Flight price prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_excel(r'C:\\Users\\anmul\\Downloads\\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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       "<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_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2h 50m     550\n",
       "1h 30m     386\n",
       "2h 55m     337\n",
       "2h 45m     337\n",
       "2h 35m     329\n",
       "          ... \n",
       "47h          1\n",
       "4h 10m       1\n",
       "28h 30m      1\n",
       "42h 5m       1\n",
       "29h 30m      1\n",
       "Name: Duration, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Duration.value_counts()"
   ]
  },
  {
   "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              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Journy_Day'] = pd.to_datetime(train_df.Date_of_Journey, format='%d/%m/%Y').dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Journy_Month'] = pd.to_datetime(train_df.Date_of_Journey, format='%d/%m/%Y').dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Journy_Day</th>\n",
       "      <th>Journy_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",
       "   Journy_Day  Journy_Month  \n",
       "0          24             3  \n",
       "1           1             5  \n",
       "2           9             6  \n",
       "3          12             5  \n",
       "4           1             3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(['Date_of_Journey'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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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>Dep_Time</th>\n",
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       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
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       "      <th>Journy_Day</th>\n",
       "      <th>Journy_Month</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>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>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>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>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>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    Source Destination                  Route Dep_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    22:20   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL    16:50   \n",
       "\n",
       "   Arrival_Time Duration Total_Stops Additional_Info  Price  Journy_Day  \\\n",
       "0  01:10 22 Mar   2h 50m    non-stop         No info   3897          24   \n",
       "1         13:15   7h 25m     2 stops         No info   7662           1   \n",
       "2  04:25 10 Jun      19h     2 stops         No info  13882           9   \n",
       "3         23:30   5h 25m      1 stop         No info   6218          12   \n",
       "4         21:35   4h 45m      1 stop         No info  13302           1   \n",
       "\n",
       "   Journy_Month  \n",
       "0             3  \n",
       "1             5  \n",
       "2             6  \n",
       "3             5  \n",
       "4             3  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Departure time is when the plane leaves the gate\n",
    "\n",
    "train_df['Dep_hour'] = pd.to_datetime(train_df.Dep_Time).dt.hour\n",
    "train_df['Dep_min'] = pd.to_datetime(train_df.Dep_Time).dt.minute\n",
    "\n",
    "train_df.drop(['Dep_Time'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>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>Journy_Day</th>\n",
       "      <th>Journy_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  Journy_Day  Journy_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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Arrival time is when the plane pulls up to the gate\n",
    "\n",
    "train_df['Arrival_hour'] = pd.to_datetime(train_df.Arrival_Time).dt.hour\n",
    "train_df['Arrival_min'] = pd.to_datetime(train_df.Arrival_Time).dt.minute\n",
    "\n",
    "train_df.drop(['Arrival_Time'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <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>Journy_Day</th>\n",
       "      <th>Journy_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",
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       "  <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",
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       "      <td>22</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journy_Day  Journy_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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Time taken to reach destination is called Duration\n",
    "## It is difference between Departure Time and Arrival Time\n",
    "\n",
    "## Assigning and converting Duration column into list\n",
    "duration = list(train_df.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\"  # Adding 0 mins\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]\n",
    "\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(\"h\")[0]))    # Extract hours from Duration\n",
    "    duration_mins.append(int(duration[i].split('m')[0].split()[-1]))  # Extract onlye minutes from Duration\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[\"Duration_hours\"] = duration_hours\n",
    "train_df[\"Duration_mins\"] = duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10678</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>2h 30m</td>\n",
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       "      <td>4107</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>10679</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4145</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10680</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>3h</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7229</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10681</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 40m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12648</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10682</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → GOI → BOM → COK</td>\n",
       "      <td>8h 20m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>11753</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Airline    Source Destination                  Route Duration  \\\n",
       "10678     Air Asia   Kolkata    Banglore              CCU → BLR   2h 30m   \n",
       "10679    Air India   Kolkata    Banglore              CCU → BLR   2h 35m   \n",
       "10680  Jet Airways  Banglore       Delhi              BLR → DEL       3h   \n",
       "10681      Vistara  Banglore   New Delhi              BLR → DEL   2h 40m   \n",
       "10682    Air India     Delhi      Cochin  DEL → GOI → BOM → COK   8h 20m   \n",
       "\n",
       "      Total_Stops Additional_Info  Price  Journy_Day  Journy_Month  Dep_hour  \\\n",
       "10678    non-stop         No info   4107           9             4        19   \n",
       "10679    non-stop         No info   4145          27             4        20   \n",
       "10680    non-stop         No info   7229          27             4         8   \n",
       "10681    non-stop         No info  12648           1             3        11   \n",
       "10682     2 stops         No info  11753           9             5        10   \n",
       "\n",
       "       Dep_min  Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "10678       55            22           25               2             30  \n",
       "10679       45            23           20               2             35  \n",
       "10680       20            11           20               3              0  \n",
       "10681       30            14           10               2             40  \n",
       "10682       55            19           15               8             20  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(['Duration'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</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>Journy_Day</th>\n",
       "      <th>Journy_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  Journy_Day  Journy_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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Airline.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2028ff965f8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"Price\", x=\"Airline\", data=train_df.sort_values(\"Price\", ascending=False), kind='boxen', height=6, aspect=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_df['Airline'].unique())"
   ]
  },
  {
   "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>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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Airline = train_df[['Airline']]\n",
    "Airline = pd.get_dummies(Airline, drop_first=True)\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2028fea9550>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y='Price', x='Source', data=train_df.sort_values('Price', ascending=False), kind='boxen', height=6, aspect=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Source = train_df[['Source']]\n",
    "Source = pd.get_dummies(Source, drop_first=True)\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Destination = train_df[['Destination']]\n",
    "Destination = pd.get_dummies(Destination, drop_first=True)\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = train_df.Additional_Info=='No info'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.781127129750983"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Additional_Info conatins almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "train_df.drop(['Route', 'Additional_Info'], 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",
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       "\n",
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       "        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>Journy_Day</th>\n",
       "      <th>Journy_Month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Total_Stops  Price  Journy_Day  \\\n",
       "0       IndiGo  Banglore   New Delhi    non-stop   3897          24   \n",
       "1    Air India   Kolkata    Banglore     2 stops   7662           1   \n",
       "2  Jet Airways     Delhi      Cochin     2 stops  13882           9   \n",
       "3       IndiGo   Kolkata    Banglore      1 stop   6218          12   \n",
       "4       IndiGo  Banglore   New Delhi      1 stop  13302           1   \n",
       "\n",
       "   Journy_Month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  Duration_hours  \\\n",
       "0             3        22       20             1           10               2   \n",
       "1             5         5       50            13           15               7   \n",
       "2             6         9       25             4           25              19   \n",
       "3             5        18        5            23           30               5   \n",
       "4             3        16       50            21           35               4   \n",
       "\n",
       "   Duration_mins  \n",
       "0             50  \n",
       "1             25  \n",
       "2              0  \n",
       "3             25  \n",
       "4             45  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Total_Stops.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As this is case of Ordinal Categorical type we perform LabelEncoder\n",
    "## Here vlaues are assigned with corresponding keys\n",
    "train_df.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = pd.concat([train_df, Airline, Source, Destination], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journy_Day</th>\n",
       "      <th>Journy_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",
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       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
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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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  Journy_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",
       "   Journy_Month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  Duration_hours  \\\n",
       "0             3        22       20             1           10               2   \n",
       "1             5         5       50            13           15               7   \n",
       "2             6         9       25             4           25              19   \n",
       "3             5        18        5            23           30               5   \n",
       "4             3        16       50            21           35               4   \n",
       "\n",
       "   Duration_mins  Airline_Air India  Airline_GoAir  Airline_IndiGo  \\\n",
       "0             50                  0              0               1   \n",
       "1             25                  1              0               0   \n",
       "2              0                  0              0               0   \n",
       "3             25                  0              0               1   \n",
       "4             45                  0              0               1   \n",
       "\n",
       "   Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0                    0                             0   \n",
       "1                    0                             0   \n",
       "2                    1                             0   \n",
       "3                    0                             0   \n",
       "4                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Trujet  Airline_Vistara  \\\n",
       "0                 0               0                0   \n",
       "1                 0               0                0   \n",
       "2                 0               0                0   \n",
       "3                 0               0                0   \n",
       "4                 0               0                0   \n",
       "\n",
       "   Airline_Vistara Premium economy  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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop(['Destination', 'Source', 'Airline'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
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      ],
      "text/plain": [
       "   Total_Stops  Price  Journy_Day  Journy_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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(r'C:\\Users\\anmul\\Downloads\\Test_set.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Banglore</td>\n",
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       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
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       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2671, 10)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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",
      "Jet Airways Business                   2\n",
      "Vistara Premium economy                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": [
    "\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2671, 28)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\n",
       "0                                  0         0        0   \n",
       "1                                  0         0        0   \n",
       "2                                  0         0        0   \n",
       "3                                  0         0        0   \n",
       "4                                  0         0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journy_Day</th>\n",
       "      <th>Journy_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  Journy_Day  Journy_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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Price', 'Journy_Day', 'Journy_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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_train.loc[:, ['Total_Stops', 'Journy_Day', 'Journy_Month', 'Dep_hour', 'Dep_min', 'Arrival_hour', \n",
    "                      'Duration_hours', 'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
    "                      'Airline_Jet Airways', 'Airline_Jet Airways Business', 'Airline_Multiple carriers',\n",
    "                      'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet', 'Airline_Trujet',\n",
    "                      'Airline_Vistara', 'Airline_Vistara Premium economy', 'Source_Chennai', 'Source_Delhi', 'Source_Kolkata',\n",
    "                      'Source_Mumbai', 'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad', 'Destination_Kolkata',\n",
    "                      'Destination_New Delhi']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journy_Day</th>\n",
       "      <th>Journy_Month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>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>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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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  Journy_Day  Journy_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",
       "   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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:, 1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 18))\n",
    "sns.heatmap(train_df.corr(), annot=True, cmap='RdYlGn')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anmul\\anaconda3.6\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
       "          max_features='auto', max_leaf_nodes=None,\n",
       "          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "          min_samples_leaf=1, min_samples_split=2,\n",
       "          min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "          oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.74067436e-01 1.44946684e-01 5.58303185e-02 2.51402986e-02\n",
      " 2.47941498e-02 3.47075458e-02 1.42522263e-01 2.23306206e-02\n",
      " 9.06416335e-03 1.77550782e-03 1.82588419e-02 1.58289750e-01\n",
      " 6.93047140e-02 1.67570443e-02 7.73368487e-04 3.23924885e-03\n",
      " 1.36627617e-04 4.57661281e-03 6.74010223e-05 3.91078934e-04\n",
      " 9.52847572e-03 2.28855645e-03 7.81345633e-03 2.44638804e-02\n",
      " 1.61230108e-02 7.12620480e-03 4.63641511e-04 2.52190985e-02]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "feature_imp = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feature_imp.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor()\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9447630310426561"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7903770234066316"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anmul\\anaconda3.6\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2028fa566d8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE:  1196.588959671587\n",
      "MSE:  4519902.428144658\n",
      "RMSE:  2126.0062154529696\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": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7903770234066316"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomized Search CV\n",
    "\n",
    "## Number of trees in ramdom 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 level 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": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "## create the random grid\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}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_random = RandomizedSearchCV(estimator=rf_model, param_distributions=random_grid, scoring='neg_mean_squared_error',\n",
    "                               n_iter=10, cv=5, verbose=2, random_state=42, n_jobs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.9s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.7s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.6s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.7s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.7s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   4.1s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   4.0s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   4.0s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   4.0s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   4.0s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   2.3s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   2.3s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   2.3s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   2.3s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   2.3s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   4.2s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   4.2s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   4.1s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   4.1s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   4.1s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   6.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   6.7s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   6.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   6.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   6.5s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   5.8s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   5.9s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   5.7s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   5.8s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   5.8s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.2s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.2s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.1s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.2s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.1s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.1s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.0s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.0s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.0s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.3s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.3s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.3s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.3s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.3s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   8.0s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   7.8s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   7.6s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   7.7s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   7.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:  3.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=2)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anmul\\anaconda3.6\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sns.distplot(y_test-prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iIvuLSmSL7EP9LsMtIvuHZiRE9pl2065K3V/WtGt6RlPDIrJxCiRE9pnOpl2GYZDPOOTSNme82Z0emogMIC1tiGyD3bSUoKZdIrKVNCMh0me7bSlBTbtEZCspkBDps922lKCmXSKylRRIiPTZXLFOLr18FXEnlxLaTbvyGYf5UoN8xuEdDx/Xrg0R2RTlSIj02fhwhkrdJ59xlo6tt5SwHfkUqp4pIltFMxIifbaRpYTdlk8hItKNAgmRPtvIUsJuy6cQEelGSxsi26DXpQRtzRSRQaMZCZFdRFszRWTQKJAQ2UW0NVNEBo0CCZFdRFszRWTQKEdCZJfR1kwRGSSakRAREZFNUyAhIiIim6ZAQkRERDZNgYSIiIhsmgIJERER2TTt2hDZBbajUZeISD9oRkJkh6lRl4gMMgUSIjtMjbpEZJApkBDZYXPFOrn08lVGNeoSkUGhQEJkh6lRl4gMMgUSIjtMjbpEZJApkBDZYWrUJSKDTNs/RXYBNeoSkUGlGQkRERHZNAUSIiIismkKJERERGTTFEiIiIjIpimQEBERkU1TICEiIiKbpkBCRERENk2BhIiIiGyaAgkRERHZNFW2FBkA0zMlznizzBXrjA9nOOVOqBKmiOwKmpEQ2eWmZ0o8+dQFKnWfsaE0lbrPk09dYHqmtNNDExFRICGy253xZsmlbfIZB8MwyGcccmmbM97sTg9NRESBhMhuN1esk0svX4XMpW3mivUdGpGIyE0KJER2ufHhDNVGsOxYtREwPpzZoRGJiNykQEJklzvlTlBtBFTqPnEcU6n7VBsBp9yJnR6aiIgCCZHdbmpyiHc8fJx8xmG+1CCfcXjHw8e1a0NEdgVt/xQZAFOTQwocRGRX0oyEiIiIbJoCCREREdk0BRIiIiKyaQokREREZNMUSIiIiMimKZAQERGRTVMgISIiIpumQEJEREQ2TYGEiIiIbJoCCREREdk0BRIiIiKyaeq1IQNpeqbEGW+WuWKd8eEMp9wJ9aIQEdkBmpGQgTM9U+LJpy5QqfuMDaWp1H2efOoC0zOlnR6aiMi+oxkJ2VV6mWk4482SS9vkMw7A0p9nvFnNSoiIbDPNSMiu0etMw1yxTi69PAbOpW3mivXtHK6IiNDnGQnXdX8P+NnWl497nve7ruu+HfggkAX+2vO897fOfQD4U2AY+Afg1z3PC1zXPQ58HJgEPOB9nueVXdcdBf4SOAHMAj/red7Vfr4f6a9eZxrGhzNU6v7S9wGqjYDx4cz2DlhERPo3I9EKGH4c+EHgAeCU67o/D3wU+CngPuAh13Xf2XrJx4Hf9DzvHsAAfrV1/EPAhzzPuxf4BvCB1vE/AE57nncf8GHgD/v1XmR79DrTcMqdoNoIqNR94jimUvepNgJOuRPbOVwREaG/SxtXgH/reV7T8zwfeAG4B3jJ87yXPc8LSIKH97iueyeQ9Tzva63Xfqx13AEeAT7Vebz1958gmZEA+Cvgna3zZUCND2eoNoJlx1abaZiaHOIdDx8nn3GYLzXIZxze8fBx5Uf0yfRMicdOn+Mjjz/PY6fPKalVRJbp29KG53nfbf/ddd1Xkyxx/DFJgNF2BbgDOLrG8YNAsRV0dB6n8zWtJZAiMAFc7mV84+OFVY9PTOhh1It+3Ke3v+EuPvul7xMbBrmsQ7XmE2Hw9jfcteLnTUwM8eBrj275GLbSXvhdevnyDb70rSsUsg53HB6mWvP50reu8NNvznP30ZEt+Rl74T71m+5Rd7pHO6fvuzZc130t8DjwO0BAMivRZgARycxI3MNxWsfb53QyOr7X1dxcmShafumJiSFmZ/Vpq5u17tPt1nYoOCZvvv8IZ7xZLl4tMj6c4c33H6HgmAP3z2Wv/C79/ddewSTGiGNq1SYGYBLz9197hXe96cRtX3+v3Kd+0j3qTveoO9M01vwAfbv6nWz5I8Cngd/yPO8Trus+ChzpOOUwyQzCxTWOzwAjrutanueFrXPaMw6XWudddF3XBoaAuX6+H1lbe8dFLm0v23Gx0SWHqckhLVHsInPFOmND6WXHtENGRDr1M9lyCngMeK/neZ9oHf568i33Va7rWsB7gSc8zzsP1FuBB8AvtI77wGng51rHfxF4ovX3v2l9Tev7p1vnyw7o3HFhGAb5jEMubXPGm93poclt6DVvRUT2r37OSPw2kAE+6Lpu+9h/An6JZJYiQxIMtBMp3wd82HXdYeAZ4I9ax38D+HPXdd8PXAB+vnX8A8DHXNf9LrDYer3skK365KrS17vLKXeCJ5+6ACT/PKuNgGoj4JH7d3d+iohsHyOOb01B2PPuAl5WjsTmrXafHjt9bkVth/bXva6ldy6PdD60BnFHxl76XepncLeX7lO/6B51p3vUXUeOxN3AK1t5bZXIli2xFZ9cVfp6d1LeioisRyWyZUtsRW0Hlb4WERk8mpGQLXO7n1xV+lpEZPBoRkJ2DZW+FhEZPAokZNdQ6WsRkcGjpQ3ZVZTYJyIyWDQjISIiIpumGQmRHqlYlojISgokRHpwu71EFISIyF6lQEL2rM6Ht2OZxHFMEMXYpoFhGPhh1PND/XaKZW1VQzMRkd1IgYTsSZ0Pb9s0ePHCAgBHx7NcnqsB4E6N9vxQv51eIrupYqdmRkRkqynZUvakzof3lfkq2bRNNm3zyrXK0t+vzFd76lI6PVNidrHGN7xZXrywwI1yA+i9WNZuqdjZDq4qdX/ZzMj0jHoUiMjmKZCQPanz4V1rBDiWiW0Z1JshtmXgWCa1Vnvs9R7q7YfvgUIa04BqPeDspSJX5ys9F8vaLa241epdRPpBgYTsSZ0P72zaxg8jgjAmk7IIwhg/jMi2Ao31Hurth++hsRyvvmOUXMYmjCMWy82ecxx2S8XO3TIzIiJ7iwIJ2ZM6H95HxnLUGgG1RsBdh/JLfz8yluv6UO98+A7nU9x7/AA/5E4yMZrtObdgt1Ts3C0zIyKytyjZUvak9sO7nVh47/EDS7s23Kn00q6NkYzDI/cfXfOhvlWNxHZDxc6taPUuInIrBRKyZ3V7eLd3MPzt09Nr7mDofPgGQcSFmTLlms9r7jzA9Expx4ODjbg1uBofzqwbRImI9EKBhOxLvdZ2aD98v/jMJZ4/v0Ah63Df8VFs2xzIWhC7YWZERPYWBRKyL22ktsPU5BDD+RQ/cHJ82RLHWueLiOwnSraUfWmjOxi040FEZHUKJGRf2ugOBu14EBFZnQIJ2Zc2WtthvfOnZ0o8dvocH3n8eR47fU6VIkVkX1EgIfvSRms7rHU+sGrZ6Zcv39jOtyMismOUbCn71kZ3MKx2/mOnz62atPnV567wYw8e27rBiojsUpqRELkNayVhzixUd2hEIiLbSzMSsq9sdRvttSpfTo4XtmK4IiK7nmYkZN/oRxvttZIw3/j6I1s4chGR3UuBhOwb/WijvVYS5t1HR7Zw5CIiu5eWNmTfmCvWGRtKLzu2FUWlVHZaRPYzBRKyb6yWz3BtocpiuckffupZao2QXMbh+GThtnMn1rPVeRoiIjtJSxuyb9yaz3B1vsK5y0VSlsncYoNqPWB2ocq1+ept506spR95GiIiO0mBhOwbt+YzLJabnDw6QjOMcByTXMYm5VgslBu3nTuxln7kaYiI7KQ1lzZc1/0iEK/1fc/z3tqXEYn0UWc+w0cefx7LgKtzSc0H2zYpZGyCMOpbQ65+5WmIiOyU9XIk/p/Wnz8NjAAfBQLgF4DFPo9LpO9s08CbXsQwDYhjwihmrtRgfDjdt4Zca9WdUPMvERlUawYSnud9GsB13d8BftjzvKj19ePAV7dneCK96TWBsfO8izNl/CAkn7EpVXwwYohiwjCm2gh45P6jWz7OU+4ETz51AUhmIqqNoG8/S0RkO/SSI3EQ6Py4NASM9Wc4IhvXawLjrec1gwjLNHEsk1zGwjSStbyFcpOU1Z/0oY02CxMR2e162f75X4Gvu677GcAA3gP8576OSmQDOhMYAW6UG7x0qci3vj/HHRN53vbgMR667zBnvFmiMGZ6pkytEdDwQxzLIJ2yuPvIMN+/lHTszKVtbNvkyacuLHvIb9W2TdWdEJG9pGsg4Xne/+G67jeAt7cO/RvP857o77BEejM9U+KbL10njiNyGQfbNHj5agnLNLBNg2o94JNfOgvAhZkyswtVUo5FNmUThhEL5SZhFBPHN/OKj4znloKSM94sU5NDS7MZubS9bNZDswkist/1WpDqKvBd4GPAg30bjcgGtB/utmVAbOEHEa/MVrAtA8sysUyDXCb5Ff/8M5cAMAwDx06WLQq5FEEYE0QxNyo+o/kUR8ZzjBSSXRWduylunfW4NdAQEdmvui4Eu677y8CfAb9Lsnvjv7mu+6v9HphIN+2H+/HJAn4YARCEEUEUEUUxhWwSRGRTFovlJtm0RbXmc/5qiXOXi5y/WiIIIqYm87zhNYeYOlRYCiJg+W6KtdqFa9umiOx3vWSU/WvgjUDR87wZ4BTwW30dlUgP2g/3kUKak0eHcWwT2zKJQjgwlCKTSh78tWbIaCGFbZqUG0lVSwOI45hyw8c2zTW7eJ5yJ4Bk22a1ESz7+btx2+b0TInHTp/jI48/z2Onz6lipoj0XS+BROh5XrH9hed50yT1JER21PhwhpmFGi9eWODcleRX9PhkHscxiCKIo5hqPaDeDHjbg8e4fqOOZZjkMg5DOYdcxsEyTK7fqHfdTdEt0NgNVH5bRHZCLzkS867rPkCryqXruu8D5vs6KpEeHDuY4yvfuUImZZNNWUtBw4++7jDnZyoslpuMFlL85A/fyUP3HebT//Ayw3mbhVITP4wxDMg4JsVqE1h/N0U70OjctfHI/Ud3VX6E8jhEZCf0Ekj8b8CngJOu614BasBP9XVUIj24dL3KiaPDLJabrc6dNkcP5shmUvy799274vxsyuLKfJ0YSLUSLhvNCIOIp1+4yqXr1RVbOwepU6fKb4vITuglkMgB9wP3ABbgeZ7n93VUIj2YK9Y5dCDH4bE8AJdmSnz/YpFvn53nxQsLS/Uj2g6OZJieKZPs9ExmJGIgCEM+9qTH4bEcUxP5pSWBB06O8+zZub5t+dzqIEXlt0VkJ/QSSPyl53n3AS/0ezAiGzE+nOHspUWuzNcoV338MMK2oJBxltWPaAcTQRRhWhBFyf/apSMaQYwRhCyUGlRqPvfdeYBc2ubzz1xiarLQl6WCftSlUPltEdkJvQQS33Zd973APwLl9kHP85QnITsq7Rh8/1IR2zIJwog4hiAA85b6Ee1AotYIyaYcgiCk7kd01KAiBhrNAD80eflqkftPHmSx3OTwgZAXZ8rJa9MWhw9kmWuGtz32fuQzDEIeh4jsPb0EEj9FUha7U0yyzCGyY751dp5CxqbSCIlaQYFpQqUeMD5ys35EWy7jkEmZzNX8VurwcvVmRD5jslhqUm0EZFIm37t4g2zaJpMy8YOI7128gTs1ettj71c+g8pvi8h266VEthZYZVe6Oleh1ggxDCOpC0GyZBGESZTQrh/RdnyyQNo2WSjNEoYrI4kYqDcDUo5FtREwOZrl4mwFgJs/IamOebuUzyAie0UvlS0t13V/w3Xd/+a67mdc1/2lbRiXyLqmZ0qt5YkkabLdrDMGonh5/Yi2U+4EpmVQyDoUsjZmKx4wjKQbHSR5E7Zt8o6Hj5NO2bhTozi2Sa0Z4Ngm7tToUhXN2zEIdSlERHrRy9LGHwOvAf6CJPD4Fdd1X+V53vv7OjLZV1bbwQCsuavhjDdLNmVRrEYE/vIHuwnkMvZS/Yi2qckhHjg5zsuXi1ydv7nk0c6VyKZMhnIpjhzMMTU5tDRrcO/xA0vnVuo+Ix2zCJulfAYR2St6CSR+DHhNe8un67ofB74FKJCQLdG5g8E2DZ47N8fpb1/BsQzuOjLEoQO5Fbsa5or1JJCorNyJfHA0y/t+7J4VD+XpmRLPnp3j+KECTT9ksdwkipPZCMc2mDiQ5fDYze2k/d4FoXwGEdkLegkkZlvntf+LHQGLfRuR7DvtHQxhGHP2chHHNomiiFoAl69XyabspWZa7V0N48MZvlnxV8uZ5EalsSzoaM92fPOlWcIoplLzCcOYoZxD049IpywOFNIOpvR8AAAgAElEQVRggGWZS7MhmjUQEemul0DiWeC067ofI+mx8XPAddd1/w2A53kf7N/wZDdpP5ArzZB8ytqyKo/tHQze9CKObbYCiXip5feV+SojhfSyXQ2n3An+x1deWfV6fhCTS9uc8WYBlmY7mn5EqdqkGURkU1aSpGmAH0T4YYhhmCvqOGz3rMEgVdIUEYHeAoks8BxJ10+AV1p/vp5VN9HJINjoA6tz+eHgWJ7r85Utq/LYzkWoNQKyrY6dpmViALZlUGskdRs6dzVMTQ5htipTxvHNZElIki3bQUdnvQY/jLAsEyuKaQQRBcfCspLA5ejBPIvlJn/79PSOlcjuR5EqEZF+62X75y+v9T3Xdf9qa4cj22EzD6zOB7JpGFta5bGdi2BbJs0g2c6ZcSziOKZYaeIHEU+/eA3LNPnJH75z6XXDhRSLpWYSTHRcL22ZS0FHZ70GxzLwgyQ4aTQj/CAijmLCIOTc5SInj44sux/9LpF9KzXdEpFB1Esb8fW4WzIK2VadDyyjFRR0LgWsZq5YJ5deHnduVUOodi7CnYeGqNSTDvXu1AiHDmQpVX0Mw2Aom+LowRzPnp1jeqbE0y9cpdG8WYiqU9ox8S4scmGmzOxijWsLVQBGCmmGsjaObZFykpmITNrCsi1OHh3h0Fhu2f34/DOXNnyfbkc/77GISL/0srQhe8xmqir2u4DS1OQQv/iOe5ctJTSCiNedGFvaRQHJ9sv//o8v8/1Li1Tqq5eqrjZC8rmY64tVohhmFmoAHBnL8dyNGn4QMZRLkcvYDOUcTNNk8kB22TVyaZvFchN3avse7CpSJSKD6HZnJGQAjQ9nqDaCZce6PbA6CyhFfSygNDU5xLvedIJf+YnXMDGa5dCB3LLv59I2L5xfoO6vXRTKD2PSjkUu7ZB2LGzT4Np8jcVKA8eyGCmkcOwkq8LAYDSfWnY/ipUm33l5nnoj4Dsvz1Os3Kw50c8Hu4pUicggUiCxD23mgdVefshnHK4v1shnnL4nAa4V8DRbuQ3riVoFIhzbpJBziGK489Awrzsxxg+5k7zqWFKxcmaxyrkrN7i+WKdS97lRbvDihQVqjYBXHRui1gh48cICN8qNvj/YO+/xfKmxLfdYROR29X1pw3XdYeArwP/sed4rruv+GfCjQKV1yu97nvdZ13XfDnyQZJfIX7crZ7qu+wDwp8Aw8A/Ar3ueF7iuexz4ODAJeMD7PM8rI11ttj5CeyvkxMQQs7OlLR/XrTskjrVyImB5Qai0Y1G7JcC41XypwdhQmnTKavXJiJgr1rFNg299/zqzizVSjsVQ1sYPYmJiwjDm3JUS2bTN8clCklORS3Nhpsy5KyV+8NUH+15HQkWqRGTQdA0kXNf9NPAfPc/7+1W+vW73Itd1/xnwYeCejsM/BDzied6VjvOywEeBR4Fp4HHXdd/ped4TJMHCv/Q872uu634E+FXgPwIfAj7ked4nXNf9APAB4H/v9n4ksdseWKvtJHn27BwPnBzn0vUqc8U69brP9WKDZrB6kmWnMIwoVpuMmmlqzQB3ahTDMHjxwgL1ZohjJ5Nx86UmB0cyTIxmyWccjh8qMDaUXmrMNVJI87p8ivlSg3e96US/b4OIyMDpZUbiM8AHXNf9EPCfgY96njff+t7PdXntrwL/CvgvAK7r5oDjwEdd1z0GfBb4feBh4CXP815unfdx4D2u6z4PZD3P+1rreh8Dft913T8FHgHe1XH8yyiQGDhPv3CVzz9ziYuzFRzb5NXHhslnHPIZh3LV5/PPXGJiNEu9EXD2SolC1iHjmARBuG4RkzCKaTSTPIqhXLLr4oXzC5RrPlEYkUnbRAAGxNysO7HdCY8qQCUig65rjoTneX/ped6jwP9CsozwtOu6/8V13Yc9z/tel9f+S8/zTnccOgx8AfhfgTcAbwJ+BTgKXOk47wpwxzrHDwJFz/OCW47LAHn6hat88ktnqdYDbNMgDCO++8oCl6+XKVaaXJwtU6r62KbBd16ep1RpUqwm/TGGcus3znIsk/GRNMcnC+TSDpZl4NgGI3mHkKRduGUajA2liGNjKVjYzoTH9ixMpe4vq1MxPbP1y0YiIv3SU46E67om8GqSJQobmAE+5Lru457n/V6vP8zzvHPAT3dc94+BXwQ+xfKaQsmidhLo9HKc1vGejY8XVj0+MaFPg7d6+fINvvrcFWYWqkweyPHG1x/h7qMjt33d0//vtzAMg/lig3ozwDAg7Vicv1bh0FhMyrEwLZPzM2XCKMa2DZqt1uGWtf61h/IpfucXHuKrz12h3Jjn22fnWSw3sEyDoZxNEMLBkSwxMQYGEQZvf8Nd3H10hAMH8jff73hh0++32+/S3z1ziYOjOQq5FACFApSrTV68WOTB125NY7BBoH/nutM96k73aOf0kiPxB8AvA+dI8hLe43me77puHrgA9BxIuK77euAez/M+3TpkkDQDuwgc6Tj1MHB5neMzwIjrupbneWHrnMu9jgNgbq6cZPZ36FcS4SDrzF3IpW1m5sp89kvf5833H7ntKfgLV4uUqh2Nt+KkBoQf1LEtsAwTM4qwTBPbMoiimDiOSTkm1fr6yZaVms+ff+47vHRxkVI1WPoZPlBvhozkHPwgpFzzec2dB3jz/UcoOCazsyUKjsmPPXhs2fU2+nvRy+/ShSs3kpmISuPmwTjmwpUb++b3UP/Odad71J3uUXemaaz5Afp29TIjMQn8T57nfavzoOd5Fdd1f36DP88A/oPrul8AysCvAX8OfB1wXdd9FfAy8F6SXIzzruvWXdf9Ec/z/gn4BeCJViBzmiRH47+SzGo8scGxSA9WK9scG8amyzZ35gSUa6t37wzCmKFcitFCiitzVRzLZLSQZnaxjmXGybTUOtmWtgmFnMOLFxaXKmXeqlzzefQHj+1oToIKUInIXtBLr41fW+d7f7uRH+Z53rdd1/33wD8BDvBpz/P+CsB13V8CPg1kgL8hWe4AeB/w4dY20meAP2od/w3gz13XfT/JzMhGgxrpwapVMLMOF68We3p9Z+DgWCbzpToTo1nGhtJr7ryIgbc9eIxnz84t9d/Ipm1G8g5BFFOtBzi2ST5jEwQhxdrNCpe2CbmMQ60REMdrBxuxwY7vwmj3GIHl21sfuX//LGuIyOAz1vuP7R51F/CyljZ689jpcys+NceGgRHHXR/Ety6LtKtE5rM2cQzTM5U1Xzs1WeDk0WFK1SYXZsoUsg5TE3kcx+LbZ+eYmsjx/UtFKvVw6Z9jDJgGZNNJ7YhmENEMVk+dcSyDP/mdt2z8hvSo19+l/b5rQ//Odad71J3uUXcdSxt3c7OL95ZQrw1Z12qfmiMM3nz/kS6vXLksUm0ElKpNFsoNTGPdEiTEcYRlGaRSFv/80RNcul7lwkyZ6kKNRsPnuZcXiKKYKFqedRvFUKmHSafPcO0g+eDI7lg+2G31PERENkqBhKxrtSqYb3/DXRSc7tXVb10WqdR8mkGMaYBtJ8mTay1vhGG8FIBcul7llDvB7I0aKdvg0gxEYcxacYJpgGGsP9P2oz9wuOv4O+33mQMRkbUokJCubv3U3Os04q3JhO2y1lEM9eb6u3WLVR+42W2zPbsxPVPGMAzSKYtaY3lRKsc2SDsWQRjhr9PUK2VDw+99SW+1qptPPnVBfTBERFDTLumjzuJON8oNwg1U+qg3k6CjvYvh/LUi09fKXJytUPcDGn6EbbU7eCbiOMYwDMaG0liWidX67e5cRDGAA0PZDbUC71yiMQyDfMYhl7Y54832/oZERPYozUjIlmmXu14sNxktpHjbg8eWlkW++dJ1DAPiOFl66NorI2KpquTYUIpzl0uEYUQUx8RxUgLbsAwsk6UAJQihVvdpBuHSjo1kmSP5uTFgmQa5jL2hLZar7lxpzZSIiOx3mpGQLdFZ7nok51CtB3zyS2e5OlfhXW86wfFDBQ4MJRUcuwURbfmMwwMnx/nqd2fIpS0s08AwDKIoCQ7CMAkqOgURNJoRYRwTRiy9xrYMLANsy2Ao52yo5PVa7cxV70FERIGEbJHPP3OJTMoml7ExWp/6LdPgr794lo88/jyzizXiDSxtmK06D5euVwmjmOF8irHhDNm0jWUmSxSWaazZfjaKknMcyyRlm8QxWLbJa+88wLsfPbmh3Ibt7L8hIjJotLQhW2Kx3GSk1Uir0QxZLDco13wMI5kJGC2k+N70jZ6vl00nv5pzxTpDWQc/jEinkgYbgR9SrgeEUbxuB9AYqPshB0ezS0stD923sd0asPrOlUfuP6pESxERFEhIj9rbHy/MlAnCGMuMufPQ8NI2yNFCimo96ag5X2rQ9EMMI5lZ+Ob358ilbQxuFo1ab3nDBO67cxRIlhX8IOTy9SoNP6RYaRLHSUGpRtB9jSSKoVhp0vRD/v7MRQ6P5zcVAKjeg4jI6rS0IV21tz9em68yu1Blvljj3OUS//TcZT78ued5+oWrvO3BY9SbAfOlBiYQRhFxfHPHhB+E684edDJMeOjeSSBZVrAsk6MHc9QbAVEEpmFQ6NJGvFMQhjT9kOmZMv/99LkNvfftMD1T4rHT5/jI48/z2OlzaiMuIgNFgYR01d7+uFBOulS2G2HFcRImfO4r5zk8nuc9bz6ZtHMNI+xWbkI242BbBvVmMkMBXWYjDLAtk2fPzvH0C1c5481Sa4RMXyuzWG7iByF+GDJXbPY8/jhOljiiKObF6cVN3YN+aQdplbq/rEaFggkRGRRa2pCuVRvb2x9rjYBSpUkzjIgiaBCSz1oEYRJstJMjK3WfMIx56oVrxHFMHBvJdk1Yc1bCADIpC9s2cWyTYrnBXzzpYRgGfhjS8JPXh2G8bunr1cSt/4vjmHozYnqmtGuWKVbrrto+vlvGKCKyHs1I7HO9fCJub380DKg0QsIwWmqIdWm2SsMPlmoqtHc4WJbBwZEMUZwsaxit6QjbBMtMqlAaJDMQpgGjBQfbNgnDiOGslbQAb4RUG8FSFcoY2MDGjyUGN2tJZNPWriokNVesk0svj+dVo0JEBokCiX2ul6qNxw7m8C4sMrtYJ4xi/FbXboNkmWKx1OTspRt85PHnOePN8sDJcfIZh7GRLCP5FK87MUYubS/VdChkHYZyKXJpa2m5o1QLMA24czLPzOLNKpi91py4Vee20LBVd8KyTdw7RnbVQ1o1KkRk0GlpYw/rpdFUt6qN0zMlnj07x7GDeYqVJjXCpfPan/KjOLnOPXeMUKn7PHt2jnc8fJx3venE0hheungDp1XPAZIKlPVmRAwcOpAhCJOli0tzNaqNjp/B2ssha8mmTILWEohpQMqxyGVs7j48xOhQellL9J22WnfVaiPgkfuP7vDIRER6o0Bij+q10dStjbVg+SfizhmLhXKDcu3G0ixB52xBEMR89bvXcGyT0UKKL5y5yL94533Ltk1ena8wfa3M9WIdv7V10zahUgsIWrs6wnh58LDRICJlJzUoMimLhh9hmQavPzm+ax/SqlEhIoNOgcQe9cVnLnF1rkoQRmTTNkfH80tLFp0PqfYn4nLVZ75Up1zzsUyTn/zhO4HlMxZDWWfNpYYYCMKYMA6ZLzY4872ZVqJkxPhwhmMHc5y/WiIm+eRdDn2iuNV7wzQIophggwkQ7YBjJO+QSSUltOt+RD7jkE1bvOpAlivzVaZnysv6f+y2h7RqVIjIIFOOxB40PVPiu6/ME8cx2ZSNH0R8/9INgiBakR8wNTnEAyfHuXS9QrkWMJRNcfRgjmfPzjE9U1q2hl+q+ev+3JikNHXDj6g1Is5fKy3Nhjx7dg7bTGYL/DAmbjXTwkhahm80iLBMGM47DOdsgjBmodRkZrGe7CppJXHMFevUGiFTkwX+2X2TTE0Wlt6XiIhsDc1I7EFnvFkK2dZShQGOncSLF2bKvP7E+IrzL12v4h4fXba8Uan7nPFml63hV+vrBxJtyXbLmCCMlhI4AS7OlHngVQdx7EVeuVrE9zeZSUmyrFJvBElQAlhGMqsBUKo2ieOYcs3nyFiO6ZkytUZINm0xWkhtaGtlL3kmIiL7mQKJPWiuWGdqIs+5K8knb9syIIZy3V+10dR6CZeda/jBBuo3RPHNfhnt64FBtRFwZCzHK1dLmObK7p29imOSICJOZieiOEnWNE3wg4gginFsg5kbdQ6OZMikTPwg4vJsUmq7F9MzJT7z5XMUq0khrJcv3+Dr373GkYk8xycLCipERNDSxp40PpzBcSxOHh3GsU3qzaQV5mvuPLDiwTc9U2J2scY3vBlevLBAsZJUjOxMuJyaHOKUO8HQBspSxyQ5FW3VRsDJo8NUGwG2bZLPWJgYmw4kIAlWYpLtnVGcBEyObZJJWeQzDpaZ1KVwbBPDSL6HAbVGb4HEF5+5xLWFKpCU5b5R9SlWm8zfqKkCpYhIiwKJPahdFMq2TdypUe6ZGuHweI63nrpj2XntnR2jhRSWYVKtB7x0cZFr89UVbbK/cObihusvPHd2jq999wrPnZvj+mKdtzx4jAdOjjM9U6bhx9iOseFdGatpXyOK4qQXh2WSTVuYpolpGvhBBHEyUxHHMbket3+evVwkk7JwbJNyPUjKfjsmi+XmqvU2RET2IwUSe1B7OSKfcZgvNchnnBXbPuHm1s7DY3lOHhsml7GJYlgoN1ac/+KFBRrNjWVENsOY6zcaLJQaVBs+V+cqPHt2jqnJAvdNjRD00L1zI8JWsNBuW55JWdx9eAjHNqk1Axzb5I6JAscnCz1eMcZolbYKggjTYOlrUAVKERFQjsSe1cuWws7ciJFCmpFCmjiOmS81Vry21ggxjdWusr4ohgNDaUpVn8e/dp7DYzleurjI9cX6hntm9MJs5UtkUzZvfO0kX/3uDGEUM5R1GC2kMC1j1TyR1Zw4Mow3vQiGjW0Z+GFMFMUcHEmWfFSBUkREgcS+1q0YVadcxmax1NjUz5m7USOMYir1kOuLdRp+uFQCe6sdPZjnnqlRgiDi4vUqxw7mmS/VKdWaVBsBP/nDd/acIPnWU3ewUG5Qqvo4lkkjCMikTO46PESl7u+64lYiIjtBgcQ+sdo2xl7KMz/9wlU+/8wl5osNmhst9tBSa4RYrdmMao+JjpuVTdvk0jbPTl/nnqlkS+uhsRyQbGm9dL3KQz1ea2pyiB953WE+/8wlYsNgfDjN5GiWIIoZyTiqQCkiggKJfWF6psSnv3yWUtUnCCIuX6/wytUi7370JHcczPH5Zy5TrQfkMjZve/Dmw/HpF67yyS+dJZOySTsmQRhtqomWYST5Epvpm7FRR8fzrQJaxm131Wz3GZmaLOBO3Qy0fvyhKQUQIiItSrbcB75w5iIzCzUAMmkLgJmFGp/4O48nvj5NueoThBHlqs8TX5/m6ReuAvD5Zy6RSdnkMnara+ftxZ39DiIALMtYttW000ZzGnrpjCoist9pRmIfOHelSDZlL1W4dGwDYpvvXSwS0ypYRUwQxjSDiL/4/77H4fE8i+UmI63aEbZtUq5uLkeiucW7M9aTby05ALfdVfP8tSK1ekitmVTFPDKWYzif0k4NEZEOCiT2BYP4lvmAmJgwBse62SMjORMq9YAnn7pAJmUyV6xTbYTUm/3NbdgKR8ayvOtNJ5a+vp2umtMzJeZuJIFTNm3hBxFnLxc5ejDH4bF8X8YvIjKIFEjsAyePDvPihYWkuqNl4ocR9WaYNM3CuBlEGMnMhEHyKd4yDRbLzU1t+9wJ6ZS17Ovb6ap5xpvl2ME8l65XCMI4uW9BxOXrVX7ijXdtwWhFRPYGBRL7wFsePMZCqUGx2qTa8HFsi0MHcgRByPVi8+aJrXrVhUyy82HuRoPRgkO1ERJssBjVTpjf5PbU1cwV60weyJJN21yeq1BrBGTTFvlsSomWItI3g9goUIHEPjA1OcTPPHpixS/nP37rEn935vKK8ycPJK3Dgyjm0IEchSDkwrXKDox8Y8qV3rqT9qJdY2M4n2I4nwJYUXNDRGQrtdsW5NI2Y0PppZ4+q1Um3k0USOwxa0Wzq03zP/fywqpbMq/M1RjKpbHNpMV4EO3+2QiArRxlLzU2RES2UudOMWDpzzPe7K4OJLT9cw9pR7OVur8smr21Q+X0TInHTp/j2nyNGFo9JFjqIlFrhlQbPpmUTa0R0PQHI5DYSr32KxER2Spzxfpt17/ZCZqR2EN6iWY7p86WumbGrJiZKFV9bMtY6jGxGe3AZPs2fybvb6se9reTrCkislEbaVuwm2hGYg/pJZrtDDasjn/6tz7sTdOgWPVvq7FWvMp1++0LZy5u808UEdkap9wJqo2ASt0njuOlnj69NhrcKZqRGGC35kM4lkm1EawZzU7PlPjmS7MQQzZjk3YMqo3VH/WVmk+tEW5LWeutYpIU3xIRGUTtJdXN1r/ZKQokBtRq2b3zpToGBoyuTBBsn29bJnEc4wcRDX/1EMGxjFaHzkEJIRLJaAek6IWIyCoGcUlVgcSAWjUfYhSCICKfcZZFswB/+Xffo1RtQgw3Kk1iWLOVdwyM5NLUm7XteTNb6OTR4S293iDu6RYR2U4KJAbUXLHO2FB62bFc2ma+2VhWJro9EzFfbNBoJssVYLSqWq4uCGPGhhzmijU22Tl8R2TTFm958NiWXW9Q93SLiGwnBRIDpPPT8exiDT8Il/V9WC2794w3SxTGNJpJ86kIiOOYuEuAsFDxiWJIWRDGa89e7BZpx+Cheye39AE/qHu6RUS2k3ZtDIhba0QcKKQ5d7nI1fnKutm9L15Y4Ftnr1Op+wRh0pwr7iH1IZu2MU2DIALbMnEsY1f33LBMkyvzVR47fW5F3YzNGtQ93SIi20mBxIDo/HRsGAaHxnKcPDrCYrnJhZkS0zNlao2QM97s0oP06ReucnGmjB9EPQUPndo1JOK4FXgYyZbQ29GvOMQAMimTai3g6nxl1SJcmzE+nJQK7zQIe7pFRLaTAokBsdqn48kDWXIZh1zGYWqywNREflk1y88/c4lCzsEyzRVbOG2LNVlGkidhWyaZtEU2Y5NxLPLZzfeZcGxWzGhYXX77eg08LCtZenEck8Vyk1za5ow3u6lxdhrUPd0iIttJORIDYnw4w7X5KgvlRqsTpc2BQppq3Wd8OL3qOv5iuUnaNknZJs1bsibXa58RxVBrBDi2ycGczdWFOn4Y41ibT5QIgiQZshlEpGyDZhBjmQZRFC0Lcgyjt6WXTmEI5VrASD4iCOMtW34Y1D3dIiLbSYHEgDh2MMdXvnOFTMomm7Ko1gPmi3UODKXXXMfPpixmFmqkUxZOR6lr04B8xqZUC1b7UcTAsfEctWbAuSslLMMgNmOC28i4jIFaI8SxDQ6N5Wn4IfVmSNNf3vq7M4jYSDwRE3Oj6nPoQHZLlx8GcU+3iMh20tLGgLh0vcqJo8PkMjZ1PyKXsTlxdBjDMNZcxz84kiEmpumHyy8WQ6NLI65izeeli0XqzYhKIyQMYyzz9n9dTMPAsUyiKCbtmMu2od7W5WPw/ZDRQkrLDyIi20gzEgNirljn0IHcsu2ecRzTDGKqjYBStcliqUmp5mOZBj/5w3cyV6xz96ECZ6+UiYGUnTy1gzAm5axc7ug0PVMmiOKlEtnJqbe3BzSdMslnbIZyDqVqk5mFOu3imQYbX9Joc2wDwzAYyqU4PJZX0SgRkW2kQGJArJUjcXyywLGDOT73lfOEUUQh65BxLD73lfP4QUSl7pNyTEzDwLZNmn5IOmW0CiyV13x4m0mxbSK4JZjYvEYzIpeJuTBTppB1OD5ZYPZGjWo9uLk7ZIMcE8aGM0weyPLuR08qgBAR2WYKJAbEWjkSD95zkEvXq7jHR8lnHIqVJt8+e51yLUi2fZIEAoWsjR9EVOsBacdk9kZ93SSEuh8uzRZsVceNGFgs+YwNG8zdSJIh/SCCOFnWsC2z65LLrfwI3KlR3nrqDgURIiI7QDkSA2KtHIlL16vLtoa+eGGBGxUfiJfyD2KSvAnTSHZFBGFMxrFw7OUbLDu/iuLu2zM3I4qhVAswzKRMdxTFmGZSryLcQDKnATgWWKbBv3jnfQoiRER2iGYkBsRaORLtbYmVuk8+43B9sUYUQ3jLhowoSh66Kcek6ceYRtwqMHWzZ6ZpGss6fvarLHajGWGbUTIbYSTvAwMsyyQiWndralsM+CFk1ihtoWZbIiLbQzMSA6KzyuKNcoMXLyzwDW+W2cUaxw7mqDaSqo7BGiWwY2Cx0iSMoJC1aAQxBgaWmQQRuYzFgYKTzFpsw/sJwgjLhDCMCSMwMJgczZBar1LWKlKOtaKK5a3lxDuLdImIyNZSIDEg2lUWr85XePH8IlfmqiyW6pRrPl/5zjUeODnOYrm5bhBgWwa2adD0Y4ZyDlOHChwayzGcdzAwuNFq1LVVORHrieOYhp+U7raMJIcDwySXsXF6jCUcy8CxrRVVLG8tJ57POFtW7VJERJZTIDEg2lUWr83XuFFtYJkGE6NZ0o7FtYUqL5xfZGI0SyG39mpVGMakUyaGYVCrJ8mYQRgxWkgzOpQi2uz+yw0ygDBKKlsmBwzSjsWJI0O8/u4xbNvq2iDMaC2JBGG0ooqlmm2JiGwf5UgMkKnJIaIYDo/lSHV8bI9ji7OXi/zgqw9irlPVKY6h2YwxjJhaM2Q4SpY3TNPANKy+5ETczMLoGEdrLHEr+TObTvp4XJmvcmQsB3FE1CWmiWMIYqjUfV44v8C/+5OvMlpI8bYHjy3LGWlTsy0Rkf5QIDFwYprNiGLVp1YPaAYRURTj2CZpx8A0knbfqz2ILdPAtAx8P2KkkMY0DbLpJCDxw2jVh/7tj3Z1pplUuUynLXJpmziOqdUDXrq4SDNIdoxEUffxBCHEccRILk21HvDJL53lLT94lNkbST5JLm1TbQRUGwGP3H90S9+biIhoaWPgTI5kmC83qNZ9as3w/2/v3iShuR8AACAASURBVIPsvOs7z7+f27n2Va1uyZIlYcn4Z3EZG2ttCGAb4lxsCMSQEGogCSQTMinIZKdqZ2e2KhAqqcylZnbITLZgZhdCkVogSy0wsxMIDIMxxgk4TmRjSGz/DJYtybq11Gr15dye6/7xPKfVklq37nP6In1eVXb3+fVzznnOT6ef8+3f5fslXYgYMh5+8ihbRyvUKz6+d+HWTs9zii2gDjdurhEnKUmSByF7tg0xVF9+dc/zeReZmuhOWSQJpFnGrTuGecXLNuE4+SrP+VZM4Dt4rovrXnwLqrPoq+O4OK5DreJTKfk89fxp7r9rJ/VKwOm5DvVKwP137dSuDRGRPtCIxAYzWC9Tr/j5wkrn7AdztZx/iDY7CTdtG2JyukWrEzPfigCKkYo8lfT4SIVKJeA1E4McP93gzHzIgWNzBH7v4sqkWESZLBpS6J5rJXCJ04yJ0QrbxgdodmK2jtW4/66dfOorzzDb6NAOE7IsD3oWj0vkgUOevCqMUwaqPvGilJvVkseZ+VDFtkREVolGJDaYKEl51U1jOI6DX+xaqFd84iSj2Y44Md1i02CF7WM1KmU/P66YwnBdhyTJd2yMDVXYvrnGgaOzNNsxZCmzjahn5+m6UKv4lHwHl3xkIfBdKmWPzSNVxgYrvHz7yAUjBnu2DRF4Z4t5ZYsWgLoOxSiFQ7nkUa94pBn4iwKgVpgX7hIRkdXR1xEJY8wQ8F3g56y1Lxpjfgr4GFAFvmCt/XBx3O3Ap4Ah4DvAb1lrY2PMTuCzwARggfdaa+eNMSPA54DdwEngl6y1x/v5WtaLsaEKB47M5Jkg04w07SZwyj95q2UPz3MolX0++OCr+LsDp/jq9w4RJxnlwAMn48CxWeIk49lD02wdrTHbCjl+ukUY93CFRHF+vu+SpCmuk+F7TlHBA+65fSudKE+oNTPf4eEnjhAlKb7rsGmoQqnkMTXTohNluC6M1AM812W2GbF5uMKebUNMz3V47qUZ6q5PluYLSNthzNtev6t3r0NERC6pbyMSxpjXAn8J3FLcrgKfBn4e2AvcaYx5oDj8s8BvW2tvIf9E/EDR/gngE9baW4G/BT5StP8h8Ki1di/wSeA/9ut1rDflwOFHR2bwPBaCiTDOSNKUNMvYs23onLwJnSjj1bvHuHFigFLgEicwVMu3ep6ebXNwcp7jU03CqHdBhFP8r9lJaLYTkjQjTqAdJuzcMsDbXr+Ll041abQjPAfs4TM8e2ga380Li2VkBJ7L8ECFXVsGeM3Nm9n7sjF+4lU38OtvuZV9ZoIkg5u2DfP2N+xiZLDMTDOiVvF515v2cOferT17LSIicmn9HJH4APAh4P8ubt8F/Mha+wKAMeazwLuMMU8DVWvtY8VxnwF+3xjzKeAe4MFF7Y8A/wJ4a/EzgD8DPm6MCay1vRubX6eeev40wwMl2mFCHEcLuzOSFG7aOsC2zQPAuXkTykEeL7Y6ST4tEHjMNiM6Ub7jo6cjEVDU0LiwrV72OTzZ4L88+gLVss/OiQGOT7eoFjkfjp1usm2szlwzolr2ee3eCU5MNzl6qplPhwzB1rH6BYHCA6+7qafnLyIiV65vgYS19jcAjDHdpm3AsUWHHANuvET7ZmDWWhuf137OYxVTILPAOHD0Ss9vbGxgyfbx8fW9QG+uFVGvBjTbMb6X72wIiwyRZxoxYbG68cCRGaIkxXVgrhkxMlBeWJx5aqZNXOzWaIfxZZ7x6jgsvfXUdaAZxpQDn1YnZrBe4uBkgzCKGR2s5CMY7ZhTsx0GayXiNCNKYfJMh8B380qmjsO3nzrGO95U56Ztwz09735Y7++l9UL9dHnqo8tTH62d1dy14XJuWgAHSK+inaK9e8xizqKfXZGpqflFWydz4+ODnDy5NvUYrrTI1GA14NhUk3YYEyfn/uzUmSbfefIwaV4Di6F6XmcijBICL99S2Q5jOlGS54zIeleYy3Pzx7rY2EaaQZaCV8pLh5+cbhEneWZNsoxyySfwXWbm2/iuSxC4HDhyBqeY5phtdHCyDJeMbz72Ig/evbs3J94na/le2kjUT5enPro89dHlua5z0T+gV/zYfXnUpb0E3LDo9lbyEYSLtU8Cw8aYbgrHGzg74nCkOA5jjA8MAlN9O/M+Ozw5x5ceeZ4fHpji0PE5fnhgii898vySRabuu2M77c6FQQTk5cE7UUacZGScndJwHJhvRQS+Uyy6zNNkNztXV7b7Ui4XkHQ3X0RJSpZlzDdD5lsx7TDl+HSL0zMtbthUw/dcZpsh7TDmpZMNzsx3mG9HC9MfSnUtIrK+rGYg8deAMcbcXAQH7wG+Zq09CLSNMW8ojvuVoj0CHgXeXbT/KvC14vu/KG5T/PzRjbw+4lv7X2JyugVApcg0OTnd4lv7X7rg2Dv3bqUUnP1nc8772t0imaQZk2faeelw3yfL4LWv2MroUJnA90iusq7GSldR5IGNky8MzfJAxy2mWrIU5tsxP3zhNO0wph1GpGmebyKMUqbnOgzV8mRZSnUtIrK+rFogYa1tA+8HvgQ8DTwLfLH48XuBPzLGPAsMAH9ctH8Q+M1iQebdwIeL9o8ArzPG/H1xzIdW4zX0y4Fjs1SLoX3HcQh8l2rJ58Cx2SWPLwUe1ZKXV78MnIUgoruLI0vzXAtJmhLGKXGSZ8DMsoxa2cf3XMi4bGGslQg8hyJZJZ4LJd/B8zy6+aU818X3XKplPy9l7jiMDZWplHwqpQC3yDvhug5DtYCZRkijHdHsxOwz4/07cRERuSp9XyNhrX3Zou8fAm5b4pinyHd1nN9+EHjTEu2ngbf38jzXlkN23t/8+e2lP+nHR6qcmesUf72nuG5+X9eFcuDTCROSJCPNMqIsJUszJjaVOT3XYdeWQX7w/Km8aNZVnGGpmBbJU227JEl6TtbKc19Nnmmz2YnIMqiUPBzyct5JmhLFKYHvUipGHFzXARzaYT5fM1QrEQQut988zsx8Jy+Z3gipVwLuuW2bMlaKiKwjSpG9DuzZNsSzh6bz0QjPJUpS2mHCrTtHlzz+vju28/9++3mG6mW2jHrMNENm5kOqZZ/Bqs/x0y2i4lPedbKFkuM/c+cOAL7/45P4nrNwzMUsTk4dJXnkkZFv42y2woUfug7nBCYZMNuM8roeQCdKKfsuSZqQpXmIlKYpWeaQFIsryiV3YR1EGCW0OnlQMTxQxvddbq0E63KB5ZUukhURuVYpRfY68OY7trNltAZAs5Mv9dgyWuPNd2xf8vg7927lXW/aQ63iM1Ns7Xz7G3YxMVplthEuBAjdnRmOA2mS8bn/8Ryf+srTgMNA7fIFurqBweLHwYEkSfLxkoU01hfe13WLnRoOjAyU2DRcZr4dE6VZXs48zphrxsRplhcO8xy2jdXZNlanHSb4nkOWZet6OuPw5Bxff/wQjXbEpsF8h8zXHz+05CJZEZFrlUYk1oEdE4O8897dV/WX7Z17t16QmGnzcJVPf/WZhdvdz/cwznj64HRe/RMH33dotJfY9rEEzwHXc4jjs5MvUZxSL/vgQKMdL+SOyLJ8PUSt7ON6LrONELf42cx8SBQXoxrZ2dEOJ4NSycV1XdI0JQg8tozWGBkocXquw9hQZd1OZ+y3J6mVfeqVPCjrft1vT3LHK1WyXESuDwok1oleVKs8cqp5yTwOPvnaizS78lWWaQZZN/NlBtWySxhnxGnG2HCZW3eNcvRUk1MzbZIkpVrycFyHTlG9M86gEyZ0ipLn3VGMwHfJsoxS4HLnrVs4eGKOF47P8ZqXj3PPveszcDjf1GybTYPlc9q0PVVErjcKJK4hU7N5xsqLCXyPdhgTLpWE4iKy4j8H8D2HJM1LeAe+x8x8xGteXmewVmL2mUmcwCVJM9qt6JzsYO1OfHb9RPFNnOTZOHFShuolXnXTJk7PddblOoiLGRuq0GhHCyMRoO2pInL90RqJa8jYUJ5q2mHprZ2dKCFNL59caikZ5Osj0oxKyWPxno8oygtztcOERismTjl3S4hTjGwUza5z9vsoznjqx6eYnG5tuA/gfWacZiem0Y7W/XoOEZF+USBxDdlnxikH3sL0wfm6UwvOMv/VsyzfsQF5Fs3RwTIz83kp7+GBEsP10kL8kGZ5LonNQyUGqiVcJ89tEXhnHwvAd+H0XIfnj86wfXNteSe2RnZMDHL/XTupVwJOz3WoVwLuv2vnhpiWERHpFU1tXEN2TAzycz+xk//vL18gTR1cJ1vYXREni4ppLTNNZZpmlEsurTClUvLYtWWAHx+ZYa4ZMVD1qZZ9gmK3he+7bBmtUS55hFFCpeTRiWLIHJqdOK9z4mSUfA8H2L1tiCOnmtzZo75YLb1Y2yIispEpkNigLpa/4IHX3cTm4SoPPXGEM/MhlVKePOroqdaK01ynGZyZj6hVfbaMVphtxUzPhQwPBAxWS0RJSsn3SNIE33MpBx5RnOfE2LtrlCzLODQ5TzidUgk8Bmo+cZJPiRw91eDYVEt5GERENhhNbWxAl8tfsHWszlAtoNGKeGmywZEeBBFdA1Wf0YEyk2fa+C5s2VSlUsq3gga+y/BgCcfJ01+fnxPjJ/fdyNaxfGtnkuYVQCenW3gOBJ6H7znKwyAissFoRGIDulT+AoDPfeM5Xjw+S5xkpL2KIAqdKKVeLRHFKc8dnuH2m8c4cCz/4Pc9h7LvUS573LZ7LN8iel5OjNv3jPH8SzOEUbqwjqLZSXDdmFt3juD7LvvtSY1KiIhsEAokNqCp2Ta+6/DsoWlanZhq2eeGTTWmZtvstyc5MtUgjHscQRSiOM2fs+Rxei4jCDz2bBvi2OkmrU6ekfK23WO874G959zv8OQc39r/Ek/+6BRpljEyUGKmEeJ7eWGucslleKBMlmXKwyAisoEokFiHLle/IfBcnj00TbXsUy35RHGKPXyGW3eOcvDELI1W3LdzS7OUl07OU/JcBms+zU5MrexjdoxwYrrJ0VNNjp1u8m8+t59aJWDnxADbN9f4q787zuR0iyRN8V2X2WY+7TFQ9RmolmiF+TkrD4OIyMaiQGKdOTw5x5cfOcBsMySK80WIB4/P8c57dy8EE1mWEcUpc60WUZTk0xcZ/CA6medw6CPXcUjihJCMG2o1bt8zxtMvTvP4gRPMNWPqFZ8wTCiVPBqtiLLv8sRzJ8myjGrZJ4xT0jQjcPOCXXPNGNd1qZa9hTwM99ym9NIiIhuFFluuMw8/cYQT000AauV87cOJ6SYPP3Fk4ZiZZkSSJIRF2ukkyUjTjEYnod3p/WhEt4qn71FkznQYqAREScq3nzzC80fzUZDAd2iHMY0wxnWhFHhMz3dI0oy5ZpSXHk9S5lsRrU5MUmwnBahXS8rDICKyAWlEYp15/ugslZJH4OcfsHlNCo9nDk7zXx89wNRsm0Mn5giTjFo1oBMmeFleUTPLIO3DiEQpcCl5LkmW0Q5TatWAesWnEyUcPDHH5uEKbpFKsxUmVAKP+VbM5qEKrU7MYDXgzHybyTOthYJdUZwSAeMjVX79rXsVPIiIbFAKJNadDOe8gaIwTJhrhgvbPR0yOmGaV91MU5wilaXvOkRZBj0OJjphSqd40Erg4nsO0/MhWZbhOg5RnOL7Lmma4bkOYZzgug5RklIt+4wMlDh0PCNMMnzPwXHyMuOu67B5qKwgQkRkA9PUxjqz+4YhWmFMFKcLayFOz3fwXIfnDp/BHj7DQLWE7+Z//cdJRpSkeC4EgYe/VJGNFcoW/RcmKWkx7NEOE2oljyhKGawGJGlG4OWFu7IMwihhdKCM57nUqj61sk9eDMShVgnYPFhhckY7NERENjKNSKwzP7nvRqbnO8w1I9qdhCRNSVMYHAxwgMnpFo12RJLkH9blkks7TGmFKWkaLUwx9EuaQquTUK/4pIFHpezTCvMRiNGBEtPzIb7vMjpUYtNQlS2bauwz47w02WConq+b6AqjhKTPi0NFRKS/FEisMzsmBvmFe/csbP88eaaF4+T1K7rTCUmRaMohTxDluw6Ok+G6Tq9nNS6Qka/F6EQJruMQxik7J+pEcZ7qerhe4m2v38Wde7eec78924Z49tA0juMQeC5RkqfOvnXnaJ/PWERE+kmBxDq0uBDUn3z1abaOVnnyR1MkaVrsmsi5LgujEqXAoxx4VMs+Lxzrb4ppx8mfd2igRK3is3m4Spxm3Lpr9KK1Mt58x3am5zrMNkOanYjA9xZSZ4uIyMalQGKdGxuq0GhH1Co+Z+Y7ZMW2B98hnxYovsZJRicMifucSMJx4MaJAXaMDzBUL9FoR9QrAQ/evfuS99sxMcg77919yURbIiKy8SiQWOf2mXG+/vghahWfRivCK+XTCWGUkpFBBp0wISMjSVKanaSv5zMxUuGuV95Ao9EBoFb2rziltUpui4hce7RrY53bMTHI/XftZOfEAGmxi8MBkkXVuBwn36IZ9yCGcAB/0bsir8zpUCt7bN1UvWAxp1Jai4hc3zQisY5crMbGjolB3vfAXiZGK3zleweJzivIVQ5cWuHKpjSqpXzbZppBmmU4FGsvfA/XdSj5LmNDZWYaEfPNELKMZidWSmsRkeucAol14vDkXD6FUfbZNFim0Y748iMHGBkoLZTjnpnvcPO2YZ45dAbPZWHr5EqDCIChepmXbR1kYrTK371wmtlGiOc61KsBgefS7MTMNCLe9vpdnGklHDo2w9hQhXtu26bpChGR65gCiXVivz1JrexTr+T1NZIk48R0k9lmyKtu2kSjHfH0wWkqJZeBakAnjGmHKb0oFu448KrdmxaeO05Shmol0iwj8N2iVLlHvVrizr1bGR8f5OTJ/u4MEemXy1XXFZGro0BinZiabbNpsLxw++hUg0rJI04yHMehXgkYqAYcn2oQeCufylgsyzhnt0e17BMWVUW7eR66uzMAXjg6wzcfe1EXYtlwlhr5+/rjh1QsTmQFtNhynRgbqtBcVLmz1YkJw4RGO+LJH53i2UPTVHyHTpTSCnu7M8MB7OEzzDZCALaN1WmHCb6XJ8LqlvfevrnGn37tGf7VZx7nhwem8F1n4UJ8eFIjFLL+LR756wbotbLPfntyrU9NZMNSILFO7DPjNDsxjXZUZK9MOTXXIYwSzsy1OTHd4sDxecggSnoxoXFWt5T3wRNzZFmG5zlsGa2xc2KA03Md6pWAGzfX+PPvHmT/cycJw5hOlPD80VmSJNOFWDaMqdl2UfPlrKvZwiwiF9LUxjrR3ebZnbvNALIUx/EIPIcoyehE+fSDAz1ZG9HluQ633DjMC8fnOD3XyRdR3nt2EeXhyTk++edPA+A6Dhkw14wYrAUcnWpgdozoQiwbQjfBW3eaDrSFWWSlFEisI4sTNn30048zNlylE6XEcUqwKLlDb8cj8sLlQeDxmpePL5mhcr89SZKmDFZL+fRLBp4L7U6C5zq6EMuG0U3wBvlIhLYwi6ycAol14vyV5I1WhzR1SLMM33cZqPqcnu305bmzNL3kxXRqts1ANSBKUgaqPjONCLKMTpwwPFDShVg2jPNH/rSFWWTlFEisA+evJD9xukmjnZcQdx2HrJMxVyyE7IcoTml1koV1DudfVMeGKsRxypFTDQLfZWy4wsnTLbLMYdeWQd58x3ZdiGXDUKp2kd7SYst14PyV5NPzHcqBR5xAGGdESe8XWC7meS47xusX3YGxz4zjeg7bN9fxPYcwShkeKPG++2/hV++/VRdlEZHrmEYk1oHFOSRmGyFHTjZotOPL3Kt3HIeFrXCQBzaLg4PFw8G+77LzhmFuvXFIAYSIiCiQWA+6K8mTJOPHR2boRKsXRAAsrsN1sa1wi4eDldlSRES6NLWxDnRzSBw8MUeSpiT9rQR+gWRRkkztwBARkauhQGId6E4dNNsRM/NRnihiFcVJdk4Gy31mfHVPQERENixNbawj/VxQuRTHyfNBuI5zNhGVtsKJiMhVUCCxhrq5I545eJrjUy06UUKWZWSrEE+UfIcsgzTLePXuUf7RW1/R/ycVEZFrjgKJNdLNHZEkKcdPt0iL+hqrNSYRxRmOA5Wyx9uXyGYpIiJyJRRI9MH5WSqXKrO9354kSVJ+fGSWZjvCdSHNep/+eimeC5WSj+s6VEveKjyjiIhcq7TYsse6Iw2NdsSmwfJFkzwdPDHL0ZNNWu2IDIji1QkiAHzPJYxTyGB8pKLKnSIismwakeixxVkqgYsmeWp1EhqdkE6Ukq7iGkvHgTDKp1DKgcvJM218f3b1TkBERK4pCiR6bHGWyq6lkjw5wGwjwnHyYaHVCiac4n8OeY2NmTikWtbbQERElkdTGz02NlTJS20vslSSp4z8wzxJVy+IgGIdRpZPb2RZHkzMt6LVOwEREbmmKJDosW6WykY7umSSp3YYE6cXeZA+cxyIkzTftVHyiNbqREREZMNTINFj3SyV9UrA6bkO9UrA/XftvGDXxtRMZ03OzyEflUgz6EQpSZpRLWvnhoiILI8mx/tgcYGri2l3VrcwV9f5syidKOXGzfU1ORcREdn4FEj0wVJ5JIBz2lY5G/YFHM4GFY67ysU9RETkmqFAose6eSRqZX8hj8SXHnkeB4fNI5WFtrXiOvliy4UgAjgxfWHZcBERkSuhNRI9tjiPhOM41CsBc82I2WZ4TltljTJKZuSLLd1iC6jnOoTR2kyziIjIxqdAosemZtvUzsvLEMcpUZyc0zYyEKzmaS3IsrOLLQFwMiolDUyJiMjyKJDosaXySPi+S+CfHYGYme8QximuA6VgddcndEciHMDzwHNdyqq3ISIiy6RAoseWyiMxWAsYqpUW2g5NzlPyPSZGq5R8D2cVY4lKyWN0qJyfU73MyEBp9Z5cRESuORrT7rFuHonFOzTuuXcbcHbXRpxkbB+rcXBynkYrXrViXQ6QZRnD9RKB5xIlKWGUUKuszTSLiIhsfAok+uBieSS6bR//8g94+uA0rU5ywTErkS+eZCFjZneLp+fmiyqjJGN0qELgu7Q6MdWyz8RIlS2baj09DxERuX4okFhlhyfnODw5TxQnOMVWzF5wHfA9h9HBCu0wZr4ZLdTzyDJI0oyS5zA+XGXzSIVa2afZiZdM3y0iInKltEZile23J/E8N18b0aPHdADXdfBcF8+Dm7cP4wcurlvkjCiKdL39jS/jnffuvmz6bhERkSulEYlVNjXbZrAaMNcM80WWvRqRcB3KgUccZ4wMlnnVrlGePzZHK0zYOlrlLa/byZ17twIocBARkZ5Zk0DCGPMwMAF0Uzz+Y2AP8GEgAP6DtfbjxbE/BXwMqAJfsNZ+uGi/HfgUMAR8B/gta+26z6w0NlQhihOOnJwn61XRTQc8ByAlSR2eOzwDZNy2Z4yf3HejAgcREembVZ/aMMY4wC3Abdba2621twMvAf8SeCNwO/CbxphXGGOqwKeBnwf2AncaYx4oHuqzwG9ba28hH93/wCq/lGXZZ8bxPJdK2cf3ezO5Ua/4BL5LGGfEacbtN49xy44RwkTlwUVEpL/WYkTCFF+/YYwZAz4JzAHfstaeBjDGfBH4ReAR4EfW2heK9s8C7zLGPA1UrbWPFY/1GeD3gf+0aq/iPEsV6rrYzo3779rJ1Ew7T1wVr3znRuA5zLdjSp5HJXAX0nBDviZDIxIiItIvaxFIjAIPAf+EfBrj28AXgGOLjjkG3AVsW6L9xku0X7GxsYEl28fHr/5D94WjM3z7qWMMVANu3DpEsxXx7aeO8Y431blp2/CSzzE6Wudf/+njtDrNq36+xVwn3+7pui6bhssMD1So18sAVGslTp1pLes1Xc5Sj/nC0Rm+98NjTE43mRit8ROvvmHJ13+96Ee/X4vUT5enPro89dHaWfVAwlr7PeB73dvGmD8hXwPxh4sOc4CUfOolu4r2KzY1NU+anrvScXx8kJMn567mYQD45mMv4pLhZBmtZpjvoiDjm4+9yIN3717yPo/uP8jUmdZVP9f5gsBluFYiJSWKUzYPlWk0OgA02hH1SrCs13QpS/XT4qqntbLP5NQ8f/bfn7lud4Us9710vVE/XZ766PLUR5fnus5F/4BeqVUPJIwxbwTK1tqHiiYHeBG4YdFhW4Gj5GsnrqZ9TUzNttk0WD6nrVb2mZrNy3OfP+3Raoc8/OQx4nT5WzacomZGEmeMjZQZqZc5Mx/ieQ4z8x0OTc4z34p4xa5RDk/O9f3DfHHVU0BTKyIi14m1yCMxAvw7Y0zFGDMIvA/4ZeA+Y8y4MaYG/ALwdeCvAWOMudkY4wHvAb5mrT0ItI0xbyge81eAr636KyksVair2YkZG6pweHKOLz9ygB8emOLg8Vn+1k7yzf1HVxREQB5EBJ5DKXDZtWWIOM0YGSgxM9/h6YPTAOzdOYLvu3z98UMcnuxvtL5U1dPFwZSIiFybVj2QsNZ+Bfgq8CSwH/i0tfavgN8FHga+D3zeWvu4tbYNvB/4EvA08CzwxeKh3gv8kTHmWWAA+OPVfB2LLVWoq5sx8uEnjnBiOl8HUSsHTM20epI6Is2gU+zSaLQjNg2W8X2X46db3HTDIK/ePcbIYIV6JaBW9tlvT/bgWS/uUsGUiIhcu9Ykj4S19iPAR85r+zzw+SWOfQi4bYn2p8gXZK65HROD3Li5xkNPHKXZjikHLts2VfnG3xzmh89PUa/6pGnGqUabTtTbEl1l3z1nOiFJU87MhWzdVF84ZjVGBvaZcb7++KGF5+um377ntm19fV4REVlbSpHdA3/zzHEefvIotbLPxEiFMEp54cQ8rXZImmVMzbQ5PDnP6dlOz5/7/BLkA9WAuVZ0TttqjAx0t7Uq/baIyPVFKbJX6PDkHF94+HnanZg0gyRJKZc8kjTjxRMN6lWf+Vb/Em62o3M3q2warNDqNGi0o1UfGbhY1VMREbl2aURiBbpbHludfDojTTMa7Zgsy/BdaIcJlaC/sVocpeeszXA9kI4mnQAADTpJREFUh7e9fpdGBkREZFVoRGIFulseq2WfKE4JfAfPdWiHCaXAo1LyWOHmjMty3XxtRHdr6T23bWPHxCB39vdpRUREAAUSK9LNH3HTlgGeOXQGgJLv0GgnOA68fPsg0/PRZR5lZRzHuWjSKxERkX7T1MYKdLc8bp8YZO/OEQLfJU5hsBbwyl2jVCsldk6sLJOYe4m6Xg55ICEiIrJWNCKxAou3PG4bH2CkCCzOX5PwyFPHLvYQl1WvBkRxQhilZMU0ieOA5+a5JAZq+icUEZG1oxGJFViNLY+B7/LKXaOMj1YZqvmUAhfXdXBdl4GKzz/YvblnzyUiInK19OfsCvV7y+MbX30DjXbEQK3EmfmQ2WZIFCcEvsdQrcSb79jet+cWERG5HAUS61it7Cyk277/rp0A5xT/2mfGta1TRETWlKY2+uxvnjm+7PtOjNaVB0JERNY1BRJ99tATR5Z931Lg8eDdu9kxMbiQ/KpboKvRjlalqqeIiMilKJDos5NnWld9H8fJ/zszHy60dZNf1SsBjuOsWlVPERGRS9EaiT44PDnHfnuSQ5PznJkLL3+H82XguQ4jA6WFpm7yq8VWo6qniIjIpWhEoscWT0FMzTRZVoZsByplj/sW7cjoJr9abDWqeoqIiFyKAokeWzwFcbVlwx3A9xy2jFb5lZ+5hTv3bl342T4zTrMTn1Ogq9mJ2WfGe/wKRERErpymNlaoO43R3ZJ58MQsO4sdFu0wueLH2bmlzu03j190S2c3+dXi5+oW6BIREVkrCiRW4PDkHF9+5AAnZ1rMNUKyDOI05cjJBtWyT5Je+v6+C67nsHmowm17Ni/sxLjYds9+J78SERG5WgokVuDhJ47w4vE5mu2zFT7jJKMTdqhXLz0aMVDxKZU8yr6L53kLOzEgnx5RwCAiIhuB1kiswLOHpplrhiRpRpJmRElGBmTAfCu+5H33mXHqFZ9Kyada9hbatRNDREQ2EgUSKzDXjEizPOfD1To21cT3XFphzA2bagvt2okhIiIbiaY2ViDLMhwHsiwv6X01zjRCdozXOdyKeO6lGQarASODJTzP5Z7btvXnhEVERHpMIxIrMFgrEXhXH0QAvGLXKKWSx003DDFQ9ZlrhRw91eT2PWNaHyEiIhuGRiRWYO+uUX5wIOHMXHjVwUSWZQv5JrYUUxuNdsSRU03u7MO5ioiI9INGJFbgzXdsZ8f41Y8evGLXCHGaBxKLaaGliIhsNAokVmDHxCDvvHc3gX/5bnQdGB4occuNw7z7vpcr5bWIiFwTNLWxQjsmBqkEHp0oxYEla2sEnsNN24a4defoOZkrv/74ISAfiWh2YpqdWAstRURkQ9GIRA9kQDlwcJfozZGBEoP1gBs21Xjw7t0LQUQ35XW9EnB6rkO9Elw0o6WIiMh6pRGJHqiWfRqtiOS84Qjfg4nRKlGUcuDY7AX3U8prERHZ6DQi0QOewwVBBOT5JQDyfJfLyFolIiKyzimQ6IHj060l25MUojilHSbs2Ta0ymclIiLSfwokVuj3Pvndy+aQ2DJa4813bF+dExIREVlFWiOxAr/3ye/y0tSl8z68evfYOTs1REREriUKJFbgckFE4MGDd+9epbMRERFZfZra6KMH775prU9BRESkrxRI9Mm73nQTD7xOgYSIiFzbFEj0iYIIERG5HiiQWIHRmndV7SIiItcaBRIr8O9/594LgobRmse//5171+iMREREVpd2bayQggYREbmeaURCRERElk2BhIiIiCybAgkRERFZNgUSIiIismwKJERERGTZFEiIiIjIsimQEBERkWVTICEiIiLLpkBCRERElk2BhIiIiCybAgkRERFZNgUSIiIismwKJERERGTZFEiIiIjIsl2PZcQ9ANd1lvzhxdrlXOqny1MfXRn10+Wpjy5PfXRpi/rH6/VjO1mW9fox17s3Ao+u9UmIiIisgbuBv+zlA16PgUQZuBM4BiRrfC4iIiKrwQNuAP4G6PTyga/HQEJERER6RIstRUREZNkUSIiIiMiyKZAQERGRZVMgISIiIsumQEJERESWTYGEiIiILJsCCREREVk2BRIiIiKybNdjrY0lGWPeA3wYCID/YK39+Bqf0qowxgwB3wV+zlr7ojHmp4CPAVXgC9baDxfH3Q58ChgCvgP8lrU2NsbsBD4LTAAWeK+1dt4YMwJ8DtgNnAR+yVp7fJVf3ooZYz4K/FJx86vW2n+uPrqQMeYPgF8EMuBPrLUfUz8tzRjzvwObrbXv71VfGGNKwJ8A/xPQAt5jrX121V9cDxhjHiZ/3VHR9I+BPSxxfe7Ve2y1XluvGGPeBnwUqAPfsNb+z2v5+6YRCcAYsx34l+R1OG4HftMY84q1Pav+M8a8ljzn+i3F7SrwaeDngb3AncaYB4rDPwv8trX2FsABPlC0fwL4hLX2VuBvgY8U7X8IPGqt3Qt8EviP/X9FvVX8Yv4M8Bry98U+Y8w/RH10DmPMvcBPAv+A/IPsnxhjbkP9dAFjzH3A+xY19aovfgdoFO3/FPhMP19HvxhjHPLr0W3W2tuttbcDL7HE9bnH16sNwxizG/jPwIPkv3N3FK97zX7fFEjkfgr4lrX2tLW2AXyR/K+ra90HgA8BR4vbdwE/sta+YK2Nyd+A7zLG7AKq1trHiuM+U7QHwD3k/bXQXnz/VvKoFuDPgAeK4zeSY8D/Yq0NrbUR8Az5RU59tIi19hHgzUV/TJCPdI6gfjqHMWYT+Qfivypu97IvFtqttd8Bxou/ODcaU3z9hjHmKWPMb3Px63Mvr1cbyTvIRxxeKq5L7waarOHvmwKJ3DbyD42uY8CNa3Quq8Za+xvW2sWVUC/WDxdr3wzMFm/cxe3nPFbx81lgvNevoZ+stX/f/QU0xrycfIojRX10AWttZIz5feBp4CH0XlrK/wn8LjBd3O5lX1wr17BR8vfPO4D7gN8CdnJ176Xl9OtGcjPgGWP+mzHm+8AHWePfNwUSOZd8brfLIf/AuN5crB+utB3O9ptzXvuG7VNjzCuB/wH8r8AB1EdLstZ+lPyCs4N85Eb9VDDG/AZw2Fr70KLmXvbFNXENs9Z+z1r7q9baGWvtKfJ1H3/Ayt5LV9KvG4lPPkrzj4CfAF5Lvp5hzX7fFEjkXiIvr9q1lbPD/deTi/XDxdongWFjjFe038DZfjtSHIcxxgcGgam+nXmfGGPeQP4X0v9mrf1T1EcXMMbcWizowlrbBL4MvAn102LvBn6m+AvyD4C3A79B7/rimriGGWPeWKwj6XKAF7m699Jy3mMbyXHgm9bak9baFvBfyAOLNft9UyCR+yZwnzFm3BhTA34B+Poan9Na+GvAGGNuLt5g7wG+Zq09CLSLD1WAXynaI+BR8oskwK8CXyu+/4viNsXPHy2O3zCMMTuA/0q+Av7/KZrVRxfaDXzSGFMudg/8PPkwvvqpYK39aWvtq4rFg78H/Ddr7a/Ru75YaDfGvBFoW2sP9ft19cEI8O+MMRVjzCD5wtRfZunrcy9/FzeSrwA/a4wZKV73A+RrHdbs902BBGCtPUI+d/kw8H3g89bax9f2rFaftbYNvB/4Evlc97OcXYzzXuCPjDHPAgPAHxftHyRfRf00cDf5Fi3IVwC/zhjz98UxH1qN19Bj/wyoAB8zxny/+Gvy/aiPzmGt/Qvgq8CTwH7gu0Xg9X7UT5fTq774P4By0f7H5B8YG4619iuc+176tLX2r1ji+tzj69WGYa39a+Dfku+4exo4CPwn1vD3zcmy86dJRERERK6MRiRERERk2RRIiIiIyLIpkBAREZFlUyAhIiIiy6ZAQkRERJZNgYSI9JUx5k5jzH9ewf1vMsZ8qZfnJCK9o0BCRPrtlayspsEuzhZzEpF1RnkkROSqGGM+CUxaa3+3uP3LwC9Ya9+xxLE7gL8ChoEvW2t/zRjzNvLkNyXyqoX/zFr7PWPMreS1FSrkqZE/RZ4h0wLbge9Ya3+27y9QRK6KRiRE5Gp9HPi1Ig8/wG8CS05dWGsPk6eEfrQIIl5OXkb7Ldba1xT3/bIxpk5eFO3PrbX7gLeQlznOyGtSPK8gQmR9UiAhIlfFWvt94AXgrcaYveRlh79xhXf/afICQQ8VKcc/R15Z8Gby4kP/3BjzZeCdwO9YazdidUaR64p/+UNERC7wceDXgeeA/8tae6VzpB7wkLW2WyyoO/1x1Fr7VDFi8dPAfcBHjTH7enzeItJjGpEQkeX4IvAa4BeBT1/m2BgIiu8fIi+nfSuAMeYtwA+AqjHm88C7i4JfHwRmgT3n3V9E1hktthSRZTHGfAzYYq1972WOu5m8RPEPrbXvNMa8i7yao0MeJPxTa+2jxTTJp8grFCbAN4F/AYySV37sAK+9itEPEVkFCiRE5KoViyO/A3zIWvvYWp+PiKwdrZEQkatijPlZ4M+AT1hrHzPGGOALFzncLl4PISLXHo1IiIiIyLJpsaWIiIgsmwIJERERWTYFEiIiIrJsCiRERERk2RRIiIiIyLL9/67kcbxxQbjMAAAAAElFTkSuQmCC\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": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1170.8509856870603\n",
      "MSE: 4053505.0083318413\n",
      "RMSE: 2013.3318177418846\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": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8120074937477273"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('flight_rf.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8120074937477273"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\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",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
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       "      <th>Duration_mins</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
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       "      <th>SpiceJet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
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       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
       "0            1            6              6        17       30             4   \n",
       "1            1           12              5         6       20            10   \n",
       "2            1           21              5        19       15            19   \n",
       "3            1           21              5         8        0            21   \n",
       "4            0           24              6        23       55             2   \n",
       "\n",
       "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
       "0           25              10             55          0      0       0   \n",
       "1           20               4              0          0      0       1   \n",
       "2            0              23             45          0      0       0   \n",
       "3            0              13              0          0      0       0   \n",
       "4           45               2             50          0      0       0   \n",
       "\n",
       "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
       "0            1                     0                  0   \n",
       "1            0                     0                  0   \n",
       "2            1                     0                  0   \n",
       "3            0                     0                  1   \n",
       "4            0                     0                  0   \n",
       "\n",
       "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\n",
       "0                                  0         0        0   \n",
       "1                                  0         0        0   \n",
       "2                                  0         0        0   \n",
       "3                                  0         0        0   \n",
       "4                                  0         0        0   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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