flight_price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flight Price Prediction\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import pickle\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing dataset\n",
    "\n",
    "1. Since data is in form of excel file we have to use pandas read_excel to load the data\n",
    "2. After loading it is important to check the complete information of data as it can indication many of the hidden infomation such as null values in a column or a row\n",
    "3. Check whether any null values are there or not. if it is present then following can be done,\n",
    "    1. Imputing data using Imputation method in sklearn\n",
    "    2. Filling NaN values with mean, median and mode using fillna() method\n",
    "4. Describe data --> which can give statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_excel(r\"Data_Train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2h 50m     550\n",
       "1h 30m     386\n",
       "2h 45m     337\n",
       "2h 55m     337\n",
       "2h 35m     329\n",
       "          ... \n",
       "31h 30m      1\n",
       "30h 25m      1\n",
       "42h 5m       1\n",
       "4h 10m       1\n",
       "47h 40m      1\n",
       "Name: Duration, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From description we can see that Date_of_Journey is a object data type,\\\n",
    "Therefore, we have to convert this datatype into timestamp so as to use this column properly for prediction\n",
    "\n",
    "For this we require pandas **to_datetime** to convert object data type to datetime dtype.\n",
    "\n",
    "<span style=\"color: red;\">**.dt.day method will extract only day of that date**</span>\\\n",
    "<span style=\"color: red;\">**.dt.month method will extract only month of that date**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_day\"] = pd.to_datetime(train_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_month\"] = pd.to_datetime(train_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <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>24.0</td>\n",
       "      <td>3.0</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>1.0</td>\n",
       "      <td>5.0</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>9.0</td>\n",
       "      <td>6.0</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>12.0</td>\n",
       "      <td>5.0</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>1.0</td>\n",
       "      <td>3.0</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         24.0            3.0  \n",
       "1          1.0            5.0  \n",
       "2          9.0            6.0  \n",
       "3         12.0            5.0  \n",
       "4          1.0            3.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we have converted Date_of_Journey column into integers, Now we can drop as it is of no use.\n",
    "\n",
    "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Departure time is when a plane leaves the gate. \n",
    "# Similar to Date_of_Journey we can extract values from Dep_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_data[\"Dep_hour\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_data[\"Dep_min\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.minute\n",
    "\n",
    "# Now we can drop Dep_Time as it is of no use\n",
    "train_data.drop([\"Dep_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>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>24.0</td>\n",
       "      <td>3.0</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>1.0</td>\n",
       "      <td>5.0</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>9.0</td>\n",
       "      <td>6.0</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>12.0</td>\n",
       "      <td>5.0</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>1.0</td>\n",
       "      <td>3.0</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         24.0            3.0   \n",
       "1   7h 25m     2 stops         No info   7662          1.0            5.0   \n",
       "2      19h     2 stops         No info  13882          9.0            6.0   \n",
       "3   5h 25m      1 stop         No info   6218         12.0            5.0   \n",
       "4   4h 45m      1 stop         No info  13302          1.0            3.0   \n",
       "\n",
       "   Dep_hour  Dep_min  \n",
       "0        22       20  \n",
       "1         5       50  \n",
       "2         9       25  \n",
       "3        18        5  \n",
       "4        16       50  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrival time is when the plane pulls up to the gate.\n",
    "# Similar to Date_of_Journey we can extract values from Arrival_Time\n",
    "\n",
    "# Extracting Hours\n",
    "train_data[\"Arrival_hour\"] = pd.to_datetime(train_data.Arrival_Time).dt.hour\n",
    "\n",
    "# Extracting Minutes\n",
    "train_data[\"Arrival_min\"] = pd.to_datetime(train_data.Arrival_Time).dt.minute\n",
    "\n",
    "# Now we can drop Arrival_Time as it is of no use\n",
    "train_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>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>24.0</td>\n",
       "      <td>3.0</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>1.0</td>\n",
       "      <td>5.0</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>9.0</td>\n",
       "      <td>6.0</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>12.0</td>\n",
       "      <td>5.0</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>1.0</td>\n",
       "      <td>3.0</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         24.0            3.0        22   \n",
       "1     2 stops         No info   7662          1.0            5.0         5   \n",
       "2     2 stops         No info  13882          9.0            6.0         9   \n",
       "3      1 stop         No info   6218         12.0            5.0        18   \n",
       "4      1 stop         No info  13302          1.0            3.0        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time taken by plane to reach destination is called Duration\n",
    "# It is the differnce betwwen Departure Time and Arrival time\n",
    "\n",
    "\n",
    "# Assigning and converting Duration column into list\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding duration_hours and duration_mins list to train_data dataframe\n",
    "\n",
    "train_data[\"Duration_hours\"] = duration_hours\n",
    "train_data[\"Duration_mins\"] = duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Duration\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>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>24.0</td>\n",
       "      <td>3.0</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>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</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>9.0</td>\n",
       "      <td>6.0</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>12.0</td>\n",
       "      <td>5.0</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>1.0</td>\n",
       "      <td>3.0</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         24.0            3.0        22       20   \n",
       "1         No info   7662          1.0            5.0         5       50   \n",
       "2         No info  13882          9.0            6.0         9       25   \n",
       "3         No info   6218         12.0            5.0        18        5   \n",
       "4         No info  13302          1.0            3.0        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Categorical Data\n",
    "\n",
    "One can find many ways to handle categorical data. Some of them categorical data are,\n",
    "1. <span style=\"color: blue;\">**Nominal data**</span> --> data are not in any order --> <span style=\"color: green;\">**OneHotEncoder**</span> is used in this case\n",
    "2. <span style=\"color: blue;\">**Ordinal data**</span> --> data are in order --> <span style=\"color: green;\">**LabelEncoder**</span> is used in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1751\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              818\n",
       "Vistara                               479\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Airline\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQQAAAGkCAYAAABnzShmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABwiUlEQVR4nO3deVxU9f7H8fcwCGroIAhC5tr9qWi7XUtLK8wtSTIX0sqW69VW2+4tbVEr8qZ16+Z2s2y5tli531BLi7pRqNm120ZaIuCGoKyiMsDM+f3BZS4kIOrMnIF5PR8PHzLfs8xnzjbnfOa7WAzDMAQAAAAAAADALwSYHQAAAAAAAAAA7yEhCAAAAAAAAPgREoIAAAAAAACAHyEhCAAAAAAAAPgREoIAAAAAAACAHyEhCAAAAAAAAPiRQLMD8FV5eSVyOg2zwwAAAAAAAABOWkREqzqnUUMQAAAAAAAA8CMkBAEAAAAAAAA/QkIQAAAAAAAA8CMkBAEAAAAAAAA/QkIQAAAAAAAA8CMkBAEAAAAAAAA/QkIQAAAAAAAA8CMkBAEAAAAAAAA/QkIQAAAAAAAA8CMkBAEAAAAAAAA/4rWE4GeffabrrrtO8fHxuvbaa7VhwwZJUkZGhhISEjRkyBAlJCQoMzPTtYwnpgEAAAAAAAD+zGIYhuHpNzEMQ3369NE777yjbt26afv27Ro3bpz+/e9/69Zbb9WoUaMUHx+vNWvWaMWKFVqyZIkkacKECW6f1lB5eSVyOj2+aTwqPz9PYWHhZocBAAAAAAAAL4uIaFXnNK/VEAwICNDhw4clSYcPH1ZkZKQKCgqUlpamuLg4SVJcXJzS0tKUn5+vvLw8t0/zJ9u3p2nKlMnaseNns0MBAAAAAACADwn0xptYLBb97W9/01133aWWLVvqyJEjWrRokbKzs9WuXTtZrVZJktVqVWRkpLKzs2UYhtunhYWFNTjm8PAQN28F73E4HFq8eKEkafHihXrttddc2wMAAAAAAAD+zSsJwYqKCi1atEgLFy5U79699e9//1sPPPCA5syZ4423PyWNucnw+vVJKigokCTl5+fr3Xc/0NChcSZHBQAAAAAAAG+pr8mwVxKCP//8s3Jzc9W7d29JUu/evdWiRQsFBwcrJydHDodDVqtVDodDubm5io6OlmEYbp/mDwoLC7R8+VLZ7XZJkt1u17JlS9W37+Wy2ULNDQ4AAAAAAACm80ofglFRUTpw4IB27dolSUpPT9ehQ4fUqVMnxcTEKCkpSZKUlJSkmJgYhYWFKTw83O3T/MGmTV/K6XTWKHM6ndq06UuTIgIAAAAAAIAv8coow5L0z3/+U6+++qosFoskacqUKbr66quVnp6uqVOnqri4WK1bt9bs2bPVtWtXSfLItIZqrE2Gi4oK9eCDd7tqCEpScHCwXnhhATUEAQAAAAAA/ER9TYa9lhBsbBprQlCq7EOwqtlwcHCwxowZRx+CAAAAAAAAfqS+hKBXmgzDuwYPHuaqDWizhWrQoGHmBgQAAAAAAACfQUKwCbJarZo06W5J0uTJ98hqtZocEQAAAAAAAHwFTYbr0JibDFfJz89TWFi42WEAAAAAAADAy2gy7KdIBgIAAAAAAOC3SAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHSAgCAAAAAAAAfoSEIAAAAAAAAOBHAr3xJnv37tXdd9/ten348GGVlJTo66+/VkZGhqZOnarCwkKFhoZq9uzZ6ty5syR5ZBoAAAAAAADgzyyGYRjeftNnnnlGDodD06dP14QJEzRq1CjFx8drzZo1WrFihZYsWSJJHpnWUHl5JXI6vb5pAAAAAAAAgNMWEdGqzmlebzJcVlamDz/8UKNGjVJeXp7S0tIUFxcnSYqLi1NaWpry8/M9Mg0AAAAAAADwd15pMlxdcnKy2rVrp169eunHH39Uu3btZLVaJUlWq1WRkZHKzs6WYRhunxYWFubtjwsAAAAAAAD4FK8nBFesWKFRo0Z5+21PWnh4iNkhAAAAAAAAAG7n1YRgTk6Otm7dqjlz5kiSoqOjlZOTI4fDIavVKofDodzcXEVHR8swDLdPOxn0IQgAAAAAAIDGymf6EFy1apWuuOIKtWnTRpIUHh6umJgYJSUlSZKSkpIUExOjsLAwj0wDAAAAAAAA/J1XRxkeMmSIHnvsMQ0YMMBVlp6erqlTp6q4uFitW7fW7Nmz1bVrV49NayhqCAIAAAAAAKCxqq+GoFcTgo0JCUEAAAAAAAA0Vj7TZBgAAAAAAACAuUgIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhGATlp+fZ3YIAAAAAAAA8DEkBJuo7dvTNGXKZO3Y8bPZoQAAAAAAAMCHkBBsghwOhxYtmi9JWrRovhwOh8kRAQAAAAAAwFeQEGyCNmxYr+LiIklSUVGhNm5cb3JEAAAAAAAA8BUkBJuYwsICLV++VHa7XZJkt9u1bNlSFRUVmhsYAAAAAAAAfAIJwSZm06Yv5XQ6a5Q5nU5t2vSlSREBAAAAAADAl5AQbGL69euvgICauzUgIEB9+15uUkQAAAAAAADwJSQEmxibLVSjR49TcHCwJCk4OFhjxoyTzRZqbmAAAAAAAADwCSQEm6DBg4e5EoA2W6gGDRpmbkAAAAAAAADwGSQEmyCr1apJk+6WJE2efI+sVqvJEQEAAAAAAMBXWAzDMMwOwhfl5ZXI6ax70yQnb1RqaooXIzp5FRUVCgwMNDuMOvXr11+xsYPMDgMAAAAAAKDJiYhoVec0380W+bjU1BTt3vmrOtrCzA6lTr68c3cX5UsSCUEAAAAAAAAv81rOyG63a9asWdq0aZOCg4N1wQUX6Omnn1ZGRoamTp2qwsJChYaGavbs2ercubMkeWSaO3W0henx/oPdvl5/kJiywewQAAAAAAAA/JLX+hB87rnnFBwcrI8//lgffvih7rvvPknSjBkzNH78eH388ccaP368pk+f7lrGE9MAAAAAAAAAf+aVhOCRI0e0evVq3XfffbJYLJKktm3bKi8vT2lpaYqLi5MkxcXFKS0tTfn5+R6ZBgAAAAAAAPg7rzQZ3rNnj0JDQzV//nxt2bJFZ5xxhu677z41b95c7dq1c42Ca7VaFRkZqezsbBmG4fZpYWG+298fAAAAAAAA4A1eSQhWVFRoz5496tmzpx555BF99913uuOOO/TSSy954+1PSXh4SL3Tg4ICVealWJqqoKDAeke8AQAAAAAAgPt5JSF45plnKjAw0NWM9/zzz1ebNm3UvHlz5eTkyOFwyGq1yuFwKDc3V9HR0TIMw+3TTkZeXomcTqPO6WVlFae1TVC5DQ8ePGx2GAAAAAAAAE1OfZWwvNKHYFhYmC655BJ99dVXkipHAc7Ly1Pnzp0VExOjpKQkSVJSUpJiYmIUFham8PBwt08DAAAAAAAA/J3FMIy6q8G50Z49e/Too4+qsLBQgYGBuv/++3XFFVcoPT1dU6dOVXFxsVq3bq3Zs2era9eukuSRaQ11ohqCiYnTpYN5erz/4FPcIv4tMWWDFBGuxx9/yuxQAAAAAAAAmpz6agh6LSHY2JAQ9CwSggAAAAAAAJ5jepNhAAAAAAAAAL6BhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAgAAAAAAAH6EhCAAAAAAAADgR0gIAl70n//82+wQAAAAAACAnyMhCHjJW2+9ruef/4vefvtNs0MBAAAAAAB+jIQg4AXHjh3Txx+vkyR99FGSjh07ZnJEAAAAAADAX5EQBLxg5sxHa7x+8slH65gTAAAAAADAs0gIAh7200/fa9++PTXK9u7do7S0H02KCAAAAAAA+LNAswNozLLz8/Tpjp/MDqNRys7PU3REuNlheMWSJa/XUf6ann32RS9HAwAAAAAA/B01BAEPu+WWibWWT5jwBy9HAgAAAAAAQA3B0xIdFq6B3XuZHUajtCl3n9kheE3PnueoffsONZoNn3VWB/XseY6JUQEAAAAAAH9FDUHAC2bOnFXj9YwZs+qYEwAAAAAAwLNICAJe0KJFCw0Zco0kaejQOLVo0cLkiAAAAAAAgL+iyTDgJTfffLvOPfd8XXBBb7NDAQAAAAAAfowagoAXkQwEAAAAAABmIyEIAAAAAAAA+BESggAAAAAAAIAfISEIAAAAAAAA+BEGFTkNu4vylZiywewwGqXdRfnqGBFudhgAAAAAAAB+h4TgKerXr7/ZIdQqPz9PBw/myjAMV5nFYlFERKTCwnwnAdcxItxntyEAAAAAAEBTZjGqZ47gkpdXIqez8W2aoqJCPfjg3bLb7a6y4OBgvfDCAtlsoeYFBgAAAAAAAK+JiGhV5zT6EGxibLZQjR49TsHBwZIqk4FjxowjGQgAAAAAAABJJASbpMGDh7kSgDZbqAYNGmZuQAAAAAAAAPAZJASbIKvVqkmT7pYkTZ58j6xWq8kRAQAAAAAAwFfQh2AdGmsfgtXl5+f51EAiAAAAAAAA8A76EPRTJAMBAAAAAADwW15LCMbGxmro0KGKj49XfHy8UlJSJEkZGRlKSEjQkCFDlJCQoMzMTNcynpgGAAAAAAAA+DOvNRmOjY3Vyy+/rG7dutUonzBhgkaNGqX4+HitWbNGK1as0JIlSzw2raGaQpNhAAAAAAAA+CefbTKcl5entLQ0xcXFSZLi4uKUlpam/Px8j0wDAAAAAAAA/F2gN9/sT3/6kwzDUO/evfXggw8qOztb7dq1c42Ca7VaFRkZqezsbBmG4fZpYWFhDY41PDzEzZ8eAAAAAAAAMJ/XEoLvvPOOoqOjVVZWpmeeeUZPPfWUbr31Vm+9/UmjyTAAAAAAAAAaK59oMhwdHS1JCgoK0vjx47Vt2zZFR0crJydHDodDkuRwOJSbm6vo6GiPTAMAAAAAAAD8nVcSgkePHtXhw4clSYZhaN26dYqJiVF4eLhiYmKUlJQkSUpKSlJMTIzCwsI8Mg0AAAAAAADwd14ZZXjPnj2699575XA45HQ6dfbZZ+vxxx9XZGSk0tPTNXXqVBUXF6t169aaPXu2unbtKkkemdZQNBkGAAAAAABAY1Vfk2GvJAQbIxKCAAAAAAAAaKx8og9BAAAAAAAAAOYjIQgAAAAAAAD4ERKCAAAAAAAAgB8hIQgAAAAAAAD4ERKCTVh+fp7ZIQAAAAAAAMDHkBBsorZvT9OUKZO1Y8fPZocCAAAAAAAAH0JCsAlyOBxatGi+JGnRovlyOBwmRwQAAAAAAABfQUKwCdqwYb2Ki4skSUVFhdq4cb3JEQEAAAAAAMBXkBBsYgoLC7R8+VLZ7XZJkt1u17JlS1VUVGhuYAAAAAAAAPAJJASbmE2bvpTT6axR5nQ6tWnTlyZFBAAAAAAAAF9CQrCJ6devvwICau7WgIAA9e17uUkRAQAAAAAAwJeQEGxibLZQjR49TsHBwZKk4OBgjRkzTjZbqLmBAQAAAAAAwCeQEGyCBg8e5koA2myhGjRomLkBAQAAAAAAwGeQEGyCrFarJk26W5I0efI9slqtJkcEAAAAAAAAX2ExDMMwOwhflJdXIqezcW+a/Pw8hYWFmx0GAAAAAAAAvCwiolWd06gh2ISRDAQAAAAAAMBvkRAEAAAAAAAA/AgJQQAAAAAAAMCPkBAEAAAAAAAA/AgJQQAAAAAAAMCPkBAEAAAAAAAA/AgJQQAAAAAAAMCPkBAEAAAAAAAA/AgJQQAAAAAAAMCPnFRCsKCgQKtXr9arr74qScrJydGBAwc8EhgAAAAAAAAA92twQvDrr7/W0KFD9eGHH2rhwoWSpKysLM2cOdNTsQEAAAAAAABwswYnBGfNmqW//e1veu211xQYGChJOv/88/X99997LDgAAAAAAAAA7tXghOC+ffvUt29fSZLFYpEkNWvWTA6HwzORAQAAAAAAAHC7BicEzz77bKWkpNQoS01NVbdu3dweFAAAAAAAAADPCGzojFOnTtXkyZN15ZVXqrS0VNOnT1dycrKrP0EAAAAAAAAAvs9iGIbR0JlzcnL0z3/+U/v371d0dLRGjBihqKgoT8Znmry8EjmdDd40AAAAAAAAgM+IiGhV57QGJwTLyspksVjUrFkzV1l5ebkMw1BQUNDpR+ljSAgCAAAAAACgsaovIdjgPgRvu+02/fTTTzXKfvrpJ/3hD3849cgAAAAAAAAAeFWDE4K//PKLzj///Bpl5513nrZv3+72oAAAAAAAAAB4RoMTgq1atdKhQ4dqlB06dEgtWrQ4qTecP3++unfvrl9++UWSlJGRoYSEBA0ZMkQJCQnKzMx0zeuJaQAAAAAAAIA/a3BCcPDgwXrooYf0yy+/6NixY9qxY4ceeeQRDRs2rMFv9tNPP+k///mPzjzzTFfZjBkzNH78eH388ccaP368pk+f7tFpAAAAAAAAgD9rcELwgQce0Nlnn60xY8booosuUkJCgrp06aIHH3ywQcuXlZXpqaee0owZM2SxWCRJeXl5SktLU1xcnCQpLi5OaWlpys/P98g0AAAAAAAAwN8FNnTG4OBgzZgxQ9OnT1dBQYHatGnjSuw1xEsvvaQRI0aoQ4cOrrLs7Gy1a9dOVqtVkmS1WhUZGans7GwZhuH2aWFhYQ2ONzw8pMHzAgAAAAAAAI1FvQnBvXv36qyzzpIk7dmzp8a0I0eOuP6unuSrzbfffqsffvhBf/rTn041Tq/LyyuR02mYHQYAAAAAAABw0iIiWtU5rd6E4LXXXqtvv/1WkjRo0CBZLBYZRs0kmcVi0c8//1xvAFu3btWuXbs0cOBASdKBAwf0hz/8QdOmTVNOTo4cDoesVqscDodyc3MVHR0twzDcPg0AAAAAAADwd/UmBKuSgZK0ffv2U36TSZMmadKkSa7XsbGxevnll9WtWzctXbpUSUlJio+PV1JSkmJiYlxNe2NiYtw+DQAAAAAAAPBnFuO3Vf5q4XA4NGTIEK1bt05BQUGn/abVE4Lp6emaOnWqiouL1bp1a82ePVtdu3aVJI9MayiaDAMAAAAAAKCxqq/JcIMSgpI0ZMgQLVu2TK1bt3ZbYL6MhCAAAAAAAAAaK7ckBN955x0lJydr8uTJioqKqjHC8IkGFWmMSAgCAAAAAACgsXJLQrBHjx61r6ABg4o0RiQEAQAAAAAA0Fid8ijDknTs2DH9/e9/15VXXqmePXtq8uTJCg4OdmuAAGCG/Pw8hYWFmx0GAAAAAABeFXCiGZ566il99tln6tq1qzZs2KA5c+Z4Iy4A8Kjt29M0Zcpk7djR9Go4AwAAAABQnxMmBFNSUvTaa6/p4Ycf1quvvqrPPvvMG3EBgMc4HA4tWjRfkrRo0Xw5HA6TIwIAAAAAwHtOmBA8evSoIiMjJUnR0dEqKSnxeFAA4EkbNqxXcXGRJKmoqFAbN643OSIAAAAAALznhH0IOhwObd68WVVjj1RUVNR4LUl9+/b1XIRAE7Jr10517fo7s8Pwa4WFBVq+fKnsdrskyW63a9myperb93LZbKHmBgcAAAAAgBeccJTh2NjY+ldgsejTTz91a1C+gFGG4W4bNqzTkiWv65ZbJmrQoKFmh+O31q//UB988K7Ky8tdZc2aNVNCwo0aOjTOxMgAAAAAAHCf+kYZPmFC0F+REIQ7lZWVaeLEm+R0OhUQYNXixW8pKCjI7LD8UlFRoR588G5XDUFJCg4O1gsvLKCGIAAAAACgyagvIXjCPgQBnL7581+U0+mUJDmdDi1Y8KLJEfkvmy1Uo0ePU3BwsKTKZOCYMeNIBgIAAAAA/AYJQcDDdu/O1LZtW2uU/fvfW7VnT5ZJEWHw4GGuBKDNFqpBg4aZGxAAAAAAAF5EQhDwsOXL3zupcnie1WrVpEl3S5ImT75HVqvV5IgAAAAAAPAeEoKAh40ZM67W8tGjb/ByJKiuR4+emjt3kbp3jzE7FAAAAAAAvIqEIOBhHTp0Uvv2HWqUnXVWB3Xo0MmkiFClsLDA7BAAAAAAAPA6EoKAhxUWFujQodwaZQcP5qqoqNCcgCBJ2rBhnaZPn6qNGz8yOxQAAAAAALyKhCDgYZs2fekaYbiK0+nUpk1fmhQRysrK9NZbb0iSlix5XWVlZSZHBAAAAACA95AQBDysX7/+CgioeaoFBASob9/LTYoI8+a9IMMwJEmG4dT8+S+YHBEAAAAAAN5DQhDwMJstVKNHj1NwcLAkKTg4WGPGjJPNFmpuYH5q9+5MffvtNzXKtm37Rnv2ZJkUEQAAAAAA3kVCEPCCwYOHuRKANluoBg0aZm5Afmzp0rdOqhwAAAAAgKaGhCDgBVarVZMm3S1Jmjz5HlmtVpMj8l8dO9Y+ujOjPgMAAAAA/IXFqOpICzXk5ZXI6WTTwL3y8/MUFhZudhh+raioUPfeO6nGQC8BAQGaN+8VmnEDAAAAAJqMiIhWdU6jhiDgRSQDzWezhWrs2PE1ysaOvZFkIAAAAADAb5AQBOB3hg27ViEhlb+UhIS00rBhcSZHBAAAAACA95AQBOB3rFar7r//z5KkBx54mD4dAQAAAAB+hT4E60AfgkDTR5+Ovod9AgAAAADuQR+CAFALEk++Zfv2NE2ZMlk7dvxsdigAAAAA0KSREAQAmM7hcGjevBckSfPmvSCHw2FyRAAAAADQdJEQBACY7qOP1qqoqFCSVFhYoI8/XmtuQAAAAADQhJEQBACYqrCwQB988E6Nsvfff8eVIAQAAAAAuBcJQcALkpM3KjFxupKTN5odCuBzkpM3HtdE2OFwcL4AAAAAgIeQEAS8IDU1Rdu3pyk1NcXsUACfY7GYHQEAAAAA+BcSggAAU8XGDlZgYGCNssDAQMXGDjIpIgAAAABo2kgIAgBMZbOFasyY8dVKLBo79kbZbKFmhQQAAAAATRoJQQCA6YYOHa42bdpIktq0aaMhQ64xOSIAAAAAaLq8lhC86667NGLECF133XUaP368fv75Z0lSRkaGEhISNGTIECUkJCgzM9O1jCemAQB8j9Vq1d13PyBJuueeB2S1Wk2OCAAAAACaLq8lBGfPnq1//vOfWr16tW6//XY9+uijkqQZM2Zo/Pjx+vjjjzV+/HhNnz7dtYwnpgFAlfz8PLNDQDU9evTU3LmL1L17jNmhAAAAAECT5rWEYKtWrVx/l5SUyGKxKC8vT2lpaYqLi5MkxcXFKS0tTfn5+R6ZBgBVtm9P05Qpk7Vjx89mh4JqwsLCzQ4BAAAAAJq8wBPP4j6PPfaYvvrqKxmGocWLFys7O1vt2rVzNQ2zWq2KjIxUdna2DMNw+7SwsLAGxxoeHuLmTw9/FhQU6Po/IqLVCeaGpzkcDi1evFCStHjxQr322ms0UQUAAAAA+A2vJgSfeeYZSdLq1as1Z84c3Xfffd58+5OSl1cip9MwOww0EWVlFa7/Dx48bHI0WL8+SQUFBZKk/Px8vfvuBxo6NM7kqAAAAAAAcJ/6KiSZMsrwddddpy1btigqKko5OTlyOBySKmvt5ObmKjo6WtHR0W6fBgCFhQVavnyp7Ha7JMlut2vZsqUqKio0NzAAAAAAALzEKwnBI0eOKDs72/U6OTlZNptN4eHhiomJUVJSkiQpKSlJMTExCgsL88g0ANi06Us5nc4aZU6nU5s2fWlSRKiOgV4AAAAAwPMshmF4vF3soUOHdNddd+nYsWMKCAiQzWbTI488ol69eik9PV1Tp05VcXGxWrdurdmzZ6tr166S5JFpDUWTYbhTYuJ0bd+eph49eurxx58yOxy/VlRUqAcfvNtVQ1CSgoOD9cILC2SzhZoXGLR9e5oSE6friSeeZqRhAAAAADhN9TUZ9kpCsDEiIQh3IiHoW9avT3I1Gw4ODtaYMePoQ9BkDodDf/rTvTp4MFeRke303HNzGegFAAAAAE6Dz/UhCABmGjx4mKs2oM0WqkGDhpkbELRhw3oVFxdJqqzFuXHjepMjAgAAAICmi4QgAL9jtVo1adLdkqTJk++hJprJGOgFAAAAALyLhCDgBYWFBTX+h/l69OipuXMX0VedD2CgFwAAAADwLhKCgBcUFRXV+B++ISws3OwQIKlfv/61lvfte7mXIwEAAAAA/0BCEABgKpstVJGRUTXK2rWLYtRnAAAAAPAQEoIAAFPt3p2pffv21Cjbu3eP9uzJMikiAAAAAGjaSAgCAEy1fPl7J1UOAAAAADg9JAQBL6ioqJAk2e2lSk7eaHI0gG8ZM2ZcreWjR9/g5UgAAAAAwD+QEAS8wOGoUMvmwWoe1EypqSlmhwP4lFatWtda3rq1zcuRAAAAAIB/ICEIeEmX9pHq0j7S7DAAn1NXrVlq0wIAAACAZwSaHQAAmCE5eaNSU1PUr19/xcYOMjscv2axmB0BAAAAAPgXaggC8EupqSnavj2NJtw+IDZ2sAIDa/4+FRgYSKIWAAAAADyEhCAAwFQ2W6jGjBlfo2zs2Btls4WaExAAAAAANHEkBAEAphs6dLirlmBgYKCGDLnG5IgAAAAAoOkiIQgAMJ3VatWZZ54lSWrf/ixZrVaTIwIAAACApouEIADAJ7Rs2VKS1KJFS5MjAQAAAICmjYQgAAAAAAAA4EdICAIAAAAAAAB+hIQgAAAAAAAA4EdICAIAgDrl5+eZHQIAAAAANyMhCHhQcvJGJSZOl9PpVMa+XGXsy1VWVqaSkzeaHRrgU5KTNyorK1OSVFhYYG4wcNm+PU1TpkzWjh0/mx0KAAAAADciIQh4UGpqinZn7lTPzlHqHNVGnaPayGJUKDU1xezQAJ9SeU441aJFcxUVFZkdDiQ5HA4tWjRfkrRo0Xw5HA6TI0IVam0CAADgdJEQBDysc1SYZt42zPWvc1SY2SEBPqlDxyh16Bhldhj4rw0b1qu4uDI5W1RUqI0b15scESRqbQIAAMA9SAgCAIAaCgsLtHz5UtntdkmS3W7XsmVLVVRUaG5gfo5am76JGpsAAKAxIiEIAABq2LTpSzmdzhplTqdTmzZ9aVJEkKi16YuosQkAABorEoIA/E71ASwY5AU4Xr9+/RUQUPMWISAgQH37Xm5SRKDWpu+hxqbv2rVrp9khAADg80gIAvA7qakpssipHt06yyIng7wAv2GzhWr06HEKDg6WJAUHB2vMmHGy2ULNDcyPUWvT91Bj0zdt2LBO06dP1caNH5kdCgAAPo2EIAC/1LFDlB5/eKI6dmAQC6A2gwcPcyUAbbZQDRo0zNyA/By1Nn0LNTZ9U1lZmd566w1J0pIlr6usrMzkiAAA8F0kBAEAwHGsVqsmTbpbkjR58j2yWq0mR+TfqLXpW6ix6ZvmzXtBhmFIkgzDqfnzXzA5IgAAfBcJQQAAUKsePXpq7txF6t49xuxQIGpt+hJqbPqe3bsz9e2339Qo27btG+3Zk2VSRAAA+DYSggAAoE5hYeFmh4D/otam76DGpu9ZuvStkyoHAMDfkRAEAABoJKi16TuoselbOnbsVGt5hw61lwMA4O9ICAIAADQi1Nr0DdTY9C3XXDOi1mbc11xzrUkRAQDg20gIAgAAAKeAGpu+w2YL1dix42uUjR17I824AQCog1cSggUFBfrjH/+oIUOG6Nprr9U999yj/Px8SVJGRoYSEhI0ZMgQJSQkKDMz07WcJ6YBAAAAaHqGDbtWISGtJEkhIa00bFicyREBAOC7vJIQtFgsmjhxoj7++GN9+OGH6tChg55//nlJ0owZMzR+/Hh9/PHHGj9+vKZPn+5azhPTAAAAGrP8/DyzQ8B/bd+epilTJmvHjp/NDgWqbMZ9//1/liQ98MDDNOMGAKAeXkkIhoaG6pJLLnG9vuCCC7R//37l5eUpLS1NcXGVv97FxcUpLS1N+fn5HpkGAADQmJGA8h0Oh0OLFs2XJC1aNF8Oh8PkiCDRjBsAgIbyeh+CTqdTS5cuVWxsrLKzs9WuXTvXr3dWq1WRkZHKzs72yDQAAIDGigSUb9mwYb2Ki4skSUVFhdq4cb3JEaEKA+8AAHBigd5+w6efflotW7bUTTfdpLS0NG+/fYOFh4eYHQKagKCgQFWU1l4eEdHK+wFBUuX2Lyut+Zr9Ya6goECV2iv/DgiwsD+AWqxcuVKHDxdLkoqLi7Rp02caOXKkyVH5p/z8fK1Y8Z7s9soLl91u1/Ll7ykubqjatGljcnQAAAAn5tWE4OzZs5WVlaWXX35ZAQEBio6OVk5OjhwOh6xWqxwOh3JzcxUdHS3DMNw+7WTk5ZXI6TQ8tCXgL8rKKmqthltWVqGDBw97PR5UKiurOO41+8Nc1feJ02mwP4DfKCws0JtvvulKQJWWluqNN97QuedezCiqJli//qPjamg6HA4lJX2koUMZyAIAAPiG+ipaeK3J8Isvvqgff/xRCxYsUFBQkCQpPDxcMTExSkpKkiQlJSUpJiZGYWFhHpkGeEty8kYlJk5XVlamMrPzNPON9a5/mdl5ysrKVGLidCUmTldy8kazwwUA+LhNm76U0+msUeZ0OrVp05cmReTf+vXrr4CAmrfRAQEB6tv3cpMiQnUMvAMAwIl5JSH466+/6uWXX1Zubq5uuOEGxcfH6+6775YkzZw5U2+//baGDBmit99+W08++aRrOU9MA7whNTVFuzN+Uefw5uoU0UpGRZnrX6eIVuoc3lyWkgPanfGLUlNTzA4XMFVy8kZlZWVqz+5s7dmdLbu9lGQ58Bv9+vWXxWKpUWaxWEhAmcRmC9Xo0eMUHBwsSQoODtaYMeOorekDGHgHAICG8UqT4f/7v//Tjh07ap129tlna9myZV6bBnhLp4jWeiKhX73zPP1+qmiYDn+XmpoiQxU6s31bV1lm1k5JUmzsILPCAnyKzRaqc845X9u2bXWVnXvu+SSgTDR48DBt3Lheubk5stlCNWjQMLND8nu/HXjnuefmugYaBAAANXl9lGEAMFNVbbSsPdlKnLNYWXuylZWVSW00k53Zvq3uujfe9a96chBAZR+CP/30fY2yH3/8XkVFheYEBFmtVk2aVNniZfLke0g8+QBGfgYAoOFICALwK6mpKZJRoQ5nhsvpKFOHM8Mlo4Km2wB8Gn0IAvUrLCzQ8uVLa4z8vGzZUpLmAADUgYQgAL/ToX1b/enu61z/OlAbDYCPYxAL3/Pb5qm/HXUY3kXSHACAk0NCEAAAwMcxiIXvoXmqbyFpDgDAySEhCAAA0AgMHjzMlQBkEAtz0TzV91QlzZs1ayZJatasGUlzAADq4ZVRhgHATMnJG119BGZlZUpGhZ5fsNo1fc++Q5KlUImJ0yVV1jJgdFsAvqZqEIvExOkMYmGy+pqnDh0aZ1JUGDhwsJYufUuS5HA4FRs72OSIAADwXdQQBNDkpaamKDPjV5Ufy9WZkS11ZrvWclSUuf6d2a61zoxsqfJjucrM+JUBRgD4rB49emru3EXq3j3G7FD8Gs1TfdOnn26Q1Vq5X6zWACUnbzA5IgAAfBc1BAH4hfZRNt17W/8TzjfvDZKBAHxbWFi42SH4varmqR988I7Ky8tpnuoDqppxl5eXS5LKy8u1bNlS9e17OfsFAIBakBAETlP15qhVsrIyZXGU6en3U+tdNiu3WEZeqaupahWarAIA4NsGDhysd99dIkmqqHDQPNVkNOMGAODk0GQYOE2pqSnavWuHVLTH9a9TqFUdw1vIcJTX+69jeAt1CrXWWHb3rh00WQUAwMdt3PiRDKMyAWUYTn3yyUcmR+Tf+vXrL4vFUqPMYrHQjBsAgDpQQxBwg47hLfXotT3dsq5ZH6a5ZT3+qq4am4azrEHNgfcdKJQl4Ci1Nj3stwO9GEaFFs5b45q+f+8hWf470AvbHoCvKSws0AcfvFOj7P3339Fllw2geapJbLZQnXPO+dq2baur7Nxzz2d/AABQBxKCAJqUygFEdig6oqWrLCrcKqmFnA77CZePjmghSbKX7HGVZR88KkkkpdwoNTVFGZm/KCrKpsh2ldvcUfG//dMuqpUkKSPzF0lsewC+JTl5oxwOR40yh8Oh5OSNGjlyjElR+bfCwgL99NP3Ncp+/PF7FRUVkhQEAKAWJAQBNDnRES31x7HuqbEpSa9+QK1NT4iKsumWifU35frH4i+9FA0ANNxvWqbCB9CHIAAAJ4c+BAEAAICTEBs7WIGBNX9XDwwMpDazifr166+AgJqPNgEBAfQh6CN27dppdggAgN8gIQgAAACcBJstVGPGjK9RNnbsjTRNNZHNFqrRo8cpODhYkhQcHKwxY8axT3zAhg3rNH36VG3cyMA7AOBLLIZhGGYH4Yvy8krkdLJp/Fltg1PUJisrU3LY1Sm85QnnbYisvKOSNVidOnU+4bz+NNjCyewPw2FXdKR79ockZecelYV9csrqGujF6SxTVLSt3mUPZBcpICDouG3PdgZgNofDoT/84UZVVFQoMDBQr732jqxWq9lh+TWHw6H77rtDhYUFCg1to5deepl9YrKysjJNnHiTnE6nAgKsWrz4LQUFBZkdFgD4jYiIVnVOow9BoA6pqSnKSt+ujqH137R0DJGkZjIqyt3yvh1tzSQ5ZeTtqne+3YVlkvxnsIXU1BRl7PpZ7cLq3x8RNkkKlKOizG3vHRkWKMmho4Xp9c6Xk+9f+6ShUlNTtCtjhyIiW7jKwtoGSGquihMM9NI2srkk6fCR3a6yg7nHJLGdAZgnOXmj1q37pyoqKiRJFRUVeuSR+3XNNSO4NvkI+nn0DfPnv+jq29HpdGjBghf1wAOPmBwVAEAiIQjUq2NokKYOPNPsMGr17Kf7zQ7B69qFBWlCXKTZYdRpSVKu2SH4rIjIFho9rodb1rV86Xa3rAdorPLz8xQWFm52GH4tNTVFeYUFand2V1dZ3r79Sk1NISFoog0b1uvYsaOSpKNHj2rjxvUMKGKi3bsztW3b1hpl//73Vu3Zk6UOHTqZFBUkvkcAVKIPQQAAUKf8/DyzQ0A127enacqUydqx42ezQ/F7Ye3P1OC773D9C2vvmz8g+ovCwgItX75UdntlzXO73a5ly5aqqKjQ3MD82PLl751UObyD7xEAVaghiCahof3LnYysrEypwu6zNfF2F9qlkkwlJk532zrplw1Addu3pykxcbqeeOJpde8eY3Y4fs/hcGjRovmSpEWL5uu55+bSPxrwX5s2felqmlrF6XRq06YvqSVokjFjxmnbtm+OKx89+gYTooFU+T3y4otzJEkvvjhHCxYs5nsE8GMkBNEkpKamKGtnmjq0dt8XWocWkhQgw+GevgHdrUOrAEl2OXN3uGV9e4odkuiXDUAlkk++Z8OG9SouLpIkFRUV0hwSqKZfv/5atmxpjTKLxaK+fS83KSJ06NBJF154sb799n9JwYsuupjmwiZat+5DHTlSIkkqKTms9es/VFzcdeYGBcA0JATRZHRobdWf+7pvZFl/89ymo25dn7trbWZlZcpZUebT/fTl5JUpoMh/am2ezMjPDmep2/r+O5h7VPkBDdvOvrz9fB3JJ99SV3PIvn0vl80Wam5wgA+w2UJ1zjnn1+iz7txzz+f8MFn37j1qJAS7daO2uVkKCwv0wQfv1Ch7//131L//lZwngJ8iIQjAI1JTU5SRnqaIUPesLzyk8n/nCUalNVPlZz2qkrw0t6zvYGHl/76a0EpNTVH6rp8V1rZZvfO1biNJgapwuGfk5zbhgZIqVFC8s9758g9V1u711e3ny0g++R6aQwL1S0paUyPxJEnbtn2jtWvXaPjweJOi8m+FhQVatWpZjbJVqz5Q//5X8F1ignXr/inDMGqUGYahdes+1LhxN5sUFQAzkRAE4DERodLYq2hieKo++MxhdggnFNa2mYbFtzU7jFqtX3PI7BAaLZJPvqdfv/7HdcQfEBBAc0jgv5KTN6h5ixbq2LmLq2x3ZoY+/XQDCUGT8F3iW/bu3V1r+Z49WV6OBICvYJRhAABQQ79+/WWxWGqU0ReXuWy2UI0ePU7NmlXWyG3WrJnGjBlHLRvgv2w2mzp27qKpTyW6/nXs3EU2m83s0PxWv379FRBQ83GTHzLMM27chDrKqR0I+CsSggAAoAabLVS9ep1Xo+ycc84j+WSygQMHy+GorG3jcDgVGzvY5IgA32G11t7wqa5yeF7VDxnBwcGSpODgYH7IMFHVIC/VXXTR7xnkxUfk5+eZHQL8EN+QAACghsLCAn3//bc1yr777lsVFRXyIGeiTz/dIKs1QE6nQ1ZrgJKTN9DsDn6t+uBWWVmZkkV6dvrjrum7MzMkQ0pMnM4gUyYZPHiYNm5cr9zcHNlsoRo0aJjZIfm1e+99UBMn3iSn06mAgADdc88DZocESdu3pykxcbqeeOJpde/OwDvwHhKCAOAnPDHyc4Wj3Gf76ss/VK7iAv8Z9dmdkpM3yuGo2Yelw+FQcvJGjRw5xqSo/FvVQC/l5ZWD5ZSXlzPQC/xKbd9hWVmZKrWXKui/NdBkSJkZu45bdscv25WVlXnc8v5yTTeT1WrVpEl3KzFxuiZPvkdWK31LmykoKEg33XSrlix5XTfffLuCgoLMDsnvORwOLVo0X5K0aNF8PffcXM4TeA0JQQDwE6mpKUpPT5OtjXvWd0aryv8dFe4ZPdjdKnMkR3Uo3z2jPhcVVP7vDw+Pv+k+ED6AzvnRVDX0x6qsrEyVlpbKGlwzgWENCpLjNyOn/pY1KEjlhlM7M/+XLHTYy2pNEtaGxOHp6dGjp+bOXaSwsHCzQ4GkwYOv0e9+101du/7O7FAgacOG9SouLpIkFRUVauPG9Xyvw2tICAKAH7G1kQbQ7dgp+WKD2RF4T+/el2jFig+OK7/44j4mRAOJUYZ9wW+bp1YYTm1Y8LJrev6+/Sq2BNA89SSlpqbol4xdahEZVe98AW0j1dID77/nyNF6px/LPSDJP34M8iSSgb6FZKBvqKr9b7fbJUl2u53a//AqEoIAAKCGb77ZUkf513Q+bhKbLVTXX5+gpUuXuMpGjUrggcGLUlNTtDMzXa2iotS8XYQkqaxa0/qQqHaSpJ2Z6ZJIIJ2MFpFR+t34iWaHUaud7y42OwQATRS1/2E2EoJoMg7kl+jzX32z6WJjcCC/TGdGmh0FAF9gtx+rtby0tNTLkaB+tO32tlZRUfr9xFvrnWfr4je9EgsgVdZcXbHifbPDaPRGjUpokkn86jWbCwsLVFRUdNw8NptNoaGV/clQu9m7qP0Ps5EQBAAANTRv3kKS1LJl5f9Hjx77b3lz02Lyd4WFBVqxouZDw/LlS3XZZf2pJQj4sdTUFGXnHlSzEJrknqrykjylpqY0yURYamqKsrKy1LFDF9lah8vWuvbjxHBKu/dkSKJ2szfZbKEaPXqcq9lwcHCwxowZx/c6vIaEIJqMqLAQXfl/nuhdxj9sPVR/HzoA/Eds7GCtWPGBOnbpKEna/tMOBQYG8pBgok2bvlRFRUWNsoqKCpoVAVB4517qGP+w2WE0WrvXzDE7BI/q2KGLpv5p1gnne/b5RyU5Tzgf3Gvw4GHauHG9cnNzZLOFatCgYWaHBD8S4I03mT17tmJjY9W9e3f98ssvrvKMjAwlJCRoyJAhSkhIUGZmpkenAQCAE7PZQhUZ2a5G2dixN/KLtZclJ29UYuJ0JSZOV2pq7f0MffVVihITpys5eaNJUQIAgFNltVo1adLdkqTJk++R1Wo1OSL4E6/UEBw4cKAmTJigG2+8sUb5jBkzNH78eMXHx2vNmjWaPn26lixZ4rFpAACgYSpHhKwcMCEwMFBDhlxjbkB+KDU1RVm7M9WxSycFtQxS914xtc6XlZEpiWZeAADUpnpfir7qd7/rpmXLlpodRp3oX7Jp8kpC8OKLLz6uLC8vT2lpaXrjjTckSXFxcXr66aeVn58vwzDcPi0sLMwbHxVANQfzjmjbz16piNwkHcxzii6B4G1VN81ZWZmSxZBU+ev1v/6VzI2gCTp26aRHnppR7zyzpz9ZlbsF4IfseXuafLNXT7Ln7ZFsXcwOAx6UmpqirJ2Z6tC6o9mh1ClAAXL6aJPtPcW7JfHDY1NkWh+C2dnZateunatKrNVqVWRkpLKzs2UYhtunkRAEAODEUlNTlJmVruiOUa6y7N0HmmyH676ittoLlUnZ/yb86rE7I0sypMTE6TXK+TUfaPr69etvdgj12rUrXWVldtfroKBgde16tokR1cLWxee3I05fh9Yd9dAl08wOo1H665a/mB0CPIRBReoQHh5idgg4CUFBgSo1O4gmICgoUBERrdy3rvAzdFEM/WCcqp0HHG7fJzg97twfviooKFDRHaP0x6m3uMpeffYffvHZzfTNN5u0K2uX2p4V7Sprc1ZlP46Hy+r/hmvTvnK+YscxV9mhvdkKCgpUQsL1HojWPwUFBaqhNxucLw0XFBQoHTE7ivr58v5MSLjeZ8/zb7/9VlOnTq1RVlZm1+2336rzzz/fpKj8S1BQoOylDa915svH+umofFYsMzuMRq2pHhv+zrSnw+joaOXk5MjhcMhqtcrhcCg3N1fR0dEyDMPt005WXl6JnE7DA58cnlBWVnHimXBCZWUVOnjwsNvWhdPn7n2Sn3dEO35yy+r8Tn6e1DbMffvDV9V17rrzWMTxysoq1PasaI184I9uWd+qF19ln7nZyXyvse0brjHcL7A/T828efNrLZ87d56effZFL0fjnyrPr4Z339NUj/XGcJ3xdU312PAH9SVyTUsIhoeHKyYmRklJSYqPj1dSUpJiYmJcTXs9MQ0AAADAqXN35/xZWZmyOx3a+e5it63TnY7lZisrwHpck/zT4S/N+W+5ZaJmzZp5XPmECX/wfjAAgON4JSGYmJioDRs26NChQ7rtttsUGhqqtWvXaubMmZo6daoWLlyo1q1ba/bs2a5lPDENTdueYoee23TU7DAarT3FDnWKNDsKeFpY+Bnq3svsKBqnnH1mRwAA5ktNTdH2XekKCHfTTUNo5ehZxyp8dGScsEjZJf1S5J6aMc68XEn+0Tl/z57nqH37Dtq3b4+r7KyzOqhnz3NMjAr+6kB+tj7fmWx2GI3SgfxsnRnZ3uww4AFeSQg+/vjjevzxx48rP/vss7Vs2bJal/HENDRdnugIOCsrU6o4po6hwW5ftzvsLrRLgS3UqVNnt6yvU6Tvd0wNAIA31DXIS7nToa2L36x32cPZB1RaS42yplQrLCA8UsFx48wOo1GyJy01OwSvmjlzlv74x5tdr2fMmGViNE1bnYNTSXr2+UdPuPzuPbskMUAV4E/oYR5NQmzsILd/USUmTpeRt0tTB57p1vW6y7Of7pclvLMef/wps0MB0ARUPUhkZWXKkEOvPvsP17Ts3Qdk0f8SHDwcoKlLTU3RrxnpatEuwlVmjQiTVVK5s/6abM3/u8zeo8WusmM5ByX5R60woLoWLVpoyJBr9PHH6zR0aJxatGhhdkhNVmpqijIzM9U+uour7Myoyr8ryk/cN37VvOX/GxRa+7IzJDWNa1dUWLSu/F2s2WE0Sv/O22J2CPAQEoIAAECpqSnalfWr2rav7HfX7vjfcKph7UMlScUVeTq0L19S03g4AOrTol2E/u/GG9yyrl/fec8t6wEao5tvvl3nnnu+Lrigt9mhNHnto7vonklPu2198195QhIDbQJNFQlBAAAgSWrbPkwj7x1a7zyr5n3kpWgav5MZfCErK1MOObXqxVfd8t6H9marQAENGgiBGp8API1kIAD4HhKCAAAAHlDZ7HSnWkaFn3DewEibAiUdLis94bwNERzZRpK071hBvfMdPZAniRqfODklhw7K/v03ZofRKJUfOijZWpkdBgAAJAQBeM7BQumDz3x01MBG4GChFHLiPAIAH9YyKlw9b40zO4w6pb2ZZHYIAAB43J7i3frrlr+YHUajtKd4tzpFdjY7DHgACUEAHuHuEYuzsjLlrDimduFBbl2vO+XklSnAjSM/h4Qz8jMAuFNDm3FnZWXK7qxwW99/R3NylRWQ32SacIe0jVDweRebHUajZN/9q9khoJE5meuWYVT1++ce+7IzZLEcP/JwbXz52sX99OnpFNmZbdhEkRAE6rG7sEzPfrrf7DBqtbuwTJ18uPaYu0d+TkycroxdP7ttfR5hkTp1YuRn+L7aHi6ysjLlMMpP2Efgob35KrAcPu7hwJcfBIAqqakp2rFrp5pFtK1/xrBQBUoqczjd8r6BbdvKIWnX4cJ65ys/eEgSTbgB/E9qaooyMjIVdYIaWpFtK6dXVLhvEJB2EZXrPHak/vkO5GZK8t1rl7ufS4CmgoQgUIeG/gqSlZUpOezqFN7SLe+blXdUsgafsJZZJz+rPXYy+8Nw2BUd6Z79IUnZuUdlacA+6RLq+/ukqED6YoPZUTRORQVS2zCzo3CP1NQU7czcIVtUiKusZbtmkprJXlF/H3atoirPrYOl+1xlRQdKJPnugwBQXbOItooYHW92GLU6uHyN2SEA8EFRkZ112w0zzA6jTm+896TZITR6+fl5Cgvz4doeaJJICAJ1aOgvSYmJ06WiPXr02p5ued9ZH6ZJtg7UMvuNk9kfmRk73PvmTaTmnyeacVc4jimsbTO3rtdd8g+VK9DqvibcbcN8P+F7MmxRIeo/8UK3rCtl8bduWY/ZTmZU4IbIyspUmbPCp/vpO3ogT1kBRQ1qDtZQ1BYFAKDhtm9PU2LidD3xxNPq3j3G7HDgR0gIAmhSakvYZGVlynCWqX1U6AmX33egUJaAoBpJpM4hTSMR5Ilm3AXFOzUs/gRN70yyfs0htWnd+BO58J7U1BRt3/WLFB5y4pkbIjRIUpAOlx1zz/o8IayljknaXuSm7jHy3Ftb1BNJ2nJHhc/WxCs/eEhZ+YVuTdBKJGkBb/LEdcvp9O1aeAdyMxUQ0LC+BhvKX65bDodDixbNlyQtWjRfzz03V1ar1eSo4C9ICAJoUmpLelXWGmxoJ96WJlEbEMApCg9RYDyDJZyqijXfuHV9lUnaX2UJd1N7/dBWkqRyp8M963O38DYqlbSjKM9tqzTy8iXRpB/wltTUFO3alamIsE5uWV+YrXI9FeXu6xvQ3dq2qYzxcKF7YjyYnyXJP65bGzasV3FxkSSpqKhQGzeu19ChcSZHBX9BQhBAk1e9dl9WVqbspccUHPy/Zq52e7mCm1c2Le3cJbJJ1AaEfzqZkQjLnXa3NfUtyi7R0YDMRj8KIXyTJTxMgXFDzA6j0apI+tjsEAC/ExHWSWOuedzsMBqtZesSzQ7BKwoLC7R8+VLZ7XZJkt1u17JlS9W37+Wy2ULNDQ5+gYQggCaveq3B5OSNWrfun8rNzZHT6VRAQIAi20XpmmtGkKRAo5eamqJfM35Wy8igeucLbCsFKlBljvoHEGmoFpGBkhzadyS93vmO5pZJ8o9f/AEAAOqzadOXcjprjmbvdDq1adOX1BKEV5AQBNxgd97RysFA3LSujja3rAq1qEoOVtWkorYSmpqWkUHqMS7a7DBqtX1pttkhAAAA+IR+/fpr+fL3apQFBASob9/LTYoI/oaEIHCa6hrEwuIoU6fI1vUum5VbLMNacwCLjramMYCFr3P3ABsAAPgLZ16u7ElLzQ6jUXLm5Uq2VmaHAcAH2GyhGj16nKvZcHBwsMaMGUdzYXgNCUHgNNU1iIWl5ICeSOhX77JPv58qIySKASzQaOUfKtf6NYfMDqNW+YfK1ab+nDxwnCOHChTwXf1Nn1E356ECyXamW9dZcihP1u9/cus6/YnjUJ5kC3fb+tz9o2VWVqbsTodaRPpmzeZjudkKDrDW+PH2tNha8cOvHzh4KFvbfvjE7DAarYOHstUq1L3fJb5q8OBh2rhxvXJzc2SzhWrQoGFmhwQ/QkIQAHBKGvpAk5WVKYezVBGRLd3yvgdzj8oa0PyED2dtWlPbFgDczd017BMTp2vPkaP63fiJblunO+18d7E6nNGSH28BeITVatWkSXcrMXG6Jk++R1ar1eyQ4EdICAIeknWwWE+/n3rCeTqGRHkpIsC9GvpQmJg4XYeP7NbocT3c8r7Ll25XqzM6NomHs4aOCtxQWVmZKnOW+WxffUdzy5TVwNGIG8rd/YCe0baNAs8/223r8zcVmQVuX2dI23AFntfL7ev1FxW795odwgkdyz2gne8uNjuMWh3LPSB16Wp2GGhkItpG66JzrzY7jEYrfc9ms0Pwqh49emru3EUKC3NfbW6gIUgIAh5QVSspMytTFme5Okf/7+KemZ0nI6CZOnXqrI4hUdRggl84mHtMy5dud9u6WnVxy6pMl5qaoh270tTMXfd/oZJVkr3C7qYVupc1THLoqHYVuWcQpvK8yv/pDxRovBp6H1RYWKCioqIaZXZ7qQxJQcHB9S5bZrfLIik4uHmNcpvNptDQNvW/cZeu3KsB8DiSgTADCUHAA6pqTiUmTldAab5m3va/viBmvrFezuZhTaJ2E9AQdQ2843SWKSq6/iG1D2QXKSCg5sA7rbo0rabAzcKltvEWs8NolA6tMcwOAV5g5OWrIuljs8NotIy8fLf2Iehup9MEOTFxuo46nbrpz9Pqne/t5/6ilgEB3HvBaw7mZ2nZukSzw2i0DuZnqVVoZ7PDgAelpHyuf/0r2ewwGrUrrohV//5XntY6SAgCHpZ5IF8z31hf43XHzmEmRgR4V10D7xwrzdEtEy+vd9l/LP5SLZq3a9IPcYcPHZG+MzuKxunwIUn155RPXl6JKtZ84+aV+pG8ErfuE08MYHGs9JgszZq5db3uYpSXq0XzFu4bwEKSbOFN6kcUwNd54rrldEpRkZ3dul53OpCbqYAAue3a1Sq0M9ctwAtICAIeVPVFlrY9TS2bVzZnMSyBfMEBgA/yxENcmbNCLaN8t3bW0QN5CgoIdOMIqu7dju4ewKKh/XZWjnxboZbtIt3yvkdzchXcwO3s7n4xm7qcPbv19nN/OeE8XdyZZAXq4YmBdzIyMt22Pk/p1Klzk/4BF+7Vv/+Vp127DaePhCDgQVU3BBMmjFWX9pUPFY5mrbjRB+DS3HKGlGl2FI1Tcze3tPbEQ9yvGTvdtj5P8aeHuJMZDOnXjHS3va9FFr/azt5SPflcWx+DUmU/gV06UdsIjVdDj92srEyVlpYe11fm6bDbS9W8efMT/pjRpQvnGNAYkRAEvCRjX64kqWPnViZHAsBXeKZG2jG1jAxy63rd5WhumYIC3Ngc0s210dztZGKrLZlRUVEhh6NCkuR0OutcNiAgQFZroAIDa97WNWzAhDY+vQ3NUlffp+VOh1pFR9W77OHsA2oWYK15nHdpzXb2AHcn8QFf1NDjvLYa0NW/W6p/p1RX/fujtu8Nai0DTZfFMAx65K5FXl6JnE42DdzjttvGq7y8TJJFt98+iS9V+L3ExOnKyPxFUVEnGFTkQJG6dO5GrZoGSkycrn1H0tVjXLTZodRq+9JstT/jbPbnKfj880+1ePHfjyufOPFOXXnlQBMi8j+JidOVU3pEv594a73zbV38pto1P4PjHAAAmC4iou4KSdQQBLwgMDDwvwlBQ8nJG0gIwu/9tplXbm6OpMofYZxOQy1atKxs5tW5G7VqTtLR3DJtX5ptdhi1OppbJnUxO4rG6corB+qtt16X3W53lTVv3pxkoJcdPnBAWxe/ecJ52nU+2zsBAQAAnCISgoAXVK+Im5mZofz8fIWFMdIw/Ndvm78kJk5XVtau/74K0KuvLjEnsEbuZPoZKnfaZYsOccv7FmWXqFlA8ImbAnfx7Sa+vu6FFxbq7rv/4Hr9178uMDEa/1P92M3KylSF4VRY+zNdZfn79ivQEqDfdT6b4xwAAPg8EoKAF5SWHqvx+oEH7tQ//vG+SdEAvqdfv/7KysrUsWNHFRXlm81dG4OTGTDhYOk+9Z94oVveN2Xxt4po3p4mkh5ms9kUE9NTP/+cpp49z5HNVn+Te7hX9fMrMXG60jN3HTcPA4cAAIDGgoQg4GFJSWuOK3M4HFq37kNdc821JkQE+J7Y2EFKTU3R9u1pJx4EAW5RdKBEKYu/ddu6Ijq7ZVU4gccee0offZSkoUPjzA7Fr/Xr11+FhQU6mJklh8Mhq9WqiIhIagYCAIBGg4Qg4GHLlr1ba/n7779NQhCopupBmgdqz6trBNVS+zE1C6r/1qC8rELNg2uOFBzRmf3mTSQDzcfotgAAoLFjlOE6MMow3OWFF57Vtm3fHFd+0UW/14MPPmJCRABwvOTkjUpNTVFhYYHy8/NUVlZ23Dzt25+lVq1aq1+//iRDAAAAAB9X3yjDJATrQEIQ7lJUVKi77554XPmCBYtls4V6PyAAOIG77/6DioqKjiu32UK1YMFiEyICAAAAcLLqSwgGeDEOwC/ZbKEaOXJMjbKRI8eSDATgs2655Y91lB//4wYAAACAxoeEIOAF1103WoGBlf1yBQYG6rrrRpkcEQDUrU+fS9W6dc0RbG22UPXpc6lJEQEAAABwJxKCgBdYrVZNnTpdkjRt2gxZrVaTIwKA+s2Z81KN17Nn/82cQAAAAAC4XZNNCGZkZCghIUFDhgxRQkKCMjMzzQ4Jfq5Hj56aO3eRunePMTsUADihkJAQV43ASy7pp5CQEJMjAgAAAOAuTXZQkQkTJmjUqFGKj4/XmjVrtGLFCi1ZsqTByzOoCAAAUkrK5+rf/0qTowAAAABwsvxulOG8vDwNGTJEW7ZskdVqlcPh0CWXXKINGzYoLCysgesgIQgAAAAAAIDGqb6EYKAX4/Ca7OxstWvXztVPm9VqVWRkpLKzsxucEAwPp2kUAAAAAAAAmp4mmRB0B2oIAgAAAAAAoLGqr4ZgkxxUJDo6Wjk5OXI4HJIkh8Oh3NxcRUdHmxwZAAAAAAAAYK4mmRAMDw9XTEyMkpKSJElJSUmKiYlpcHNhAAAAAAAAoKlqkoOKSFJ6erqmTp2q4uJitW7dWrNnz1bXrl0bvDxNhgEAAAAAANBY+d0ow+5AQhAAAAAAAACNld/1IQgAAAAAAACgdiQEAQAAAAAAAD8SaHYAviogwGJ2CAAAAAAAAIDb0YcgAAAAAAAA4EdoMgwAAAAAAAD4ERKCAAAAAAAAgB8hIQgAAAAAAAD4ERKCAAAAAAAAgB8hIQgAAAAAAAD4ERKCAAAAAAAAgB8hIQgAAAAAAAD4ERKCAAAAAAAAgB8hIQgAAAAAAAD4Eb9PCMbGxuqXX36pd54333xTeXl59c5TVFSkc889V88880yN8qVLl+rNN9883TA9Yu/everZs6fi4+MVHx+vYcOGacmSJae8vpycHN18881ujNCz/Hnfe1JsbKwuv/xyORwOV9mKFSvUvXt3vf322ydcfurUqa75tmzZoi+//NI17WSOse7du+vIkSMnGb1nvPTSS1q3bp3ZYbidO479vXv36pJLLjml96++j+Pj41VaWnpK62lK1q9fr+uuu07x8fEaOnSoHnrooRMuc7rb7uabb9Znn312wvlWrlypjIyMU36fpqi8vFzz5s3TkCFDNHz4cMXHx2vKlCnauXNng5YfPXq04uPja5T98MMPDdrv+J+G3A/8VvXj/rHHHtM333zjmvbll19q/Pjxuvrqq3X99ddrzJgxWrlypVtjbor+8Ic/6L333qtRZhiGYmNjNWHChBrbuDZ13bNxX3I8T9+XTJ06VQMGDFB8fLyGDBmi559/3mPvJUmffvqpZs+e7dH38BXufO7417/+pe7du+uTTz5p0Pync79Q3/m9devW466jtWnIc5k73HzzzRo4cKDrXuof//iHR9/PX58ZvWHMmDGKj4/XNddcUyPvMW3atAYt39Bry88//9xon/UCzQ6gMViyZIn69eun8PDwOuf58MMPdcEFF2jt2rX685//rKCgIEnSuHHj6lzG4XDIarW6Pd6T0apVK61Zs0aSlJ+fr4EDB2rYsGGKiIg46XW1a9dOb731lrtDNFVT3veeFBERoS+//FJXXHGFJGn16tXq1avXSa/n66+/1tGjR3X55ZdL8v1jrKKiQoGBNS+rDodD9913n1vW5Wt86divuo75s9zcXD355JNatWqVoqOjZRiGtm/ffsLlvLXtVq1apTZt2qhLly5eeb/GYNq0aSotLdWyZcvUunVrGYahjz76SOnp6frd735X77K//vqr8vLy1KxZM/3000+ua+y5556rv/71r7Uu0xiuK41R9QfzL7/8Uo8++qjmzp2rCy64QJK0e/duLV++3KToGo9Ro0bpzTff1A033OAq27JliwIDAxv0g3V992z13ZeczHnBfUnDrx+TJk3STTfdpMOHDys+Pl4XXnihBg4ceFws7rgnGDhw4HHrbqrcee+1YsUKXXrppVq+fLmuvvrqE7736dwv1Hd+//73v9fvf//7E66jIc9ltTmV777HH39cV111lbKzsxUXF6dLLrlEPXr0cE13Op2yWCyyWCwntd7a1LfvcHqWLVsmqbICwqhRo447hk90bDT02vLzzz/r888/1zXXXHN6AZuAu8L/ys3NVWJiovbv3y+73a7hw4frjjvu0N///nfl5uZqypQpCg4O1l//+tdab9JXrFihhx9+WIsWLVJycrKGDh0qSZo3b56OHj2qRx55RCtXrtTatWsVFham9PR0zZgxQ48++qjWrl2riooKXXLJJbrzzjs1ceJErVu3Tp9++qn++te/6vXXX9fatWvlcDgUHBysmTNnKiYmRq+++qqys7M1ffp0SdKhQ4c0YsQIffrpp/rqq6/00ksvKSAgQA6HQ0888cQJa+EcOXJEzZo1U/PmzSVV/qL68ssvq1u3bjVe/+53v9NTTz2lzZs3KygoSC1bttR7773nOtG2bNkiqfKX0AceeEAbN25UYWGhHn74YQ0ZMkSS9N133+n55593/VI6ZcoUXXnllcrLy9NDDz3k+vWnb9++evTRR7Vt2zY9/fTTcjqdqqio0J133qm4uLjT3e2S2PeeMHLkSK1cuVJXXHGF9uzZo2PHjrmOI6nyl+NzzjlHN910U62vJWnHjh1677335HQ6lZqaquHDh+uaa6457hi755579NVXX6mgoEAPPvig6xirbteuXZo1a5YKCgpUXl6uW265RaNGjTpuvrKyMr344otKSUlRQECAOnTooAULFmjHjh168skndezYMdntdo0dO1a33nqrK/YzzjhDmZmZKigo0COPPKJZs2bp4osv1g8//KA777xTH3/8sevzVb3H1q1bVV5erm7dumnmzJk644wzjlvXO++8o0ceeUQ7d+5UYGCgunTpopdeesmdu+q0nMqx/8wzzygmJqbOddZ33diwYYNeeOEFhYaGasCAAcctt23bNp1xxhmaPXu2vv76a5WXl6tNmzaaNWuW2rdv77kN4SMOHTqkwMBAhYaGSpIsFotrW9d3rlTfdlX76ODBg5Kk22+/XSNHjqzzOvlbJSUl+stf/qIdO3bIbrfrkksu0bRp07R69Wr9+OOPSkxM1N/+9jc98sgj6tevn3c2jI/KzMzUJ598on/9619q3bq1pMp9NmzYMEmV38mJiYn64YcfJEkjRozQpEmTXMsvX75c8fHxCgoK0ooVK1zJjS1btmj27NlauXKl63v5pptuUmpqqkaMGMGDRz1uvvlmnXPOOfrPf/6j3NxcDRs2TH/6058kSTt37tS0adNUUVGhs88+W3a7vcZyt99+u6666iotWLBAd911lysZKEkdO3bUgw8+6Hq9evVqvfbaa65pTz311Ek/4DZFV199tZ588knt3LnTdb+1cuVKXX/99TW28fvvv68333xTQUFBcjqd+tvf/qYNGzYcd8928OBB/e1vf1Nubq7Kysq0YMEC133JTz/9pPbt2+utt97SF198odDQUP3www9q0aKF7Ha7ysrKdOONN7q+6yXuS071vqRVq1Y699xzlZGRUes9QVlZWa3PBFXXr7FjxyolJUWlpaV6/vnn9d577+m7775T8+bNtXDhQkVERGjlypX6/PPPNXfu3Bp/Vx1D1aclJSWpVatW2rFjh9q1a6cnnnhCc+bMUVZWls455xw9//zztSZ5/vWvf+nvf/+7ysrK1KxZM02bNs11ni9fvtyVtG7WrJkWLVqktm3b1nmuV8XRunVr/frrr2rVqpXmzZuniIgIORwOPf/880pJSZEk9e/fX3/6059ktVo1depUffbZZ4qKitLhw4d1zz336LbbbtO8efP0yy+/qGfPnlqyZIlmzpypjz76SP3791d6erpmzpypu+66S8uWLVN0dLQkqaCgQJs2bdL69es1fPhwHTx40FUhZP78+UpKSlJwcLAsFouWLFmi1q1bn9a9Vn3nt6TTOsftdrscDofuuOMODR8+3LW+Cy+8UN99952Cg4O1cOFCTZ48WQUFBbLb7TrvvPP05JNPuhKqdYmOjlaXLl2UkZGhjRs3KisrS0ePHtWePXv09ttv6z//+U+tx8WWLVv0zDPP6LzzztN3332nwMBAzZkzR/Pnz9evv/6q6OhozZs3Ty1btqxx31z9b0nHTdu1a5dKSkqUmZmpXr16adKkSXr22We1f/9+DRo0yLXcb61atUrvvvuuHA6HQkJCNHPmTHXt2lWStGjRIiUlJclisahly5Z69913FRAQoFdeeUX//Oc/JVX+2Pj444/rjDPO0Lx585SRkaHDhw9rz5496tixo1566SW1aNGi3nuXm2++Wb169dL333+vffv2acKECWrXrp3efvtt5ebm6s9//rOGDRtW73NuixYt6t1fDREbG6tRo0Zp8+bN6tChgy6++OJ6rxnVp9W2Hdu0aaO5c+eqpKRE8fHx+v3vf6/HH3/8tOP0GsPPXXXVVcaOHTuMW2+91fj6668NwzAMu91ujBs3zvjyyy9rzFOXn3/+2bjqqqsMp9NprFmzxpg4caJr2ty5c41nn33WMAzDWLFihXHBBRcYWVlZrukDBgwwcnJyjG3bthkJCQnG7bffbhiGYTzxxBPGBx98YBiGYeTl5bnm/+qrr4wxY8YYhmEYhYWFRr9+/YySkhLDMAxj/vz5xjPPPGMYhmFce+21xtatWw3DMIyKigrj8OHDx8W9Z88eIyYmxhgxYoQxfPhwo1evXsYbb7xx3Lb57euffvrJGDx4sOFwOFxxVK2vT58+rvm7detmvPXWW4ZhGMY333xjXH755YZhGEZRUZERHx9v5OTkGIZhGDk5OUb//v2NoqIi44033jCmTZvmWkfVuu+44w5j1apVhmEYhtPpNIqKimrfGSfBn/e9J1111VXG9u3bjSFDhhiFhYXGSy+9ZLz11lvGI4884joeqv/929fV/66+DQ2j9mNs3rx5hmEYRnp6utGnTx/j0KFDrmklJSVGeXm5MXLkSGPnzp2GYRjG4cOHjcGDB7teVzdv3jzj7rvvNux2u2EY/9v+hw8fdpWVlJQYw4YNcy3/yCOPGCNHjjSOHDliGIZhbN682ejRo4exbdu2Wj/fggULjAULFrimzZkzx3jhhRdqXdeGDRuMW265xTVv1fngC07n2K+uodeNQ4cOGX369DHS09MNwzCMV155xbWPq5ar+rv6efPBBx8Y999/v7s+tk9zOBzGnXfeafTp08e49957jTfeeMPIz883DKPh58rgwYONdevWudZZtXx918mbbrrJSE5ONgzDMB599FHXtdrhcBgPPPCA8f777x83Hwxj7dq1xogRI+qcPmfOHOPhhx82nE6ncfjwYeOaa64xPv/8c8MwDKOsrMzo27evkZWVZezfv9/o06eP6xq1efNmY+TIkYZhVJ5f3bp1M9auXev5D9SIVX3X33TTTcZ9991nOBwOo7i42OjTp4+RkZFhGIZhjBw50li5cqVhGIbx7bffGj169HAdz9WP7fPOO89IS0ur87127NhhXHbZZa57oBdffNG47777PPfhGpmnn37amD17tmEYld+9F154oXHgwIEa2/iiiy4y9u/fbxhG5fXo6NGjhmEcf89WWFhoVFRUGFdddZWxefNmIyYmxti9e7fx0ksvGYMGDTIGDBhgvPnmm4ZhGMbDDz9sLFq0yDCMyvvMgQMHuvYL9yUnf19Sff0HDhwwLr/8cuOrr7467p6gvmeCquvXZ599ZhiGYbz66qtG7969XefXjBkzXHGuWLHCuPfee4/7u7ZpF198sZGdnW0YhmFMmjTJGDFihFFcXGyUl5cbcXFxxldffXXc58nKyjLGjh3ruqf+5ZdfjCuuuMK1fa+++mojNzfXtT9KS0vrPder4qg6jh977DHXZ3nnnXeMW265xbDb7YbdbjcmTJhgvPPOO4ZhVD4PnXPOOUZpaamxbNkyo1evXsbUqVMNh8NhzJo1y+jZs6dRUlJifPDBB0b37t2NzZs3G4ZhGKtWrTLuuuuuGp/pjTfeMP785z+73v+VV15x7dMLLrjAOHbsmGEYlcdaeXm5YRinf69V1/ltGMZpneOGYRgHDx40+vfv7zomb7rpJmPy5Mmu2J1Op+uexul0Gn/+85+Nd999t9Y4q8fy66+/GhdeeKGRnp5uzJ0717jiiitcn/1Ex0XPnj1dx+vMmTON/v37u469iRMnup73ql9XfnuN+e20QYMGGcXFxUZFRYVx7bXXGrfffrtht9uNI0eOGJdeeqnrO6u6rVu3Gn/84x9d14zPP//cSEhIMAzDMFauXFnjM1Rto88//9wYPny4cfjwYdf2mjNnTo04ioqKDKfTadx2222ue7367l2qf8ceOHDAOO+881zH/XfffWf079/ftV/res49VdWv1VdddZUxY8YM17QTXTOq/q5vO/52HY0JNQRVWeX366+/Vn5+vqvsyJEjSk9P12WXXXbC5at+pbdYLBo8eLASExOVk5Ojdu3aHTfvRRddpI4dO7peX3LJJdq0aZP27t2rhIQELV68WGVlZUpNTdUf//hHSdKPP/6oRYsWqaioSBaLRZmZmZIkm82m2NhYrVmzRmPHjtWyZcv0xhtvSJIuvfRSPfvssxo6dKgGDBhQo3ZWddWbDOfk5OiGG25Q7969de6559b5eTt06CCHw6HHHntMl1xyia666qo6562qNnvBBRcoNzdXdrtd3377rfbu3ev6fFJlrYisrCydf/75euONNzR79mz16dPH1STjkksu0SuvvKL9+/frsssu0/nnn1/ne54Mf973nlRVy2Xt2rVat26dli5dqh9//NEj7zVmzBhJUteuXdWzZ0/95z//qVG1OzMzU+np6TVqZ5SXl2vXrl06++yza6zrs88+09SpU12/FoaFhUmSSktLNXPmTO3YsUMWi0W5ubnavn27a/mhQ4eqZcuWrvV06tRJF154Ya3xJicnq6SkRB9//LGkyl//qzdBqL6uHj16aNeuXXryySfVp08fXXnllae0jTzhdI79E6ntuvGf//xHPXv2dP2amZCQUGefRF988YXeffddHT16VBUVFafw6RqngIAALVy4UL/88ou2bt2qTz75RK+99po+/PBDSSc+VzIyMlRRUeGqoSZJbdq00dGjRxt8nUxOTtb333/vuh6VlpbWekzgeDt37tRDDz2k0tJS9e/fX9u2bdOjjz4qi8WikJAQDR8+XJs2bdIVV1yhzz77TF26dHGdVz179tTGjRtdNSOqCw4OrrFPUb+hQ4cqICBArVq10tlnn63du3erbdu2+uWXX1z9NV5wwQUN/m6dMmWKMjMzlZeXp6+++kpbtmzRFVdcocjISEnSDTfccFw/kP5s9OjRmjhxoh588EGtX79evXv3Pu4acumll2ratGkaOHCgrrzySnXo0KHWdeXn5+vRRx9VTk6OZsyYIamyr67k5GSFhYUpMDCwRjPKTZs2ae3atXI6nTp48KBycnJO6TNwX1LplVde0bJly2S1WjVx4kT169dPK1eurHFPUN8zQZs2bdSyZUvXe/Tq1UtRUVGumu+9evVSampqne9fl4suukhRUVGSpJiYGLVv316tWrVyfb6srKzjarCnpKRo9+7duvHGG11lFRUVOnTokD7//HPFx8e7atedccYZknTCc/2iiy5y1dY7//zzXZ9l06ZNGjlypGufX3/99frkk080fvx4ZWZmqnfv3goODlZcXJymT5+u888/XwEBAQoJCVFQUJAOHDggq9Wqjh076osvvtAll1yid999V/fff3+Nz7Ry5UpXP2ojR47UY489pj/+8Y8KCQlRly5d9Oc//1n9+/fXlVdeqZCQkOO246ncazXk/JZO/hzPysqS1WpVUVGRMjIyXDU3r732WldzUKfTqddff11ffPGFnE6nioqKXK3ialPVoiE4OFhPPfWU6/5zwIABrvOwvuNCkrp06eI6Xnv27Kn9+/e7jr1evXopKyurQdutussvv9x1vHbv3l09evRQUFCQgoKC1KVLF+3evVudO3eusUxycrK2b9/uujYZhqHi4mJJldeYcePGufZxmzZtJFUeh9dcc42rfOzYsZo1a1aNOKpaN5x33nnavXu3a7m67l2k/33HtmvXTqGhoa6m6r169VJOTo7sdnu9z7nuct111530MvVtx8aMhKDkav+/fPlyNWvW7KSWLSsr04cffqjg4GBXYq28vFyrVq2qtTlV1ZdElb59+2rz5s3au3evnnvuOW3dulVr166VVJl4Kysr03333ae3337bdaJUbyp3880366GHHlJ4eLjOPvtsV99Mjz76qHbs2KHNmzfrvvvu02233aaxY8fW+1natWun888/X1u2bNG5554rq9Uqp9Ppml7VPKZVq1Zau3attmzZok2bNun555/XqlWral1ncHCwJLluuCoqKmQYhrp376533nmn1mVWr16t1NRUrVmzRq+88oqWLl2qW2+9VbGxsUpNTdXTTz+tyy67TA888EC9n6ch2PeeU9WRep8+fVxfLlXqOrZOl2EYxzXzMAxDbdq0aVC/J4Zh1Fr+wgsvKCIiQs8++6wCAwN1++2314i5+k13ba9/+x4zZsxQ3759a51efdkOHTpo3bp12rx5s7744gu9+OKLrmPOTKd77J9IXdeNhti3b5/+8pe/aPny5erQoYO2bdvmavLnL7p166Zu3brpxhtv1DXXXKOvv/76uHnqOldqU9VPTkOuk4ZhaOHChXXevON/evbsqaysLBUXF6t169b63e9+pzVr1ujtt9/Wjz/+WOs+qnq9YsUK7dy5U7GxsZKko0ePasWKFbUmBFu0aOGWPo78RfXrq9VqdQ1E0dBtGBMTox9++MH1EDh37lwdOXJEF110kaTazz38T48ePRQREaGUlBStWLGiRpPdKvPnz9cPP/ygzZs3a8KECZo5c6brYbO6mTNnKjY2VmlpaZo7d67uuOMOffDBB+rXr58KCgoUEPC/sRX37dunAwcOaPXq1QoJCdG1117rlh+U/Pm+pKoPwd+qfk9Q3zPB3r17azTnDAgIqPG6+vlZ3YnuMX97jtd1zv9W//79NWfOnFqn1eZE53pd71vXtb+srEx79+7VwYMHXdd+wzC0bds2V798Vd0FSZUJ6aSkJMXGxqq4uLjG/v3hhx+Unp6uxx57zFWWm5urbdu26aKLLtIHH3ygbdu2afPmzbr++uu1ePHiGkniU73Xasj5LZ38OT5//nxZLBYNGTKkzvPgww8/1L///W+98847CgkJ0csvv+yqaFGbqj4Ef+u397R1HRfp6enHHa+/3ee1Pf944vg1DEOjRo06qb5DT/b4rYqzvnuX+uKvfs8fHBxc53Ouu1Q/Nhr6XHoq27Ex8PtRhqXKg7R379565ZVXXGXZ2dmuPpTOOOMMHT58uNZlP/nkE3Xt2lVffPGFkpOTlZycrNdff73Bo8n17dtXKSkpKioqUlRUlPr166d58+bp0ksvlVT54F1RUeH6Bendd9+tsXy3bt0UGhqqWbNmafz48a7yXbt2qXv37rrllls0YsQIVzv++pSUlOinn35y/arQsWNH13KbNm1y/dqRn5+v0tJSDRgwQH/605/UqlUr7dmzp0GfV5IuvPBCZWVlafPmza6y77//XoZhaM+ePa5fE6ZNm6affvpJTqdTGRkZ6tixo2644QZNmDChQZ+nIdj3ntOhQwc98MADuuuuu46bVv3Yys3NdfW981shISF1bv8qK1askFT5i/vPP/98XO3RLl26qHnz5lq9erWrLD09XSUlJcetKzY2Vv/4xz9UVlYmSa4aUYcPH1ZUVJQCAwP1yy+/nHAUtPrExsbqzTffdI3SVlJSovT09FrnrfqV9+qrr9a0adOUn5+vwsLCU35vdzndY/9UXHjhhUpLS3PdvFV1EvxbJSUlatasmSIiIuR0Oo8b0a4py8nJ0bfffut6feDAAeXn5+uss86SdOJzpWvXrgoMDNT69etdZQUFBQoJCan3OlldbGysXnnlFdcNaX5+vuv7ob7rqT/q3LmzBg4cqMcff7zGdjl69KgkqV+/flq+fLkMw1BJSYnWrVunvn37Kjc3V1u3btWnn37qOv/+9a9/6ccff9T+/fvN+jhNWkhIiP7v//7PVdv2+++/r3NU4rvuuksLFizQ999/7yo7duyY6+++ffvqX//6l+v8qUpQ4X9GjRqlefPmKTMz05X4qFJRUaE9e/bovPPO06RJk3TZZZfp559/lnT8Nebw4cNq3769LBaLvv32W+3fv1+jR4+u9b4kJCRE5eXlCgkJUUZGRp3fy9yXuPe+pL5nglPVsWNH7dixQ2VlZSorK3PVfDwdl112mVJSUvTrr7/WiFOSrrrqKq1Zs8b1nHTkyBGVlZWd8rner18/rVq1SuXl5SovL9fq1avVt29fffLJJwoJCdH999/vuvZ37969zlqSQUFB6tevnx588EGNHz++RlJmxYoVmjhxoms9ycnJuvfee7VixQqVlJQoPz9fffr00ZQpU9StW7can1s6vXut+s5v6dTP8a+++qreGneHDx9WmzZtXOdwUlJSg2OuS33Hxanq2LGj6/m3pKREn3/++WlGKVdtuwMHDkiqHGSmquXWVVddpaVLl7quPwUFBZIqj8N169appKREhmFo+fLlDT5+a7t3OVl1Ped6QkOvGfVtx4Z8N/gqv68hWJWFfv755/WXv/xF1157raTKC84zzzyjiIgITZgwQY8++qiaN29+3MASK1eudC1T5cILL5TT6dTWrVtP+P5RUVE644wz1Lt3b0mVVaT379/vSgqFhIRoypQpGj16tKKjo4/rSF+qbJrw4osv1qi2/9e//tVVfbp169bHDU1fpWrkL6kyG37NNde4qu7ed999mjp1qpYtW6aLLrpIZ555pqTKB8EnnnhCFRUVcjgcGjBggC644IIGP4jYbDYtXLhQzz33nGbNmqXy8nJ16NBBL7/8sr7++mu98cYbrkz9k08+qYCAAL311lvasmWLmjVrpqCgILd01Onv+94bEhISai0fO3aspkyZohEjRqhz584677zzap3v6quv1po1axQfH+/qvPu3goKCdMMNN6igoKDWjtkDAwP18ssva9asWXrttdfkdDoVHh6uv/3tb8eta9KkSfrrX/+q6667Ts2aNVOnTp00d+5c3XnnnXr44Yf1z3/+Ux07dmzQSGh1mTRpkubPn6/Ro0e7aqjec889xzUTkio7MK8aLdTpdGrSpEk+0fzydI/9UxEeHq6nn35ad9xxh0JDQ10DmPxW9+7dNXToUA0fPlxnnnmmfv/735/Wg1JjUlFRoXnz5mnfvn1q3ry5nE6n7r//fvXs2VNSw86VhQsX6qmnntLChQtlsVh0++2367rrrqv3Oll1LZUqayg/99xzrubkzZo106OPPqoOHTooISFBs2fP1uuvv66HH36YJIikv/zlL1q4cKFGjx6twMBAtW7dWpGRkZo0aZI6d+6sp59+2rXNR4wYoQEDBuiVV17RgAEDajThCg4O1sCBA7Vy5crTuj6hbnPmzNG0adP05ptvqlevXnV2XTJgwAA99dRTmjVrlg4ePKi2bdsqKChITz31lCTp//7v//TQQw/p9ttvl1T541nVNFS69tprNWfOHCUkJBzX4b/T6dTUqVN1+PBhWSwWRUdH66GHHpKk4+7ZHnroIT355JPKzc3VV199pe7du+uqq66qUdOpymOPPabrr79e5557rlq1alXndy33Je69L6nvmeBUXXjhherbt6/i4uJ01lln6eyzz671B6yT0blzZz333HN67LHHVFpaqvLycl100UU677zz1KdPH02aNEm33XabLBaLgoKC9PLLL5/yuZ6QkKDdu3dr5MiRkiqbZo4dO1aTJ08+rvZ9q1atdPDgwTrvvUaPHq2PPvrItS6p8nmvqjuf6uLi4jRixAjddddduv/++1VaWirDMNSzZ08NHjy4xrync69V3/ktndo5/uqrr6p79+7q3r17ne973XXX6dNPP9Xw4cPVrl079e7d+7RbKNV3XJyqwYMHuwZ66dSpk2vAsNPx+9//Xvfff7/uvPNOORwOlZeXa+jQoTrnnHN03XXXKScnRwkJCbJarTrjjDP0zjvv6IorrtCOHTtctU/POecc3XnnnSd8r7vuuqvWe5dTUdtzric09JpR33bs27evXn/9dY0YMUJ9+vRpVIOKWIzT+QmmkasaQe6rr76qtw8BX/fYY4+pS5cumjhxotmhNBrs+6ah+mhnAOrmqXPl2LFjGjhwoD744ANXTUQA8Ffcl8CXLFy4UAcPHnT1oQk0Jr7wnPvqq69qz549TfqHO79tMrxkyRJNmDBBjzzySKNNCOXk5GjIkCHKysqq0Zkp6se+B4DTV1XrYNy4cSQDAQDwIcOHD9dHH31UaxN5wJf5ynPuSy+9pFWrVmncuHGmxeANfl1DEAAAAAAAAPA3fltDEAAAAAAAAPBHJAQBAAAAAAAAP0JCEAAAAAAAAPAjJAQBAADgFtOnT9eCBQvqnL5lyxYNGDDA9Xr48OHasmWLN0IDAABANYFmBwAAAIDG5+abb9b27dv11VdfKSgoSJL01FNPndQ61q5d64nQAAAAcALUEAQAAMBJ2bt3r7755htZLBZ9+umnDVqmoqLCw1EBAACgoUgIAgAA4KSsXr1a559/vkaOHKnVq1e7yqdOnaoXX3xR0v+aB7/yyiu67LLLNG3atOPWExsbq9TUVEnSvHnzdN999+nhhx/WhRdeqOHDh+uHH35wzZuTk6N7771Xl156qWJjY7VkyRLPfkgAAIAmjIQgAAAATsqaNWt07bXX6tprr9WXX36pQ4cO1TrfoUOHVFRUpM8++0xPP/30CdebnJys4cOH65tvvlFsbKxrGafTqTvvvFPdu3fXF198oX/84x/6xz/+oZSUFLd+LgAAAH9BQhAAAAAN9s0332j//v0aNmyYzjnnHHXo0EFJSUm1zhsQEKApU6YoKChIzZs3P+G6e/furSuuuEJWq1Xx8fHavn27JOmHH35Qfn6+7rnnHgUFBalDhw4aO3as1q1b59bPBgAA4C8YVAQAAAANtnr1al122WUKCwuTJMXFxWnVqlW69dZbj5u3TZs2Cg4ObvC627Zt6/q7efPmstvtqqio0L59+5Sbm6uLL77YNd3hcNR4DQAAgIYjIQgAAIAGKS0t1fr16+V0OnXZZZdJksrKylRcXOyqzVedxWJxy/tGR0frrLPO0oYNG9yyPgAAAH9HQhAAAAAN8sknn8hqterDDz9Us2bNXOX3339/jcFF3O28885TSEiIXnnlFU2YMEHNmjVTenq6SktLdd5553nsfQEAAJoq+hAEAABAg6xatUrXX3+9zjzzTEVERLj+3Xjjjfrwww9VUVHhkfe1Wq36+9//ru3bt2vgwIG69NJL9fjjj6ukpMQj7wcAANDUWQzDMMwOAgAAAAAAAIB3UEMQAAAAAAAA8CMkBAEAAAAAAAA/QkIQAAAAAAAA8CMkBAEAAAAAAAA/QkIQAAAAAAAA8CMkBAEAAAAAAAA/QkIQAAAAAAAA8CMkBAEAAAAAAAA/QkIQAAAAAAAA8CP/DyZO1ZA/yTKIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graph we can see that Jet Airways Business have the highest Price.\n",
    "# Apart from the first Airline almost all are having similar median\n",
    "\n",
    "# Airline vs Price\n",
    "sns.catplot(y = \"Price\", x = \"Airline\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \n",
       "0               0                0                                0  \n",
       "1               0                0                                0  \n",
       "2               0                0                                0  \n",
       "3               0                0                                0  \n",
       "4               0                0                                0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Airline = train_data[[\"Airline\"]]\n",
    "\n",
    "Airline = pd.get_dummies(Airline, drop_first= True)\n",
    "\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4536\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source vs Price\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Source\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0               0             0               0              0\n",
       "1               0             0               1              0\n",
       "2               0             1               0              0\n",
       "3               0             0               1              0\n",
       "4               0             0               0              0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Source = train_data[[\"Source\"]]\n",
    "\n",
    "Source = pd.get_dummies(Source, drop_first= True)\n",
    "\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination = train_data[[\"Destination\"]]\n",
    "\n",
    "Destination = pd.get_dummies(Destination, drop_first = True)\n",
    "\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    BLR → DEL\n",
       "1        CCU → IXR → BBI → BLR\n",
       "2        DEL → LKO → BOM → COK\n",
       "3              CCU → NAG → BLR\n",
       "4              BLR → NAG → DEL\n",
       "                 ...          \n",
       "10678                CCU → BLR\n",
       "10679                CCU → BLR\n",
       "10680                BLR → DEL\n",
       "10681                BLR → DEL\n",
       "10682    DEL → GOI → BOM → COK\n",
       "Name: Route, Length: 10682, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Route\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "train_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      5625\n",
       "non-stop    3491\n",
       "2 stops     1520\n",
       "3 stops       45\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Total_Stops\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "# Here Values are assigned with corresponding keys\n",
    "\n",
    "train_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</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>1.0</td>\n",
       "      <td>5.0</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>9.0</td>\n",
       "      <td>6.0</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>12.0</td>\n",
       "      <td>5.0</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>1.0</td>\n",
       "      <td>3.0</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         24.0   \n",
       "1    Air India   Kolkata    Banglore            2   7662          1.0   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882          9.0   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218         12.0   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302          1.0   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0            3.0        22       20             1           10   \n",
       "1            5.0         5       50            13           15   \n",
       "2            6.0         9       25             4           25   \n",
       "3            5.0        18        5            23           30   \n",
       "4            3.0        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  \n",
       "0               2             50  \n",
       "1               7             25  \n",
       "2              19              0  \n",
       "3               5             25  \n",
       "4               4             45  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate dataframe --> train_data + Airline + Source + Destination\n",
    "\n",
    "data_train = pd.concat([train_data, Airline, Source, Destination], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air 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>24.0</td>\n",
       "      <td>3.0</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>1.0</td>\n",
       "      <td>5.0</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>9.0</td>\n",
       "      <td>6.0</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>12.0</td>\n",
       "      <td>5.0</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>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</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": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897         24.0   \n",
       "1    Air India   Kolkata    Banglore            2   7662          1.0   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882          9.0   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218         12.0   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302          1.0   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0            3.0        22       20             1           10   \n",
       "1            5.0         5       50            13           15   \n",
       "2            6.0         9       25             4           25   \n",
       "3            5.0        18        5            23           30   \n",
       "4            3.0        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  Airline_Air India  Airline_GoAir  \\\n",
       "0               2             50                  0              0   \n",
       "1               7             25                  1              0   \n",
       "2              19              0                  0              0   \n",
       "3               5             25                  0              0   \n",
       "4               4             45                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0               1                    0                             0   \n",
       "1               0                    0                             0   \n",
       "2               0                    1                             0   \n",
       "3               1                    0                             0   \n",
       "4               1                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Trujet  Airline_Vistara  \\\n",
       "0                 0               0                0   \n",
       "1                 0               0                0   \n",
       "2                 0               0                0   \n",
       "3                 0               0                0   \n",
       "4                 0               0                0   \n",
       "\n",
       "   Airline_Vistara Premium economy  Source_Chennai  Source_Delhi  \\\n",
       "0                                0               0             0   \n",
       "1                                0               0             0   \n",
       "2                                0               0             1   \n",
       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "\n",
       "   Source_Kolkata  Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0               0              0                   0                  0   \n",
       "1               1              0                   0                  0   \n",
       "2               0              0                   1                  0   \n",
       "3               1              0                   0                  0   \n",
       "4               0              0                   0                  0   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  Destination_New Delhi  \n",
       "0                      0                    0                      1  \n",
       "1                      0                    0                      0  \n",
       "2                      0                    0                      0  \n",
       "3                      0                    0                      0  \n",
       "4                      0                    0                      1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>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>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897         24.0            3.0        22       20   \n",
       "1            2   7662          1.0            5.0         5       50   \n",
       "2            2  13882          9.0            6.0         9       25   \n",
       "3            1   6218         12.0            5.0        18        5   \n",
       "4            1  13302          1.0            3.0        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0             1           10               2             50   \n",
       "1            13           15               7             25   \n",
       "2             4           25              19              0   \n",
       "3            23           30               5             25   \n",
       "4            21           35               4             45   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(r\"Test_set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Info\n",
      "---------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2671 entries, 0 to 2670\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          2671 non-null   object\n",
      " 1   Date_of_Journey  2671 non-null   object\n",
      " 2   Source           2671 non-null   object\n",
      " 3   Destination      2671 non-null   object\n",
      " 4   Route            2671 non-null   object\n",
      " 5   Dep_Time         2671 non-null   object\n",
      " 6   Arrival_Time     2671 non-null   object\n",
      " 7   Duration         2671 non-null   object\n",
      " 8   Total_Stops      2671 non-null   object\n",
      " 9   Additional_Info  2671 non-null   object\n",
      "dtypes: object(10)\n",
      "memory usage: 208.8+ KB\n",
      "None\n",
      "\n",
      "\n",
      "Null values :\n",
      "---------------------------------------------------------------------------\n",
      "Airline            0\n",
      "Date_of_Journey    0\n",
      "Source             0\n",
      "Destination        0\n",
      "Route              0\n",
      "Dep_Time           0\n",
      "Arrival_Time       0\n",
      "Duration           0\n",
      "Total_Stops        0\n",
      "Additional_Info    0\n",
      "dtype: int64\n",
      "Airline\n",
      "---------------------------------------------------------------------------\n",
      "Jet Airways                          897\n",
      "IndiGo                               511\n",
      "Air India                            440\n",
      "Multiple carriers                    347\n",
      "SpiceJet                             208\n",
      "Vistara                              129\n",
      "Air Asia                              86\n",
      "GoAir                                 46\n",
      "Multiple carriers Premium economy      3\n",
      "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",
    "# Categorical data\n",
    "\n",
    "print(\"Airline\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Airline\"].value_counts())\n",
    "Airline = pd.get_dummies(test_data[\"Airline\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Source\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Source\"].value_counts())\n",
    "Source = pd.get_dummies(test_data[\"Source\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Destination\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Destination\"].value_counts())\n",
    "Destination = pd.get_dummies(test_data[\"Destination\"], drop_first = True)\n",
    "\n",
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "test_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)\n",
    "\n",
    "# Replacing Total_Stops\n",
    "test_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)\n",
    "\n",
    "# Concatenate dataframe --> test_data + Airline + Source + Destination\n",
    "data_test = pd.concat([test_data, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "data_test.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Shape of test data : \", data_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1          6.0            6.0        17       30             4   \n",
       "1            1         12.0            5.0         6       20            10   \n",
       "2            1         21.0            5.0        19       15            19   \n",
       "3            1         21.0            5.0         8        0            21   \n",
       "4            0         24.0            6.0        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\n",
       "0                                  0         0        0   \n",
       "1                                  0         0        0   \n",
       "2                                  0         0        0   \n",
       "3                                  0         0        0   \n",
       "4                                  0         0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "\n",
    "Finding out the best feature which will contribute and have good relation with target variable.\n",
    "Following are some of the feature selection methods,\n",
    "\n",
    "\n",
    "1. <span style=\"color: purple;\">**heatmap**</span>\n",
    "2. <span style=\"color: purple;\">**feature_importance_**</span>\n",
    "3. <span style=\"color: purple;\">**SelectKBest**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lucky/miniconda3/envs/ds_env/lib/python3.9/site-packages/pandas/core/generic.py:6383: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return self._update_inplace(result)\n"
     ]
    }
   ],
   "source": [
    "data_train['Journey_day'].fillna(data_train['Journey_day'].mean(), inplace = True)\n",
    "data_train['Journey_month'].fillna(data_train['Journey_month'].mean(), inplace = True)\n",
    "\n",
    "data_test['Journey_day'].fillna(data_test['Journey_day'].mean(), inplace = True)\n",
    "data_test['Journey_month'].fillna(data_test['Journey_month'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</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>1.0</td>\n",
       "      <td>5.0</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>9.0</td>\n",
       "      <td>6.0</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>12.0</td>\n",
       "      <td>5.0</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>1.0</td>\n",
       "      <td>3.0</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         24.0            3.0        22       20             1   \n",
       "1            2          1.0            5.0         5       50            13   \n",
       "2            2          9.0            6.0         9       25             4   \n",
       "3            1         12.0            5.0        18        5            23   \n",
       "4            1          1.0            3.0        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": 46,
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:, 1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total_Stops                                  0\n",
       "Journey_day                                  0\n",
       "Journey_month                                0\n",
       "Dep_hour                                     0\n",
       "Dep_min                                      0\n",
       "Arrival_hour                                 0\n",
       "Arrival_min                                  0\n",
       "Duration_hours                               0\n",
       "Duration_mins                                0\n",
       "Airline_Air India                            0\n",
       "Airline_GoAir                                0\n",
       "Airline_IndiGo                               0\n",
       "Airline_Jet Airways                          0\n",
       "Airline_Jet Airways Business                 0\n",
       "Airline_Multiple carriers                    0\n",
       "Airline_Multiple carriers Premium economy    0\n",
       "Airline_SpiceJet                             0\n",
       "Airline_Trujet                               0\n",
       "Airline_Vistara                              0\n",
       "Airline_Vistara Premium economy              0\n",
       "Source_Chennai                               0\n",
       "Source_Delhi                                 0\n",
       "Source_Kolkata                               0\n",
       "Source_Mumbai                                0\n",
       "Destination_Cochin                           0\n",
       "Destination_Delhi                            0\n",
       "Destination_Hyderabad                        0\n",
       "Destination_Kolkata                          0\n",
       "Destination_New Delhi                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "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",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.15913217e-01 1.28416465e-01 6.34432418e-02 2.60966854e-02\n",
      " 2.18266244e-02 2.78335105e-02 1.99872614e-02 1.26677495e-01\n",
      " 1.82421523e-02 1.00664988e-02 1.90499346e-03 1.58414490e-02\n",
      " 1.42636976e-01 6.74151392e-02 1.97760683e-02 9.20389628e-04\n",
      " 2.96789861e-03 1.18131435e-04 5.26978391e-03 1.56910875e-04\n",
      " 3.97649823e-04 1.07016977e-02 3.32260731e-03 8.25728238e-03\n",
      " 1.10480820e-02 1.83111125e-02 6.46567300e-03 4.93266163e-04\n",
      " 2.54917369e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting model using Random Forest\n",
    "\n",
    "1. Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "2. If needed do scaling of data\n",
    "    * Scaling is not done in Random forest\n",
    "3. Import model\n",
    "4. Fit the data\n",
    "5. Predict w.r.t X_test\n",
    "6. In regression check **RSME** Score\n",
    "7. Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "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": [
       "0.9517346365739916"
      ]
     },
     "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": [
       "0.7955458469860053"
      ]
     },
     "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": [
      "/home/lucky/miniconda3/envs/ds_env/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1186.1735289626536\n",
      "MSE: 4408451.962996547\n",
      "RMSE: 2099.6313874098346\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": [
       "0.7955458469860053"
      ]
     },
     "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": []
  },
  {
   "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=   5.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.1s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.4s\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=   8.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   8.2s\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=   8.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  12.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  12.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  12.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  13.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  12.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  10.7s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  11.3s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  11.3s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  11.0s\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=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  15.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  15.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  15.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  15.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  15.2s\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": [
      "/home/lucky/miniconda3/envs/ds_env/lib/python3.9/site-packages/seaborn/distributions.py:2619: 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: 1170.5764367318218\n",
      "MSE: 4081221.1264660736\n",
      "RMSE: 2020.2032389009958\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": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open a file, where you ant to store the data\n",
    "file = open('flight_rf.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(reg_rf, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7955458469860053"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}