flight_price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flight Price Prediction\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing dataset\n",
    "\n"
   ]
  },
  {
   "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": {
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       "    }\n",
       "\n",
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       "        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": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dropna(inplace = True)\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "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": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302   \n",
       "\n",
       "   Journey_day  Journey_month  \n",
       "0           24              3  \n",
       "1            1              5  \n",
       "2            9              6  \n",
       "3           12              5  \n",
       "4            1              3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route  Arrival_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL  01:10 22 Mar   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR         13:15   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK  04:25 10 Jun   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR         23:30   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL         21:35   \n",
       "\n",
       "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0   2h 50m    non-stop         No info   3897           24              3   \n",
       "1   7h 25m     2 stops         No info   7662            1              5   \n",
       "2      19h     2 stops         No info  13882            9              6   \n",
       "3   5h 25m      1 stop         No info   6218           12              5   \n",
       "4   4h 45m      1 stop         No info  13302            1              3   \n",
       "\n",
       "   Dep_hour  Dep_min  \n",
       "0        22       20  \n",
       "1         5       50  \n",
       "2         9       25  \n",
       "3        18        5  \n",
       "4        16       50  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "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</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\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": 19,
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Duration\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Categorical Data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Airline\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graph we can see that Jet Airways Business have the highest Price.\n",
    "# Apart from the first Airline almost all are having similar median\n",
    "\n",
    "# Airline vs Price\n",
    "sns.catplot(y = \"Price\", x = \"Airline\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "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": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "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": 27,
     "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": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "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": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Route\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Total_Stops\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  \n",
       "0               2             50  \n",
       "1               7             25  \n",
       "2              19              0  \n",
       "3               5             25  \n",
       "4               4             45  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
       "1    Air India   Kolkata    Banglore            2   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
       "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
       "\n",
       "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0              3        22       20             1           10   \n",
       "1              5         5       50            13           15   \n",
       "2              6         9       25             4           25   \n",
       "3              5        18        5            23           30   \n",
       "4              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  Airline_Air India  Airline_GoAir  \\\n",
       "0               2             50                  0              0   \n",
       "1               7             25                  1              0   \n",
       "2              19              0                  0              0   \n",
       "3               5             25                  0              0   \n",
       "4               4             45                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0               1                    0                             0   \n",
       "1               0                    0                             0   \n",
       "2               0                    1                             0   \n",
       "3               1                    0                             0   \n",
       "4               1                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Trujet  Airline_Vistara  \\\n",
       "0                 0               0                0   \n",
       "1                 0               0                0   \n",
       "2                 0               0                0   \n",
       "3                 0               0                0   \n",
       "4                 0               0                0   \n",
       "\n",
       "   Airline_Vistara Premium economy  Source_Chennai  Source_Delhi  \\\n",
       "0                                0               0             0   \n",
       "1                                0               0             0   \n",
       "2                                0               0             1   \n",
       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "\n",
       "   Source_Kolkata  Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0               0              0                   0                  0   \n",
       "1               1              0                   0                  0   \n",
       "2               0              0                   1                  0   \n",
       "3               1              0                   0                  0   \n",
       "4               0              0                   0                  0   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  Destination_New Delhi  \n",
       "0                      0                    0                      1  \n",
       "1                      0                    0                      0  \n",
       "2                      0                    0                      0  \n",
       "3                      0                    0                      0  \n",
       "4                      0                    0                      1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0            0   3897           24              3        22       20   \n",
       "1            2   7662            1              5         5       50   \n",
       "2            2  13882            9              6         9       25   \n",
       "3            1   6218           12              5        18        5   \n",
       "4            1  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0             1           10               2             50   \n",
       "1            13           15               7             25   \n",
       "2             4           25              19              0   \n",
       "3            23           30               5             25   \n",
       "4            21           35               4             45   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(r\"Test_set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Info\n",
      "---------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2671 entries, 0 to 2670\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          2671 non-null   object\n",
      " 1   Date_of_Journey  2671 non-null   object\n",
      " 2   Source           2671 non-null   object\n",
      " 3   Destination      2671 non-null   object\n",
      " 4   Route            2671 non-null   object\n",
      " 5   Dep_Time         2671 non-null   object\n",
      " 6   Arrival_Time     2671 non-null   object\n",
      " 7   Duration         2671 non-null   object\n",
      " 8   Total_Stops      2671 non-null   object\n",
      " 9   Additional_Info  2671 non-null   object\n",
      "dtypes: object(10)\n",
      "memory usage: 208.8+ KB\n",
      "None\n",
      "\n",
      "\n",
      "Null values :\n",
      "---------------------------------------------------------------------------\n",
      "Airline            0\n",
      "Date_of_Journey    0\n",
      "Source             0\n",
      "Destination        0\n",
      "Route              0\n",
      "Dep_Time           0\n",
      "Arrival_Time       0\n",
      "Duration           0\n",
      "Total_Stops        0\n",
      "Additional_Info    0\n",
      "dtype: int64\n",
      "Airline\n",
      "---------------------------------------------------------------------------\n",
      "Jet Airways                          897\n",
      "IndiGo                               511\n",
      "Air India                            440\n",
      "Multiple carriers                    347\n",
      "SpiceJet                             208\n",
      "Vistara                              129\n",
      "Air Asia                              86\n",
      "GoAir                                 46\n",
      "Multiple carriers Premium economy      3\n",
      "Vistara Premium economy                2\n",
      "Jet Airways Business                   2\n",
      "Name: Airline, dtype: int64\n",
      "\n",
      "Source\n",
      "---------------------------------------------------------------------------\n",
      "Delhi       1145\n",
      "Kolkata      710\n",
      "Banglore     555\n",
      "Mumbai       186\n",
      "Chennai       75\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "Destination\n",
      "---------------------------------------------------------------------------\n",
      "Cochin       1145\n",
      "Banglore      710\n",
      "Delhi         317\n",
      "New Delhi     238\n",
      "Hyderabad     186\n",
      "Kolkata        75\n",
      "Name: Destination, dtype: int64\n",
      "\n",
      "\n",
      "Shape of test data :  (2671, 28)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "\n",
    "print(\"Test data Info\")\n",
    "print(\"-\"*75)\n",
    "print(test_data.info())\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Null values :\")\n",
    "print(\"-\"*75)\n",
    "test_data.dropna(inplace = True)\n",
    "print(test_data.isnull().sum())\n",
    "\n",
    "# EDA\n",
    "\n",
    "# Date_of_Journey\n",
    "test_data[\"Journey_day\"] = pd.to_datetime(test_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day\n",
    "test_data[\"Journey_month\"] = pd.to_datetime(test_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month\n",
    "test_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)\n",
    "\n",
    "# Dep_Time\n",
    "test_data[\"Dep_hour\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.hour\n",
    "test_data[\"Dep_min\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.minute\n",
    "test_data.drop([\"Dep_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Arrival_Time\n",
    "test_data[\"Arrival_hour\"] = pd.to_datetime(test_data.Arrival_Time).dt.hour\n",
    "test_data[\"Arrival_min\"] = pd.to_datetime(test_data.Arrival_Time).dt.minute\n",
    "test_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)\n",
    "\n",
    "# Duration\n",
    "duration = list(test_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration\n",
    "\n",
    "# Adding Duration column to test set\n",
    "test_data[\"Duration_hours\"] = duration_hours\n",
    "test_data[\"Duration_mins\"] = duration_mins\n",
    "test_data.drop([\"Duration\"], axis = 1, inplace = True)\n",
    "\n",
    "\n",
    "# Categorical data\n",
    "\n",
    "print(\"Airline\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Airline\"].value_counts())\n",
    "Airline = pd.get_dummies(test_data[\"Airline\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Source\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Source\"].value_counts())\n",
    "Source = pd.get_dummies(test_data[\"Source\"], drop_first= True)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Destination\")\n",
    "print(\"-\"*75)\n",
    "print(test_data[\"Destination\"].value_counts())\n",
    "Destination = pd.get_dummies(test_data[\"Destination\"], drop_first = True)\n",
    "\n",
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "test_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)\n",
    "\n",
    "# Replacing Total_Stops\n",
    "test_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)\n",
    "\n",
    "# Concatenate dataframe --> test_data + Airline + Source + Destination\n",
    "data_test = pd.concat([test_data, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "data_test.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "print(\"Shape of test data : \", data_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
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       "      <th>Duration_hours</th>\n",
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       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
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       "      <th>Jet Airways Business</th>\n",
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       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
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       "      <th>New Delhi</th>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\n",
       "0                                  0         0        0   \n",
       "1                                  0         0        0   \n",
       "2                                  0         0        0   \n",
       "3                                  0         0        0   \n",
       "4                                  0         0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  "
      ]
     },
     "execution_count": 43,
     "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "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": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Airline_Multiple carriers Premium economy</th>\n",
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       "      <th>Airline_Vistara</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            0           24              3        22       20             1   \n",
       "1            2            1              5         5       50            13   \n",
       "2            2            9              6         9       25             4   \n",
       "3            1           12              5        18        5            23   \n",
       "4            1            1              3        16       50            21   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  \\\n",
       "0           10               2             50                  0   \n",
       "1           15               7             25                  1   \n",
       "2           25              19              0                  0   \n",
       "3           30               5             25                  0   \n",
       "4           35               4             45                  0   \n",
       "\n",
       "   Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0              0               1                    0   \n",
       "1              0               0                    0   \n",
       "2              0               0                    1   \n",
       "3              0               1                    0   \n",
       "4              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 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": 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": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.39796250e-01 1.43559644e-01 5.42099378e-02 2.41913461e-02\n",
      " 2.09894788e-02 2.82473503e-02 1.94111025e-02 1.09198253e-01\n",
      " 1.71611580e-02 1.01654669e-02 1.85235374e-03 1.77992115e-02\n",
      " 1.40603207e-01 6.73469037e-02 1.81456085e-02 9.05573873e-04\n",
      " 2.95550340e-03 1.26557301e-04 5.18064308e-03 7.84388966e-05\n",
      " 5.67758823e-04 1.11812863e-02 3.17500592e-03 6.94884667e-03\n",
      " 1.17398938e-02 1.36434459e-02 5.42328636e-03 5.29791714e-04\n",
      " 2.48666965e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EunJ0BV9fXyIiIhg4cCCFhYUAtG7dmsGDB+Pn58f48eO5dOkSVatWZdq0aQD8+c9/JioqioSEhOuuaOfs7MyUKVMYMWIEhYWF1K5dm8mTJ+Pk5MTRo0cJDg7GZDIREhKCj4/PLdXavHlzTp06xcsvv3zVa0OGDGH8+PHMmjWL559//rbPg6enJz179iQ/P58333yTevXq8fLLLzN8+HC+/PJLnJycaN68uTVElsT86E4l3vd3efmmmw8SEREREblFBovujRI7OHPmAmazWk8u00O1Yks9IbbUE2JLPSG2tCDDA+jYsWMMHjz4mq/FxMTw1FNP3eOKREREREQeDgpHZUzt2rVJTEy0dxkiIiIiIg8dLcggIiIiIiKCwpGIiIiIiAigcCQiIiIiIgIoHImIiIiIiAAKRyIiIiIiIoDCkYiIiIiICKBwJCIiIiIiAuh7jsROSvKNxSWRl28iJ/vSPTmWiIiIiNzfFI7ELsJj1pN57u6HlpWxQeTc9aOIiIiIyINAt9WJiIiIiIigcHRLDh48iLe3N+vWrbNuGzBgABkZGVeNjYqKwmg0kpGRwYABA+5ZPTeq6UpGo5GoqKjbOp6fnx/p6els3LiRGTNm3Ha9IiIiIiL3A4WjW2A0GuncuTPx8fHWbXPnzqVGjRrX3adGjRrMnTv3ntVzKzXdqQ4dOjB06NC7Nr+IiIiIiD3pmaObMJlMJCUlsWjRInr27MmxY8eoXbs2fn5+LFiwgJ07d7J8+XKysrJo3769db/09HT69OlDcnIyUVFRVKpUiR9//JGMjAwGDRpEaGgoubm5fPjhhxw6dIiioiIGDBhAQEBAieoBrlvTsGHDrjnX9erKysoiMjKSkydPUq9ePfLz84HLoWznzp1MnDiRtWvX8s9//pO8vDzy8/OJiYnh2WefLaWzLiIiIiJy7ykc3cTmzZvx9PSkbt26dOzYkfj4eEaMGFFsTEZGBmvWrMHR0fG6t6ydPHmSxYsXc/DgQfr06UNoaCiffPIJjRs3ZtKkSVy4cIGePXvSrFkzvLy87qge25pu5Fp1zZw5k0aNGjF37ly+/fZb1q5dW2wfs9lMfHw8s2fP5tFHH2XZsmXMnz+/zIYjD4/K9i5BboE+J7GlnhBb6gmxpZ4QW3faEwpHN2E0Gq1Xc7p06cLw4cN55513io1p1KjRTUNIq1atMBgMPPnkk2RlZQGwbds28vLySEhIAODixYscOnTohuHoevU4OTnddk3Xq2vnzp3ExsYC8Oyzz15VT7ly5fjb3/5GcnIyaWlp7Ny5k3Llyu4dmqdOab26ss7Do7I+JylGPSG21BNiSz0htq7siXLlDCX66hiFoxs4c+YMKSkp7Nu3jwULFmCxWMjOzmb9+vXFxrm4uNx0LmdnZwAMBoN1m9lsZsqUKTRu3BiA06dPU7Vq1RLVY3s73q3UdL26DAYDFovF+mcHB4di++Tm5hIaGkpQUBDPPvss3t7eLFq06JaOJyIiIiJSVpXdf+4vA5KSkvD19SUlJYXk5GQ2bdrEwIEDWbJkSanM7+vryxdffAFAZmYm3bp148SJE3ar53d/+tOfSExMBGDPnj0cO3as2OtHjhyhXLlyDBw40FpPUVFRqdYgIiIiInKvKRzdgNFopHfv3sW29e7dmz179lgXKbgTERER5OXlERAQQN++fYmMjLQurnC79fz88893XM/vhgwZwvHjx+natStz58696ra6Bg0a0LBhQ/z9/QkODqZChQr89ttvpXZ8ERERERF7MFiuvH9K5AGTl28iJ/uSvcuQm9B942JLPSG21BNiSz0htvTM0QNo165djB8//pqvzZkz565+j9G9dObMBcxm5XIRERERKTsUjsqYFi1aWJ/3ERERERGRe0fPHImIiIiIiKBwJCIiIiIiAigciYiIiIiIAApHIiIiIiIigMKRiIiIiIgIoHAkIiIiIiICKByJiIiIiIgACkciIiIiIiKAvgRW7MTdvdI9P2Zevomc7Ev3/LgiIiIicn9QOBK7CI9ZT+a5extUVsYGkXNPjygiIiIi9xPdViciIiIiIoKuHJWaL7/8kjlz5mAymbBYLAQFBfHnP//Z3mVZeXt706BBAwAKCgr44x//yPvvv0/dunVvut+BAweIi4sDYPDgwcVe37hxI/v27WPo0KF3p3ARERERkXtE4agUZGRkMGnSJIxGI25ubuTm5hIWFkbdunXp0KGDvcuzSkxMtP78xRdfMGDAANasWYOTk1OJ5+zQoUOZeo8iIiIiIiWl2+pKwblz5ygsLCQvLw+AihUrMnHiRJ544gl++OEHXn75Zbp160bfvn05evQoAGFhYezYsQOA9PR0/Pz8AIiKimLgwIH4+/uTnJzMtm3b6NatG4GBgbz55ptcuHCBoqIiPv74Y4KDg+nWrRv/+te/brvmXr164eTkxJYtWwBYsWIFwcHBBAUFMWrUKPLz86/aZ8+ePfTs2ZP27dtbryQZjUaioqJu+/giIiIiImWNrhyVggYNGtChQwc6duxIw4YN8fHxITAwkD/+8Y/079+f6dOn07RpU9auXcuwYcNISEi44Xyurq7Mnj2bgoIC2rVrx/z582nYsCFTp05l+fLlODpe/tiWL19OQUEB4eHhNGnShBYtWtxW3fXr1+eXX36hdu3aLF26lPj4eJydnYmNjWX+/Pm8/fbbxcafOXOG+Ph4Lly4gJ+fH/3797+9E1UGeHhUtncJch36bMSWekJsqSfElnpCbN1pTygclZIPPviAt99+m6+//pqvv/6aHj168MYbb1ClShWaNm0KgL+/P2PGjCEn58Zrpv0+/sCBA9SoUYOGDRsCMGzYMACGDBnC/v372b59OwAXL17kwIEDtx2ODAYDLi4u7Nixg6NHj9KjRw8ACgsLadSo0VXjW7dujZOTE48++ihubm6cP3/+to5XFpw6pfXqyiIPj8r6bKQY9YTYUk+ILfWE2LqyJ8qVM5Toq2MUjkrB5s2buXjxIl26dCE0NJTQ0FCWLl3KypUrrxprsVgoKiqy/gxgMpmKjXFxcQGgfPnyxbbn5OSQm5tLUVERkZGRdOrUCYCzZ89SoUKF2677wIEDvPLKKxw8eBB/f3+io6MBrMew9fsVK7gcrH6vX0RERETkQaBnjkqBi4sLsbGxpKenA5dDz+HDh2nevDlZWVns2bMHgDVr1uDp6Ymrqytubm4cPnwYgA0bNlxz3rp163L27FnruHnz5vHFF1/g6+vL0qVLKSwsJDc3l969e7N79+7bqnnx4sUYDAZ8fHzw8fHhP//5D2fOnMFisTBu3Dg+++yzkp4OEREREZH7kq4clQJfX18iIiIYOHAghYWFwOVb0AYPHoyfnx/jx4/n0qVLVK1alWnTpgHw5z//maioKBISEq672puzszNTpkxhxIgRFBYWUrt2bSZPnoyTkxNHjx4lODgYk8lESEgIPj4+N60zKCgIALPZjJeXF3PnzqVcuXI0aNCAiIgI+vbti9lspmHDhrzxxhuldHZERERERO4PBovujZKHRF6+iZzsS/YuQ65B942LLfWE2FJPiC31hNjSM0didezYsau+oPV3MTExPPXUU/e4ohs7c+YCZrNyuYiIiIiUHQpHD4jatWsX+5JXERERERG5PVqQQUREREREBIUjERERERERQOFIREREREQEUDgSEREREREBFI5EREREREQAhSMRERERERFA4UhERERERARQOBIREREREQH0JbBiJ+7ulexy3Lx8EznZl+xybBEREREp2xSOxC7CY9aTee7eh5SVsUHk3POjioiIiMj9QLfViYiIiIiI8ACGo/T0dJo0aUJQUBBBQUF07tyZIUOGcPr06duea8mSJaxatQqAGTNmsHHjxhLVNHPmTHbt2gXA6NGj2bt3b4nmuZEVK1YQGhpKUFAQgYGBLFiwoETz7Nixg7CwsKu2Z2RkMGDAgDstU0RERESkzHogb6urXr06iYmJAFgsFqZOncqQIUNYvHjxbc3z/fff07JlSwCGDh1a4nq+/fZbfHx8AJgwYUKJ57meJUuWEB8fzz/+8Q+qV69OdnY2r7/+Oo888ggvv/xyqRyjRo0azJ07t1TmEhEREREpix7IcHQlg8HA4MGDadWqFampqaSkpLB27VqKiop4/vnniYyMJDc3l2HDhlmvLg0aNIhHHnmE5ORktm/fjoeHB6tXr6Zly5a0bNmSiIgI6tevz/79+3F3d2fGjBm4urry+eefk5iYyKVLlzAYDEyfPp29e/eyb98+oqOjmTVrFjExMURERODj48Ps2bNJSkrCwcGBVq1aERkZyYkTJ647//V88sknTJo0ierVqwNQpUoVJk2axIULFwD44YcfmDBhAvn5+bi5ufHhhx9Sp04d9u/fz5gxY8jLy6Nq1ar89a9/BeDs2bMMGDCAY8eOUbduXWbOnElmZiZ9+vQhOTmZqKgoKlWqxI8//khGRgaDBg0iNDT07n6QpcjDo7K9S5Br0OcittQTYks9IbbUE2LrTnvigQ9HAE5OTtSpU4fU1FT27dvHsmXLMBgMREZGkpSUhNlspmbNmsyZM4eff/6ZZcuWMXLkSPz8/GjZsiWtW7dm9erV1vlSU1P56KOPaNSoEYMHD2blypUEBwezYcMGFi5ciIuLCzNmzGDx4sW8//77JCQkEBERgbe3t3WOr776iuTkZIxGI46OjgwePJj4+Hjatm17zfmvdasbXA4yJ06coFmzZsW216tXD4CCggKGDRvG9OnTadq0KWvXrmXYsGEkJCQwfPhwhg8fTvv27Vm8eDGfffYZ7dq147fffmP27NnUrFmTHj16sG3bNp544oli8588eZLFixdz8OBB+vTpc1+Fo1OntCRDWePhUVmfixSjnhBb6gmxpZ4QW1f2RLlyhhKtjvxQhCO4fAVpwYIFnD17lpCQEADy8vLw9PQkNDSUqVOnkpGRQbt27Rg0aNAN53J3d6dRo0YA1K9fn/Pnz1OpUiViY2NZvXo1R44cYcuWLTRs2PC6c2zfvp2uXbvi4uICQGhoKCtWrKBt27bXnP96ypW7/NiYxWK55utHjhyhSpUqNG3aFAB/f3/GjBnDr7/+yqlTp2jfvj0AvXv3Bi4/c9SgQQO8vLyAyyHr3LlzV83bqlUrDAYDTz75JFlZWdetT0RERETkfvFQhKOCggLS0tLw8fEhMDCQ/v37A5CdnY2DgwMVK1Zk7dq1bNmyhU2bNvHpp5+ydu3a687n7Oxs/dlgMGCxWDhx4gRhYWG89tprtGnThmrVqrF///7rzmE2m6/aZjKZrjv/9bi6uuLl5cW+fft49tlnrdt37txJSkoKAQEBV+1zrfny8/PJzMwEwNHxf21xveP/XqPBYLhubSIiIiIi95MHbrU6W2azmbi4OJo1a0ZoaCiJiYnk5uZiMpkYNGgQ69at4/PPPycuLg5/f3/Gjh3L2bNnycnJwcHBgaKiols6zt69e6lTpw79+vWjWbNmpKSkWPe91jy+vr6sXr2avLw8TCYTCQkJ+Pr6lug9hoeHM3HiRE6dOgVcvtVu4sSJ1KlTh8cff5ysrCz27NkDwJo1a/D09KRmzZr84Q9/YOvWrQAkJiYyY8aMEh1fRERERORB8EBeOcrMzCQoKAi4HI4aNmxIbGwsrq6upKam0qNHD4qKimjdujXBwcHWBRkCAwNxdHQkIiKCKlWq8NxzzzF16lQqV775g12tWrXiiy++oEuXLjg5OdG0aVMOHToEQOvWrRk7diyTJk2yjm/fvj379+8nNDQUk8lE69atee211zh58uRtv99evXpRWFjI66+/br3S88orr1hXqps2bRrjx4/n0qVLVK1alWnTpgEwZcoUxo0bx+TJk3Fzc2Py5MmkpaXd9vFFRERERB4EBsuN7tkSecDk5ZvIyb5k7zLEhh6qFVvqCbGlnhBb6gmxpQUZHiJhYWFkZ2dftb1nz5706tXLDhXdmTNnLmA2K5eLiIiISNmhcHSfWLhwob1LEBERERF5oD3wCzKIiIiIiIjcCoUjERERERERFI5EREREREQAhSMRERERERFA4UhERERERARQOBIREREREQEUjkRERERERACFIxEREREREUBfAit24u5eyd4lWOXlm8jJvmTvMkRERETEzhSOxC7CY9aTea5sBJKVsUHk2LsIEREREbE73VYnIiIiIiLCQxyO0tPTadKkCUFBQQQFBdG5c2eGDBnC6dOnb3uuJUuWsGrVKgBmzJjBxo0bS1TTzJkz2bVrFwCjR49m7969JZrnesLCwnjhhRcICgoiICCAkJAQvvrqq1vab8eOHezYsYOwsLCrXs/IyGDAgAGlWquIiIiIyL32UN9WV716dRITEwGwWCxMnTqVIUOGsHjx4tua5/vvv6dly5YADB06tMT1fPvtt/j4+AAwYcKEEs9zIzExMdZj7N27lz//+c8sWrSIJ554osRz1qhRg7lz55ZWiSIiIiIidvFQh6MrGQwGBg8eTKtWrUhNTSUlJYW1a9dSVFTE888/T2RkJLm5uQwbNsx6dWnQoEE88sgjJCcns337djw8PFi9ejUtW7akZcuWREREUL9+ffbv34+7uzszZszA1dWVzz//nMTERC5duoTBYGD69Ons3buXffv2ER0dzaxZs4iJiSEiIgIfHx9mz55NUlISDg4OtGrVisjISE6cOHHd+W/VU089hb+/P//+979577332LNnDx9//DF5eXm4ubnxwQcf4OXlVWyfs2fPMmDAAI4dO0bdunWZOXMmmZmZ9OnTh+Tk5NL8SO4pD4/K9i7hoafPQGypJ8SWekJsqSfE1p32hMLRFZycnKhTpw6pqans27ePZcuWYTAYiIyMJCkpCbPZTM2aNZkzZw4///wzy5YtY+TIkfj5+dGyZUtat27N6tWrrfOlpqby0Ucf0ahRIwYPHszKlSsJDg5mw4YNLFy4EBcXF2bMmMHixYt5//33SUhIICIiAm9vb+scX331FcnJyRiNRhwdHRk8eDDx8fG0bdv2mvNf67a3G6lfvz6bN2+moKCA6OhoZs+ejaenJ1u2bOH999/nX//6V7Hxv/32G7Nnz6ZmzZr06NGDbdu23dFVp7Li1CktyWBPHh6V9RlIMeoJsaWeEFvqCbF1ZU+UK2co0erICkc2DAYDCxYs4OzZs4SEhACQl5eHp6cnoaGhTJ06lYyMDNq1a8egQYNuOJe7uzuNGjUCLoeQ8+fPU6lSJWJjY1m9ejVHjhxhy5YtNGzY8LpzbN++na5du+Li4gJAaGgoK1asoG3bttecvyTv18XFhSNHjnD8+HHeeust62sXLly4anyDBg2sV5Pq1avHuXPnbvuYIiIiIiJlkcLRFQoKCkhLS8PHx4fAwED69+8PQHZ2Ng4ODlSsWJG1a9eyZcsWNm3axKeffsratWuvO5+zs7P1Z4PBgMVi4cSJE4SFhfHaa6/Rpk0bqlWrxv79+687h9lsvmqbyWS67vy368CBA9SrVw+z2UytWrWsz2AVFRVdc3EKR8f/tUxJjykiIiIiUhY9tKvV2TKbzcTFxdGsWTNCQ0NJTEwkNzcXk8nEoEGDWLduHZ9//jlxcXH4+/szduxYzp49S05ODg4ODhQVFd3Scfbu3UudOnXo168fzZo1IyUlxbrvtebx9fVl9erV5OXlYTKZSEhIwNfXt1Te8549e1i3bh0vvfQSjz/+OOfPn7eulpeQkMDw4cNL5TgiIiIiIveDh/rKUWZmJkFBQcDlcNSwYUNiY2NxdXUlNTWVHj16UFRUROvWrQkODrYuyBAYGIijoyMRERFUqVKF5557jqlTp1K58s0fAGvVqhVffPEFXbp0wcnJiaZNm3Lo0CEAWrduzdixY5k0aZJ1fPv27dm/fz+hoaGYTCZat27Na6+9xsmTJ0v0nqOjo6lQoQIGg4FHHnmEadOmUatWLeDyMuQTJkwgPz+fSpUqFaujtM2P7nTX5r5defkme5cgIiIiImWAwaL7osQOzpy5gNms1pPL9FCt2FJPiC31hNhST4gtLcggVwkLCyM7O/uq7T179qRXr152qEhERERE5P6gcPSAWbhwob1LEBERERG5L2lBBhERERERERSOREREREREAIUjERERERERQOFIREREREQEUDgSEREREREBFI5EREREREQAhSMRERERERFA33MkdlKSbyy2p7x8EznZl+xdhoiIiIjcRQpHYhfhMevJPHf/hI2VsUHk2LsIEREREbmrdFudiIiIiIgICkclkp6eTpMmTQgKCiIoKIjOnTszZMgQTp8+fcdz5+Tk8PbbbwOQkZHBgAED7njOkggKCrLLcUVERERE7EXhqISqV69OYmIiiYmJfPnll9SpU4chQ4bc8bznz58nNTUVgBo1ajB37tw7nrMkEhMT7XJcERERERF70TNHpcBgMDB48GBatWrFggUL+M9//sPChQsBiIqKomXLlrRs2ZI///nPuLm54ezszKxZsxg1ahQZGRlkZmbSokULJk+eTExMDJmZmQwaNIj33nuPPn36kJyczOnTpxk9ejS//fYbjo6OvPvuu7Rp04a4uDgyMjI4evQov/76Ky+//DJvvfXWdWvdsWMHs2fPxmKxcOzYMTp37kzlypXZsGEDAHPmzKFatWp4e3tz4MCB686fmprKmDFjMJlMODs78/HHH/PYY4/di9MtIiIiInJXKByVEicnJ+rUqUO1atWuOyYtLY158+ZRq1YtVq1aRcOGDZk5cyYFBQV07dqVH3/8kejoaPr06cPf/vY30tPTrfuOHz8eX19f+vfvz/Hjx+nVqxcrVqwA4MCBAyxatIicnBw6duzIq6++SpUqVa5bx+7du1m9ejWurq4899xzjBw5EqPRyHvvvcfq1avp27dvsfHXmv+zzz6jf//++Pv7s2bNGn744YcHPhx5eFS2dwkPNJ1fsaWeEFvqCbGlnhBbd9oTCkelyGAw4OLict3X3d3dqVWrFgABAQHs2bOHf/3rX/zyyy9kZWVx8eJFXF1dr7nv9u3biYmJAcDLy4tmzZqxe/duAHx8fHBycsLd3R1XV1dycnJuGI6efPJJ/vjHPwLg5ubGn/70JwA8PT3Jzs6+avy15m/bti0ffvghW7ZsoX379nTu3PnmJ+g+d+qU1qu7Wzw8Kuv8SjHqCbGlnhBb6gmxdWVPlCtnKNFXx+iZo1JSUFBAWloalSpVwmKxWLcXFhZaf74yOC1cuJDJkyfz6KOP8tprr1GvXr1i+9myfc1isVBUVASAs7OzdbvBYLjhPADly5cv9mcHB4cbjr/W/C+++CLLly+nadOmfPbZZ4wdO/aGc4iIiIiIlHUKR6XAbDYTFxdHs2bNcHNz4/jx4+Tn55OVlcV///vfa+6zdetWXnnlFbp164bBYCA1NRWz2YyjoyMmk+mq8b6+vixbtgyA48eP891339G8efO7+bZu6J133mHPnj307NmToUOH8tNPP9mtFhERERGR0qDb6kooMzPTuty12WymYcOGxMbG4urqStu2benatSs1a9bkmWeeueb+ffv2Zdy4cXz66adUrFiRp59+mvT0dFq0aIGnpydhYWF8/PHH1vGjR49mzJgxGI1GAGJiYqhevfrdf6PXMXDgQEaPHs3f//53HBwciIqKslstIiIiIiKlwWC52T1YIkJevomc7Ev2LuOBpfvGxZZ6QmypJ8SWekJslcYzR7py9ADatWsX48ePv+Zrc+bMoUaNGve4oqudOXMBs1m5XERERETKDoWjB1CLFi30Ja4iIiIiIrdJCzKIiIiIiIigcCQiIiIiIgIoHImIiIiIiAAKRyIiIiIiIoDCkYiIiIiICKBwJCIiIiIiAigciYiIiIiIAApHIiIiIiIigL4EVuzE3b2SvUsokbx8EznZl+xdhoiIiIjcBQpHYhfhMevJPHf/hYyVsUHk2LsIEREREbkrdFudiIiIiIgICkel4uDBg3h7e7Nu3TrrtgEDBpCRkXHV2KioKIxGIxkZGQwYMKDUa/H29r6t8XFxccTFxQEQFBRk3X727FlGjx7NCy+8gL+/P8HBwWzcuLFUaxURERERKUt0W10pMBqNdO7cmfj4eDp37gzA3Llzb7hPjRo1bjrmXktMTASgoKCAvn370rlzZ7788kscHBz45ZdfCA8Pp2bNmjRo0MDOlYqIiIiIlD6FoztkMplISkpi0aJF9OzZk2PHjlG7dm38/PxYsGABO3fuZPny5WRlZdG+fXvrfunp6fTp04fk5GSioqKoVKkSP/74IxkZGQwaNIjQ0FByc3P58MMPOXToEEVFRQwYMICAgIBbqmvHjh384x//wMXFhZ9//hlvb2/++te/4uTkxLx581i6dClubm5UqVKFpk2bApevOh04cIB169bh7OxMRESEdb7HH3+ccePGUVRUBMAPP/zAhAkTyM/Px83NjQ8//JA6deqU4pkVEREREbm3FI7u0ObNm/H09KRu3bp07NiR+Ph4RowYUWxMRkYGa9aswdHRkaioqGvOc/LkSRYvXszBgwfp06cPoaGhfPLJJzRu3JhJkyZx4cIFevbsSbNmzfDy8rql2r7//nvWrl1L9erV6dGjB19//TUeHh4kJCSwfPlyDAYDr7zyijUc/W737t08++yzV83Xtm1b4PKVpWHDhjF9+nSaNm3K2rVrGTZsGAkJCbdU1/3Ow6OyvUt4IOm8ii31hNhST4gt9YTYutOeUDi6Q0aj0Xo1p0uXLgwfPpx33nmn2JhGjRrh6HjjU92qVSsMBgNPPvkkWVlZAGzbto28vDxr6Lh48SKHDh265XBUv359/vCHPwBQr149zp8/T1paGm3btqVixYoAvPjii5jN5hvO89e//pUtW7aQl5dH69at6dGjR7ErTv7+/owZM4acnBwqV37wf0mdOqX16kqbh0dlnVcpRj0httQTYks9Ibau7Ily5Qwl+uoYhaM7cObMGVJSUti3bx8LFizAYrGQnZ3N+vXri41zcXG56VzOzs4AGAwG6zaz2cyUKVNo3LgxAKdPn6Zq1aq3XN/vc/4+r8ViwWAwFAtDjo6OFBQUFNuvSZMmxMfHW/88fPhwhg8fjtFoZOfOndcMUxaLxXrLnYiIiIjI/Uir1d2BpKQkfH19SUlJITk5mU2bNjFw4ECWLFlSKvP7+vryxRdfAJCZmUm3bt04ceLEHc35pz/9ic2bN5OTk0N+fj7/+c9/rhrTpUsXLl26xCeffEJhYSEAFy5cYMeOHZQrV47HH3+crKws9uzZA8CaNWvw9PTE1dX1jmoTEREREbEnXTm6A0ajkXfffbfYtt69ezNv3jwqVbr9y3i2IiIiGDduHAEBARQVFREZGUnt2rXvaM6GDRvSt29fXnrpJapUqYKnp+dVY5ycnFiwYAHTp0+ne/fuODo6Yjab8fPz489//jNOTk5MmzaN8ePHc+nSJapWrcq0adPuqC4REREREXszWCwWi72LELlf5OWbyMm+ZO8yHji6b1xsqSfElnpCbKknxJaeOXoI7dq1i/Hjx1/ztTlz5lCjRo17XFHJnDlzAbNZuVxEREREyg6Fo/tMixYtrF/WKiIiIiIipUcLMoiIiIiIiKBwJCIiIiIiAigciYiIiIiIAApHIiIiIiIigMKRiIiIiIgIoHAkIiIiIiICKByJiIiIiIgACkciIiIiIiKAvgRW7MTdvZK9SyixvHwTOdmX7F2GiIiIiJQyhSOxi/CY9WSeuz8DxsrYIHLsXYSIiIiIlLoydVvdwYMH8fb2Zt26ddZtAwYMICMj46qxUVFRGI1GMjIyGDBgQKnWkZ6ejre3N2PGjCm2ff/+/Xh7e2M0Gm+4v5+fH+np6Rw/fpxRo0YBsHfvXkaPHn3D/X5/T/ZwvfMsIiIiIvKwKFNXjoxGI507dyY+Pp7OnTsDMHfu3BvuU6NGjZuOKQlXV1e2bNlCUVERDg4OAKxZs4ZHH330luf47bffOH78OABPPfUUTz31VKnXWVruxjkUEREREbmflJlwZDKZSEpKYtGiRfTs2ZNjx45Ru3Zt/Pz8WLBgATt37mT58uVkZWXRvn17637p6en06dOH5ORkoqKiqFSpEj/++CMZGRkMGjSI0NBQcnNz+fDDDzl06BBFRUUMGDCAgICAG9ZTsWJFGjRowLfffouvry8AW7du5bnnnrOO8fb25sCBA8DlYLdz504mTpxofT0mJob09HQ++OADXnzxRWbNmsXChQsJCwvj8ccfZ8+ePeTn5zNq1Cief/75YsdfsWIFn332GWazmcaNGzN27FicnZ2LjVm5ciWffPIJBoOBp556ivHjx3P27FlGjRpFTk4Op06domvXrgwfPhyj0Vjs/GVmZpKVlcXRo0eJjIwkJiaGBQsW8Mc//pHJkyezc+dOioqKCAkJoV+/fuzYsYMpU6ZgNpupX78+3bt3Z8qUKQBUrVqV2NjY2wqOIiIiIiJlTZkJR5s3b8bT05O6devSsWNH4uPjGTFiRLExGRkZrFmzBkdHR6Kioq45z8mTJ1m8eDEHDx6kT58+hIaG8sknn9C4cWMmTZrEhQsX6NmzJ82aNcPLy+uGNfn7+7Nu3Tp8fX3Zs2cP3t7eWCyWW35P0dHRzJo1i7Fjx7Jjx45irxUUFLB8+XL279/PgAEDSE5Otr526NAhli5dSnx8PM7OzsTGxjJ//nzefvvtYufi448/xmg08oc//IHIyEi++uorjh49SkBAAMHBweTk5NC2bVtef/31a54/V1dXZs+eDVwOcgBLly4FYPny5RQUFBAeHk6TJk0AOHLkCJs2baJy5cqEhYUxbtw4mjZtyoIFC/jpp5+uCngPMg+PyvYu4YGjcyq21BNiSz0httQTYutOe6LMhCOj0Wi9mtOlSxeGDx/OO++8U2xMo0aNcHS8ccmtWrXCYDDw5JNPkpWVBcC2bdvIy8sjISEBgIsXL3Lo0KGbhqP27dszffp0zGYza9euxd/fnzVr1pTsDdro0aMHAA0bNsTDw8N6BQpgx44dHD161DqmsLCQRo0aFdv/+++/5//+7//4wx/+AGC9igOwfft25s+fz6FDhygsLOTSpcsLH9iev6ZNm15V1zfffMP+/fvZvn07cPlcHThwgCeeeIK6detSufLlhuvQoQMRERF07NiRDh060KpVqzs+J/eTU6e0JENp8vCorHMqxagnxJZ6QmypJ8TWlT1RrpyhRKsjl4lwdObMGVJSUti3bx8LFizAYrGQnZ3N+vXri41zcXG56Vy/33pmMBis28xmM1OmTKFx48YAnD59mqpVq950rkqVKtGgQQP++9//sn37dv7yl79cFY4sFgsGgwGTyXTT+a70+3NMv9d3ZWgpKirC39+f6OhoAHJzcykqKiq2v21IPHv2LABz5szh+PHjBAQE0LFjR7Zt22a92mV7/q51PouKioiMjKRTp07WeStUqMDu3buLje/Xrx/t27dn06ZNTJkyhT179vDWW2/d1jkQERERESlLysRqdUlJSfj6+pKSkkJycjKbNm1i4MCBLFmypFTm9/X15YsvvgAgMzOTbt26ceLEiVva19/fn9jYWJo0aXJVIHFzc+PQoUNYLJZit8X9zsHB4bqh6feQtXfvXrKzs3nyySetr/n4+PCf//yHM2fOYLFYGDduHJ999lmx/Z966il2797NqVOnAPjoo4/YuHEjW7duJTw8HH9/f06cOEFGRgZms/mW3itcPldLly6lsLCQ3Nxcevfuze7du68a9/LLL5Obm0u/fv3o168fP/300y0fQ0RERESkLCoTV46MRiPvvvtusW29e/dm3rx5VKp0518WGhERwbhx4wgICLBeGaldu/Yt7du+fXtGjx7N0KFDr3rtL3/5CwMHDqRatWo888wznDt3rtjr9erVIycnh8jISF566aVirx0/fpzg4GAApk2bVuxKUoMGDYiIiKBv376YzWYaNmzIG2+8UWz/GjVqMHr0aMLDwzGbzTRv3pyQkBAeeeQRRowYQZUqVXB3d6dJkyakp6ff0nsF6NmzJ0ePHiU4OBiTyURISAg+Pj5XPTM1bNgwoqKicHR0xNnZmQ8++OCWjyEiIiIiUhYZLLezwoCUirCwMCIiIvDx8bF3KXZzv38JrO5xLl26b1xsqSfElnpCbKknxNYD88yRPezatYvx48df87U5c+ZQo0aNe1zRw2V+dCd7l1Biefm393yZiIiIiNwfdOVI7OLMmQuYzWo9uUz/+ie21BNiSz0httQTYqs0rhyViQUZRERERERE7E3hSEREREREBIUjERERERERQOFIREREREQEUDgSEREREREBFI5EREREREQAhSMRERERERFA4UhERERERARQOBIREREREQHA0d4FyMOpJN9YXBbl5ZvIyb5k7zJEREREpBQoHIldhMesJ/Pc/R8qVsYGkWPvIkRERESkVOi2OhERERERERSO7OrgwYN4e3uzbt26Wxq/ceNGZsyYcdvH2bFjB2FhYbe9X2kdX0RERETkfqDb6uzIaDTSuXNn4uPj6dy5803Hd+jQgQ4dOtyDysrm8UVERERE7iaFIzsxmUwkJSWxaNEievbsybFjx6hduzZ+fn40bdqU/fv3M2XKFEaMGIGbmxvOzs5069aNnTt38sILL7B06VL+8Y9/APD5559z5MgR3nnnHUaNGkVGRgaZmZm0aNGCyZMn31I9UVFRPPLII/z3v/8lJyeHUaNGkZiYSGpqKh07diQqKgqj0cjOnTuZOHEifn5+dOvWja+//ppLly4xadIkmjRpcjdPWZnl4VHZ3iU8EHQexZZ6QmypJ8SWekJs3WlPKBzZyebNm/H09KRu3bp07NiR+Ph4RowYAUCbNm2YPn066enppKWlMW/ePGrVqoXRaLS+PnbsWM6fP0/VqlVZtWoVo0aNYvPmzTRs2JCZM2dSUFBA165d+fHHH2+5pszMTJKSkli+fDnvvfce69atw9nZmTZt2jBo0KCrxru6urJs2TIWLlzIP/7xD+Li4krn5NxnTp3Skgx3ysOjss6jFKOeEFvqCbGlnhBbV/ZEuXKGEq2OrGeO7MRoNBIQEABAly5dWL58OQUFBQA0a9bMOs7d3Z1atWoV27d8+fJ06tSJ9evX8+uvv5KVlUXTpk0JCAigVatW/Otf/yImJoasrCwuXrx4yzW1adMGAE9PT+rXr4+7uzuVKlXC1dWV8+fPXzW+devWANSvX5+srKzbev8iIiIiImWNrhzZwZkzZ0hJSWHfvn0sWLAAi8VCdnY269evB8DZ2dk61sXF5ZpzdOvWjRkzZnD+/HlryFq4cCHr1q2jR48ePPfccxw8eBCLxXLLdZUvX976s6PjzVvj9zoNBsMtH0NEREREpKxSOLKDpKQkfH19mTdvnnVbXFwcS5YsueU5mjdvTmZmJomJifz9738HYOvWrbzyyisEBgZy6NAhUlNTMZvNlCunC4QiIiIiIjejcGQHRqORd999t9i23r17M2/ePCpVuvV7I/39/fn666/x8vICoG/fvowbN45PP/2UihUr8vTTT5Oenk7t2rVLtf7SMD+6k71LKBV5+SZ7lyAiIiIipcRguZ37rkRKyZkzFzCb1XpymR6qFVvqCbGlnhBb6gmxVRoLMujK0UNk0qRJbNu27artTZo0YcKECXaoSERERESk7FA4eoiMHDnS3iWIiIiIiJRZelJfREREREQEhSMRERERERFA4UhERERERARQOBIREREREQEUjkRERERERACFIxEREREREUDhSEREREREBND3HImdlOQbi+8XefkmcrIv2bsMEREREblNCkdiF+Ex68k892AGiJWxQeTYuwgRERERuW26rU5ERERERARdOSrz0tPTefHFF6lXrx4AeXl5eHt7M2bMGKpVq3ZPa5kxYwZNmjShQ4cO9/S4IiIiIiL3gsLRfaB69eokJiYCYLFYmDp1KkOGDGHx4sX3tI6hQ4fe0+OJiIiIiNxLCkf3GYPBwODBg2nVqhWpqamkpKSwdu1aioqKeP7554mMjOTXX3/lrbfewsvLi6NHj+Lp6cmUKVNwdXW97rx+fn74+/uzefNmHBwcGDZsGJ9++ilHjx5l5MiRdOnShaioKFq2bEnLli2JiIigfv367N+/H3d3d2bMmHHD+UVEREREyjqFo/uQk5MTderUITU1lX379rFs2TIMBgORkZEkJSXxzDPPcPDgQaKjo/Hx8WHixInMmjWL6OjoG85bvXp1Vq9ezXvvvcecOXNYsGAB3333HR999BFdunQpNjY1NZWPPvqIRo0aMXjwYFauXElYWNjdfNv3FQ+PyvYu4b6jcya21BNiSz0httQTYutOe0Lh6D5lMBhYsGABZ8+eJSQkBLj8PJKnpyfPPPMMjz32GD4+PgB0796d4cOH33TONm3aAODp6Un16tVxdHTE09OT7Ozsq8a6u7vTqFEjAOrXr8/58+dL6609EE6d0np1t8PDo7LOmRSjnhBb6gmxpZ4QW1f2RLlyhhJ9dYzC0X2ooKCAtLQ0fHx8CAwMpH///gBkZ2fj4ODAuXPncHT830drsVhwcHC46bzly5e3/nzl/tfi7Oxs/dlgMGCxWG73bYiIiIiIlClayvs+YzabiYuLo1mzZoSGhpKYmEhubi4mk4lBgwaxbt06ANLS0ti/fz8ACQkJ1qtCIiIiIiJybbpydB/IzMwkKCgIuByOGjZsSGxsLK6urqSmptKjRw+Kiopo3bo1wcHB/Prrr1StWpWZM2dy7NgxvL29iYmJsfO7EBEREREp2wwW3Q/1wElPT6dPnz4kJyfbu5SHUl6+iZzsS/Yu476i+8bFlnpCbKknxJZ6QmzpmSO5LWFhYddcXKFnz5706tXrntZy5swFzGblchEREREpOxSOHkC1atW65lWjhQsX2qEaEREREZH7gxZkEBERERERQeFIREREREQEUDgSEREREREBFI5EREREREQAhSMRERERERFA4UhERERERARQOBIREREREQEUjkRERERERAB9CazYibt7JXuXYDd5+SZysi/ZuwwRERERsaFwJHYRHrOezHMPZ0BYGRtEjr2LEBEREZGrKByVMenp6bz44ovUq1cPgLy8PLy9vRkzZgzVqlUrteP4+fmxYMECatWqVWpzioiIiIjcz/TMURlUvXp1EhMTSUxM5Msvv6ROnToMGTLE3mWJiIiIiDzQFI7KOIPBwODBgzl06BCpqanMmTOH4OBgunXrxuTJk7FYLKSnpxMYGMjbb79N165dGTBgAFlZWTed+29/+xvdu3enc+fO7N69G4C0tDTCwsIIDAzklVdeYc+ePQBERUVhNBqt+3p7ewMQFxdHeHg4Xbp0YdGiRaV/AkRERERE7hHdVncfcHJyok6dOqSmprJv3z6WLVuGwWAgMjKSpKQknnnmGQ4ePEh0dDQ+Pj5MnDiRWbNmER0dfcN5n3jiCT7++GM+//xz5s+fz8yZM4mMjOSNN96gU6dO/PDDDwwdOpR169bdcJ6CggLWrFlTmm/5gefhUdneJZQ5OidiSz0httQTYks9IbbutCcUju4TBoOBBQsWcPbsWUJCQoDLzyN5enryzDPP8Nhjj+Hj4wNA9+7dGT58+E3n7NixI3A5JK1bt47c3FyOHTtGp06dAGjevDlVq1bll19+ueE8TZs2vZO39lA6dUpLMlzJw6OyzokUo54QW+oJsaWeEFtX9kS5coYSrY6scHQfKCgoIC0tDR8fHwIDA+nfvz8A2dnZODg4cO7cORwd//dRWiwWHBwcbjrv72MMBoN1P4vFUmyMxWKhqKgIg8Fgfa2wsLDYGBcXl5K/ORERERGRMkLPHJVxZrOZuLg4mjVrRmhoKImJieTm5mIymRg0aJD1lre0tDT2798PQEJCAm3atLntY1WqVAkvLy/Wr18PwA8//MDp06epX78+rq6uHD58GIANGzaU0rsTERERESk7dOWoDMrMzCQoKAi4HI4aNmxIbGwsrq6upKam0qNHD4qKimjdujXBwcH8+uuvVK1alZkzZ3Ls2DG8vb2JiYkp0bGnTJnCuHHjiIuLo3z58sTFxeHk5ETv3r155513CAwMxNfXFw8Pj9J8yyIiIiIidmew2N5HJfed9PR0+vTpQ3Jysr1LuWUP+5fA6h7p4nTfuNhST4gt9YTYUk+ILT1zJDcUFhZGdnb2Vdt79uxJr1697FDR/8yP7mTX49tTXr7J3iWIiIiIyDUoHD0AatWqdc2rRgsXLrRDNbfmzJkLmM26aCkiIiIiZYcWZBAREREREUHhSEREREREBFA4EhERERERARSOREREREREAIUjERERERERQOFIREREREQEUDgSEREREREBFI5EREREREQAhSMREREREREAHO1dgDyc3N0r2bsEu8vLN5GTfcneZYiIiIjI/6dwJHYRHrOezHMPdzBYGRtEjr2LEBEREREr3VYnIiIiIiJCGQxH6enpNGnShKCgIIKCgujcuTNDhgzh9OnTJZpvyZIlrFq1CoAZM2awcePGEs0zc+ZMdu3aBcDo0aPZu3dviea5nrCwMP7yl78U2xYXF0dcXFypHSMqKop27doRFBREYGAggYGBLF++/Jb2MxqNpKen4+fnd80xQUFBpVaniIiIiIg9lMnb6qpXr05iYiIAFouFqVOnMmTIEBYvXnzbc33//fe0bNkSgKFDh5a4pm+//RYfHx8AJkyYUOJ5bmTdunX4+/vTsWPHuzI/wJAhQwgJCQHg+PHj9O7dmxo1avDcc8/d0by/f14iIiIiIverMhmOrmQwGBg8eDCtWrUiNTWVBg0aMGfOHNauXUtRURHPP/88kZGR5ObmMmzYMOsVpkGDBvHII4+QnJzM9u3b8fDwYPXq1bRs2ZKWLVsSERFB/fr12b9/P+7u7syYMQNXV1c+//xzEhMTuXTpEgaDgenTp7N371727dtHdHQ0s2bNIiYmhoiICHx8fJg9ezZJSUk4ODjQqlUrIiMjOXHixHXnv5G33nqLDz74gBYtWlw1ds+ePXz88cfk5eXh5ubGBx98wE8//cTatWuZPn06R44coXPnzmzdupVq1aoRHh7O0KFDadq06XWP5+XlRZ8+fVi8eDHPPfccR48eZdy4cWRlZeHi4sL7779Po0aNiu2Tl5fHu+++y6FDh6hSpQp/+9vfcHNzw9vbmwMHDpToM36YeXhUtncJZYbOhdhST4gt9YTYUk+IrTvtiTIfjgCcnJyoU6cOv/zyC5mZmezbt49ly5ZhMBiIjIwkKSkJs9lMzZo1mTNnDj///DPLli1j5MiR+Pn50bJlS1q3bs3q1autc6ampvLRRx/RqFEjBg8ezMqVKwkODmbDhg0sXLgQFxcXZsyYweLFi3n//fdJSEggIiICb29v6xxfffUVycnJGI1GHB0dGTx4MPHx8bRt2/aa84eFhd3wfbZo0YKsrCzGjx9PbGysdXtBQQHR0dHMnj0bT09PtmzZwvvvv28NahaLhW+++QZ3d3d27tyJn58faWlpPPXUUzc9t08++aT11rqRI0cyZswYGjVqxOHDhxk0aBDr1q0rNv7s2bP079+fpk2bMmTIENasWcOrr756S5+jXO3UKS3JAJd/kelcyJXUE2JLPSG21BNi68qeKFfOUKLVke+LcASXryC5uLjwzTffsGfPHuutYXl5eXh6ehIaGsrUqVPJyMigXbt2DBo06Ibzubu7W6+K1K9fn/Pnz1OpUiViY2NZvXo1R44cYcuWLTRs2PC6c2zfvp2uXbvi4uICQGhoKCtWrKBt27bXnP9WDBs2jKCgIDZs2GDdduTIEY4fP85bb71l3XbhwgUqVarE448/zoEDB9i+fTt9+/bl22+/pWLFivj4+GAwGG7pmC4uLuTm5rJv3z7ee+896/aLFy9y7ty5YmOrV69uvRr1xBNPXPW6iIiIiMj96r4IRwUFBaSlpfHEE09YQ0D//v0ByM7OxsHBgYoVK7J27Vq2bNnCpk2b+PTTT1m7du1153R2drb+bDAYsFgsnDhxgrCwMF577TXatGlDtWrV2L9//3XnMJvNV20zmUzXnf9WPPLII3z00Ue8++67dO7cmapVq2I2m6lVq5b1uZ6ioiLr7YNt27Zl69at/PLLL4wbN44+ffpQrlw52rdvf0vHO3DgAPXq1cNsNuPk5FTs2aGTJ09edXufo+P/WuZ23peIiIiISFlX5lars2U2m4mLi6NZs2bUrl0bX19fEhMTyc3NxWQyWW/9+vzzz4mLi8Pf35+xY8dy9uxZcnJycHBwoKio6JaOtXfvXurUqUO/fv1o1qwZKSkp1n2vNY+vry+rV68mLy8Pk8lEQkICvr6+d/yeW7RowYsvvkh8fDwAjz/+OOfPn7eulpeQkMDw4cOBy+EoPj6eJ554Ajc3N8qXL8+mTZto1arVTY9z5MgRFi9eTK9evahcuTKPPfaYNRxt3bpVt8uJiIiIyEOlTF45yszMtC4NbTabadiwofUZHD8/P1JTU+nRowdFRUW0bt2a4OBg64IMgYGBODo6EhERQZUqVXjuueeYOnUqlSvf/OGsVq1a8cUXX9ClSxecnJxo2rQphw4dAqB169aMHTuWSZMmWce3b9+e/fv3ExoaislkonXr1rz22mucPHnyjs/BsGHD+Oqrr4DLz1zNmDGDCRMmkJ+fT6VKlax11KtXD4vFYl2Rr2XLlhw8eJCKFStec96ZM2fy2WefYTAYcHBwYOTIkfzf//0fAFOmTGHcuHHMmzeP8uXLM23atFu+NU9ERERE5H5nsOi+KBG7yMs3kZN9yd5llAl6qFZsqSfElnpCbKknxNZDtSDDgyAsLIzs7Oyrtvfs2ZNevXrZoSL7OXPmAmazcrmIiIiIlB0KR/fQwoUL7V2CiIiIiIhcR5lfkEFEREREROReUDgSERERERFB4UhERERERARQOBIREREREQEUjkRERERERACFIxEREREREUDhSEREREREBFA4EhERERERAfQlsGIn7u6V7F3CfSEv30RO9iV7lyEiIiLyUFA4ErsIj1lP5jn9pf9mVsYGkWPvIkREREQeErqtTkREREREBIWje+rgwYN4e3uzbt26Wxq/ceNGZsyYcdvH2bFjB2FhYTccExUVhdFovO25RUREREQeVApH95DRaKRz587Ex8ff0vgOHTowdOjQu1yViIiIiIiAnjm6Z0wmE0lJSSxatIiePXty7NgxateujZ+fH02bNmX//v1MmTKFESNG4ObmhrOzM926dWPnzp288MILLF26lH/84x8AfP755xw5coR33nmHUaNGkZGRQWZmJi1atGDy5Mm3XNPmzZtZvHgxZ86cYeDAgbzyyitcunSJ6OhoDhw4gMFgIDw8nO7du2M0Gtm5cycTJ04EICwsjIiICACmTJmC2Wymfv36TJo0qfRP3kPOw6OyvUu4Jx6W9ym3Tj0httQTYks9IbbutCcUju6RzZs34+npSd26denYsSPx8fGMGDECgDZt2jB9+nTS09NJS0tj3rx51KpVy3rbW5s2bRg7diznz5+natWqrFq1ilGjRrF582YaNmzIzJkzKSgooGvXrvz444+3XFNBQQH//ve/OXToEH369OGVV14hLi4ONzc3Vq1axdmzZ3n55Zdp0KDBDec5cuQImzZtonJl/YK6G06devCXZPDwqPxQvE+5deoJsaWeEFvqCbF1ZU+UK2co0erIuq3uHjEajQQEBADQpUsXli9fTkFBAQDNmjWzjnN3d6dWrVrF9i1fvjydOnVi/fr1/Prrr2RlZdG0aVMCAgJo1aoV//rXv4iJiSErK4uLFy/eck0dOnTAYDBQv359zp07B8D27dt56aWXAHj00Ufp0KEDO3fuvOE8devWVTASERERkfuerhzdA2fOnCElJYV9+/axYMECLBYL2dnZrF+/HgBnZ2frWBcXl2vO0a1bN2bMmMH58+etIWvhwoWsW7eOHj168Nxzz3Hw4EEsFsst1+Xg4ACAwWCwbrPd32KxUFRUhMFgKPZaYWHhTWsWEREREbmf6MrRPZCUlISvry8pKSkkJyezadMmBg4cyJIlS255jubNm5OZmUliYiJBQUEAbN26lVdeeYVu3bphMBhITU3FbDbfUa2+vr4sW7YMgLNnz7Jx40ZatmyJm5sbP//8MxaLhePHj3PgwIE7Oo6IiIiISFmjK0f3gNFo5N133y22rXfv3sybN49KlW79Xkh/f3++/vprvLy8AOjbty/jxo3j008/pWLFijz99NOkp6dTu3btEtc6aNAgxo0bR2BgIEVFRQwcOJDGjRtTUFBAQkICL774InXr1uWZZ54p8TEA5kd3uqP9HxZ5+SZ7lyAiIiLy0DBYbuc+LJFScubMBcxmtZ5cpodqxZZ6QmypJ8SWekJslcaCDLpy9ACbNGkS27Ztu2p7kyZNmDBhgh0qEhEREREpuxSOHmAjR460dwkiIiIiIvcNLcggIiIiIiKCwpGIiIiIiAigcCQiIiIiIgIoHImIiIiIiAAKRyIiIiIiIoDCkYiIiIiICKBwJCIiIiIiAuh7jsROSvKNxfJg8/CofEvj8vJN5GRfusvViIiIyMNI4UjsIjxmPZnn9BdcuX0rY4PIsXcRIiIi8kDSbXUiIiIiIiIoHN0yo9FIVFSUvcuwi+PHjzNq1CgAduzYQVhYmJ0rEhEREREpfQpHclO//fYbx48ft3cZIiIiIiJ3lcLRbUpLSyMsLIzAwEBeeeUV9uzZA0BUVBRGo9E6ztvbG4C4uDjCw8Pp0qULixYtIiwsjMmTJ/PKK6/wwgsv8NVXXwFw+vRp3n77bUJCQggNDWXbtm2YzWb8/PxIS0sD4OLFi7Rt25b8/Pzr1ufn58eUKVPo2rUr3bp1Y/PmzfTp04e2bduyZs0a67HefPNNAgMDCQ4OJiUlxVprdHQ0YWFh+Pn58cknnwAQExPDvn37+OCDDwA4e/YsAwYMoHPnzgwcOJCCgoLSPMUiIiIiInahBRluU2RkJG+88QadOnXihx9+YOjQoaxbt+6G+xQUFFiDyZdffklhYSFLliwhOTmZGTNm0LZtWyZMmEBoaCgdOnQgMzOT3r17s2LFCrp3705SUhJDhw5l/fr1tGvXDmdn5xser3r16qxevZr33nuPOXPmsGDBAr777js++ugjunTpwvjx4/H19aV///4cP36cXr16sWLFCgAOHDjAokWLyMnJoWPHjrz66qtER0cza9Ysxo4dy44dO/jtt9+YPXs2NWvWpEePHmzbto127dqVxukVuSW3urKd3N/0OYst9YTYUk+IrTvtCYWj23Dx4kWOHTtGp06dAGjevDlVq1bll19+ueF+TZs2Lfbn1q1bA1C/fn2ysrIA2LZtG7/88gszZ84EwGQycfz4cUJCQujfvz9Dhw5l+fLlDBs27KZ1tmnTBgBPT0+qV6+Oo6Mjnp6eZGdnA7B9+3ZiYmIA8PLyolmzZuzevRsAHx8fnJyccHd3x9XVlZycq9cFa9CgAV5eXgDUq1ePc+fO3bQmkdJ06pTWq3vQeXhU1ucsxagnxJZ6Qmxd2RPlyhlK9NUxCkc3sGvXLry8vKhRowYWi4WKFStisViKjbFYLBQVFWEwGKyvFRYWFhvj4uJS7M+/X/kxGAzWbWazmc8++wxXV1cAMjIyqFatGg4ODnh6erJ+/XrOnDlDs2bNblp3+fLlrT87Ol79EV/vPVxZ2+/12Y61nfN6Y0RERERE7jd65ugGEhIS2LBhA3D5drM6derg5eXF+vXrAfjhhx84ffo09evXx9XVlcOHDwNY97kdvr6+LF68GIDDhw/TrVs3Ll26/D1AoaGhxMTE0K1bt9J4W/j6+rJs2TLg8kp03333Hc2bN7/ueAcHB0wmU6kcW0RERESkrFI4uoE33niDFStW4O/vz6FDh+jVqxdTpkxh4cKFBAYG8uGHHxIXF4eTkxO9e/dm586dBAYG8t133+Hh4XFbx4qOjmb37t0EBgby7rvvMnnyZCpVunwpsFOnTpw/f56goKBSeV+jR49m+/btBAYG8vbbbxMTE0P16tWvO75evXrk5OQQGRlZKscXERERESmLDBbdE1WmWSwWUlJS+OKLL5g9e7a9yxGxu7x8EznZl+xdhtxlepZAbKknxJZ6QmzpmaOHwEcffcSmTZuYO3eudVtYWJh1cYUr9ezZk169et3L8krszJkLmM3K5XKZ/gcnIiIiZYGuHIldKBzJlRSOxJZ6QmypJ8SWekJslcaVIz1zJCIiIiIigsKRiIiIiIgIoHAkIiIiIiICKByJiIiIiIgACkciIiIiIiKAwpGIiIiIiAigcCQiIiIiIgIoHImIiIiIiADgaO8C5OFUki/lkgebh0dle5dw2/LyTeRkX7J3GSIiIlJKFI7ELsJj1pN5Tn+plPvbytgg9N3sIiIiD45bvq3u4MGDeHt7s27dOuu2AQMGkJGRcdXYqKgojEYjGRkZDBgwoHQqvQaj0UhUVNQNxyQnJ/PPf/7zmq+dO3eOp556ik8//bTY9tGjR7N3795Sq7Ok4uLiaNWqFUFBQXTr1o3AwEA2bdpUorlmzJjBxo0bS7lCEREREZEHxy1fOTIajXTu3Jn4+Hg6d+4MwNy5c2+4T40aNW465m778ccfr/vaqlWraN++PUuWLKF///4YDAYAJkyYcK/Ku6mePXsyePBgAPbv38/rr7/ON998c9vzDB06tLRLExERERF5oNzSlSOTyURSUhLvvvsuP/30E8eOHQPAz8+P9PR0jEYjYWFhBAYGMnXqVOt+6enp+Pn5AZevJsXExNCrVy/8/PxISEgAIDc3l5EjRxISEkJQUBCrVq0q0Rs5evQo/fv3Jzg4mF69evHTTz9x+PBh4uPjiY+Ptx7vSkajkVdffRUnJye2b99u3R4WFsaOHTvYsWMHL730EiEhIbzxxhs8//zz1jGtW7dmzZo1AMyZM4e5c+eSkZFBeHg4PXr0oH379vz1r38FoHfv3nz99dcAWCwWOnXqREZGBpMmTaJbt24EBwcza9asm77HnJwcqlWrBhQ/t3D5KlNcXByFhYVERkbSvXt3unfvztKlS4H/Xc1LT0+ne/fuREZGEhAQQN++fcnKygIgJSWFl156ie7duxMREcG5c+cArlnnN998Q0hICCEhIfTv35+zZ8/e2gclIiIiIlJG3dKVo82bN+Pp6UndunXp2LEj8fHxjBgxotiYjIwM1qxZg6Oj43VvdTt58iSLFy/m4MGD9OnTh9DQUD755BMaN27MpEmTuHDhAj179qRZs2Z4eXnd1hsZOXIkY8aMoVGjRhw+fJhBgwaxbt06evbsCUBoaGix8ampqZw6dYoWLVrg7+9PfHw8f/rTn66a98iRI2zatInKlSvTo0cPDh48iIODA0VFRezcuZMuXbqQkpLCuHHjWLVqFQEBAQQHB5OTk0Pbtm15/fXXCQ0NJSkpieeff55du3ZRu3ZtTCYTKSkprF69mvz8fEaPHk1+fj7Ozs7Fjh8fH8+GDRsoKCjg6NGjfPjhhzc8D99//z3nz59nxYoVnDt3jkmTJtGjR4+r3vtHH31Eo0aNGDx4MCtXrqRr167ExsayYMECqlatSnx8PH/96195++23r1nn3//+d8aNG0fTpk1ZsGABP/30U7HwKPKwuB8Xkrhf6NyKLfWE2FJPiK077YlbCkdGo5GAgAAAunTpwvDhw3nnnXeKjWnUqBGOjjeerlWrVhgMBp588knr1Ypt27aRl5dnvbJz8eJFDh06dFvhKDc3l3379vHee+9Zt128eNF65eNaEhISePHFF3FwcKBLly78/e9/5/Tp09YrM7+rW7culStfPslt27blm2++wdHRkT59+rB69WpycnI4deoUTzzxBE888QTbt29n/vz5HDp0iMLCQi5duoS/vz/Tpk3j0qVLLF++nJCQEGrUqIGzszM9e/akffv2vPPOO1cFIyh+W116ejo9evSgbt261KhR45rvq379+qSlpREeHk6bNm0YPnz4VWPc3d1p1KiRdfz58+fZvXs3J06coE+fPgCYzWaqVq163To7dOhAREQEHTt2pEOHDrRq1epGH5HIA+vUKS3JcDd4eFTWuZVi1BNiSz0htq7siXLlDCVaHfmm4ejMmTOkpKSwb98+FixYgMViITs7m/Xr1xcb5+LictOD/f6X/9+f7YHLfwmfMmUKjRs3BuD06dNUrVr1unPs2rULLy8vatSogcViwcHBAbPZjJOTE4mJidZxJ0+exNXV9ZpzFBYWsnLlShwdHUlOTrZuT0hI4M0337zu+2rbti2zZs3CycmJoUOHsnbtWlauXEnr1q0BmDhxIsePHycgIICOHTuybds2LBYLFSpUoE2bNnz55Zds376dcePG4ejoyL///W927txJSkoKPXv2ZOHChdStW/e6771WrVo8/fTT/PDDD7z44otYLBbrayaTCUdHR9zc3Fi9ejVbt27lq6++Ijg4mNWrVxeb58oQZjAYsFgsFBUV8X//93/Mnj0bgPz8fHJzc69bZ79+/Wjfvj2bNm1iypQp7Nmzh7feeuu6tYuIiIiIlHU3feYoKSkJX19fUlJSSE5OZtOmTQwcOJAlS5aUSgG+vr588cUXAGRmZtKtWzdOnDhx3fEJCQls2LABgAMHDuDl5UXlypV57LHHrOFo69atvPrqqwA4ODhgMpmKzbFp0yYeffRRvv76a5KTk0lOTubDDz9k6dKlxQKHrcaNG5OWlsaRI0eoV68ePj4+fPLJJ7Rr18563PDwcPz9/Tlx4gQZGRmYzWbg8m1906ZNo3Xr1jg5OfHTTz/x2muv8eyzzzJy5Ejq1atHWlraDc9VdnY2P/30E40aNaJKlSqcP3+es2fPUlBQwJYtWwDYuHEjw4cPp127dkRHR1OhQoUbns/fNWvWjB9++MFaw9///ncmT5583TpffvllcnNz6devH/369eOnn3666TFERERERMqym145MhqNvPvuu8W29e7dm3nz5lGp0p1/kWdERATjxo0jICCAoqIiIiMjqV279nXHv/HGG4wYMYLPP/+cP/zhD0yfPh2AKVOmMG7cOObNm0f58uWZNm0aBoPB+pf6atWqERYWZn1PvXr1KjZvQEAAU6dOtYaMazEYDDzzzDNcunT5+3l8fX3597//TcuWLQF48803GTFiBFWqVMHd3Z0mTZqQnp5O7dq1eeaZZzAYDNZnnxo1akTz5s0JCAjgkUceoWHDhrRp0+aqY/7+zJHBYCA3N5eXX37Z+mxUeHg4L730En/4wx946qmnAGjTpg3r1q2ja9euODs706lTJ7y9vW/6OXh4ePDRRx/xzjvvYDabqVGjBlOmTMHNze2adT7yyCNERUXh6OiIs7MzH3zwwU2PISIiIiJSlhksN7pUIqXCYrFw8OBBRo4cyYoVK+xdTpmgL4GVB8HK2CDd736X6FkCsaWeEFvqCbF1T545soddu3Yxfvz4a742Z86c6y5GUFZ99tlnzJs3jxkzZti7lDJjfnQne5cgcsfy8k03HyQiIiL3DV05Ers4c+YCZrNaTy7Tv/6JLfWE2FJPiC31hNgqjStHt/QlsCIiIiIiIg86hSMREREREREUjkRERERERACFIxEREREREUDhSEREREREBFA4EhERERERARSOREREREREAIUjERERERERQOFIREREREQEAEd7FyAPp5J8Y7E82Dw8Ktu7BClj7mZP5OWbyMm+dNfmFxGR+5PCkdhFeMx6Ms/pLyYiYh8rY4PIsXcRIiJS5ui2OhERERERERSObkl6ejpNmjQhKCiIoKAgOnfuzJAhQzh9+vQdz52Tk8Pbb78NQEZGBgMGDLjjOa+0Y8cOwsLCSnVOEREREZEHkcLRLapevTqJiYkkJiby5ZdfUqdOHYYMGXLH854/f57U1FQAatSowdy5c+94ThERERERuX165qgEDAYDgwcPplWrVixYsID//Oc/LFy4EICoqChatmxJy5Yt+fOf/4ybmxvOzs7MmjWLUaNGkZGRQWZmJi1atGDy5MnExMSQmZnJoEGDeO+99+jTpw/JycmcPn2a0aNH89tvv+Ho6Mi7775LmzZtiIuLIyMjg6NHj/Lrr7/y8ssv89Zbb92w3rNnzzJgwACOHTtG3bp1mTlzJk5OTiQkJPDPf/4Tg8FA48aNef/996lYsSLe3t4cOHAAAKPRyM6dO5k4cSJ+fn40bdqU/fv3889//pNx48ZZr54NGjSIDh063N0TLyJSirQIyP1Hn5nYUk+IrTvtCYWjEnJycqJOnTpUq1btumPS0tKYN28etWrVYtWqVTRs2JCZM2dSUFBA165d+fHHH4mOjqZPnz787W9/Iz093brv+PHj8fX1pX///hw/fpxevXqxYsUKAA4cOMCiRYvIycmhY8eOvPrqq1SpUuW6dfz222/Mnj2bmjVr0qNHD7Zt28Yf//hHZs+ezdKlS3Fzc+ODDz5g1qxZjBw58obvu02bNkyfPp3ly5dTs2ZN5syZw88//8yyZcsUjkTkvnLqlJZkuJ94eFTWZybFqCfE1pU9Ua6coUSrIysc3QGDwYCLi8t1X3d3d6dWrVoABAQEsGfPHv71r3/xyy+/kJWVxcWLF3F1db3mvtu3bycmJgYALy8vmjVrxu7duwHw8fHByckJd3d3XF1dycnJuWE4atCgAV5eXgDUq1ePc+fOkZ6eTvv27XFzcwPglVde4b333rvpe27WrBkATz/9NFOnTiUjI4N27doxaNCgm+4rIiIiIlKW6ZmjEiooKCAtLY1KlSphsVis2wsLC60/XxmcFi5cyOTJk3n00Ud57bXXqFevXrH9bNm+ZrFYKCoqAsDZ2dm63WAw3HAeAEfH/2Xg38ebzear5jeZTFcd/8ptVx77scceY+3atQQGBrJr1y5eeumlm9YhIiIiIlKWKRyVgNlsJi4ujmbNmuHm5sbx48fJz88nKyuL//73v9fcZ+vWrbzyyit069YNg8FAamoqZrMZR0fHqwIIgK+vL8uWLQPg+PHjfPfddzRv3rzU3kPLli1JTk4mKysLgKVLl+Lj4wOAm5sbhw4dwmKxkJycfM39P//8c+Li4vD392fs2LGcPXuWnBxd2hYRERGR+5duq7tFmZmZBAUFAZfDUcOGDYmNjcXV1ZW2bdvStWtXatasyTPPPHPN/fv27cu4ceP49NNPqVixIk8//TTp6em0aNECT09PwsLC+Pjjj63jR48ezZgxYzAajQDExMRQvXr1Uns/DRo04M033yQsLIzCwkIaN27MBx98AMBf/vIXBg4cSLVq1XjmmWc4d+7cVft3796dYcOGERgYiKOjIxERETe8tU9EREREpKwzWHQvlIiIPGTy8k3kZF+ydxlyG/TwvdhST4gtLcggAOzatYvx48df87U5c+ZQo0aNe1zRzZ05cwGzWblcLtP/4MSWekJEROxB4egB0KJFCxITE+1dhoiIiIjIfU0LMoiIiIiIiKBwJCIiIiIiAigciYiIiIiIAApHIiIiIiIigMKRiIiIiIgIoHAkIiIiIiICKByJiIiIiIgACkciIiIiIiKAvgRW7MTdvZK9S5AyxsOjsr1LkDJGPXH/yMs3kZN9yd5liIjcMYUjsYvwmPVkntP/SEVEHgQrY4PIsXcRIiKl4KG5re7gwYN4e3uzbt0667YBAwaQkZFx1dioqCiMRiMZGRkMGDDgrtVkNBqJioq64Zjk5GT++c9/XvO1c+fO8dRTT/Hpp58W2z569Gj27t1banWKiIiIiDwMHppwZDQa6dy5M/Hx8dZtc+fOpUaNGtfdp0aNGsydO/delHddP/74IxcuXLjma6tWraJ9+/YsWbIEi8Vi3T5hwgSeeuqpe1WiiIiIiMgD4aEIRyaTiaSkJN59911++uknjh07BoCfnx/p6ekYjUbCwsIIDAxk6tSp1v3S09Px8/MDLl9NiomJoVevXvj5+ZGQkABAbm4uI0eOJCQkhKCgIFatWlWiGo8ePUr//v0JDg6mV69e/PTTTxw+fJj4+Hji4+Otx7uS0Wjk1VdfxcnJie3bt1u3h4WFsWPHDnbs2MFLL71ESEgIb7zxBs8//7x1TOvWrVmzZg0Ac+bMYe7cuWRkZBAeHk6PHj1o3749f/3rXwHo3bs3X3/9NQAWi4VOnTqRkZHBpEmT6NatG8HBwcyaNatE71tEREREpKx4KJ452rx5M56entStW5eOHTsSHx/PiBEjio3JyMhgzZo1ODo6XvdWt5MnT7J48WIOHjxInz59CA0N5ZNPPqFx48ZMmjSJCxcu0LNnT5o1a4aXl9dt1Thy5EjGjBlDo0aNOHz4MIMGDWLdunX07NkTgNDQ0GLjU1NTOXXqFC1atMDf35/4+Hj+9Kc/XTXvkSNH2LRpE5UrV6ZHjx4cPHgQBwcHioqK2LlzJ126dCElJYVx48axatUqAgICCA4OJicnh7Zt2/L6668TGhpKUlISzz//PLt27aJ27dqYTCZSUlJYvXo1+fn5jB49mvz8fJydnW/rfYuIyIPhXiygoUU6xJZ6QmzdaU88FOHIaDQSEBAAQJcuXRg+fDjvvPNOsTGNGjXC0fHGp6NVq1YYDAaefPJJsrKyANi2bRt5eXnWKzsXL17k0KFDtxWOcnNz2bdvH++9955128WLFzl37tx190lISODFF1/EwcGBLl268Pe//53Tp09TrVq1YuPq1q1L5cqXm6Rt27Z88803ODo60qdPH1avXk1OTg6nTp3iiSee4IknnmD79u3Mnz+fQ4cOUVhYyKVLl/D392fatGlcunSJ5cuXExISQo0aNXB2dqZnz560b9+ed955R8FIROQhdurU3V2SwcOj8l0/htxf1BNi68qeKFfOUKLVkR/4cHTmzBlSUlLYt28fCxYswGKxkJ2dzfr164uNc3Fxuelcv//l32AwWLeZzWamTJlC48aNATh9+jRVq1a97hy7du3Cy8uLGjVqYLFYcHBwwGw24+TkRGJionXcyZMncXV1veYchYWFrFy5EkdHR5KTk63bExISePPNN6/7vtq2bcusWbNwcnJi6NChrF27lpUrV9K6dWsAJk6cyPHjxwkICKBjx45s27YNi8VChQoVaNOmDV9++SXbt29n3LhxODo68u9//5udO3eSkpJCz549WbhwIXXr1r3peRQRERERKYse+GeOkpKS8PX1JSUlheTkZDZt2sTAgQNZsmRJqczv6+vLF198AUBmZibdunXjxIkT1x2fkJDAhg0bADhw4ABeXl5UrlyZxx57zBqOtm7dyquvvgqAg4MDJpOp2BybNm3i0Ucf5euvvyY5OZnk5GQ+/PBDli5dWmxhBluNGzcmLS2NI0eOUK9ePXx8fPjkk09o166d9bjh4eH4+/tz4sQJMjIyMJvNwOXb+qZNm0br1q1xcnLip59+4rXXXuPZZ59l5MiR1KtXj7S0tJKdRBERERGRMuCBD0dGo5HevXsX29a7d2/27NlDfn7+Hc8fERFBXl4eAQEB9O3bl8jISGrXrn3d8W+88QYrVqzA39+fQ4cO0atXLwCmTJnCsmXLCAwMJDY2lmnTpmEwGHj22WdZuXIlCxcuLPaeft/vdwEBAeTn57Nly5brHttgMPDMM89Qr1494HKwu3DhAi1btgTgzTffZMSIEYSEhDB//nyaNGlCeno6AM888wwGg8H67FOjRo1o3ry59RmlmjVr0qZNmxKcQRERERGRssFgudGlBhEur1B38OBBRo4cyYoVK+xdjoiIlDF5+SZysu/uF3vr+RKxpZ4QW3rmqIzatWsX48ePv+Zrc+bMueF3K5VFn332GfPmzWPGjBmlNueZMxcwm5XL5TL9D05sqSdERMQedOVI7ELhSK6kvwiLLfWE2FJPiC31hNgqjStHD/wzRyIiIiIiIrdC4UhERERERASFIxEREREREUDhSEREREREBFA4EhERERERARSOREREREREAIUjERERERERQOFIREREREQEAEd7FyAPp5J8KZc82Dw8Ktu7BClj1BMPjrx8EznZl+xdhojITSkciV2Ex6wn85z+Ryki8jBYGRtEjr2LEBG5Bbqt7i4yGo1ERUXZu4zbsmPHDsLCwuxdhoiIiIjIPadwJCIiIiIigsLRPZGWlkZYWBiBgYG88sor7NmzB4CoqCiMRqN1nLe3NwBxcXGEh4fTpUsXFi1aRFhYGJMnT+aVV17hhRde4KuvvgLg9OnTvP3224SEhBAaGsq2bdswm834+fmRlpYGwMWLF2nbti35+fnXre/rr7+ma9euhISEsHTpUuv2nTt30qtXL4KDg/Hz82Pt2rVcuHABHx8fLly4AEB6ejpdu3Yt3RMmIiIiImIHCkf3QGRkJGFhYaxcuZL33nuPoUOHUlBQcMN9CgoKWLNmDa+++ioAhYWFLFmyhPfee48ZM2YAMGHCBEJDQzEajXzyySeMGTOGixcv0r17d5KSkgBYv3497dq1w9nZ+brHiYqKYubMmRiNRlxcXKyvff7558TExLB8+XImTJjA3//+dypVqkS7du348ssvAVixYgVBQUF3fI5EREREROxNCzLcZRcvXuTYsWN06tQJgObNm1O1alV++eWXG+7XtGnTYn9u3bo1APXr1ycrKwuAbdu28csvvzBz5kwATCYTx48fJyQkhP79+zN06FCWL1/OsGHDrnucAwcOUL16derVqwdAcHCwNXxNmTKFTZs28eWXX7J7925yc3MBCA0NJS4ujpdeeolVq1bx2Wef3eZZERGRh01prD6oFQzFlnpCbN1pTygclbJdu3bh5eVFjRo1sFgsVKxYEYvFUmyMxWKhqKgIg8Fgfa2wsLDYmCuv4ADWKz8Gg8G6zWw289lnn+Hq6gpARkYG1apVw8HBAU9PT9avX8+ZM2do1qzZdes1GAyYzWbrnx0cHKw/9+7dGx8fH3x8fPjTn/7E8OHDAXj22WfJzMxk/fr11KpVixo1atzq6RERkYfUqVN3tl6dh0flO55DHizqCbF1ZU+UK2co0VfH6La6UpaQkMCGDRuAy1dl6tSpg5eXF+vXrwfghx9+4PTp09SvXx9XV1cOHz4MYN3ndvj6+rJ48WIADh8+TLdu3bh06fLy2KGhocTExNCtW7cbzuHt7c2ZM2dITU0FYPXq1QBkZWVx5MgRhg4dStu2bdm6dStFRUXA5UDVvXt3YmJiCAkJue26RURERETKIoWjUvbGG2+wYsUK/P39OXToEL169WLKlCksXLiQwMBAPvzwQ+Li4nBycqJ3797s3LmTwMBAvvvuOzw8PG7rWNHR0ezevZvAwEDeffddJk+eTKVKlxNyp06dOH/+/E2fBypfvjxTp04lMjKS4OBga7hydXXl5ZdfpmvXrnTv3p0zZ86Ql5fHxYsXAejatSt5eXl07NixBGdJRERERKTsMVhs7/mS+57FYiElJYUvvviC2bNnl/r8ZrOZL774grS0NKKjo0s0h74EVkTk4bEyNki31UmpU0+IrdK4rU7PHD2APvroIzZt2sTcuXOt28LCwsjOzr5qbM+ePenVq9dtzR8REcGJEyeYP39+iWucH92pxPuKiMj9JS/fZO8SRERuia4ciV2cOXMBs1mtJ5fpX//ElnpCbKknxJZ6QmxpQQYREREREZFSonAkIiIiIiKCwpGIiIiIiAigcCQiIiIiIgIoHImIiIiIiAAKRyIiIiIiIoDCkYiIiIiICKBwJCIiIiIiAigciYiIiIiIAGCwWCwWexchIiIiIiIPnrx8EznZl+7JsTw8KnPqVA4A5coZcHevdNtzOJZ2USK3IjxmPZnn7s1/KCIiIiJiHytjg8ixdxG3QeHoHvnggw/47rvvKCws5NixY9SrVw+APn36EBoaWmxscnIyR48epX///tedz2g0snPnTiZOnHjdMdnZ2XzwwQccPHgQgOrVq/P+++/z2GOPcfz4cT755BM++uijUnh3IiIiIiL3P4Wje2Ts2LEApKen06dPHxITE6879scffyyVY8bGxvLkk08SGxsLwKpVq3j33XdZvnw5v/32G8ePHy+V44iIiIiIPAgUjuwoLS2NMWPGkJWVRYUKFRg9ejQVKlQgPj4eAE9PT55//nlGjRpFTk4Op06domvXrgwfPvyW5j99+jTu7u6YzWbKlStHly5dqFChAgAxMTGkp6fzwQcfMHbsWGbPnk1SUhIODg60atWKyMhITpw4wVtvvYWXlxdHjx7F09OTKVOmULFiRUaNGsWhQ4cA6N27Nz169Lg7J0lERERE5B5ROLKjyMhI3njjDTp16sQPP/zA0KFDWbduHT179gQgNDSU+fPnExAQQHBwMDk5ObRt25bXX3/9luZ/6623GDRoEIsXL8bX15dWrVrRrVs3AKKjo5k1axZjx47lq6++Ijk5GaPRiKOjI4MHDyY+Pp62bdty8OBBoqOj8fHxYeLEicyaNYtOnTpx/vx5VqxYwblz55g0aZLCkYiIiIhck4dH5fvmWApHdpKbm8uxY8fo1KkTAM2bN6dq1ar88ssvxcaFh4ezfft25s+fz6FDhygsLOTSpVtbyKBJkyZs3LiR7777jm3btvHpp58SHx/PkiVLio3bvn07Xbt2xcXFBbgcylasWEHbtm157LHH8PHxAaB79+4MHz6cQYMGkZaWRnh4OG3atLnlK1kiIiIi8vD5fQW5u600VqvT9xzZicViwXYVdYvFQlFRUbFtEydOZOHChXh6evLWW2/h5uZ21X7Xm3/s2LEUFRXRsmVL3nnnHZKSkjh37hw//fRTsbFms/mq/U0mEwCOjv/LzxaLBQcHB9zc3Fi9ejWvvfYaaWlpBAcHk52dfcvvXURERESkLFI4spNKlSrh5eXF+vXrAfjhhx84ffo09evXx8HBwRpOtm7dSnh4OP7+/pw4cYKMjIxrhhlbBoOBn3/+mfnz51vHZ2ZmYjKZqF27drFj+Pr6snr1avLy8jCZTCQkJODr6wtcfi5q//79ACQkJNCmTRs2btzI8OHDadeuHdHR0VSoUIETJ06U+jkSEREREbmXdFudHU2ZMoVx48YRFxdH+fLliYuLw8nJiWeffZaRI0dSrVo13nzzTUaMGEGVKlVwd3enSZMmpKen39L8U6dO5eOPP6ZDhw488sgjVK5cmdjYWFxdXalXrx45OTlERkYyZcoU9u/fT2hoKCaTidatW/Paa69x8uRJqlatysyZMzl27Bje3t7ExMRQvnx51q1bR9euXXF2dqZTp054e3vf5bMlIiIiInJ3GSy3co+WPJR+X3Y8OTm51OfWl8CKiIiIPPhWxgbdV88c6crRfe4vf/kLhw8fvmq7n58fQ4cOtUNFt2Z+dCd7lyAiIiIid1levsneJdwWXTkSuzhz5gJms1pPLrvyX3pEQD0hV1NPiC31hNjSanUiIiIiIiKlROFIREREREQEPXMkdlKunMHeJUgZo54QW+oJsaWeEFvqCbH1e0+UtDf0zJGIiIiIiAi6rU5ERERERARQOBIREREREQEUjkRERERERACFIxEREREREUDhSEREREREBFA4EhERERERARSOREREREREAIUjERERERERQOFIREREREQEUDgSEREREREBFI6klK1cuZIuXbrwwgsvsGjRoqte379/P6GhoXTu3JnRo0djMpkA+O2333j11Vd58cUXeeutt8jNzb3XpctdUtKeWLFiBc8//zxBQUEEBQUxbdq0e1263CU364nfjRw5EqPRaP2zfk88uEraE/o98WC6WT9s2LCBoKAgunXrxttvv8358+cB/Y54kJW0J0r0O8IiUkpOnjxpad++veXcuXOW3NxcS2BgoOXQoUPFxnTt2tXy/fffWywWi+W9996zLFq0yGKxWCxvvPGGZdWqVRaLxWKZNWuWZfLkyfe0drk77qQnPvzwQ8vKlSvvdclyl91KT5w8edLy5ptvWpo2bWpJSEiwbtfviQfTnfSEfk88eG7WDzk5OZZWrVpZTp48abFYLJbp06dbxo8fb7FY9DviQXUnPVGS3xG6ciSlZtu2bfj6+uLq6kqFChXo3LkzX375pfX1X3/9lby8PJo3bw5ASEgIX375JYWFhXz77bd07ty52Ha5/5W0JwD27t3LihUr6NatG8OHD7f+K5Dc327WE3D5Xwg7dOiAv7+/dZt+Tzy4StoToN8TD6Kb9UNhYSHjxo2jRo0aAHh7e3PixAn9jniAlbQnoGS/IxSOpNRkZmbi4eFh/XP16tXJyMi47useHh5kZGRw7tw5KlWqhKOjY7Htcv/7f+3cvUojURyG8SewKggiWkSCjZ1BtBMLi1SxsFS0sU5AhFgHtBIGwcLOQrGw8AZEsPAGbLSw8wYEiR9NFAMSYrE4IKs7cja7ktnnVyVnEphD3rzwzwwJzcTb40qlwtHREblcjo2NjX934vprkjIBUCqVWFxcfLdmT6RXaCbAnkijpDwMDAxQLBYBaDQa7O3tUSwW7YgUC80EhHXEjzafv/5jrVbrl7VMJpN4POl96lyhmQDY2dmJ10qlUlx06myh33d7Ir3+5LO1J9Lnq3mo1+usrKyQz+eZm5v7cBCyI9IhNBMQ1hFeOVLbDA0NcXd3Fz+v1Wpks9lPj9/e3pLNZhkcHOTx8ZFms/luXZ0vNBP1ep2Dg4N4vdVqxb8GqrMlZeIz9kR6hWbCnkinr+ShVquxtLREPp8niiLAjkiz0EyEdoTDkdpmenqas7MzHh4eeH5+5vT0lEKhEB8fHh6mp6eHi4sL4Oc/iBQKBbq6upicnOTk5OTdujpfaCZ6e3vZ39/n8vISgMPDQ2ZmZr5lD2qvpEx8xp5Ir9BM2BPplJSHZrPJ8vIys7OzrK2txVcQ7Ij0Cs1EaEdkWh9dq5ICHR8fs7u7y8vLCwsLC5TLZcrlMqurq0xMTHB1dcX6+jpPT0+MjY2xublJd3c319fXVKtV7u/vyeVybG9v09/f/93bURuEZuL8/Jwoimg0GoyMjLC1tUVfX993b0dtkJSJN9VqlampKebn5wHsiRQLzYQ9kU6/y8PNzQ2VSoXR0dH49ePj40RRZEekWGgmQjrC4UiSJEmS8LY6SZIkSQIcjiRJkiQJcDiSJEmSJMDhSJIkSZIAhyNJkiRJAhyOJEmSJAlwOJIkSZIkAF4BG60NybHjxIkAAAAASUVORK5CYII=\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "reg_rf = RandomForestRegressor()\n",
    "reg_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9522403138855877"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7966948086086671"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Programs\\Python\\Python310\\lib\\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": 58,
   "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": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1180.1615510086156\n",
      "MSE: 4383677.987774411\n",
      "RMSE: 2093.7234745243727\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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026887077025966846"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE/(max(DV)-min(DV))\n",
    "\n",
    "2090.5509/(max(y)-min(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7966948086086671"
      ]
     },
     "execution_count": 62,
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 65,
   "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": 66,
   "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": 67,
   "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=   7.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   7.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   7.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   7.7s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   7.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  12.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  11.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  11.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  12.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  11.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; 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=   7.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   7.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   7.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   7.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=  13.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=  13.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=  13.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=  13.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; 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=  20.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  20.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  20.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  20.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  20.7s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  18.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  18.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  17.8s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  18.2s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  18.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   6.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   6.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   6.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   6.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   6.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   3.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   3.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   3.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   3.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   3.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   3.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   3.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   3.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   3.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   3.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  25.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  25.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  24.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  24.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  25.0s\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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Programs\\Python\\Python310\\lib\\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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QpAoGzUsKHiFUwwBoi2sREYtKaGAoKytj9uzZzJgxg61bt15yvqqqiocffpjCwkLWrFmD3+8H4MyZMyxevJiZM2eyfPlyWlpaAGhqamLZsmXMmjWLxYsXU1tbC0BNTQ2PPvoo9913HwsXLqSqqgoA0zR57rnnmDlzJrNnz+bAgQOJvFzpgUA3gcHhCB0z/BphEBGxooQFhurqajZt2sS2bdsoLS1l+/btHD58uNNzVq1axbp169i1axemabJjxw4ANmzYwKJFi6ioqGDSpEls2bIFgM2bN5Ofn8/OnTtZsGABxcXFAGzatInCwkJ+9atf8Z3vfIcNGzYAsGvXLo4cOUJ5eTkvv/wyRUVFkVAiqdHtlESkhkGBQUTEihIWGPbu3cu0adPIysoiIyODwsJCKioqIudPnz5Ne3s7U6dOBeChhx6ioqICwzDYv38/hYWFnY4DVFZWMm/ePADmzp3Lnj17MAyD4uJivvGNbwBw6tQpRowYAcDbb7/N7NmzsdvtTJgwgXHjxvHhhx8m6pKlB4LBYNQpCb+mJERELMmZqDeuqakhJycn8jg3N5eDBw92ez4nJ4fq6moaGxvxeDw4nc5Oxy9+jdPpxOPx0NDQQF5eHgAzZ87k9OnTkRGJmpoacnNzO/2Ms2fP9uo6Ro/29Or53cnJyYzL+wxkZkMrYCPN5SDTk97pXMawNAAyRwxTX3VBfRKb+ig69U906p/YEhYYzC7up79wsZ7uzsd63cXsF9yiV1FRQVVVFd/+9rfZuXNnl+9l7+KWvmjq672R5Yz7Kicnk9ra5n69x2DQ6vPTYQRwOe00e9sjxzM96QT8AQBq65oZ7uz+f++hSJ+f2NRH0al/olP/hNjttqhfkhM2JZGXl0ddXV3k8cXf9i8+X1tbS25uLtnZ2Xi9XgKBQKfjEBqlCL/G7/fj9XrJysqisrIyUhh54403Mm7cOE6ePEleXl6kMPLi95LUCJrd3SURvq1SUxIiIlaUsMBw5513sm/fPhoaGmhra2P37t0UFBREzo8fPx632x25c6GkpISCggJcLhf5+fmUl5d3Og4wffp0SkpKACgvLyc/Px+Xy8Vbb70VKZg8fPgwdXV1XH311RQUFFBWVkYgEOD48eMcO3aMyZMnJ+qSpQe6v0vifA2D7pIQEbGkhE1J5OXl8fjjj7NkyRIMw2D+/PlMmTKFpUuXsmLFCiZPnszGjRtZu3YtLS0tTJw4kSVLlgCwfv16ioqKeOWVVxg7diwvvvgiACtXrqSoqIg5c+aQmZnJxo0bAVi9ejWrV6/mrbfewu1288ILLzB8+HBmzpzJwYMHue+++wAoLi4mPT296wZLUnS7NHR44aagAoOIiBXZzK4m+iVCNQzx0+Lz8/0texl32XDunDwmcjzTk06Gy84Pf/4hK+ZPYeqXL0thK61Hn5/Y1EfRqX+iU/+EpKyGQaQrgaAZWaTpQuFjWodBRMSaFBgkqYJBE3sXd72EaxgMBQYREUtSYJCk6q7oMVzDoN0qRUSsSYFBkiZomgTNbooenefvktAIg4iIJSkwSNKERw+6rGGwax0GERErU2CQpAmPHkTdS0IjDCIilqTAIEkTDgPRdqtUYBARsSYFBkmaaCMMmpIQEbE2BQZJGsN/voahi8Bgt9uw22waYRARsSgFBkmaL0YYuv7YOZ023VYpImJRCgySNNFqGACcdrsWbhIRsSgFBkmaaDUMENriWktDi4hYkwKDJI3hjzHC4LSr6FFExKIUGCRpwmGg2xEGu13bW4uIWJQCgyRNrCkJh8OG36/AICJiRQoMkjQxix4dmpIQEbEqBQZJmphTEg5NSYiIWJUCgyRNeLoh2l0SmpIQEbEmBQZJmi+mJLpZuMlhxx/UlISIiBUpMEjSxF6Hwa51GERELEqBQZImdg2DLbLfhIiIWIsCgyRNeNlnW9d5AYfDTkBFjyIilqTAIEnj9wdx2G3YukkMLod2qxQRsSoFBkkafyDY7XQEhEYYtA6DiIg1KTBI0vgDZreLNkF44SaNMIiIWJECgyRNrBEGp8OmEQYREYtSYJCkMQLBmCMMuq1SRMSaFBgkaXoywmAoMIiIWJICgySNP2Di6GaVRwhtb22aENRqjyIilqPAIEnj98eYknCGPo4qfBQRsR4FBkmamFMS588pMIiIWI8CgySNPxDE4Yi+DkPoeZqSEBGxGgUGSZpQDUP3gcGlKQkREctSYJCk8QeC2LvbSIIvNqVSYBARsR4FBkma2LdVakpCRMSqFBgkaYxYd0k4NCUhImJVCgySNP6AGbXo0emwRZ4nIiLWosAgSRNzSkJFjyIilqXAIElhmmao6DHqSo+hMKHloUVErEeBQZIiEDQxTXo0wqANqERErEeBQZIiPM0QdR2G80WPhl81DCIiVqPAIEkRLmTUXRIiIgOTAoMkheGPPcKgokcREetSYJCk6NWUhAKDiIjlJDQwlJWVMXv2bGbMmMHWrVsvOV9VVcXDDz9MYWEha9aswe/3A3DmzBkWL17MzJkzWb58OS0tLQA0NTWxbNkyZs2axeLFi6mtrQWgpqaG//Jf/gv3338/Dz74IPv27QPAMAxuueUW7r///si/QCCQyEuWboRHGKJNSYTXaPD7FRhERKwmYYGhurqaTZs2sW3bNkpLS9m+fTuHDx/u9JxVq1axbt06du3ahWma7NixA4ANGzawaNEiKioqmDRpElu2bAFg8+bN5Ofns3PnThYsWEBxcTEAzz//PHfddRelpaW88MILPPHEEwQCAQ4dOsTNN99MaWlp5J/D4UjUJUsUvRlh0MJNIiLWk7DAsHfvXqZNm0ZWVhYZGRkUFhZSUVEROX/69Gna29uZOnUqAA899BAVFRUYhsH+/fspLCzsdBygsrKSefPmATB37lz27NmDYRjce++9keNXXnklPp+P1tZWPv74YxoaGli4cCELFy7kvffeS9TlSgxGDwKDih5FRKzLmag3rqmpIScnJ/I4NzeXgwcPdns+JyeH6upqGhsb8Xg8OJ3OTscvfo3T6cTj8dDQ0MC9994beZ+f/vSn3HjjjWRmZmKz2bj77rt57LHHqKqqYunSpZSVlZGdnd3j6xg92tO3DrhITk5mXN5noKpu8gHgGe4m05N+yfmMDDc5o4YBkOZ2Dfn+upj6Izb1UXTqn+jUP7ElLDCY5qXDyrYLtjbu7nys113swpUDX3vtNbZv384bb7wBwCOPPBI5N3HiRKZMmcIHH3zAPffc07OLAOrrvQSD/Rsiz8nJpLa2uV/vMdDV1nsB8PkMmr3tnc5letJpbfVRFwjgdNj4vLltyPfXhfT5iU19FJ36Jzr1T4jdbov6JTlhUxJ5eXnU1dVFHtfU1JCbm9vt+draWnJzc8nOzsbr9UaKE8PHITRKEX6N3+/H6/WSlZUFhOoYfvGLX7B161bGjh0LQElJCSdOnIj8DNM0cblciblgicp/fjEmR5SloSE0LeHXwk0iIpaTsMBw5513sm/fPhoaGmhra2P37t0UFBREzo8fPx63282BAweA0B/3goICXC4X+fn5lJeXdzoOMH36dEpKSgAoLy8nPz8fl8vFa6+9xrvvvsvPf/5zxowZE/kZhw4d4tVXXwXg6NGjVFVVceuttybqkiWKcF1CtLsk4HxgUA2DiIjlJGxKIi8vj8cff5wlS5ZgGAbz589nypQpLF26lBUrVjB58mQ2btzI2rVraWlpYeLEiSxZsgSA9evXU1RUxCuvvMLYsWN58cUXAVi5ciVFRUXMmTOHzMxMNm7ciGmavPzyy3g8Hr75zW9Gfv5PfvITHnvsMVavXs3cuXOx2Ww899xzeDzxqUmQ3ulJ0SOEtrhWYBARsR6b2VXRgESohiE+fv/RGX628y88NP1qPMM6TwtletK54fKRDHc7+d4re7n2SyNZOu+mFLXUevT5iU19FJ36Jzr1T0jKahhELtTTEQaX046hdRhERCxHgUGSwt+DvSQgXPSoKQkREatRYJCk6HkNg4oeRUSsSIFBkqIn21sDuFT0KCJiSQoMkhSGP4jDbou6CBeAw2HXbpUiIhakwCBJ4Q8EcTljf9xcTrs2nxIRsSAFBkkKIxCMbC4VjWoYRESsSYFBksLvD+J0RJ+OgPMLN+kuCRERy1FgkKTw93CEwaURBhERS1JgkKQw/D2cktDCTSIilqTAIEnhD5g4e1D06LRr4SYREStSYJCkCBU99qCGwWnDH1RgEBGxGgUGSQp/T6ckHHb8fk1JiIhYjQKDJEVPb6t0OewETbPfO4SKiEh8KTBIUvj9QVw9LHoEtNqjiIjFKDBIUvS4huF8qNCtlSIi1qLAIEnR83UYQqFCd0qIiFiLAoMkRU9vq3RERhhUwyAiYiUKDJIUPV24yaUpCRERS1JgkKTo+ToMKnoUEbEiBQZJip6vw3C+hkGBQUTEUhQYJOGCpkkgaPZuSkKLN4mIWIoCgyRc+I6H3txWqSkJERFrUWCQhAtPL7h6svmUih5FRCxJgUESLrxddc+2t1YNg4iIFfUoMHz3u99l7969iW6LDFJfTEn0ZoRBNQwiIlbSo8Bw7733smXLFgoLC/npT3/KuXPnEtwsGUzCowU9qWH4ouhRIwwiIlbSo8Awb9483njjDbZs2UJ9fT0LFixg1apVHDx4MNHtk0HA8Pe+hkFFjyIi1tLjGoZgMMjx48c5duwYfr+f0aNH88///M/88Ic/TGT7ZBAwAr2YknCq6FFExIqcPXnSpk2bePPNN7n88stZtGgRL730Ei6Xi9bWVu666y5WrVqV6HbKAObvTWDQ5lMiIpbUo8DQ0NDAv/7rv3LDDTd0Op6RkcELL7yQkIbJ4NGXdRj8QRU9iohYSY+mJAKBwCVh4bvf/S4AX/va1+LfKhlUejMloaJHERFrijrCsH79eqqrqzlw4AANDQ2R436/n6NHjya8cTI4GOeXee7J9tZ2uw27zaaiRxERi4kaGObPn8+nn37KoUOHKCwsjBx3OBzcfPPNCW+cDA69qWGA0OJNKnoUEbGWqIFh8uTJTJ48mb/5m78hLy8vWW2SQaY36zBAaFpCm0+JiFhL1MCwcuVKXnrpJf7+7/++y/NlZWUJaZQMLuHpBVcPRxgcDrumJERELCZqYFi6dCkA69atS0pjZHAKL9zUkxoGAJfDRkCBQUTEUqL+Bp80aRIAt99+O2PHjuX222/n3LlzvPfee9x4441JaaAMfL2uYdAIg4iI5fToN/hTTz3Fv/7rv3LkyBF+8IMfcPr0adasWZPotskg0ZvNpyA0EqHNp0RErKVHv8H/9Kc/8c///M/85je/4cEHH+TZZ5/l9OnTiW6bDBId/iAOuw2HvWdFj06HXXdJiIhYTI8Cg2ma2O123nnnHaZNmwZAW1tbQhsmg4fPCJDmcvT4+S6HPVL3ICIi1tCjwHDFFVewdOlSTp06xe23384//dM/cf311ye6bTJIdBgB3K4e73OG06F1GERErKZHe0k8++yz/OY3v+HWW2/F5XKRn5/PAw88kOCmyWDhM4K4ezHC4HTY8RlGAlskIiK91aOvfRkZGeTn59PU1MSf//xnpkyZoqWhpcd8Hb2bkgjVMKjoUUTESno0wvDDH/6QN954g9GjR0eO2Ww2fvvb30Z9XVlZGa+88gqGYfDoo4+yePHiTuerqqpYu3YtXq+X/Px8NmzYgNPp5MyZM6xatYr6+nomTJjAxo0bGT58OE1NTTzxxBOcPHmS7OxsNm/eTE5ODjU1NTz55JPU1dVht9v53ve+xx133IFpmjz//PP87ne/w2638/TTT3Prrbf2oZukP3xGoHcjDE4VPYqIWE2PRhh27tzJ7t27+fd///fIv1hhobq6mk2bNrFt2zZKS0vZvn07hw8f7vScVatWsW7dOnbt2oVpmuzYsQOADRs2sGjRIioqKpg0aRJbtmwBYPPmzeTn57Nz504WLFhAcXExAM8//zx33XUXpaWlvPDCCzzxxBMEAgF27drFkSNHKC8v5+WXX6aoqAi/39/rTpL+6W0Ng8thU9GjiIjF9Oi3+NixY3u9l8TevXuZNm0aWVlZZGRkUFhYSEVFReT86dOnaW9vZ+rUqQA89NBDVFRUYBgG+/fvj2x2FT4OUFlZybx58wCYO3cue/bswTAM7r333sjxK6+8Ep/PR2trK2+//TazZ8/GbrczYcIExo0bx4cfftir65D+8xnBPkxJKDCIiFhJj6Yk7rjjDp5//nnuvvtu0tPTI8dvuummbl9TU1NDTk5O5HFubi4HDx7s9nxOTg7V1dU0Njbi8XhwOp2djl/8GqfTicfjoaGhgXvvvTfyPj/96U+58cYbyczMpKamhtzc3E4/4+zZsz255IjRoz29en53cnIy4/I+A1EgaDIyM52MDDeZnvQun5OR4SYnOwOATI+bQHBo99nF1BexqY+iU/9Ep/6JrUeB4c033wToNEIQq4bBNC8tWrPZbDHPx3rdxez2LwZJXnvtNbZv384bb7zR7c+48Pk9UV/vJRjsXwFeTk4mtbXN/XqPgay13cAMBmlt9dHsbb/kfKYnndZWH7WBAABGR4AOf2BI99mFhvrnpyfUR9Gpf6JT/4TY7baoX5J7FBj+/d//vdc/OC8vj/fffz/y+OJv+3l5edTV1UUe19bWkpubS3Z2Nl6vl0AggMPhiByH0ChFXV0dY8aMwe/34/V6ycrKAkJ1DG+//TZbt25lzJgxkZ9RW1t7yc+Q5PIZAdKcvSl61OZTIiJW06Ov2y0tLfzgBz/gW9/6FufOneOpp56ipaUl6mvuvPNO9u3bR0NDA21tbezevZuCgoLI+fHjx+N2uzlw4AAAJSUlFBQURNZ5KC8v73QcYPr06ZSUlABQXl5Ofn4+LpeL1157jXfffZef//znkbAAUFBQQFlZGYFAgOPHj3Ps2DEmT57c896RfjNNM3SXRFpvih5Dt1V2NUIkIiKp0aMRhmeeeYbc3Fzq6+txu914vV6eeuopXnjhhW5fk5eXx+OPP86SJUswDIP58+czZcoUli5dyooVK5g8eTIbN25k7dq1tLS0MHHiRJYsWQLA+vXrKSoq4pVXXmHs2LG8+OKLAKxcuZKioiLmzJlDZmYmGzduxDRNXn75ZTweD9/85jcjP/8nP/kJM2fO5ODBg9x3330AFBcXd6rBkMQL/eGn1ws3hV/rcvZs/wkREUksm9mDr3EPPPAAJSUlkf8MBoPMnTs3MgowmKmGoX+8bQYrXvo9f3vPtdw5eSz7q6oveU6mJ50bLh/JcHcov1a8e4IdvzvMy48XMMzdo0w7qA3lz09PqY+iU/9Ep/4JiVXD0KNx4osLBQOBQK+LB2Vo6jBChYy9GWFwOUOfLUN1DCIiltGjr2+33XYbP/zhD2lvb+f3v/89b7zxBl/96lcT3TYZBHznA0NaLxZucjhC0xB+Ld4kImIZPfot/sQTT5CRkUFmZiabN2/mhhtu4Hvf+16i2yaDgK8vIwzhGoZ+TgWJiEj8xBxh+M1vfsNPf/pTDh06RHp6Otdffz233HILbrc7Ge2TAc7X0fvAECl61AiDiIhlRA0MJSUlbNmyhRUrVnDDDTdgs9n4+OOPKS4uxufzdVphUaQrHef/6PftLgkFBhERq4gaGF5//XVee+01xo0bFzl2zTXX8JWvfIXVq1crMEhMfRlhCN9KqaJHERHriFrDYBhGp7AQNmHCBHw+X8IaJYNHX4oeNSUhImI9UX+LOxzdfyvUKnzSE325rTK8s6XPUGAQEbEKLaYgCRX+o9+b7a3D4SIcNkREJPWi1jAcOnSIW2655ZLjpmnS0dGRsEbJ4HHhCENbDwOA+/z0hU+BQUTEMqIGht/85jfJaocMUj4jgMtpx27v+Z4QaRphEBGxnKiBYfz48clqhwxSoa2tezfz5VYNg4iI5aiGQRIqtLV1z+sXQDUMIiJWpMAgCeUzgr26QwJCO6Y5HXbVMIiIWIgCgyRUhxHo1R0SYW6XAoOIiJUoMEhCdRiBXo8wQKjwUYFBRMQ6FBgkoXx9DAxul4MOFT2KiFiGAoMklM8I9mpZ6DC3RhhERCxFgUESytfR1ykJu+6SEBGxEAUGSaj+TElohEFExDoUGCShOvyqYRARGQwUGCRhgqZJRx9rGHSXhIiItSgwSMIY50cI+jbCoHUYRESsRIFBEib8B78vCzdphEFExFoUGCRhfBdsbd1b4RoG0zTj3SwREekDBQZJmPBtkb3dfOrC13T4VfgoImIFUbe3FukPX6SGIXYutdlttPj8lxxv9PrIzEjD7XLSy12yRUQkjhQYJGEiNQzO2CMMPiPAR5/URh6frmsB4P2qGjwZLm67MQ+nWx9XEZFU0Xc2SRhfP6YknA4bAP6gpiRERKxAgUESpqMfd0m4HKGPpj+gwCAiYgUKDJIwvo7wXRK9/5g5wiMMft0lISJiBQoMkjDhOxz6clulUyMMIiKWosAgCdOfdRgigSGoEQYREStQYJCE8XUEsAGuPtwPGSl61DoMIiKWoMAgCdPeEcCd5sBms/X6tZqSEBGxFgUGSZjWdoPh6X1bO0GBQUTEWhQYJGFa2v0Mc7v69NrIlERANQwiIlagwCAJ0+rz93mEwWaz4bDbNMIgImIRCgySMK3tBhl9DAwQmpbQCIOIiDUoMEjCtLT7GZ7etykJCE1LaIRBRMQaFBgkYVrb/f0eYQgoMIiIWIICgySEPxDEZwQ0JSEiMkgoMEhCtPr8AJqSEBEZJBQYJCFa20OBof8jDAoMIiJWkNDAUFZWxuzZs5kxYwZbt2695HxVVRUPP/wwhYWFrFmzBr8/9EfmzJkzLF68mJkzZ7J8+XJaWloAaGpqYtmyZcyaNYvFixdTW1vb6f3eeecdvvWtb0UeG4bBLbfcwv333x/5FwgEEnjFEtbSbgCQ4e5PYLBpSkJExCISFhiqq6vZtGkT27Zto7S0lO3bt3P48OFOz1m1ahXr1q1j165dmKbJjh07ANiwYQOLFi2ioqKCSZMmsWXLFgA2b95Mfn4+O3fuZMGCBRQXFwMQDAZ59dVX+cd//EeCwS++kR46dIibb76Z0tLSyD+Ho/cbIUnvhUcY+jcloREGERGrSFhg2Lt3L9OmTSMrK4uMjAwKCwupqKiInD99+jTt7e1MnToVgIceeoiKigoMw2D//v0UFhZ2Og5QWVnJvHnzAJg7dy579uzBMAyOHDnCkSNHePrppzu14eOPP6ahoYGFCxeycOFC3nvvvURdrlwkHlMSDhU9iohYRt9/m8dQU1NDTk5O5HFubi4HDx7s9nxOTg7V1dU0Njbi8XhwOp2djl/8GqfTicfjoaGhgWuvvZbi4mLefffdTm2w2WzcfffdPPbYY1RVVbF06VLKysrIzs7u8XWMHu3p/cV3IScnMy7vM1DYXXUAXDE+i1Ej0gEwG1rJ9KR3+XyXy3nJuYxhLvyBIJmedDIy3ORkZyS20RY21D4/faE+ik79E536J7aEBQbTvPSb4YW7FnZ3PtbrLma3dz9I8sgjj0T++8SJE5kyZQoffPAB99xzT7evuVh9vZdgsH/fcnNyMqmtbe7Xeww01bVeANpa2vH7QvUMrT4/zd72S56b6UnHMC49FwwECQRNmprbaG31UTtE60+G4uent9RH0al/olP/hNjttqhfkhM2JZGXl0ddXV3kcU1NDbm5ud2er62tJTc3l+zsbLxeb6Q4MXwcQqMU4df4/X68Xi9ZWVndtqGkpIQTJ05EHpumicvV9zl16bnWdj8upx2Xs+81I05neMdKTUuIiKRawgLDnXfeyb59+2hoaKCtrY3du3dTUFAQOT9+/HjcbjcHDhwAQn/cCwoKcLlc5OfnU15e3uk4wPTp0ykpKQGgvLyc/Pz8qAHg0KFDvPrqqwAcPXqUqqoqbr311kRcrlyk1de/fSQAnPbwjpUqfBQRSbWEjjA8/vjjLFmyhAceeIC5c+cyZcoUli5dyscffwzAxo0befbZZ5k1axZtbW0sWbIEgPXr17Njxw5mz57N+++/zz/8wz8AsHLlSv74xz8yZ84ctm3bxlNPPRW1DY899hgNDQ3MnTuXlStX8txzz+HxxKcmQaLr7z4SELpLAhQYRESswGZ2VTQgEaph6Jsf/vxDjECQ1X/3xYhOi8/P/qrqS56b6Unn6nGZfPRJ53U1jp1tZs8fzzDvb67i3tuvYHg/1nQYyIbi56e31EfRqX+iU/+EpKyGQYa2lnajX4s2gaYkRESsRIFBEqK13c/w/tYwaEpCRMQyFBgkIUJbW/evhsF1/i4Jw6/AICKSagoMEndB06TN1/8RhjRX6OPZYSgwiIikmgKDxF2bz49J/zaeAkhzhdZw6DCG5oJNIiJWosAgcdcS2Ueif1MSaeenJHyakhARSTkFBom7tshOlf0bYbDZbKS57BphEBGxAAUGibuW9tDeEf1d6REgzelQYBARsQAFBom71jhNSQC4XXYVPYqIWMDQXDpPEsIfBJ/hp8HrA8BmD63uGNaXBTPTXA58GmEQEUk5BQaJG58RWvr5k5PnAKg61hhZSwHgK9fl9Po901yOSBGliIikjqYkJO46jAA2Gzgdtn6/V5pTRY8iIlagwCBx12EEcLsc2GxxCAznpyS0R5qISGopMEjctXcEcKc54vJebpcd09RqjyIiqabAIHHX3hEg3RWfwBBe7bHVZ8Tl/UREpG8UGCTufHEdYTgfGFT4KCKSUgoMEnftHQHS4xQYwhtQtfoUGEREUkmBQeIqaJr4jADpafG5YzfNqREGERErUGCQuArfAhmvKYnwCEObRhhERFJKgUHiqr0jFBjiNSWhGgYREWtQYJC4indgcDnt2IDWdt0lISKSSgoMEle+OAcGm82Gy2VX0aOISIopMEhctXeE/rC7XfHbpsTtcmhKQkQkxRQYJK7CIwzxKnqE0J0SGmEQEUktBQaJq/aOAC6nHYe9//tIhKW57LRphEFEJKUUGCSu4rloU1iaSyMMIiKppsAgcdVuxD8wuF121TCIiKSYAoPEVWgfifgVPMIXNQza4lpEJHUUGCSu2jv8cdupMiwtzUEwGFpyWkREUkOBQeLGNM247lQZ5naGPqYtbZqWEBFJFQUGiZv2jgBBM36LNoWlnR+xaNFqjyIiKaPAIHHjbQv9QY9/YDi/xbUKH0VEUkaBQeLG25qYwODWCIOISMopMEjchEcY4n2XRDiANLcpMIiIpIoCg8SNt60DIO53SYQDSHNLR1zfV0REek6BQeKmuTU8whDfwOCw2xjmdtLUqhEGEZFUUWCQuPG2GTjsNlzO+H+sMjNcNLdqhEFEJFUUGCRuvG1G3AsewzzDXDRpSkJEJGUUGCRuvG0G6e74FjyGZWakRaY8REQk+RQYJG68rYkbYcjMcNGkKQkRkZRRYJC4aW7tSOiUhLfVIBjUBlQiIqmgwCBxYZomza0G6XFegyEsMyMNky/WehARkeRSYJC4aPP5CQRNhiVwSgLQtISISIooMEhchNdISHcnaErifGDQ4k0iIqmR0MBQVlbG7NmzmTFjBlu3br3kfFVVFQ8//DCFhYWsWbMGvz+0udCZM2dYvHgxM2fOZPny5bS0tADQ1NTEsmXLmDVrFosXL6a2trbT+73zzjt861vfijw2TZPnnnuOmTNnMnv2bA4cOJDAqx3awrc8JmxKYlha6OfoTgkRkZRIWGCorq5m06ZNbNu2jdLSUrZv387hw4c7PWfVqlWsW7eOXbt2YZomO3bsAGDDhg0sWrSIiooKJk2axJYtWwDYvHkz+fn57Ny5kwULFlBcXAxAMBjk1Vdf5R//8R8JBoOR99+1axdHjhyhvLycl19+maKiokgokfgKL6qUyLskQFMSIiKpkrDAsHfvXqZNm0ZWVhYZGRkUFhZSUVEROX/69Gna29uZOnUqAA899BAVFRUYhsH+/fspLCzsdBygsrKSefPmATB37lz27NmDYRgcOXKEI0eO8PTTT3dqw9tvv83s2bOx2+1MmDCBcePG8eGHHybqkoe0yJREgkYYhqU7sdtsWu1RRCRFEvPbHaipqSEnJyfyODc3l4MHD3Z7Picnh+rqahobG/F4PDidzk7HL36N0+nE4/HQ0NDAtddeS3FxMe++++4lbcjNze30M86ePdur6xg92tOr53cnJyczLu9jVQFsAFyWPRyH3dblc1wuJ5me9F6fA/AMT2ekJw0jOPj7sitD8Zp7S30UnfonOvVPbAkLDKZ56f3yNpst5vlYr7uY3d79IElX7xXt+V2pr/f2+97/nJxMamub+/UeVne2tpmMdCetrb5un2MYfpq97Zccz/Skd3surLXVx/B0FzX1LYO+Ly82FD4//aU+ik79E536J8Rut0X9kpywKYm8vDzq6uoijy/+tn/x+draWnJzc8nOzsbr9RIIBDodh9AoRfg1fr8fr9dLVlZW1DZcWBh54XtJfDW1GpE6g0QZMVyrPYqIpErCAsOdd97Jvn37aGhooK2tjd27d1NQUBA5P378eNxud+TOhZKSEgoKCnC5XOTn51NeXt7pOMD06dMpKSkBoLy8nPz8fFyu7v9IFRQUUFZWRiAQ4Pjx4xw7dozJkycn6IqHtuaWjsidDIkyIiON5hbdJSEikgoJm5LIy8vj8ccfZ8mSJRiGwfz585kyZQpLly5lxYoVTJ48mY0bN7J27VpaWlqYOHEiS5YsAWD9+vUUFRXxyiuvMHbsWF588UUAVq5cSVFREXPmzCEzM5ONGzdGbcPMmTM5ePAg9913HwDFxcWkp3c/Ty5919TaQV52RkJ/RmZGmkYYRERSxGZ2NdEvEaph6JkVL/2em6+7jAljR3T7nK9cl8NHn9RecjzTk87V4zK7PBd22415VH5wil++fZT/9U/TSXMl5vZNKxoKn5/+Uh9Fp/6JTv0TkrIaBhk6AsEg3jYDT4KnJDIzQu+vba5FRJJPgUH6zXv+D3jCix4zwqs9alpCRCTZFBik35qSFBgyh5/fT0KBQUQk6RQYpN/C3/gTPSUx8vwIwzmvAoOISLIpMEi/hTeeSvQIQ1amG4Bzzd0vDiUiIomhwCD91pykwOB02BkxPI0GBQYRkaRTYJB+a2o1cNhtDHMnbFmPiFGZbhoVGEREkk6BQfqtqbWDEcPTou75ES/ZmW4am7vfc0JERBJDgUH6ramlI+HTEWEaYRARSQ0FBum3c14fWR53Un7WqEw3Le1+fEYgKT9PRERCFBik3855O5IaGEB3SoiIJJsCg/RLIBikuaWDLE9i12AIG5UZ2jxMd0qIiCSXAoP0S1OLgQlJG2HIPj/CoMJHEZHkUmCQfjnnDX3TH5mkEYasSGDQCIOISDIpMEi/hANDskYY3C4Hw9OdmpIQEUkyBQbpl8/P7+uQrMAAoTqGxiYFBhGRZFJgkH455/VhA0YMT846DADZI7QWg4hIsikwSL+c83aQOTwNhz15H6VRWu1RRCTpFBikX855fWQNT07BY9ioTDdNrQaGP5jUnysiMpQpMEi/fO7tiNy5kCyRxZu8mpYQEUkWBQbpl3NeHyOTPMKQfX7xJtUxiIgkjwKD9FkgGKSpNTnLQtvsNlp8flp8ftLPb6P9WUNr5JhmJ0REEsuZ6gbIwNXUYmCaJGVZaJ8R4KNPagHwB0Lp4I+f1mKaJgC33ZiH062Ps4hIomiEQfrs85bkLtoU5nTY8QxzRdaAEBGRxFNgkD471xz6gz0yyYEBQqMaKnoUEUkeBQbps3OREYbkFj1CKKQ0tXQQDJpJ/9kiIkORAoP02bnzdymMSPJdEhAKKUETmls1LSEikgwKDNJnn7d0kJnhwulI/scoXDdxTnUMIiJJocAgfdbY7Et6wWNYeFTjc9UxiIgkhQKD9FntuTYuG5mekp/tcobulNAIg4hIcigwSJ8ETZO6z9vJHTUsZW0YqTslRESSRoFB+uRzbweGP0hOVuoCQ5bHTVOLoTslRESSQIFB+qT2XBtAigNDGkHTpLnVSFkbRESGCgUG6ZNwYMhNYWAY6dGulSIiyaLAIH1Se64Nmw1Gp6joEUIjDDYb1H/enrI2iIgMFQoM0ic159rIzkxPyRoMYU6HnZysYXxW35KyNoiIDBUKDNIntefayMlK3ehC2NjRGdQ3+WhpUx2DiEgiKTBIn9Sea09pwWPYuNHDAfjk5LnUNkREZJBTYJBea+/w09TSkdI1GMJGj0zH5bTzl+ONqW6KiMigpsAgvVZ3LlRkaIURBrvdxpjsDP5yohHT1HoMIiKJosAgvWaFNRguNPayDBqafNScb5eIiMSfAoP0mD8ILT4/p+pCdyUMz3DR4vNH/qVqwcVwHcOf/9qQmgaIiAwBzlQ3QAYOn+Fnf1U1/+9YAy6nnT8frcdms0XOf+W6nJS0KzPDxWUj0/nocD1fv+VLKWmDiMhgpxEG6bXm1g4yM1ydwkIq2Ww2Jl8zmqrjDbR3+FPdHBGRQSmhgaGsrIzZs2czY8YMtm7desn5qqoqHn74YQoLC1mzZg1+f+iX/ZkzZ1i8eDEzZ85k+fLltLSEhsCbmppYtmwZs2bNYvHixdTW1gLQ0dHBqlWrmDVrFg8++CBHjhwBwDAMbrnlFu6///7Iv0AgkMhLHhIam31knV+W2SomXzMaf8DUtISISIIkLDBUV1ezadMmtm3bRmlpKdu3b+fw4cOdnrNq1SrWrVvHrl27ME2THTt2ALBhwwYWLVpERUUFkyZNYsuWLQBs3ryZ/Px8du7cyYIFCyguLgbg9ddfZ9iwYezcuZPVq1dTVFQEwKFDh7j55pspLS2N/HM4HIm65CGhzeenzRcge4S1AsPV40YyPN3Jh5/WpbopIiKDUsICw969e5k2bRpZWVlkZGRQWFhIRUVF5Pzp06dpb29n6tSpADz00ENUVFRgGAb79++nsLCw03GAyspK5s2bB8DcuXPZs2cPhmFQWVnJfffdB8Btt91GY2MjZ86c4eOPP6ahoYGFCxeycOFC3nvvvURd7pDR0BS6pTJ7ROpXebyQw25jyjWXcfBIPYFgMNXNEREZdBJW9FhTU0NOzhdFcLm5uRw8eLDb8zk5OVRXV9PY2IjH48HpdHY6fvFrnE4nHo+HhoaGLt/r7Nmz2Gw27r77bh577DGqqqpYunQpZWVlZGdn9/g6Ro/29K0DLpKTkxmX90kls6EVb3toSueKsSNxuzqP1rhcTjI93QeJaOf781qAjAw302+9nH1/Pkud12DSNZdFu5QBZzB8fhJNfRSd+ic69U9sCQsMXS2ic2GRXHfnY73uYnZ714MkdrudRx55JPJ44sSJTJkyhQ8++IB77rknatsvVF/vJdjP+wVzcjKprW3u13tYQavPz9k6L55hLjp8Bh2+zvs3GIafZm/3O0d2dz7Tk97n10ba1urj8tHDcNht7DlwkjyLTZn0x2D5/CSS+ig69U906p8Qu90W9UtywqYk8vLyqKv7Yj65pqaG3Nzcbs/X1taSm5tLdnY2Xq83UpwYPg6hUYrwa/x+P16vl6ysLHJzcyMFkBe+pqSkhBMnTkSOm6aJy+VKzAUPEQ3NPsvVL4QNczuZMG4Eh7SvhIhI3CUsMNx5553s27ePhoYG2tra2L17NwUFBZHz48ePx+12c+DAAQBKSkooKCjA5XKRn59PeXl5p+MA06dPp6SkBIDy8nLy8/NxuVxMnz6d0tJSAN5//33cbjfjxo3j0KFDvPrqqwAcPXqUqqoqbr311kRd8qDX5vPT3GpYrn7hQtdfnsWxz5p1e6WISJwldITh8ccfZ8mSJTzwwAPMnTuXKVOmsHTpUj7++GMANm7cyLPPPsusWbNoa2tjyZIlAKxfv54dO3Ywe/Zs3n//ff7hH/4BgJUrV/LHP/6ROXPmsG3bNp566ikAvvnNb9LR0cGcOXMoLi7m+eefB+Cxxx6joaGBuXPnsnLlSp577jk8nvjUJAxFp2q9AJYdYQC4/oosgqbJkdNNqW6KiMigYjO1Y09UqmH4wq/3HePNt48y/z9dQ0b6peUvX7kuh48+qe3ildHPZ3rSuXpcZp9eG3b7TWMwgybtHX6+v2UvM267nLl/MyFy3u1y4hygy5QNls9PIqmPolP/RKf+CYlVw6CloaXHTtV4SU9zdBkWUs1nBCKBIntEOh9+Wkdedkbk/G035uF0W6/dIiIDxQD9ziWpcLy6mdEjrVu/EJaXPYy6c+34A1qPQUQkXhQYpEc+b+mguqGNvFHW2NI6mrxRGQRNk7pz3d+GKSIivaPAID1y6EQjQKdhfqvKPR9qqhtbU9wSEZHBQ4FBeuTQyXO4XQ5GW/iWyrA0l4MsTxp1n2uEQUQkXhQYpEcOnTjH1eNGYLdbY0vrWEaPTKf+8/YuVw4VEZHeU2CQmJpaOzhT18KXvzQy1U3psdEj0mnvCNDSrgWcRETiQYFBYvrkxDkArr08K6Xt6I3Lzt/NUa9pCRGRuFBgkJgOnQjVL1yRO3BWyRyV6cZmU2AQEYkXBQaJqepEI1/+0kgcjoHzcXE47IzKdFPfpMAgIhIPA+cvgKRE3edtnKlrYfKE7FQ3pddGj1Dho4hIvCgwSFQHj9QDMOXLl6W4Jb03emQ6Hf4gza1GqpsiIjLgKTBIVAeP1JM7ahhjBsCCTRcLL2OtaQkRkf5TYJBudRgBqo43MuWa0aluSp+M8rix220qfBQRiQMFBunWX040YviDAzYw2O02sjPdCgwiInGgwCDd+uhIPW6Xg+svH5XqpvTZ6JHp1De1Ewyq8FFEpD8UGKRLpmly8HAdE68ahcs5cD8ml41Mxx8wqWlsS3VTREQGtIH7l0AS6tjZZuqbfNxyXU6qm9Iv4c2yjlc3p7glIiIDmzPVDRDr8AfBZ4T2Xtj357M47DauuyKLFl/o2EAc1R/hScPpsHFSgUFEpF8UGCTCZ/jZX1WNaZr84c/V5GVn8Oe/NkTOf2UAjjbYbTayR6Rzotqb6qaIiAxompKQSzQ0+/C2GVw5ZuDsHRHN6BHpnKrx4g8EU90UEZEBS4FBLnHibDM2G1w+gDabimb0yHSMQJAzdS2pboqIyIClwCCdmKbJ8bPNjMnOID1tcMxYhbe6PnZWdQwiIn2lwCCdnPN20NRqcEVeZqqbEjeZGS6GuR389bOmVDdFRGTAUmCQTo6f/xZ+Rd7gmI4AsNlsTBg7gk9Onkt1U0REBiwFBunkRHUzeaOGMcw9OKYjwq79Uhaf1bfyudeX6qaIiAxICgwScbahlXPeDq4cM3imI8KuvTwLgEMaZRAR6RMFBon46NM6YHBNR4R9KddDepqDv5w4l+qmiIgMSAoMEvHhp7XkZKWTke5KdVPizmG3cd3lWRw60ZjqpoiIDEgKDALAqRovp2tbuGrMiFQ3JWGuv0J1DCIifaXAIAD8x8ef4bDbmDBu8NUvhN1wRWibbk1LiIj0ngKD4A8E2ffns0y+evSgWaypK1fkeRjmdlB1XNMSIiK9pcAgHDxST3OrwVdvykt1UxLKYbdz04TRfPhprfaVEBHpJQUG4T8OfsZITxo3XpWd6qYk3LSJeTS3GhplEBHpJQWGIe6z+hY+OlLH30wai8NuS3VzEsZmt9Hi83P1+JEMczv5j4Of0eLz0+Lz49dgg4hITIN3wlp65JdvH8XtcnDv7ZenuikJ5TMCfPRJLQDjc4bz4ae1XHv5SJwOO7fdmIdzkK1sKSISbxphGMKOnP6cDz6pZebtVzAiIy3VzUmaq8eOwB8wOVnjTXVTREQGDAWGISpomvyi8ggjMlyDfnThYnnZw8hwO/n05OepboqIyIChwDBElfz+KJ+cPMcDBVcP6lspu2Kz2Zg4YRRnG1o5U9eS6uaIiAwICgxDjD8Iv/vwNL/ee5w7Jo0h/4bcSPFf0Ex165Ln+iuy8AxzceBQLcGhdOEiIn2kwDCEmKbJb98/wRu7DzEmO4Nrxo3g/b/UsL+qmv1V1fiDQ+d2AYfdzs3XXUZjs4/3qqpT3RwREctTYBgiWtoNXtv5F7b/+2HGXTac/3TzOOyD+DbKnrhqTCaXjUznl5VH+OtnTalujoiIpSkwDHKfe33s/MNxiv7XPv7j4Gfce/vl3HXLeNJcjlQ3LeVsNhvTbx5HRrqTF7f/kVO6a0JEpFtDq9ptkAuaJtUNrRyvbub42WaOnGniyKnPMYFJV2ez4D99meyR6ezXEHzE8HQX3314Ci/94iM2vLafO24awx035eHJSMNut9Hu82MCmRkusoa7cacpaInI0JTQwFBWVsYrr7yCYRg8+uijLF68uNP5qqoq1q5di9frJT8/nw0bNuB0Ojlz5gyrVq2ivr6eCRMmsHHjRoYPH05TUxNPPPEEJ0+eJDs7m82bN5OTk0NHRwdr1qzhT3/6E+np6WzcuJFrrrkG0zR5/vnn+d3vfofdbufpp5/m1ltvTeQlJ1Wbz89fjjdSdaKR42ebOVHjxdcRAMDpsDHuMg8zp13B1GtzGHfZcIAhVdjYU5dlDWPtknx2/uEEvz94hv/4+LNunzt6RDo5Wek4nXZME7ytBu0dflxOB55hTq7Iy+SqsZlMGDuC3Kxh2GxDe9pHRAaPhAWG6upqNm3axJtvvklaWhqPPPIIX/3qV/nyl78cec6qVat45plnmDp1KqtXr2bHjh0sWrSIDRs2sGjRIubMmcPLL7/Mli1bWLVqFZs3byY/P5+f/OQnlJSUUFxczObNm3n99dcZNmwYO3fuZP/+/RQVFfGLX/yCXbt2ceTIEcrLyzl+/DjLli1j586dOJ0Dc2DF8Af462fNHDp5jj8frefImSYCQZM0l50r8jL52uSxjBmdQVOLj5HD3ZEahdO1Xk7Xhobbv3JdTiovwbKyR6Sz+N7ruO9rV3Gixktbu5+gaZKe5iBg2mhsbqexuZ2z9a00NvtoOx/Mhme4yBk1DJ8RoLmlg999cBrj/MZWw9wORmWmMyrTzegRbrI8bkZ63IwcnsbI4WmMGJ5GmsuB02HD6bDjdNhw2DVLKCLWlLC/nHv37mXatGlkZWUBUFhYSEVFBd/5zncAOH36NO3t7UydOhWAhx56iB/96EcsWLCA/fv38/LLL0eO/93f/R2rVq2isrKSrVu3AjB37lx+8IMfYBgGlZWVrFy5EoDbbruNxsZGzpw5w9tvv83s2bOx2+1MmDCBcePG8eGHH3Lbbbf1+DriVRhY+3k7/++v9fiDJoGAiT8QpMMI0NLup7XdoMXnx+cLEL5Pwemw4XI6cDvtYIOmlg6aWjoInB8iGDt6OPcXXM2Xx4/k8lwPTkfoD03QhI+P1HXbDqfDTka6q9fnEnl+mNuZsp8N4HQ58J3fUCItzcmXv5TV6Xy4Tz3DXFyem3nJ62+ckE3VXxtCzw2aNLV20Njs43Ovj1ZfAJsNTta2cOjEOWIN8NhsobY67KEAYbPZsNttkVs/05x2MtKdDHOH/mWc/0+XM/w5tWE7/z7hN7SFDl9wtjOzq1Z109AuD5s9H7bq6qldv7oXR00YNiyNtraOGK8O3SnUEz1vZ3dv0PVhIxCkzWfQ5gvQ7vPT5gvQ5vMTJBQwh6WF/vdMT3OQ5nKQ5rST5rLjctgv+B81pMvfTF0ctGEjI6Oe1taOS0922fQeXGl8ntKrz0483qq7pwzLcNHWasR4Vuz36e2TgqZJe0eQdp9BW0eAVp+fNp+fdl8Ak1CRodvtID0t9P/19DTH+f/vO3A57Njtdr5yzWiyMt09aVFMsf7eJSww1NTUkJPzxbfZ3NxcDh482O35nJwcqquraWxsxOPxREYBwscvfo3T6cTj8dDQ0NDle509e5aamhpyc3MvOd4bo0YN79XzuzPxyzlM/HJyvt1fMW5k1PNXf2lUn84l+nwqf3Ys/elTEZHBIGHjn10l+Qvnc7s7H+t1F7N3M4Rrt9u7fK/uni8iIiLdS9hfz7y8POrqvhgav/jb/sXna2tryc3NJTs7G6/XSyAQ6HQcQqMU4df4/X68Xi9ZWVnk5uZSW1t7yXvl5eV1eVxERER6J2GB4c4772Tfvn00NDTQ1tbG7t27KSgoiJwfP348brebAwcOAFBSUkJBQQEul4v8/HzKy8s7HQeYPn06JSUlAJSXl5Ofn4/L5WL69OmUlpYC8P777+N2uxk3bhwFBQWUlZURCAQ4fvw4x44dY/LkyYm6ZBERkUHLZva0CqgPysrK+PGPf4xhGMyfP5+lS5eydOlSVqxYweTJk/nLX/7C2rVraWlpYeLEiTz77LOkpaVx+vRpioqKqK+vZ+zYsbz44ouMHDmSc+fOUVRUxMmTJ8nMzGTjxo186Utfwufz8dRTT/GnP/2JtLQ0nnnmGW666abIbZV79uwB4Mknn+RrX/taoi5XRERk0EpoYBAREZHBQRWAIiIiEpMCg4iIiMSkwCAiIiIxKTCIiIhITAoMIiIiEpMCQz+89NJL/M//+T8jj5uamli2bBmzZs1i8eLFkUWjOjo6WLVqFbNmzeLBBx/kyJEjQGi1y+eee46ZM2cye/bsyJoUAK+++iozZ86ksLCQ3bt3J/fCkqSsrIzZs2czY8aMyB4hg53X62Xu3LmcOnUKCO25Mm/ePO699142bdoUeV5VVRUPP/wwhYWFrFmzBr/fD8CZM2dYvHgxM2fOZPny5bS0tADdf/YGmn/5l39hzpw5zJkzh+effx5QH13opZdeYvbs2cyZM4ef/exngPqnK8899xxFRUVA/Pqhu9/jQ4opvdbU1GQ++eST5pQpU8wf/ehHkeMbNmwwf/zjH5umaZpvvfWWuXLlStM0TfN//+//ba5bt840TdN87733zPnz55umaZo7d+40ly5dagYCAfPo0aPmPffcYxqGYX700Ufm/fffb7a3t5t1dXXm3XffbTY2Nib1GhPt7Nmz5l133WU2NjaaLS0t5rx588xPP/001c1KqD/+8Y/m3LlzzZtuusk8efKk2dbWZk6fPt08ceKEaRiG+e1vf9usrKw0TdM058yZY3744YemaZrmk08+aW7dutU0TdNctmyZ+etf/9o0TdP8l3/5F/P55583TbP7z95A8s4775jf+MY3TJ/PZ3Z0dJhLliwxy8rK1Efnvfvuu+YjjzxiGoZhtrW1mXfddZdZVVWl/rnI3r17za9+9avm97//fdM049cP3f0eH0o0wtAHv/3tb7nqqqv4z//5P3c6XllZybx584DQbpp79uyJ7KZ53333AT3bTXPPnj3MmDEDt9vN6NGjuf3226msrEz2ZSbUhbuZZmRkRHYzHcx27NjB+vXrI8uTHzx4kCuvvJLLL78cp9PJvHnzqKio6HIn14qKCgzDYP/+/RQWFnY6Dt1/9gaSnJwcioqKSEtLw+Vycc0113Ds2DH10Xm33347//Zv/4bT6aS+vp5AIEBTU5P65wLnzp1j06ZN/Lf/9t+ArndF7ms/dPd7fChRYOiDBx54gGXLluFwODodj9dumvHYZdPqutrNNLwr6WBVXFxMfn5+5HF3fRDPnVwHkmuvvTbyi/3YsWOUl5djs9nURxdwuVz86Ec/Ys6cOdxxxx36DF3kqaee4vHHH2fEiBFAcnZFHkoUGKLYuXMnBQUFnf49+uijvXqPvuym2d3xwaSra4y2K+lg1F0f9PZ4dwbqZ+bTTz/l29/+Nt///ve54oorLjk/1PtoxYoV7Nu3j88++4xjx45dcn6o9s8vfvELxo4dyx133BE5luh+GEj9Ew/OVDfAymbNmsWsWbN6/PzwbppjxozpcjfNK6+8Eoi9m2ZXxydMmBC/C7OAvLw83n///cjji0dVhoLudnTtyU6uDoejy51cL/7sDTQHDhxgxYoVrF69mjlz5vDee++pj847cuQIHR0d3HjjjQwbNox7772XioqKTiOdQ7l/ysvLqa2t5f777+fzzz+ntbUVm80Wt37o7vf4UDK04lGCxWs3zYKCAnbv3k1bWxsNDQ384Q9/6JSaB4NYu5kOBV/5ylf461//yvHjxwkEAvz617+moKAgrju5DiSfffYZjz32GBs3bmTOnDmA+uhCp06dYu3atXR0dNDR0cFvf/tbHnnkEfXPeT/72c/49a9/TWlpKStWrODrX/86zz77bMJ3RR5KtPlUP4Rvqfzud78LENfdNF999VV++ctf4vf7Wb58OQ888EBKrjGRutrNdCj4+te/zr/927/xpS99iX379vHss8/i8/mYPn06Tz75JDabLW47uQ4kzzzzDL/85S87TUM88sgjXHXVVeqj8370ox9FRhXuvfdevvvd7+oz1IU333yT9957j//xP/5HwndFHkoUGERERCQmTUmIiIhITAoMIiIiEpMCg4iIiMSkwCAiIiIxKTCIiIhITFq4SUQS6tSpU8yYMYPrrrsucsw0TZYsWcL8+fM7Pfe3v/0t+/btY+3atclupojEoNsqRSShTp06xbx58/jwww8jx6qrq5k7dy6vv/46N9xwQwpbJyI9pREGEUm6vLw8rrzySt555x1+8IMf0NbWhsfj4cEHH2TXrl38+Mc/pra2lvXr13P06FHsdjuPPPIIS5Ysobm5meLiYj755BMMw+COO+7ge9/7XmQjIRFJDNUwiEjSffjhh5w4cYL29nYOHz7M66+/zuuvv97pORs2bOCqq66ioqKC7du3s2PHDo4fP85//+//nZtuuok333yTkpISGhsb+dnPfpaiKxEZOhTJRSTh2tvbuf/++wEIBAKMGjWKH/7wh9TX13P99dfj8Xguec3evXtZtWoVAJmZmfz6178GoLKyko8//pj/+3//b+S9RSTxFBhEJOHS09MjG/dc6M033yQjI6PL1zidzk5bDp88eZJRo0YRDAZ56aWXuOaaawBoamoacluji6SCpiRExJLuuOMOfvnLXwLQ3NzMt771LY4dO8bXvvY1XnvtNUzTpKOjg+XLl/PGG2+kuLUig58Cg4hY0lNPPcXRo0eZN28ef/u3f8t//a//lUmTJrFmzRpaW1uZN28e8+bN47rrruPv//7vU91ckUFPt1WKiIhITBphEBERkZgUGERERCQmBQYRERGJSYFBREREYlJgEBERkZgUGERERCQmBQYRERGJ6f8DzCOrAJteCK8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sns.distplot(y_test-prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "plt.scatter(y_test, prediction, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1165.7304099776718\n",
      "MSE: 4051492.994168836\n",
      "RMSE: 2012.8320829539746\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": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you want to store the data\n",
    "file = open('flight_rf.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8121008064695177"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "metrics.r2_score(y_test, y_prediction)"
   ]
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   "source": [
    "---"
   ]
  }
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