Qv4gj1S+1yrWeedOVJapXTp01YLMr7IiIiInL7KTzdwU6fPk10dPQV23v06MG4ceOcUJGIiIiISM2l8HQH8/HxYePGjc4uQ0RERESkVtBqeyIiIiIiInZQeBIREREREbGDwpOIiIiIiIgdFJ5ERERERETsoPAkIiIiIiJiB4UnEREREREROyg8iYiIiIiI2EHf8yS1io+Pl7NLuGklpeUUXSh2dhkiIiIi4iCFJ6lVRs3eSsHZ2h08Ni2IoMjZRYiIiIiIw3TbnoiIiIiIiB1qRHg6f/48wcHBzJ49u9L21atXk5aWVuVxw4cP55NPPgFgyZIlbN68+ZbUFxQUxKBBgyptS05OJigoyDb/tfyyzm3btpGTk2N7Lzc3l4kTJ153jPz8fDp06OBg5bfOG2+8wf79+51dhoiIiIjIbVMjbtvLysoiJCSEDz74gEmTJuHu7g7Ac889V+UxFRUVlV6/+uqrt7RGq9XK119/TYsWLbBarWRlZREYGOjwONu2baNNmzYEBwcD0LZtWxYsWFDd5Vab8vJy3Nwqt0lFRQVvvfVWtYwlIiIiIlJb1Ih/yWZkZPD666/zzjvvsH37dvr06QNcvrpz6dIl4uLiMBqNZGZm4unpyfHjx0lMTKw0Rnx8PG3atGHYsGEkJydz7NgxioqKOHnyJP7+/ixZsoQGDRpgNptZtGgRn3/+OWazmaCgIGbMmIGnp+c1a4yMjMRoNDJp0iT27t1LYGAg586ds70/fPhwoqOj6d69+1VfA3z22WdkZ2eza9cu1q1bx8iRI2natCnz5s3DaDSSn5/PoEGDiIyM5B//+AcA06dP57HHHruingMHDpCUlMTFixcBGD9+PN26dbtiv6KiIhISEvjqq68wGAw89thjTJs2jd27d7N48WJKS0upqKjg5Zdfpl+/frbaW7ZsyYEDB7j77rvp06fPFec+ISHB9vlMJhNz5szh8OHDlJaW0qFDByZPnoyrq+sVY82dO5eJEydy+vRpADp27MiUKVOuee5FRERERGoCp4enQ4cOce7cOcLCwigsLCQjI8MWnn7twIEDbNy4EX9//+uO+9VXX5Geno63tzejRo1i06ZNREVFsWLFCry9vUlPTwcgMTGRlJQUYmJirjle7969GTZsGBMnTmT9+vVERkbyl7/8xaHP2qVLF8LDw20hD2Dv3r2V9jl37hwtW7YkPj6evXv38tprr7Ft27ZK+1y4cIHp06eTkpJCkyZNKCgoYPDgwWRlZXHXXXdV2jchIYGGDRuyceNGXFxcOHPmDACtW7dm1apVuLq68tNPP/HMM8/wxBNPcPfddwNw8uRJVq1ahZubG0aj8Zrnfs6cObRv35633noLi8VCbGwsGRkZREVFXTFWWloa/v7+ttsxz58/79A5rCt8fb2dXcIdQedZHKWeEUeoX8QR6pe6wenhKT09nYiICAwGAz179mT27NmcOnWK++6774p927VrZ1dwAnjiiSdsQSI4OJgTJ04AkJ2djclk4qOPPgLAbDbTsmXL647XsGFDQkND+fjjj/nnP//JW2+95XB4ske9evUYMGAAAB06dKB+/fp88803eHn9/0t0f/nll+Tn5zN69GjbNoPBwPHjx2nbtm2l8T755BOMRiMuLpcfb2vcuDEAZ86cYcqUKRw/fhxXV1fOnz/PsWPHCA0NBaB///6VbrG71rnPzs4mJyfHdj5KSkoq/f5+OVZISAhpaWnMmzePxx9/nCeeeOKGzlNtV1io9fZuNV9fb51ncYh6RhyhfhFHqF9qFxcXQ5Vfj+PU8GQ2m8nKysLd3Z2NGzcCUFZWhtFoZOzYsVfsf71b637Jw8PD9rOrqyulpaXA5WeXpk+fTseOHR2uNzIykldffZXIyMgrnt1xdXXFYrHYXv88361gtVoJCgrivffeu+ExZsyYQXh4OMuWLcNgMNCrV69KNTds2LDS/tc691arleXLl+Pn53fV93851qOPPsr69evZtWsXGzduJCUlhdWrV9/w5xARERERuV2cutre9u3bad68OTt37iQ7O5vs7Gz+/Oc/s379+ls2Z3h4OGlpaZSUlABgMpnIy8uz69gOHTrwxz/+kRdeeOGK9/z9/cnNzQXg66+/5uDBg1cdw8vLi6Kiqv/PQ1lZGZs2bQJg//79lJSU8PDDD1fa59FHH+X48ePs2bPHti0nJwer1XrFeN27dyc1NdX23s+37RUVFfHggw9iMBj4xz/+wfHjx6/10a8pPDyclJQU2yIeZ86c4eTJk1fd9+TJk3h5edGvXz8mT57Mv//970qhU0RERESkpnLqlaeMjAz69+9fadujjz6KxWJh3759t2TOMWPGsGzZMgYPHozBYMBgMDBu3DgCAgKue6zBYCA6Ovqq740ePZpXX32V7du307p1a1q3bn3V/QYMGMDkyZP58MMPbQtG/NI999zDoUOHWLFiBQALFy60rT74s7vvvpvly5fbFm4oKyvDz8+Pt99+G4PBUGnfyZMnk5CQwO9//3tcXV15/PHHmTp1KhMnTmTmzJkkJyfTtm1bgoKCrvv5qzJlyhQSExNtt1/Wq1ePKVOmXPVK1L59+0hLS8PFxQWLxcLMmTNttxSKiIiIiNRkBuvVLleIU/y82t6vF5GQuqWktJyiC8XOLqPO0/3l4ij1jDhC/SKOUL/ULjX2mScRR50+bcJiUd4XERERkdtP4en/mTZtGgcOHKi0zdXVFaPReNtq+M1vfqOrTiIiIiIiNZTC0/8za9YsZ5cgIiIiIiI1mJ7UFxERERERsYPCk4iIiIiIiB0UnkREREREROyg8CQiIiIiImIHhScRERERERE7KDyJiIiIiIjYQeFJRERERETEDvqeJ6lVfHy8nF1CtSopLafoQrGzyxAREREROyg8Sa0yavZWCs7WnbCxaUEERc4uQkRERETsotv2RERERERE7KDwdAudP3+e4OBgZs+ebdu2evVq0tLSqjxm+PDhfPLJJwAsWbKEzZs335LagoKCuHjxosPHhYeHc+TIEQBGjx7NiRMnbO9lZWXxzDPP0LNnT5555hmGDRtm+ywiIiIiIrWdbtu7hbKysggJCeGDDz5g0qRJuLu789xzz1W5f0VFRaXXr7766q0u8aa8++67tp/XrVvHX/7yF5KTkwkICADgyJEj7Nq1y1nliYiIiIhUK4WnWygjI4PXX3+dd955h+3bt9OnTx+Sk5O5dOkScXFxGI1GMjMz8fT05Pjx4yQmJlY6Pj4+njZt2jBs2DCSk5M5duwYRUVFnDx5En9/f5YsWUKDBg0wm80sWrSIzz//HLPZTFBQEDNmzMDT09OuOsPDw4mIiGDXrl0UFhYSHR3NsGHDANi/fz8zZ84EoH379lit1krHvf322wQGBpKcnMxbb71lC04AgYGBBAYG2l6npKSQmZkJQNu2bZk6dardNdZlvr7ezi6hztK5FUepZ8QR6hdxhPqlblB4ukUOHTrEuXPnCAsLo7CwkIyMDPr06XPFfgcOHGDjxo34+/tfd8yvvvqK9PR0vL29GTVqFJs2bSIqKooVK1bg7e1Neno6AImJiaSkpBATE2N3vSUlJbz//vvk5+fTv39/IiMjqVevHjExMSQlJdGhQwc2b97Me++9d8Wxp0+f5tSpU4SEhFQ5/qeffkpmZiZr1qzB09OTuLg4li9fzuuvv253jXVVYaGWjLgVfH29dW7FIeoZcYT6RRyhfqldXFwMVa7wrPB0i6SnpxMREYHBYKBnz57Mnj2bU6dOXbFfu3bt7ApOAE888QR33XUXAMHBwbbnjbKzszGZTHz00UcAmM1mWrZs6VC9ffv2BeA3v/kNd911Fz/++CNlZWU0aNCADh062PaZNm2aXeMNHToUk8mEh4cHGRkZ7N69m759++LldbkRo6KiSEhIcKhGERERERFnUni6BcxmM1lZWbi7u7Nx40YAysrKMBqNV+zryG1rHh4etp9dXV0pLS0FwGq1Mn36dDp27HjDNf967F8/f/Uzg8FwxTYfHx/uu+8+cnNz6dy5MwBr1qzhyJEjvPzyyzdck4iIiIhITaLV9m6B7du307x5c3bu3El2djbZ2dn8+c9/Zv369bdkvvDwcNLS0igpKQHAZDKRl5d30+M+/PDDlJSUsH//fgA+/PBDLly4cNV9/+u//os5c+Zw7Ngx27bi4v//+5g6duzIli1bMJlMWK1W0tPT6dSp003XKCIiIiJyu+jK0y2QkZFB//79K2179NFHsVgs7Nu3jzZt2lTrfGPGjGHZsmUMHjwYg8GAwWBg3LhxlRZvuBHu7u4sXLiw0oIRDzzwwFX3ffbZZ6lfvz4TJ07EZDLRuHFjGjRoQHx8PABdu3bl8OHDDB06FIA2bdowduzYm6pPREREROR2Mlh/uXyaiNxWJaXlFF0ovv6O4jA9nCuOUs+II9Qv4gj1S+2iBSOkzjh92oTForwvIiIiIrefwlMdNm3aNA4cOFBpm6ur61UXrhARERERkWtTeKrDZs2a5ewSRERERETqDK22JyIiIiIiYgeFJxERERERETsoPImIiIiIiNhB4UlERERERMQOCk8iIiIiIiJ2UHgSERERERGxg8KTiIiIiIiIHfQ9T1Kr+Ph4ObuEW6KktJyiC8XOLkNERERErkHhSWqVUbO3UnC27oWMTQsiKHJ2ESIiIiJyTbptT0RERERExA4KTzXQ+fPnCQ4OZvbs2Q4dt3r1atLS0m543vz8fDp06HDDx//aG2+8wf79+6ttPBERERERZ9JtezVQVlYWISEhfPDBB0yaNAl3d/dK71ssFgwGAwaDwbatvLyc55577naXek1vvfWWs0sQEREREak2Ck81UEZGBq+//jrvvPMO27dvp0+fPiQnJ3P06FFMJhPff/89CxcuZOTIkTzzzDPs2bOHqKgofvrpJy5dukRcXBw9e/Zk6dKltGzZEoCVK1fy73//mzlz5jBv3jz27dtHWVkZjRo1IiEhgQcffNCu2pKTk/nmm28wmUx8++23/Pa3v2XMmDHMnTuX77//nh49ehAXFwfA8OHDiY6Opnv37sTHx+Pu7s63337Ljz/+SGhoKPPmzasUAO90vr7ezi6hztE5FUepZ8QR6hdxhPqlblB4qmEOHTrEuXPnCAsLo7CwkIyMDPr06QNATk4ORqORxo0bk5+fz7lz52jbtq0trCQnJ9vGGThwIOvXr2fy5MkAGI1G28+jR4+2HbNu3TqSkpJYtGiR3TX++9//JiMjg4YNGxIZGcmCBQtYsWIF5eXlPPXUUzz77LM89NBDVxx39OhR0tLSMBgMREZGsmvXLjp37nxD56kuKizUkhHVydfXW+dUHKKeEUeoX8QR6pfaxcXFUOUKz3rmqYZJT08nIiICg8FAz549ycnJ4dSpUwA8+eSTNG7c2Lavh4eHLVj92sCBA/nggw8oLy/n8OHDXLhwgcceewyAnTt3EhUVxe9//3tSU1M5ePCgQzU+8cQTeHt74+rqSlBQEJ06dcLd3Z2GDRvSvHlzTpw4cdXjnn76aTw8PHB3d6d169ZV7iciIiIiUhPpylMNYjabycrKwt3dnY0bNwJQVlaG0WgEwNPTs9L+DRo0qPK2twceeIAWLVqwc+dO9u3bR2RkJAaDge+++445c+aQnp6On58fX3zxBbGxsQ7V6eHhYfvZ1dX1itcVFRV2HVfVfiIiIiIiNZHCUw2yfft2mjdvzurVq23bvvzyS+Li4ujfv7/D40VGRrJu3Tpyc3NZu3YtACaTiXr16uHr64vFYmHNmjXVVr+IiIiISF2m8FSDZGRkXBGSHn30USwWC/v27aNNmzYOjdezZ09mzZpF27ZteeCBBwAICgqid+/e9O3bl0aNGtG1a9datZx46tSezi7hligpLXd2CSIiIiJyHQar1Wp1dhEi9jp92oTFopaV69PDueIo9Yw4Qv0ijlC/1C5aMEJEREREROQm6bY9ucLp06eJjo6+YnuPHj0YN26cEyoSEREREXE+hSe5go+Pj221PxERERERuUy37YmIiIiIiNhB4UlERERERMQOCk8iIiIiIiJ2UHgSERERERGxg8KTiIiIiIiIHRSeRERERERE7KClyqVWqerbnuuaktJyii4UO7sMEREREfkFhSepVUbN3krB2bofKjYtiKDI2UWIiIiISCW6bU9ERERERMQOuvJUR4SHh+Pu7o67uzvFxcW0aNGC0aNH065dO6fVtH37dvbv309cXJzTahARERERqS4KT3XI0qVLCQwMBGDr1q2MGTOG1NRUQkJCnFLPU089xVNPPeWUuUVEREREqpvCUx3Vs2dPcnJySE1NJSkpiUWLFvH5559jNpsJCgpixowZeHp6Eh8fj5ubG19//TVnz56lffv2TJs2DXd396uOm5+fz6BBg4iKiuKzzz6jpKSEpKQk1qxZw4EDB6hfvz7Lly/H19cXo9HIjh07WLp0KXv37iUhIYGQkBC+/PJLDAYDixYtIiAg4DafGRERERGRG6PwVIeFhISQnZ3NihUr8Pb2Jj09HYDExERSUlKIiYkB4MCBA6xZswYPDw/GjBnD2rVrGTZsWJXjnjt3jt/97ndMnDiRFStWMGLECP72t78xe/ZsZsyYwcqVK21j/9LXX3/NnDlzmDVrFn/6059Yvnw5CxYsuDUfvg7w9fV2dgm1ns6hOEo9I45Qv4gj1C91g8JTHWa1WgHIzs7GZDLx0UcfAWA2m2nZsqVtv759++Lp6QnAwIED2bp16zXDU8OGDenWrRsAv/3tb7n//vtp1aqV7fWuXbuuelzz5s1p3bo1AKGhoXzyySc39wHruMJCrbd3M3x9vXUOxSHqGXGE+kUcoX6pXVxcDFV+PY7CUx2Wm5vLI488Qn5+PtOnT6djx47VMu4vb+lzcXGp9NrV1ZWKigq7jisvL6+WekREREREbgctVV5Hbdu2jdWrVxMdHU14eDhpaWmUlJQAYDKZyMvLs+374YcfcunSJcrLy9m4cSNhYWHOKltEREREpMbSlac6ZPz48balygMCAkhJSSEkJITWrVuzbNkyBg8ejMFgwGAwMG7cONtiDW3btiU6OpozZ87w+OOPExUV5eRPIiIiIiJS8xisPz8YI3ek+Ph42rRpc81nnOT2Kyktp+hCsbPLqNV0f7k4Sj0jjlC/iCPUL7WLnnmSOuP0aRMWi/K+iIiIiNx+Ck93uLlz5151+7Rp0zhw4EClba6urhiNxttRloiIiIhIjaPwJFc1a9YsZ5cgIiIiIlKjaLU9EREREREROyg8iYiIiIiI2EHhSURERERExA4KTyIiIiIiInZQeBIREREREbGDwpOIiIiIiIgdFJ5ERERERETsoO95klrFx8fL2SXUGCWl5RRdKHZ2GSIiIiJ3DIUnqVVGzd5KwVkFBoBNCyIocnYRIiIiIncQ3bYnIiIiIiJiB115qoXCw8Nxd3fH3d2d4uJiWrRowejRo2nXrl21zpOfn8+gQYPYu3dvtY4rIiIiIlIbKTzVUkuXLiUwMBCArVu3MmbMGFJTUwkJCXFyZfazWCwYDAYMBoOzSxERERERuS6FpzqgZ8+e5OTkkJqaSlJSEosWLeLzzz/HbDYTFBTEjBkz8PT0JD4+Hjc3N77++mvOnj1L+/btmTZtGu7u7tccf9GiRXz66acUFxfz1ltv8dhjjwGwYcMGUlNTAfD392fWrFn4+PiQnJzMpUuXiIuLA6j0Ojk5maNHj2Iymfj+++95//33ufvuu2/tCarDfH29nV1CjabzI45Sz4gj1C/iCPVL3aDwVEeEhISQnZ3NihUr8Pb2Jj09HYDExERSUlKIiYkB4MCBA6xZswYPDw/GjBnD2rVrGTZsWJXjnjt3jtDQUGJiYsjMzCQpKYk1a9Zw5MgRkpKSMBqNNGnShMWLF/Pmm2+yePHi69aak5OD0WikcePG1fLZ72SFhVoyoiq+vt46P+IQ9Yw4Qv0ijlC/1C4uLoYqV3jWghF1hNVqBSA7O5vMzEwiIiKIiIggOzubEydO2Pbr27cvnp6euLm5MXDgQPbs2XPNcRs2bEj37t0BCA0N5eTJkwDs3buXrl270qRJEwCGDh3K7t277ar1ySefVHASERERkVpHV57qiNzcXB555BHy8/OZPn06HTt2rJZxf3lLn4uLC+Xl5dc9xtXVFYvFYntdWlpa6X1PT89qqU1ERERE5HbSlac6YNu2baxevZro6GjCw8NJS0ujpKQEAJPJRF5enm3fDz/8kEuXLlFeXs7GjRsJCwu7oTk7dOjAp59+SmFhIQBr166lU6dOADRr1ox///vfWCwWTCYTO3bsuLkPKCIiIiJSA+jKUy01fvx421LlAQEBpKSkEBISQuvWrVm2bBmDBw+2rWQ3btw4AgICAGjbti3R0dGcOXOGxx9/nKioqBuaPzAwkNjYWKKjowHw8/Nj1qxZAPTo0YPNmzfTp08fHnjgAX77299Wz4cWEREREXEig/Xnh2WkzouPj6dNmzbXXCBCao+S0nKKLhQ7u4waSw/niqPUM+II9Ys4Qv1Su1xrwQhdeZJa5fRpExaL8r6IiIiI3H4KT3eQuXPnXnX7tGnTOHDgQKVtrq6uGI3G21GWiIiIiEitoPAktmeVRERERESkalptT0RERERExA4KTyIiIiIiInZQeBIREREREbGDwpOIiIiIiIgdFJ5ERERERETsoPAkIiIiIiJiB4UnERERERERO+h7nqRW8fHxcnYJNU5JaTlFF4qdXYaIiIhInafwJLXKqNlbKTiroPBLmxZEUOTsIkRERETuALptT0RERERExA41MjyFh4fTu3dvBgwYQI8ePRg7dixffPHFTY2ZlpbG6dOnba9Xr15NWlraTY25bds2cnJybK9zc3OZOHHiTY1ZleHDh9OxY0cuXrxYadsnn3xS7XPl5+fTunVrIiIi6N+/P71792bq1Kn8+OOPdh0fHh7OkSNHrlvjG2+8wf79+6utbhERERGRW6lGhieApUuXkpmZyccff0xkZCRjxozhwIEDNzzeX//610rh6bnnnmPEiBE3VeOvw1Pbtm1ZsGDBTY15LQ0aNOAvf/nLLRv/l7y9vdm4cSObNm0iMzMTX19fhg4dSlFR9d0g9tZbb/HYY49V23giIiIiIrdSrXjmqWfPnuTk5JCamsrSpUsxm80sWrSIzz//HLPZTFBQEDNmzMDT05P333+ftLQ03N3dsVgsLF68mK1bt1JQUMD48ePx8PBgwYIFbNmyhUuXLhEXF4fRaCQrK4u77rqLo0eP4u3tTXJyMr6+vhw+fJiZM2dSXFxMaWkpUVFRjBgxgs8++4zs7Gx27drFunXrGDlyJE2bNmXevHkYjUYANmzYQGpqKgD+/v7MmjULHx+fa853LWPGjGHp0qU8//zzNG7cuNJ7JpOJOXPmcPjwYUpLS+nQoQOTJ0/m+PHj/Pd//zcffPAB5eXldOjQgbFjx/KHP/yBzZs3s3379usGPnd3d1599VV27dpFZmYmL7zwAgUFBcyePZvvv/+e0tJS+vXrx8svv3zV4/ft20dKSgoFBQX06dOH2NhY4PJVqejoaLp3725XH0jVfH29nV1CjaTzIo5Sz4gj1C/iCPVL3VArwhNASEgI2dnZAKxYsQJvb2/S09MBSExMJCUlhZiYGObPn8+WLVto0qQJZrOZiooKxo4dy7p161i6dCmBgYFXHT83N5fMzEyaNm3K1KlTWblyJTExMTz44IO2MHbx4kWGDBlCly5d6NKlC+Hh4bRp04Zhw4YBsHfvXtt4R44cISkpCaPRSJMmTVi8eDFvvvkmixcvvuZ813LfffcRERHB22+/zZQpUyq9N2fOHNq3b89bb72FxWIhNjaWjIwMoqKiMJlMFBQU8N133/HII4+we/du/vCHP7Bnzx7CwsLs/h20bduWo0ePAhAXF8crr7xC+/btMZvNjBgxgrZt29K5c+crjvvhhx947733uHjxIk8//TSDBw/moYcesnteub7CQi0Z8Wu+vt46L+IQ9Yw4Qv0ijlC/1C4uLoYqV3iuNeHJarXafs7OzsZkMvHRRx8BYDabadmyJQBhYWHEx8fTvXt3unXrhp+fn13jt2vXjqZNmwKXg9quXbsAKCkpYcaMGRw+fBiDwUBBQQGHDh0iICDgmuPt3buXrl270qRJEwCGDh1KRETEdee7njFjxtCvXz9GjhxZaXt2djY5OTm22/pKSkq47777gMvnZPfu3eTn5/Pss8+yYsUKzGYzu3btYvTo0XbN+0uXLl1i3759nDlzxrbt4sWL5OXlXTU89e7dGxcXF7y9vQkICODEiRMKTyIiIiJS69Sa8JSbm8sjjzwCXA5S06dPp2PHjlfst2zZMnJzc9mzZw8vvvgiM2bMoGvXrtcd38PDw/azq6srFRUVACxcuBBfX1/mzp2Lm5sb0dHRlJaW3vTnqWq+62nUqBHDhg1j6dKllbZbrVaWL19+1bAYFhbGnj17yM/PJzExkc8//5wPPvgAq9Vqd7iEy7+DAQMGYLFYMBgMpKenU69evesed6OfVURERESkJqmxC0b80rZt21i9ejXR0dHA5dXc0tLSKCkpAS4/75OXl0d5eTknT54kODiYMWPG0LlzZw4ePAiAp6fnDS12UFRUxP3334+bmxtHjhyptDqcl5dXlWN26NCBTz/9lMLCQgDWrl1Lp06dHJ7/akaMGMHf//53Tp48adsWHh5OSkqKLZicOXPG9n7Hjh357LPPOH/+PPfffz+dOnUiOTn5quHzasxmM8uWLePHH39kwIABeHl58bvf/Y6UlBTbPj/88IPts4qIiIiI1EU19srT+PHjcXd3p7i4mICAAFJSUggJCQEu37q2bNkyBg8ejMFgwGAwMG7cOPz8/IiPj6eoqAiDwUDTpk1tS4e/+OKLTJkyhfr16zu0It7YsWOZNGkS6enpNG/enPbt29veGzBgAJMnT+bDDz+0LRjxs8DAQGJjY22Bz8/Pj1mzZlXHqaFhw4b88Y9/5M0337RtmzJlComJiURERGAwGKhXrx5TpkzBz8+P+++/H09PT373u98Bl69Eff/999d83qmoqIiIiAgqKiooKyvjscceY82aNXh7X37YMSkpiTlz5tC/f3/gcjh96623rrvohYiIiIhIbWWw/vJhIhGpdUpKyym6UOzsMmocPZwrjlLPiCPUL+II9UvtUicWjBABOH3ahMWivC8iIiIit5/CUw2ybt06Vq5cecX2uXPn0qpVKydUJCIiIiIiP1N4qkGGDBnCkCFDnF2GiIiIiIhcRa1YbU9ERERERMTZFJ5ERERERETsoPAkIiIiIiJiB4UnEREREREROyg8iYiIiIiI2EHhSURERERExA4KTyIiIiIiInbQ9zxJreLj4+XsEmqdktJyii4UO7sMERERkVpP4UlqlVGzt1JwVkHAEZsWRFDk7CJERERE6gDdticiIiIiImIHhaca4Pz58wQHBzN79myHjlu9ejVpaWk3PG9+fj4dOnS45j7JycnMmzfvhucQEREREakrFJ5qgKysLEJCQvjggw8wm81XvG+xWLBarZW2lZeX89xzzzFixIjbVOWtUV5e7uwSRERERETsomeeaoCMjAxef/113nnnHbZv306fPn1ITk7m6NGjmEwmvv/+exYuXMjIkSN55pln2LNnD1FRUfz0009cunSJuLg4evbsydKlS2nZsiUAK1eu5N///jdz5sxh3rx57Nu3j7KyMho1akRCQgIPPvig3fWdOnWK0aNHc/LkSfz9/VmyZAkNGjTg4sWLzJ49m9zcXAAiIiIYPXo0AOHh4bz99tsEBgZe8To8PJy+ffuyZ88eAgMDSUhIqOYzKr/m6+vt7BKc4k793HLj1DPiCPWLOEL9UjcoPDnZoUOHOHfuHGFhYRQWFpKRkUGfPn0AyMnJwWg00rhxY/Lz8zl37hxt27YlLi4OuHxL3c8GDhzI+vXrmTx5MgBGo9H28+jRo23HrFu3jqSkJBYtWmR3jV999RXp6el4e3szatQoNm3aRFRUFMuXL8disbBp0yYuXrzIs88+S2BgIF27dr3umCaTifT0dLtrkJtTWHjnLRnh6+t9R35uuXHqGXGE+kUcoX6pXVxcDFWu8Kzb9pwsPT2diIgIDAYDPXv2JCcnh1OnTgHw5JNP0rhxY9u+Hh4etmD1awMHDuSDDz6gvLycw4cPc+HCBR577DEAdu7cSVRUFL///e9JTU3l4MGDDtX4xBNPcNddd2EwGAgODubEiRMA7N69myFDhmAwGPDy8qJfv37s3r3brjEHDhzoUA0iIiIiIs6mK09OZDabycrKwt3dnY0bNwJQVlaG0WgEwNPTs9L+DRo0wGAwXHWsBx54gBYtWrBz50727dtHZGQkBoOB7777jjlz5pCeno6fnx9ffPEFsbGxDtXp4eFh+9nV1ZXS0tLrHuPq6orFYrG9/vUxDRs2dKgGERERERFn05UnJ9q+fTvNmzdn586dZGdnk52dzZ///GfWr19/Q+NFRkaybt06srKyiIyMBC7fHlevXj18fX2xWCysWbOm2urv2LEjGRkZWK1WTCYTmzdvplOnTgD4+/vbnoXavXs3P/30U7XNKyIiIiLiDLry5EQZGRn079+/0rZHH30Ui8XCvn37aNOmjUPj9ezZk1mzZtG2bVseeOABAIKCgujduzd9+/alUaNGdO3alf3791dL/a+88gpvvvmm7TMMGDCAJ598EoBXX32V+Ph4Vq5cSVhYmK2em5U6tWe1jHMnKSnVioYiIiIi1cFg/fUa2CI12OnTJiwWtaxcnx7OFUepZ8QR6hdxhPqldtGCESIiIiIiIjdJt+0Jp0+fJjo6+ortPXr0YNy4cU6oSERERESk5lF4Enx8fGyr/YmIiIiIyNXptj0RERERERE7KDyJiIiIiIjYQeFJRERERETEDgpPIiIiIiIidlB4EhERERERsYPCk4iIiIiIiB20VLnUKlV927PI1fj6etu1X0lpOUUXim9xNSIiIlLbKTxJrTJq9lYKzuofuVK9Ni2IoMjZRYiIiEiNp9v2RERERERE7KDwdJPCw8M5cuSIs8uocQ4ePMjmzZsrbQsKCuLixYtOqkhERERE5OYoPNUg5eXlzi6h2hw8eJAPP/zQ2WWIiIiIiFQbPfNUTY4fP860adM4c+YMbm5uxMTE8OSTT5Kfn8+gQYPYu3cvQKXXP//8zDPPsGfPHqKioti8eTNt2rThX//6FwUFBfTp04fY2FgACgoKmD17Nt9//z2lpaX069ePl19+mS1btrB+/XpSUlIAMJvNhIeHs3btWh544IGr1hseHk7//v3Zs2cPp06dYuLEiZw+fZqsrCzOnz9PQkIC7du3B2DDhg2kpqYC4O/vz6xZs/Dx8cFoNJKVlcVdd93F0aNH8fb2Jjk5GTc3N5YuXYrJZCIiIoL27dszdepUAP72t7/x8ccfc+7cOSZNmkSvXr1u6e9FRERERKS6KDxVk9jYWKKiohgyZAhff/01L7zwAlu2bLnucefOnaNt27bExcUBsHnzZn744Qfee+89Ll68yNNPP83gwYN56KGHiIuL45VXXqF9+/aYzWZGjBhB27Zt6dGjB/Pnz+fkyZP4+fmxefNmQkJCqgxOPzObzbz//vvk5OTw4osv8vrrr5Oens7mzZtZuHAhq1ev5siRIyQlJWE0GmnSpAmLFy/mzTffZPHixQDk5uaSmZlJ06ZNmTp1KitXriQmJobx48ezY8cOli5dWmlOLy8vMjIy+Oc//8mECRMUnqTGsHdlPqnb1AfiCPWLOEL9UjcoPFUDq9XKwYMHGTRoEAAtWrSgVatW/Otf/yIwMPCax3p4eNCnT59K23r37o2Liwve3t4EBARw4sQJmjRpwr59+zhz5oxtv4sXL5KXl0fnzp159tlnWbNmDa+//jqrVq1iwoQJ1627b9++APz2t7+luLjYVkebNm04ceIEAHv37qVr1640adIEgKFDhxIREWEbo127djRt2hSAkJAQdu3aZdecoaGhFBQUUFpaioeHx3VrFbnVCgu13t6dztfXW30gdlO/iCPUL7WLi4uhyq/HUXi6xdzc3LBarbbXpaWlld5v0KABBoOh0rZfhglXV1cqKiqwWCwYDAbS09OpV6/eFfNERUURGRlJeHg4Fy5coGPHjtet7ed5XF1dK712cXGx+/mrq9XqyJzl5eUKTyIiIiJSK2jBiGpgMBho1aoV69evByAvL49Dhw4RGhrKvffeS1lZGcePHwcgKyvrhubw8vLid7/7ne25JoAffviBwsJCABo3bkynTp147bXXeP75568IZDeqQ4cOfPrpp7Z51q5dS6dOneyqt6hI/4dFREREROoOhaeb9POVk6SkJDIzM+nfvz+xsbHMnz+fxo0b4+bmxhtvvMHIkSMZPHiw7YrLjUhKSiIvL4/+/fvTv39/YmJiuHDhgu39wYMHc+HCBSIjI6vjowEQGBhIbGws0dHR9O/fn0OHDvHGG29c97iOHTtSXFzMgAEDmD17drXVIyIiIiLiLAbrL+8pE4f8vBreP/7xD+rXr+/scli+fDmFhYVMnz7d2aWI1ColpeUUXSh2dhniZHomQRyhfhFHqF9qFz3zdAv89a9/ZdWqVcTFxdWI4NSvXz9cXV1tS4rXVadPm7BYlPfl+vQXlYiIiFQ3XXmqw9atW8fKlSuv2D537lxatWrlhIpunsKT2EvhSRylnhFHqF/EEeqX2kVXnu5QQ4YMYciQIc4uQ0RERESkTtCCESIiIiIiInZQeBIREREREbGDwpOIiIiIiIgdFJ5ERERERETsoPAkIiIiIiJiB4UnEREREREROyg8iYiIiIiI2EHf8yS1SlVfWCZyNb6+3s4uoVqUlJZTdKHY2WWIiIjc8RSepFYZNXsrBWf1j0i5s2xaEIG+l15ERMT5dNueiIiIiIiIHewKT+fPnyc4OJjZs2dX2r569WrS0tKqPG748OF88sknACxZsoTNmzffeKXXEBQUxMWLF6+7X3JyMmaz+Zr75OXlERQUdMXnupX136y9e/cSEhJCREQEERER9O/f/6Zqzc3NZeLEidVYoYiIiIhI7WfXbXtZWVmEhITwwQcfMGnSJNzd3QF47rnnqjymoqKi0utXX331JsqsHsuWLSM6OtpW/9Wkp6cTFhZGRkYGI0aMsG2vqn6LxYLBYMBgMFR3uQ4JCAjAaDQC8PXXXzNkyBB69+6Ni4vjFxfbtm3LggULqrtEEREREZFaza7wlJGRweuvv84777zD9u3b6dOnD3D5Ss6lS5eIi4vDaDSSmZmJp6cnx48fJzExsdIY8fHxtGnThmHDhpGcnMyxY8coKiri5MmT+Pv7s2TJEho0aIDZbGbRokV8/vnnmM1mgoKCmDFjBp6ennZ9oG+++YaEhATOnj1LWVkZL730EoMGDWLmzJkADB06FBcXF/72t79x1113VTq2vLycTZs28d577zF69GhycnIIDg6+av1Hjx7FZDLx/fffM378eDZs2EBKSgqnT5+mc+fOLFq0iD59+vDuu+9SVFTEa6+9xrx589i3bx9lZWU0atSIhIQEHnzwQWbOnMmDDz7IH/7wBwD+85//EBMTw4cffsjatWtJS0vD3d0di8XC4sWLCQgIuOY5KCoqwsvLyxacgoKC+OKLL2zn8OfXLi4uxMXF8fXXX+Pm5kbz5s1ZsmQJe/fuZd68eRiNRvLz8xk0aBBDhw7l008/pbi4mLfeeovHHnsMgE8//ZQ//elPmM1m6tWrx+TJkwkNDeWbb75h8uTJFBcXY7FYiIyMZNSoUWzbto0lS5bg4uJCRUUF//M//0OHDh3s+t2K3MnqyuIXNZ3OszhC/SKOUL/UDdcNT4cOHeLcuXOEhYVRWFhIRkaGLTz92oEDB9i4cSP+/v7Xnfirr74iPT0db29vRo0axaZNm4iKimLFihV4e3uTnp4OQGJiIikpKcTExFx3zPLycmJjY0lMTCQgIACTycSgQYMIDQ1l+vTprFq1ijVr1lQZxHbs2EGzZs1o1qwZkZGRZGRk2MLTr+Xk5GA0GmncuDHFxcXMnDmTsrIydu/eTWhoKLt376ZPnz7s2bPHFopGjx5NXFwcAOvWrSMpKYlFixYxbNgwXn75ZUaNGoXBYGDlypU8//zzGAwG5s+fz5YtW2jSpAlms/mKK3o/y8vLIyIiArPZzHfffcf8+fOve77+/ve/c/HiRdstfufPn7/qfufOnSM0NJSYmBgyMzNJSkpizZo1nDhxguXLl5OamoqXlxdHjx5l9OjR7Nixg1WrVhEeHs4f//jHSmMvXbqUWbNm8eijj1JRUUFxsRZ/ELFHYaGWjLjVfH29dZ7FbuoXcYT6pXZxcTFUucLzdcNTeno6ERERGAwGevbsyezZszl16hT33XffFfu2a9fOruAE8MQTT9iu/AQHB3PixAkAsrOzMZlMfPTRRwCYzWZatmxp15jffvsteXl5vPbaa7ZtZWVlfPPNN9e9WgOXr7BFRkYCMHDgQAYOHMiUKVPw8PC4Yt8nn3ySxo0bA9CgQQMeeeQRDhw4wK5du3jllVdITEzEbDaTm5tLu3btANi5cyerVq3i0qVLlJeX28YKCAjAz8+PnTt3EhoaSnZ2NpMnTwYgLCyM+Ph4unfvTrdu3fDz87tq7b+8bS8vL4/hw4fz6KOPXvX39LOWLVuSl5fHzJkzefzxx+nWrdtV92vYsCHdu3cHIDQ0lHnz5gHw2WefceLECV544QXbvuXl5fz000+0b9+exMREiouL6dChA2FhYbbPM2fOHHr27MmTTz5JYGBglfWJiIiIiNQk1wxPZrOZrKws3N3d2bhxI3A5jBiNRsaOHXvF/vbeWgdUCiSurq6UlpYCYLVamT59Oh07drR7rJ9ZrVYaNWpkq9URP/30E3//+985ePAgy5cvB6C4uJiPPvqIAQMGXLH/rz9rWFgYe/bs4cCBA8yYMQMfHx8++OADWrZsiYeHB9999x1z5swhPT0dPz8/vvjiC2JjY23HDx8+nNWrV5OXl0fPnj3x9r58aXfZsmXk5uayZ88eXnzxRWbMmEHXrl2v+VkCAgJ48MEH+eKLL+jTpw+urq5YrVYA23kG8PPzIysriz179rBz504WLVrEpk2brhjvl8+Iubi4VAp+Xbp0uepVrl69ehEaGso//vEP3n33XTIyMkhKSmLKlCkcPnyYPXv28OqrrzJy5EiioqKu+XlERERERGqCa64msH37dpo3b87OnTvJzs4mOzubP//5z6xfv/6WFRQeHk5aWholJSUAmEwm8vLy7Dq2efPm1K9fnw0bNti25eXlYTKZgMuB5+eff23Dhg306tWLHTt22D5rQkICGRkZds0dFhaG0Wjk/vvvx93dnY4dO5KcnGwLgSaTiXr16uHr64vFYmHNmjWVju/atSvHjh3jL3/5C88//zxw+SrOyZMnCQ4OZsyYMXTu3JmDBw9et5ZTp07x7bff8tBDDwHg7+9Pbm4uQKVw9OOPP+Lq6srTTz/N5MmTOXPmDOfOnbPr8wJ07tyZzz77jKNHj9q25eTkAHD8+HF8fX155pln+K//+i/b/N988w1BQUG89NJLDBgwwLZdRERERKSmu+aVp4yMDPr3719p26OPPorFYmHfvn23pKAxY8awbNkyBg8ebFvFbty4cVXedldeXo7BYKBevXq4ubnx9ttvk5CQQGpqKhaLBR8fHxYvXgxAdHQ0L774IvXr179iwQij0Wh7HulnTz31FNOnTyc/P/+6dYeEhHD27Flb8OnYsSMLFy603a4WFBRE79696du3L40aNaJr167s37/fdryLiwsDBw5k586dttsULRYL8fHxFBUVYTAYaNq0aZVLiP/8zJPVaqW8vJxXX32VVq1aATB58mSmTZuGt7c3vXv3th1z+PBh26p6FouFMWPGcN999/Htt99e9/MCPPTQQyQmJvLGG29QUlJCWVkZ7dq1Izg4mC1btrBp0ybq1auHwWBgypQpACxYsIDjx4/j6urKXXfdxVtvvWXXXCIiIiIizmaw/nw/Vy2Vk5PDhAkTyM7OdnYpN+3nW9iqWpBDRO5MJaXlFF3Q4iq3mh7oFkeoX8QR6pfa5aYWjKjJEhMT+eSTT5g6daqzS7kpubm5xMTE0Lp1a3r16uXscmq006dNWCy1Ou/LbaK/qERERKS61ZrwNG3aNA4cOFBpm6urq22Z7dqsbdu2bNu2zdlliIiIiIjINdSa8DRr1ixnlyAiIiIiInewa662JyIiIiIiIpcpPImIiIiIiNhB4UlERERERMQOCk8iIiIiIiJ2UHgSERERERGxg8KTiIiIiIiIHRSeRERERERE7FBrvudJBMDHx8vZJUgt4uvr7ewSpJa5E3umpLScogvFzi5DRKRWUHiSWmXU7K0UnNVf8iIi1WXTggiKnF2EiEgtodv2RERERERE7KDwdIPCw8Pp3bs3AwYMoEePHowdO5YvvviiWufIz8/n/fffr7Rt9OjRnDhxolrnAUhOTmbevHnVPq6IiIiISF2h8HQTli5dSmZmJh9//DGRkZGMGTOGAwcO2H18eXn5Nd//7rvvrghP7777Lv7+/jdUrzNd77OKiIiIiNR0euapmvTs2ZOcnBxSU1Np2LAhbdq0YdiwYQDEx8fbXsfHx+Pq6sqxY8e4ePEiGzduZOLEiRw7doyysjL8/f1JSEjg7rvvZtasWeTn5xMREUGzZs1YunQp4eHhvP322wQGBnL8+HGmTZvGmTNncHNzIyYmhieffBKAoKAgYmJi+Pjjjzl37hyTJk2iV69e1/wMp06dYvTo0Zw8eRJ/f3+WLFlCgwYNuHjxIrNnzyY3NxeAiIgIRo8eDVCpnl+/Dg8Pp2/fvuzZs4fAwED+8Ic/MHnyZIqLi7FYLERGRjJq1Khb9SsRERE73YkLZVQHnTdxhPqlblB4qkYhISFkZ2cTHBx8zf0OHjzIypUradiwIQBvvPEGjRs3BmDRokW8++67xMbGMm3aNObNm4fRaLzqOLGxsURFRTFkyBC+/vprXnjhBbZs2WIby8vLi4yMDP75z38yYcKE64anr776ivT0dLy9vRk1ahSbNm0iKiqK5cuXY7FY2LRpExcvXuTZZ58lMDCQrl27XvecmEwm0tPTAZg9ezbh4eH88Y9/BOD8+fPXPV5ERG69wkItGeEoX19vnTexm/qldnFxMVS5wrPCUzWyWq127de7d29bcALYuHEjmzZtoqysjEuXLvHQQw9ddwyTycTBgwcZNGgQAC1atKBVq1b861//Ijw8HIC+ffsCEBoaSkFBAaWlpXh4eFQ55hNPPMFdd90FQHBwsO3Zqt27dzNlyhQMBgNeXl7069eP3bt32xWeBg4caPu5ffv2JCYmUlxcTIcOHQgLC7vu8SIiIiIiNYWeeapGubm5PPLII7i6umKxWGzbS0tLK+33y+C0f/9+Vq9ezYoVK9i0aRMTJkzAbDZXSz0/ByVXV1fg+s8d/TJYubq6UlFRcd05HPmsvXr14r333sPf3593332X119//fofQkRERESkhlB4qibbtm1j9erVREdH06xZM9vzQQUFBezdu7fK4y5cuICXlxf33HMPZrOZjIwM23teXl6YTKarHufl5UWrVq1Yv349AHl5eRw6dIjQ0NDq+1D/T8eOHcnIyMBqtWIymdi8eTOdOnUCwN/f3/ZZd+/ezU8//VTlOMePH8fX15dnnnmG//qv/7IdJyIiIiJSG+i2vZswfvx43N3dKS4uJiAggJSUFEJCQvD392f8+PH07duXhx566JrPQHXp0oXMzEx69epFo0aNeOyxx2yhIigoiObNm/P73/+ehx9+mKVLl1Y6NikpiWnTppGWloabmxvz58+3Pe9UnV555RXefPNN+vfvD8CAAQNsC1O8+uqrxMfHs3LlSsLCwnjggQeqHGfLli1s2rSJevXqYTAYmDJlSrXXKiIiIiJyqxis9j6oIyIiInVOSWk5RReKnV1GraMFAMQR6pfaRQtGSJ1x+rQJi0V5X65Pf1GJo9QzIiJyPQpPd5DTp08THR19xfYePXowbtw4J1QkIiIiIlJ7KDzdQXx8fNi4caOzyxARERERqZW02p6IiIiIiIgdFJ5ERERERETsoPAkIiIiIiJiB4UnEREREREROyg8iYiIiIiI2EHhSURERERExA4KTyIiIiIiInbQ9zxJreLj4+XsEqQW8fX1dnYJUsuoZ2qmktJyii4UO7sMERGFJ6ldRs3eSsFZ/QUqInIn2bQggiJnFyEigm7bExERERERscsdGZ7Onz9PcHAws2fPrrR99erVpKWlVXnc8OHD+eSTTwBYsmQJmzdvviX1BQUFcfHixevul5ycjNlsvuY+eXl5BAUFXfG5bmX9IiIiIiJ10R0ZnrKysggJCeGDDz6oFD6ee+45RowYcdVjKioqKr1+9dVX6du3760s87qWLVtGWVnZNfdJT08nLCyMjIyMSturqt9isWC1Wqu1ThERERGRuuCOfOYpIyOD119/nXfeeYft27fTp08f4PKVnEuXLhEXF4fRaCQzMxNPT0+OHz9OYmJipTHi4+Np06YNw4YNIzk5mWPHjlFUVMTJkyfx9/dnyZIlNGjQALPZzKJFi/j8888xm80EBQUxY8YMPD097ar1m2++ISEhgbNnz1JWVsZLL73EoEGDmDlzJgBDhw7FxcWFv/3tb9x1112Vji0vL2fTpk289957jB49mpycHIKDg69a/9GjRzGZTHz//feMHz+eDRs2kJKSwunTp+ncuTOLFi2iT58+vPvuuxQVFfHaa68xb9489u3bR1lZGY0aNSIhIYEHH3yQmTNn8uCDD/KHP/wBgP/85z/ExMTw4YcfsnbtWtLS0nB3d8disbB48WICAgJu6vcpIiJ1X01czKMm1iQ1l/qlbrjjwtOhQ4c4d+4cYWFhFBYWkpGRYQtPv3bgwAE2btyIv7//dcf96quvSE9Px9vbm1GjRrFp0yaioqJYsWIF3t7epKenA5CYmEhKSgoxMTHXHbO8vJzY2FgSExMJCAjAZDIxaNAgQkNDmT59OqtWrWLNmjVVBrEdO3bQrFkzmjVrRmRkJBkZGbbw9Gs5OTkYjUYaN25McXExM2fOpKysjN27dxMaGsru3bvp06cPe/bssYWi0aNHExcXB8C6detISkpi0aJFDBs2jJdffplRo0ZhMBhYuXIlzz//PAaDgfnz57NlyxaaNGmC2Wy+4oqeiIjI1RQW1qwlI3x9vWtcTVJzqV9qFxcXQ5UrPN9x4Sk9PZ2IiAgMBgM9e/Zk9uzZnDp1ivvuu++Kfdu1a2dXcAJ44oknbFd+goODOXHiBADZ2dmYTCY++ugjAMxmMy1btrRrzG+//Za8vDxee+0127aysjK++eYbu67WZGRkEBkZCcDAgQMZOHAgU6ZMwcPD44p9n3zySRo3bgxAgwYNeOSRRzhw4AC7du3ilVdeITExEbPZTG5uLu3atQNg586drFq1ikuXLlFeXm4bKyAgAD8/P3bu3EloaCjZ2dlMnjwZgLCwMOLj4+nevTvdunXDz8/PrnMhIiIiIuJsd1R4MpvNZGVl4e7uzsaNG4HLYcRoNDJ27Ngr9rf31jqgUiBxdXWltLQUAKvVyvTp0+nYsaPD9VqtVho1amSr1RE//fQTf//73zl48CDLly8HoLi4mI8++ogBAwZcsf+vP2tYWBh79uzhwIEDzJgxAx8fHz744ANatmyJh4cH3333HXPmzCE9PR0/Pz+++OILYmNjbccPHz6c1atXk5eXR8+ePfH2vnypetmyZeTm5rJnzx5efPFFZsyYQdeuXR3+fCIiIiIit9sdtWDE9u3bad68OTt37iQ7O5vs7Gz+/Oc/s379+ls2Z3h4OGlpaZSUlABgMpnIy8uz69jmzZtTv359NmzYYNuWl5eHyWQCLgeen3/+tQ0bNtCrVy927Nhh+6wJCQlXLBxRlbCwMIxGI/fffz/u7u507NiR5ORkWwg0mUzUq1cPX19fLBYLa9asqXR8165dOXbsGH/5y194/vnngcu3IZ48eZLg4GDGjBlD586dOXjwoF31iIiIiIg42x115SkjI4P+/ftX2vboo49isVjYt2/fLZlzzJgxLFu2jMGDB2MwGDAYDIwbN67K2+7Ky8sxGAzUq1cPNzc33n77bRISEkhNTcViseDj48PixYsBiI6O5sUXX6R+/fpXLBhhNBptzyP97KmnnmL69Onk5+dft+6QkBDOnj1rCz4dO3Zk4cKFhIWFAZeXU+/duzd9+/alUaNGdO3alf3799uOd3FxYeDAgezcudN2m6LFYiE+Pp6ioiIMBgNNmzZl4sSJ9p9MIHVqT4f2FxGR2q+ktPz6O4mI3AYGq9alrlFycnKYMGEC2dnZzi7lpo0cOZKoqKgqF+S4EadPm7BY1LJyfXo4VxylnhFHqF/EEeqX2uVaC0bcUbft1XSJiYnEx8czdepUZ5dyU3Jzc3n66afx9vamV69ezi5HRERERKRa3FG37dUk06ZN48CBA5W2ubq6snnzZidVVH3atm3Ltm3bnF2GiIiIiEi1UnhyklmzZjm7BBERERERcYBu2xMREREREbGDwpOIiIiIiIgdFJ5ERERERETsoPAkIiIiIiJiB4UnEREREREROyg8iYiIiIiI2EFLlUutUtW3PYtcja+vt7NLkFpGPVO3lZSWU3Sh2NlliEgtpvAktcqo2VspOKu/+ERExHGbFkRQ5OwiRKRW0217IiIiIiIidlB4crLw8HCOHDni7DJuWl35HCIiIiIiVVF4qkPKy8udXYKIiIiISJ2l8FRDHD9+nJdeeon+/fsTGRnJzp07AcjPz6dDhw62/X75+uef582bR2RkJOvWrWP48OHMmzeP5557jqeeeoqkpCTbsQUFBYwfP57BgwfTv39/3n77bQC2bNnCmDFjbPuZzWaeeOIJvv/++yrr3b9/P/3796d///7MmjULq9Vqe2/evHkMGjSIAQMG8NJLL/Hdd98BMHPmTFasWGHb7z//+Q+9evWqdKyIiIiISE2lBSNqiNjYWKKiohgyZAhff/01L7zwAlu2bLnucefOnaNt27bExcUBsHnzZn744Qfee+89Ll68yNNPP83gwYN56KGHiIuL45VXXqF9+/aYzWZGjBhB27Zt6dGjB/Pnz+fkyZP4+fmxefNmQkJCeOCBB646p9lsJiYmhqSkJDp06MDmzZt57733bO+PHj3aVs+6detISkpi0aJFDBs2jJdffplRo0ZhMBhYuXIlzz//PAaDoRrOoIiIyPVV54qKWp1RHKF+qRsUnmoAq9XKwYMHGTRoEAAtWrSgVatW/Otf/yIwMPCax3p4eNCnT59K23r37o2Liwve3t4EBARw4sQJmjRpwr59+zhz5oxtv4sXL5KXl0fnzp159tlnWbNmDa+//jqrVq1iwoQJVc75zTff0KBBA9sVsL59+zJt2jTb+zt37mTVqlVcunSp0q2EAQEB+Pn5sXPnTkJDQ8nOzmby5Ml2nycREZGbVVhYPevt+fp6V9tYUvepX2oXFxdDlV+Po/BUw7m5uVW6ra20tLTS+w0aNLjiyo2Hh4ftZ1dXVyoqKrBYLBgMBtLT06lXr94V80RFRREZGUl4eDgXLlygY8eODtX5cw3fffcdc+bMIT09HT8/P7744gtiY2Nt+w0fPpzVq1eTl5dHz5498fbW/4URERERkdpBzzzVAAaDgVatWrF+/XoA8vLyOHToEKGhodx7772UlZVx/PhxALKysm5oDi8vL373u9+RkpJi2/bDDz9QWFgIQOPGjenUqROvvfbadW+le/jhhykpKWH//v0AfPjhh1y4cAEAk8lEvXr18PX1xWKxsGbNmkrHdu3alWPHjvGXv/yF559//oY+i4iIiIiIMyg8OVl5eTkeHh4kJSWRmZlJ//79iY2NZf78+TRu3Bg3NzfeeOMNRo4cyeDBg3F1db3huZKSksjLy7Mt9BATE2MLPQCDBw/mwoULREZGXnMcd3d3Fi5cyMyZM+nfvz/79u2zPR8VFBRE79696du3L0OGDOE3v/lNpWNdXFwYOHAgv/nNb2jZsuUNfxYRERERkdvNYNVSZ05TUFBAnz59+Mc//kH9+vWdXQ7Lly+nsLCQ6dOn39J5Ro4cSVRU1BXPaomIiNxKJaXlFF0orpax9AyLOEL9Urvomaca6K9//SurVq0iLi6uRgSnfv364erqSmpq6i2bIzc3l5iYGFq3bk2vXr1uaIzTp01YLMr7cn36i0ocpZ4REZHr0ZUnqdK6detYuXLlFdvnzp1Lq1atnFCRwpPYT/8QFkepZ8QR6hdxhPqldtGVJ7khQ4YMYciQIc4uQ0RERESkRtCCESIiIiIiInZQeBIREREREbGDwpOIiIiIiIgdFJ5ERERERETsoPAkIiIiIiJiB4UnEREREREROyg8iYiIiIiI2EFfkisiIiIiIjVKSWk5RReKnTK3viRX6oxRs7dScNY5/yGJiIiIyO2xaUEERc4u4ip0256IiIiIiIgddOWpBhkyZAhms5mysjK+/fZbHnnkEQBat27NnDlzKu178OBBjh07Rt++fa877t69e5k3bx5Go7HKfb777jtmzpzJDz/8AEC9evWYO3cugYGBbNu2jSZNmhAcHHwTn05EREREpHZTeKpB1q1bB0B+fj6DBg1i48aNVe578OBBduzYYVd4ssfMmTN58sknGTZsGACnTp3Cze1ye2zbto02bdooPImIiIjIHU3hqYbbsGEDqampAPj7+zNr1ixcXFxYunQpJpOJiIgI2rdvz9SpU5k4cSLHjh2jrKwMf39/EhISuPvuu+2a58cff+S+++6zvf75588++4zs7Gx27drFunXrGDlyJAMHDiQlJYXMzEwA2rZty9SpU/H09CQ5OZmvv/6as2fPUlBQwCOPPEJCQgLe3t5s27aNJUuW4OLiQkVFBf/zP/9Dhw4dqvmMiYiIiEhd4Ovr7ewSrqDwVIMdOXKEpKQkjEYjTZo0YfHixbz55pssXryY8ePHs2PHDpYuXWrb/4033qBx48YALFq0iHfffZfY2Fi75vrDH/7ApEmT+O1vf0tISAi9evUiODiYLl26EB4eTps2bWxXpT799FMyMzNZs2YNnp6exMXFsXz5cl5//XUA/vnPf7JhwwbuvfdeJk+ezPLly4mLi2Pp0qXMmjWLRx99lIqKCoqLtfCDiIiIiFxdYaFzloy41mp7WjCiBtu7dy9du3alSZMmAAwdOpTdu3dXuf/GjRt55pln6N+/P1lZWRw8eNDuuQYMGEB2djbPP/88xcXFvPTSS2RlZV113927d9O3b1+8vLwwGAxERUVVqqtbt27ce++9AAwePJg9e/YAEBYWxpw5c1ixYgV5eXl4eV29KUVEREREaiJdeaoj9u/fz+rVq1mzZg2NGzdm06ZNrF271qExGjVqRN++fenbty/3338/WVlZ/P73v6+2GqdMmcLhw4fZs2cPr776KiNHjiQqKqraxhcRERERuZV05akG69ChA59++imFhYUArF27lk6dOgHg5eVFUdH/fynzwoULeHl5cc8992A2m8nIyHBorh07dlBaWgpARUUFhw8f5je/+c1V5+rYsSNbtmzBZDJhtVpJT0+31fXzWGfOnAHAaDQSFhYGwDfffENQUBAvvfQSAwYMIDc319FTIiIiIiLiNLryVIMFBgYSGxtLdHQ0AH5+fsyaNQu4HGD+/Oc/M2DAAB5//HHi4uLIzMykV69eNGrUiMcee8yhcPLzcuZubm5UVFTQpk0bXn31VeDyLX2TJ0/mww8/tC0YcfjwYYYOHQpAmzZtGDt2rG2sxx57jJiYGE6dOkWLFi2Ij48HYMGCBRw/fhxXV1fuuusu3nrrrWo5TyIiIiIit4PBarVanV2E1B3JyclcunSJuLg4Z5ciIiIiIrVUSWk5RRecs7jYtRaM0JUnqVVOnzZhsSjvy/X5+no7bZUeqZ3UM+II9Ys4Qv1Sdyg83UEOHjxou4Xul4YNG8aQIUOqZY7//u//rpZxRERERERqGoWnO0irVq3YuHGjs8sQEREREamVFJ6kVnFxMTi7BKlF1C/iKPWMOEL9Io5Qv9Qe1/pdacEIERERERERO+h7nkREREREROyg8CQiIiIiImIHhScRERERERE7KDyJiIiIiIjYQeFJRERERETEDgpPIiIiIiIidlB4EhERERERsYPCk4iIiIiIiB0UnkREREREROyg8CQiIiIiImIHhSdxumPHjvHss8/Sq1cvnn32Wb799tsr9qmoqGDmzJk8/fTT9OjRg3Xr1tn1ntQ9N9svycnJdOzYkYiICCIiIpg5c+ZtrF5uN3v65e9//zvPPPMMbdq0Yd68eZXe058vd5ab7Rf9+XLnsadn/vd//5d+/frRv39/nnnmGT777DPbe8XFxUyYMIEePXrQu3dvPvnkk9tYvdwQq4iTDR8+3Lphwwar1Wq1btiwwTp8+PAr9lm/fr01OjraWlFRYT19+rS1S5cu1pMnT173Pal7brZfli5dap07d+5trVmcx55++fbbb63/+c9/rAsXLryiN/Tny53lZvtFf77ceezpmZ07d1ovXbpktVqt1oMHD1p/97vfWYuLi61Wq9WanJxsfeONN6xWq9V67Ngxa6dOnawmk+k2VS83QleexKlOnz7Nf/7zH37/+98D8Pvf/57//Oc/nDlzptJ+mzdvZsiQIbi4uNC4cWOefvppPvzww+u+J3VLdfSL3Dns7ZdmzZrRqlUr3NzcrhhDvXTnqI5+kTuLvT3TpUsXGjRoAEBQUBBWq5Vz584BsGXLFp599lkAHnroIdq0acPOnTtv34cQhyk8iVP98MMP3Hfffbi6ugLg6upKkyZN+OGHH67Y74EHHrC9btq0KT/++ON135O6pTr6BeCDDz6gf//+REdH8+WXX96e4uW2s7dfrjeG/ny5M1RHv4D+fLmT3EjPbNiwAX9/f/6/9u2YNZEoCsPwt8KUEWKQOEkVmyDBXxDERoi9Tf6AEFIIESTWgRRJF8HWPtiKla1NEARFLEIgzRhMYRHETmeLhYGwoHc3rpN13qcb7x25Bz4OnNGJxWKSpNFopMPDQ2+dHvP98dgEQKCcn5/r4uJClmWp3W7r8vJSzWZTu7u7fh8NwH+O/oJlnp6e9PDwoFqt5vdR8AX88gRf2bat8Xis+Xwu6dfL2e/v77Jt+7d9o9HIu357e/Oe2ixbw3ZZR16i0agsy5IknZ6eyrZtPT8/b6gCbJJpXlZ9B/0lGNaRF/pLsPxJZrrdrkqlkqrVquLxuPf5wcGBHMfxrukx3x/DE3y1t7enRCKhRqMhSWo0GkokEopEIp/2ZbNZ1et1LRYLTSYTtVotnZ2drVzDdllHXsbjsbdvOBzKcRwdHR1trghsjGlelqG/BMc68kJ/CRbTzPR6PV1dXalSqejk5OTTWjab1ePjoyTp9fVV/X5fqVRqMwXgr/xwXdf1+xAItpeXF5XLZX18fCgcDuvu7k7xeFz5fF6FQkHJZFLz+Vw3Nzdqt9uSpHw+771guWwN2+erebm+vtZgMFAoFJJlWSoUCkqn036WhH/IJC+dTkfFYlHT6VSu62pnZ0e3t7dKpVL0l4D5al7oL8FjkplcLifHcbS/v+/dd39/r+PjY81mM5XLZQ2HQ4VCIZVKJWUyGR8rwioMTwAAAABggL/tAQAAAIABhicAAAAAMMDwBAAAAAAGGJ4AAAAAwADDEwAAAAAYYHgCAAAAAAMMTwAAAABg4CcQ3cmIozXIXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting model using Random Forest\n",
    "\n",
    "1. Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "2. If needed do scaling of data\n",
    "    * Scaling is not done in Random forest\n",
    "3. Import model\n",
    "4. Fit the data\n",
    "5. Predict w.r.t X_test\n",
    "6. In regression check **RSME** Score\n",
    "7. Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "reg_rf = RandomForestRegressor()\n",
    "reg_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9538001893226467"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7974507722196585"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/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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QPS0i0pJhPbm4vb0dtbW16Ozs7HN83rx5Y1opUp8Qonf14MWHd02yzJ4WEWlK0KH1l7/8BQ888ADi4+MRGxsbOG4wGPDmm2+qUjlSj9vjhcDAO7z7mU0S3OxpEZGGBB1ajz76KB577DHMnz9fzfpQiLgCjyUZpKdllLjLOxFpStBL3hVF4ZOJI4j/OVmDDg8aJQ4PEpGmBB1aK1aswJNPPgmvl7/EIoF/9/ZBF2IYZQ4PEpGmBD08+Nxzz6G5uRlPP/00UlJS+pRxl3f98feghhoe5KNJiEhLgg6t0e7yXltbizVr1qC1tRUpKSkoKyvDpEmT+pyjKAoefPBB7N27FwaDAStXrkRpaemoyjZt2oTKykpIkgSTyYS7774beXl5o2pLJPAvZR9sIYbJJKG9yxWqKhERDSno0BrtLu/r1q3DsmXLYLVaUV5ejrVr12LLli19ztm+fTvq6uqwY8cOtLa2YsmSJZg3bx6ys7NHXDZjxgzccccdiIuLw+HDh3Hbbbdh3759fVZARqNAT+siu7z7yrgQg4i0Jeg5LZfLhUcffRQLFy7ErFmzAAD79u3D888/P+Rr7XY7ampqUFxcDAAoLi5GTU0NHA5Hn/MqKytRWloKSZKQmpqK/Px8VFVVjaosLy8PcXFxAIApU6ZACIHW1tZgmx2xgluIIQfOIyLSgqBD6+GHH8aRI0fw29/+NvAE4yuuuAIvvvjikK+12WzIyMgI7FMoyzImTJgAm83W77ysrKzA9xaLBQ0NDaMqO9+2bdtw6aWXIjMzM9hmRyxnEMODZqPEDXOJSFOCHh7cuXMnduzYgfj4eEi9T7XNyMhAY2OjapUbSx988AEee+wx/OlPfxr2a9PSEgc8np6eNNpqhY05xtfLtWSMgxACSYnnhkv9X8fFOeHyeHXdzgtFUlvOF6ntAtg2PbrY78yxEHRomUwmKErfoSKHw9FvJeFALBYLGhsboSgKZFmGoihoamqCxWLpd159fT1mzJgBoG8PaqRlAHDgwAH87Gc/w+bNm3H55ZcH2+QAu70DXq/ocyw9PQlnzrQP+720wt7i24qro70bBsmA9o4eAL7A8n8tQ8Dt8aKh8SxkKehOuWbp/ZpdTKS2C2Db9Cg9PQl2e4dqwRX0b6KioiLce++9OHXqFACgqakJDzzwAL7xjW8M+dq0tDTk5uaioqICAFBRUYHc3Fykpqb2+4ytW7fC6/XC4XBg586dKCwsHFXZJ598grvvvhuPP/44/uEf/iHY5kY8VxD3acWafX/T9Lg4r0VE2hB0aN19993IyclBSUkJ2traUFhYiPT0dKxatSqo169fvx7PP/88CgsL8fzzz2PDhg0AfDctf/rppwAAq9WK7OxsFBQU4NZbb8WqVauQk5MzqrINGzagp6cHa9euhdVqhdVqxWeffRb8v1CE8q8eNA0WWjG+OchupyckdSIiGopBCCGGPg04duwYPvzwQ7S2tkJRFOTn52PKlClq108TInF48OVdR7H7wBd46p7r0On0YP8h39zk+cODJqOEZyoOYcMdc5EzQb0x6lDR+zW7mEhtF8C26ZHaw4NDzmkJIXDfffdh27ZtyMzMxIQJE9DY2IhNmzbBarXi4YcfDqwmJP3wPUtr8KdO+4cH2dMiIq0YMrRefvllfPDBB3j55ZcDCx0A31zRPffcg5deegnf/va3Va0kjT3fs7QGHx32Dw/2uBhaRKQNQ85plZeX4xe/+EWfwAKAGTNm4L777kN5eblqlSP1OD3eQfcdBM7vaXEhBhFpw5Chdfz4ccyZM2fAsjlz5uD48eNjXilSn9OlDBlacebehRjsaRGRRgwZWoqiIDFx4Am1xMREPqpEp7qcbiTEDj46HFjyzp4WEWnEkHNaHo8H7733Hi62yPDCG45JH7p6PBifGDPoOWaTBAO4EIOItGPI0EpLS8N999130fILbxAmfejq8SB+iJ6WwWBAbIyRoUVEmjFkaO3atSsU9aAQ63J6EB9rGvK8uBiZc1pEpBn631COhs3tUeD2eBEfM/TWk3ExRs5pEZFmMLSiUFdvCA01PAgAcWYje1pEpBkMrSjU1eMGgKB6WrExMu/TIiLNYGhFoa4eX88p2J4Wd8QgIq1gaEWhLqc/tIJbiNHF1YNEpBEMrSgU6GkFMzxo5kIMItIOhlYUOtfTCm71oNOt9Hs0CxFRODC0otBwFmL49x/kvBYRaQFDKwp19XhglA2DPrXYLy6GO70TkXYwtKJQl9OD+BhjUA/vDIQWe1pEpAEMrSjk23dw6JWDwHkPgmRPi4g0gKEVhXz7Dg49nwX47tPyv4aIKNwYWlGoq8cT1CIMAIjtPY8LMYhICxhaUairxz2Mnlbv04vZ0yIiDWBoRSH/QoxgcPUgEWkJQyvKCCGGtRAjxizDAM5pEZE2MLSijMvjheIVQQ8PSgYDkuJNaO9yqVwzIqKhBfebiyJGe5evxyTLBnT29p6G2qEpOSEGZzsYWkQUfgytKNPa0QMAqG/uxP5DjQCAKyenD/qalEQzznY6Va8bEdFQODwYZTp79x2MMclBv2Zcghmt7GkRkQYwtKJMS7uvx5QQ5JwWAIxLjEFbpwtewZ3eiSi8GFpRxtHWG1pxwa0eBIBxiWYoXoHObrda1SIiCgpDK8o42noQa5ZhlIO/9OMSzADAxRhEFHYMrSjjaHciIch7tPxSEmMAAGc7GVpEFF4MrSjjaOtBQtzwFo2OS/T1tFo7uIKQiMKLoRVFhBBoGUFPyz882MaeFhGFGUMrirR3ueH2eJEY5CIMg+S7AVkRviXyZ872oNPpgcerckWJiC6CNxdHEXub78biYIcHnW4F1UfOAADMJgknbG3Yf6gRc3IzYAxyw10iorHEnlYUsZ/1h9bwhgcB327vfDwJEYUbQyuK+HtaicOc0wIYWkSkDQytKGI/24MYkwyzafiXPS5GRreLz9QiovAKWWjV1tZi6dKlKCwsxNKlS3HixIl+5yiKgg0bNiA/Px+LFi3C1q1bR122b98+3HzzzZg2bRrKyspUbaPW2dt6kJocA4PBMOzXxscY4fZ44eYqDCIKo5DNpq9btw7Lli2D1WpFeXk51q5diy1btvQ5Z/v27airq8OOHTvQ2tqKJUuWYN68ecjOzh5xWU5ODh566CFUVVXB5YruJdv1zZ3ISI0f0WuT4n3L3vlcLSIKp5D0tOx2O2pqalBcXAwAKC4uRk1NDRwOR5/zKisrUVpaCkmSkJqaivz8fFRVVY2q7LLLLkNubi6Mxuhe7dbV40ZjSzcuzUga0euTE3zzYLxXi4jCKSShZbPZkJGRAVn2PQ5DlmVMmDABNput33lZWVmB7y0WCxoaGkZVRj4nG9oBAJdmJo7o9f6eVlsXN80lovCJ7u5HkNLSBv5Fn54+sl5LOOz51BfiUy5Lw5FTrX3KTCYjkhJjA9/7v77weGKcCd1OBfHxMUgf4TBjuOnpmg1HpLYLYNv06GK/M8dCSELLYrGgsbERiqJAlmUoioKmpiZYLJZ+59XX12PGjBkA+vagRlo2Fuz2DngveCZ9enoSzpxpH7PPUNvB4824ZFwsJAi09z692M/t9gSOJSXGBr4+/zgAJMabYD/bja4uJ84o+ltJqLdrFqxIbRfAtulRenoS7PYO1YIrJMODaWlpyM3NRUVFBQCgoqICubm5SE1N7XNeUVERtm7dCq/XC4fDgZ07d6KwsHBUZeRzwtaGSZbkUb1HcrwZbZ0uCD4MkojCJGTDg+vXr8eaNWuwefNmJCcnB5afr1ixAqtXr8b06dNhtVpRXV2NgoICAMCqVauQk5MDACMu+/DDD/GTn/wEHR0dEELgr3/9Kx566CHk5eWFqulh197lQvPZHlz/tYmjep/kBBNcHi86ezwjukGZiGi0QhZaX/7yl/vcP+X3xz/+MfC1LMvYsGHDgK8fadns2bOxZ8+eEdQ4ctTafEMQkzJH2dPq3e29qaULGSlxo64XEdFwcUeMKHD0dCskgwFfsoxu0jc53h9a3WNRLSKiYWNoRYGjp1pxWWYiYs2j61gnxplgMDC0iCh8GFoRzu3x4nNbO67IThn1e0mSAeMSzPjiTMfoK0ZENAIMrQh3oqENHsWLyTkpY/J+acmxONXUwRWERBQWDK0I57+R+IrscWPyfqnjYtHe5UZLu3NM3o+IaDgYWhHu6OmzsKTFB7ZhGq20ZN8OGf5toYiIQomhFaE8XqC9242jp1vxJUsyOp0edDo98I5yVM/3aBPgBEOLiMKAew9GKKfbgzf216HbqcBgAPYfagQAXDk5fVTva5QlZKbG42QjQ4uIQo89rQjmX5qeMX5sN7fNmZCIEw3tXIxBRCHH0IpgTS3diI81IiFubDvUl2Ykoa3TBUcbF2MQUWgxtCKUEAKNLd2YMD4OBoNhTN/7y70rEQ/XtYzp+xIRDYWhFaHsZ3vQ7fQgY/zY7xGYdUkCEuNMDC0iCjmGVoQ6evosAGDCGM9nAYBkMGDqpSk4fLKF81pEFFIMrQj191oH4mONSEkcm/uzLjT1svGwtzlxppX7EBJR6DC0IpDb48Xhky3ITk8Y8/ksv9zLxgMADp3kECERhQ5DKwIdOd0Kp1vBxHR1HncNAJmp8UhJNOPvtQ7VPoOI6EIMrQj0yTE7jLIBmaljP58FAAbJgC6XgmmXp+GTz+1o6XSi0+mBx6vKxxERBTC0IoxXCBw4egZX5KTAZFTn8jrdCvYfakSsWYbL7cX2fbXYf6gRTrdHlc8jIvJjaEWYv9c60Hy2B3NzM1T/rMzUeJhNEuoa+XwtIgoNhlaE2f23L5Acb8LMKy5R/bMkyYCcCYk41dQBZbQ78RIRBYGhFUGaW7tRfawZ187MglEOzaW9LDMJbo8Xp5vY2yIi9TG0IkjleychSQZcN3NiyD4z65IEJMQa8VnvwyaJiNTE0IoQNnsn9lTbcN3MiUjtfVBjKEgGA67ISUGDvQtNLV0h+1wiik4MrQjxytufw2ySsPjqSSH/7Cuyx8FgAPZW20L+2UQUXRhaEeDY6bP425EzuOHrlyI5QZ1tmwYTF2PE5ZZk7PukHs3c1omIVMTQ0jm3IvDSrqNITjDj6iuz0On0oNPpQagX882cfAkMBgO2vnU8tB9MRFGFoaVz+w834vP6Nnx10nh8cqwZ+w81Yv+hRni8od2eIiHWhPzZ2dh/uAkHP7eH9LOJKHowtHSs2+nBf+8+hpREM74ycVy4q4P82TmYmJ6AP2yvgaOtJ9zVIaIIxNDSsb/s+RxnO1yYNy0TkqTObu7DERNjxPduzIXb48XmbQfR1uXinoRENKYYWjr19xMO7ProNPJmZiE9ZeyfTjwSTreCusZ2zM2dgM/r2/DH7X/nnoRENKYYWjrU1unC09trkJkWD+s1Xwp3dfr5UlYyJuek4O+1LTjFnTKIaAwxtHTGKwSe/msNOns8uNM6DWaTHO4qDWjO1HSkJsfgnU9ssJ/l/BYRjQ2Gls68sf8UDn7uwLcWfgXZE9R7yONoybKE+TOzIAD86a+H4ObEFhGNAYaWTni8wKG6Fvz3W8cx4ytpmPvVjLDcjzUcSfFmXD09E3WN7fjz7mPhrg4RRQCGlk6c7ezBU9sOIsYsI/fS8fjwcFNY7scarkszknD91ybizY9OY091fbirQ0Q6Zwx3BWhoXiHw/P8/go4uNwrm5iDGrM15rIspueZLONPSjf/3+mHIkgFXT7eEu0pEpFPsaWmcEAJ/3nUM1ceaMWtqOjJS48NdpWEzmWR8rzgXV+Sk4E+Vh7Dnk3reu0VEI8KeloZ5vQIvvnkUb350GvNnZuGyzKRwV2lEnG4F1UebMWtKOlo7nL09LglXT8sMd9WISGfY09Koti4XHn/lE7z50WkUzMnBzdd9OdxVGjWTUcLCWdm4ZFwsnq08hI+PNoe7SkSkMwwtjVG8Xuyprse6Zz5AzQkHvrNoMr618ApIhvBv0zQW/MGVnZ6IJ/7yKXZ+eApCaHgJJBFpSsiGB2tra7FmzRq0trYiJSUFZWVlmDRpUp9zFEXBgw8+iL1798JgMGDlypUoLS1VrUwL3B4vTjS04cipVhw9fRZHT59Ft9ODSZYk/PCmachOT9T80vbhMptk/Pib0/FfO47gv3Ye9T0L7P9chqmXpsBk1NciEyIKrZCF1rp167Bs2TJYrVaUl5dj7dq12LJlS59ztm/fjrq6OuzYsQOtra1YsmQJ5s2bh+zsbFXKwqWl3YnqY804cLQZh0+2wK34ViVkpMZh5hWXIM4sY2J6AmzNnbA1dwIArpycHrb6qiEuxogff3M6dn10Gn999yQe/XM1jLKEr0xMxleyU5CVFo+M1Hikp8QhxiTBKEswREhvk4hGLiShZbfbUVNTg2effRYAUFxcjI0bN8LhcCA1NTVwXmVlJUpLSyFJElJTU5Gfn4+qqir84Ac/UKUsWBfbQX2wndW7nR58eLgJTo8CjyLgcntxtsOJentnYFuj8YkxmD01Henj45A2LhYxJiNyv5SKQ7WOfu9nlCXEx5pUOX7+sbgYIxSPSf3PNMlwKwJ5Myfi69Mz8fnpNpxoaMPJhg7sP9yIgUYMjUYJJkmC0STBKBlgNskwSpLvuOy7Fk63Fy6PAiEE4mKMvv+ZjYiNkTF+XBy8bgV9sq/3m8Ahg/8//a+twACVEoN+23swuG7yhacN/Kr+R+PizOjqdg36woHeK5hh2YFOGehVXq+AW/FCUQQ8ihder4AkGWAy+v7gMMkSjEYDjNLw/viIj7ejq8s19IkaEfgZ6fuffv+QQgBx8ebz2iYuOL/v6y+8VuLC97/gdf3PO/cCRQi43QpcHi8EAFkywCRLkGQDTFLvf2UDYsxGzJ4yAbEjuMVGzadOhCS0bDYbMjIyIMu+xsuyjAkTJsBms/UJLZvNhqysrMD3FosFDQ0NqpUFa/z4hAGPp6UNvo1SdlbKsD7H7/Ls8SE/Ho7P7FueOmg5EenHxX5njgUuxCAiIt0ISWhZLBY0NjZCURQAvsURTU1NsFgs/c6rrz+31Y/NZkNmZqZqZUREpC8hCa20tDTk5uaioqICAFBRUYHc3Nw+Q4MAUFRUhK1bt8Lr9cLhcGDnzp0oLCxUrYyIiPQlZKsH169fjzVr1mDz5s1ITk5GWVkZAGDFihVYvXo1pk+fDqvViurqahQUFAAAVq1ahZycHABQpYyIiPTFIHhnJxER6QQXYhARkW4wtIiISDcYWkREpBsMLSIi0g2GFoDy8nIsXrwYX/3qV/H888/3Kevu7sa//uu/YtGiRSgqKsLu3btVLQuX2tpaLF26FIWFhVi6dClOnDgR7ir1UVZWhgULFmDKlCk4cuRI4Phg9VajTA0tLS1YsWIFCgsLsXjxYvz4xz+Gw+Hbyuvjjz9GSUkJCgsLcccdd8Butwdep0bZWPvRj36EkpISLFmyBMuWLcOhQ4cARMZ183viiSf6/Fzq/ZoBwIIFC1BUVASr1Qqr1Yq9e/dqp22CxGeffSaOHj0qfvazn4n//M//7FP2+9//Xtx///1CCCFqa2vFVVddJTo6OlQrC5fly5eLbdu2CSGE2LZtm1i+fHlY63Oh/fv3i/r6enH99deLzz77LHB8sHqrUaaGlpYW8d577wW+//Wvfy3+7d/+TSiKIvLz88X+/fuFEEJs2rRJrFmzRgghVClTQ1tbW+DrN954QyxZskQIERnXTQghDh48KL7//e8Hfi4j4ZoJIfr9/0yt+o+kbQyt89x77739QuvGG28Un3zySeD7lStXisrKStXKwqG5uVnMmjVLeDweIYQQHo9HzJo1S9jt9rDV6WLO/z/TYPVWoyxUqqqqxO233y6qq6vFN77xjcBxu90uZs6cKYQQqpSp7dVXXxU33XRTxFw3p9Mpbr31VnHq1KnAz2WkXLOBQksrbQvZzcV6VV9fj4kTJwa+P3/DXTXKwiHYDY21ZrB6CyHGvCwU/xZerxcvvvgiFixY0G+z59TUVHi9XrS2tqpSlpKSokqb7r//frzzzjsQQuDpp5+OmOv22GOPoaSkpM9jjiLlmgHAT3/6UwghMGvWLPzkJz/RTNuiIrRuuummPvsPnu9//ud/Aj/oROG2ceNGxMfH47bbbsMbb7wR7uqMiYceeggAsG3bNjzyyCP4l3/5lzDXaPQOHDiAgwcP4qc//Wm4q6KKF154ARaLBS6XCw899BAeeOABLFq0KNzVAhAlofXqq6+O+LVZWVn44osvAn+t2Ww2fP3rX1etLBzO39BYluWLbmisNYPVWwgx5mVqKysrw8mTJ/HUU09BkqR+mz07HA5IkoSUlBRVytS2ZMkSrF27FpmZmbq/bvv378fx48excOFCAEBDQwO+//3vY/ny5RFxzfz/bmazGcuWLcOdd96Jf/qnf9JE27h6cAhFRUV4+eWXAQAnTpzAp59+iry8PNXKwiHYDY21ZrB6q1Gmpt/97nc4ePAgNm3aBLPZDACYNm0aenp68OGHHwIAXnrpJRQVFalWNtY6Ozths9kC3+/atQvjxo2LiOu2cuVK7Nu3D7t27cKuXbuQmZmJZ555Bj/4wQ90fc0AoKurC+3t7QB8D5+srKxEbm6udn4eg52Yi2Tbt28XeXl54sorrxSzZ88WeXl54ujRo0IIITo7O8Vdd90l8vPzRUFBgXjjjTcCr1OjLFyOHTsmbrnlFlFQUCBuueUWcfz48XBXqY+NGzeKvLw8kZubK6666ipx4403CiEGr7caZWo4cuSImDx5sigoKBAlJSWipKRE/OhHPxJCCPHRRx+J4uJisWjRIvHd735XnDlzJvA6NcrG0pkzZ0RpaakoLi4WJSUlYvny5eLgwYNCiMi4buc7f+GCnq+ZEELU1dUJq9UqiouLxY033ijuuusu0djYqJm2ccNcIiLSDQ4PEhGRbjC0iIhINxhaRESkGwwtIiLSDYYWERHpBkOLSGf+8R//EadOnQp3NYjCgkveiTRgwYIFaG5uhizLiIuLw7XXXotf/vKXSEhICHfViDSFPS0ijXjqqadw4MABvPrqqzh48CCefPLJPuUejydMNSPSDoYWkcZkZGQgLy8PR48exZQpU/DCCy+goKAABQUFAIApU6bg5MmTAICenh78+te/xvXXX49Zs2bh29/+Nnp6egD4Hq73rW99C7Nnz0ZJSQnef//9sLWJaKxExYa5RHpis9mwZ88eLFq0CG+99RZ27tyJP//5z4iNje13bllZGY4dO4aXXnoJl1xyCaqrqyFJEhobG/HP//zPeOSRR5CXl4d3330Xq1evxuuvv675PSWJBsOeFpFGrFq1CrNnz8ayZcswZ84c/PCHPwTg25w1JSWlX2h5vV688soruP/++wPPkvra174Gs9mM8vJyXHvttZg/fz4kScLVV1+NadOm4e233w5H04jGDHtaRBqxadMmXHXVVf2OX+zxGi0tLXA6ncjJyelXVl9fj6qqKuzevTtwzOPxhPUROERjgaFFpHEGg2HA4+PHj0dMTAxOnTqFqVOn9imzWCywWq148MEHQ1FFopDh8CCRTkmShG9+85v41a9+FXgA4oEDB+ByuVBSUoLdu3dj7969UBQFTqcT77//PhoaGsJdbaJRYWgR6di9996LyZMn45ZbbsHcuXPx29/+Fl6vFxaLBZs3b8Z//Md/YN68eZg/fz6eeeYZeL3ecFeZaFR4czEREekGe1pERKQbDC0iItINhhYREekGQ4uIiHSDoUVERLrB0CIiIt1gaBERkW4wtIiISDcYWkREpBv/Cw1gUY0QaKuLAAAAAElFTkSuQmCC\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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1175.3687078386313\n",
      "MSE: 4367377.857815197\n",
      "RMSE: 2089.8272315708773\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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026887077025966846"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE/(max(DV)-min(DV))\n",
    "\n",
    "2090.5509/(max(y)-min(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7974507722196585"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "\n",
    "\n",
    "* Choose following method for hyperparameter tuning\n",
    "    1. **RandomizedSearchCV** --> Fast\n",
    "    2. **GridSearchCV**\n",
    "* Assign hyperparameters in form of dictionery\n",
    "* Fit the model\n",
    "* Check best paramters and best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 66,
   "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": 67,
   "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": 68,
   "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=   4.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   6.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   6.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.6s\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=   4.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   8.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   8.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  10.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  12.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  14.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   9.7s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  10.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   8.7s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  11.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   4.8s\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=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   4.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   5.2s\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=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   2.1s\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=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   2.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   2.0s\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=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   3.0s\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",
      "[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=  13.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  13.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  14.4s\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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/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": 72,
   "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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1163.7561543768327\n",
      "MSE: 4049103.1648841826\n",
      "RMSE: 2012.2383469371075\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8122116414125642"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}