flight_price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "78fc367d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a74ef7a",
   "metadata": {},
   "source": [
    "Training dataset - Data_Train.xlsx\n",
    "\n",
    "Test dataset - Test_set.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62da1fe6",
   "metadata": {},
   "source": [
    "# Training Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c12f298",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_excel(r\"Data_Train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86899a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\",None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ee0f3d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46368e12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd3e4251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e09f97c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10683.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9087.064121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4611.359167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79512.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price\n",
       "count  10683.000000\n",
       "mean    9087.064121\n",
       "std     4611.359167\n",
       "min     1759.000000\n",
       "25%     5277.000000\n",
       "50%     8372.000000\n",
       "75%    12373.000000\n",
       "max    79512.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4595dfa",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Null value in training data\n",
    "\n",
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd5bd19f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting null value columns since it has only 1 null value in Route and Total_Stops columns\n",
    "\n",
    "train_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d5b22329",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking again null value\n",
    "\n",
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3103a1c",
   "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",
       "      <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>683</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>1/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → NAG → BOM → COK</td>\n",
       "      <td>14:35</td>\n",
       "      <td>04:25 02 Jun</td>\n",
       "      <td>13h 50m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>Air India</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → GOI → BOM → COK</td>\n",
       "      <td>22:00</td>\n",
       "      <td>19:15 22 May</td>\n",
       "      <td>21h 15m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>10231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>Air India</td>\n",
       "      <td>18/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → HYD → BOM → COK</td>\n",
       "      <td>17:15</td>\n",
       "      <td>19:15 19 May</td>\n",
       "      <td>26h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>12392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → JAI → BOM → COK</td>\n",
       "      <td>05:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>22h 55m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>10368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</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>18:25</td>\n",
       "      <td>21:20</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10594</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → AMD → BOM → COK</td>\n",
       "      <td>23:05</td>\n",
       "      <td>12:35 28 Jun</td>\n",
       "      <td>13h 30m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>12819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10616</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>1/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → JAI → BOM → COK</td>\n",
       "      <td>09:40</td>\n",
       "      <td>12:35 02 Jun</td>\n",
       "      <td>26h 55m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10634</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → JAI → BOM → COK</td>\n",
       "      <td>09:40</td>\n",
       "      <td>12:35 07 Jun</td>\n",
       "      <td>26h 55m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10672</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → AMD → BOM → COK</td>\n",
       "      <td>23:05</td>\n",
       "      <td>19:00 28 Jun</td>\n",
       "      <td>19h 55m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10673</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → AMD → BOM → COK</td>\n",
       "      <td>13:25</td>\n",
       "      <td>04:25 28 May</td>\n",
       "      <td>15h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>16704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Airline Date_of_Journey    Source Destination  \\\n",
       "683    Jet Airways       1/06/2019     Delhi      Cochin   \n",
       "1061     Air India      21/05/2019     Delhi      Cochin   \n",
       "1348     Air India      18/05/2019     Delhi      Cochin   \n",
       "1418   Jet Airways       6/06/2019     Delhi      Cochin   \n",
       "1674        IndiGo      24/03/2019  Banglore   New Delhi   \n",
       "...            ...             ...       ...         ...   \n",
       "10594  Jet Airways      27/06/2019     Delhi      Cochin   \n",
       "10616  Jet Airways       1/06/2019     Delhi      Cochin   \n",
       "10634  Jet Airways       6/06/2019     Delhi      Cochin   \n",
       "10672  Jet Airways      27/06/2019     Delhi      Cochin   \n",
       "10673  Jet Airways      27/05/2019     Delhi      Cochin   \n",
       "\n",
       "                       Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
       "683    DEL → NAG → BOM → COK    14:35  04:25 02 Jun  13h 50m     2 stops   \n",
       "1061   DEL → GOI → BOM → COK    22:00  19:15 22 May  21h 15m     2 stops   \n",
       "1348   DEL → HYD → BOM → COK    17:15  19:15 19 May      26h     2 stops   \n",
       "1418   DEL → JAI → BOM → COK    05:30  04:25 07 Jun  22h 55m     2 stops   \n",
       "1674               BLR → DEL    18:25         21:20   2h 55m    non-stop   \n",
       "...                      ...      ...           ...      ...         ...   \n",
       "10594  DEL → AMD → BOM → COK    23:05  12:35 28 Jun  13h 30m     2 stops   \n",
       "10616  DEL → JAI → BOM → COK    09:40  12:35 02 Jun  26h 55m     2 stops   \n",
       "10634  DEL → JAI → BOM → COK    09:40  12:35 07 Jun  26h 55m     2 stops   \n",
       "10672  DEL → AMD → BOM → COK    23:05  19:00 28 Jun  19h 55m     2 stops   \n",
       "10673  DEL → AMD → BOM → COK    13:25  04:25 28 May      15h     2 stops   \n",
       "\n",
       "                   Additional_Info  Price  \n",
       "683                        No info  13376  \n",
       "1061                       No info  10231  \n",
       "1348                       No info  12392  \n",
       "1418   In-flight meal not included  10368  \n",
       "1674                       No info   7303  \n",
       "...                            ...    ...  \n",
       "10594                      No info  12819  \n",
       "10616                      No info  13014  \n",
       "10634  In-flight meal not included  11733  \n",
       "10672  In-flight meal not included  11150  \n",
       "10673                      No info  16704  \n",
       "\n",
       "[220 rows x 11 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if there are any Duplicate values\n",
    "\n",
    "train_data[train_data.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "210299ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop duplicates value\n",
    "\n",
    "train_data.drop_duplicates(keep='first',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca0da3a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3df467d5",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1131f840",
   "metadata": {},
   "source": [
    "## Handling Numerical value\n",
    "\n",
    "1. Date_of_Journey\n",
    "2. Dep_Time\n",
    "3. Arrival_Time\n",
    "4. Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c75fbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting day from Date of Journey and formatting in (D/M/Y) format, after that appending in train_data\n",
    "\n",
    "train_data['Journey_day'] = pd.to_datetime(train_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2c489281",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting month from Date of Journey and formatting in (D/M/Y) format, after that appending in train_data\n",
    "\n",
    "train_data['Journey_month'] = pd.to_datetime(train_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d2ef581d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </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",
       "\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",
       "\n",
       "   Journey_day  Journey_month  \n",
       "0           24              3  \n",
       "1            1              5  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36d05539",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now Date_of_Journey is not important so delete it from training data as I extracted useful information from it.\n",
    "\n",
    "train_data.drop([\"Date_of_Journey\"],axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b4a67b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Minute and Hour from Dep_Time, after that appending in train_data\n",
    "# And Deleting Dep_Time \n",
    "\n",
    "train_data[\"Dep_hour\"] = pd.to_datetime(train_data['Dep_Time']).dt.hour\n",
    "train_data[\"Dep_min\"] = pd.to_datetime(train_data['Dep_Time']).dt.minute\n",
    "train_data.drop([\"Dep_Time\"],axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "79ba82bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </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",
       "\n",
       "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0   2h 50m    non-stop         No info   3897           24              3   \n",
       "1   7h 25m     2 stops         No info   7662            1              5   \n",
       "\n",
       "   Dep_hour  Dep_min  \n",
       "0        22       20  \n",
       "1         5       50  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "19b9a19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Minute and Hour from Arrival_Time, after that appending in train_data\n",
    "# And Deleting Arrival_Time \n",
    "\n",
    "train_data[\"Arrival_hour\"] = pd.to_datetime(train_data['Arrival_Time']).dt.hour\n",
    "train_data[\"Arrival_min\"] = pd.to_datetime(train_data['Arrival_Time']).dt.minute\n",
    "\n",
    "train_data.drop([\"Arrival_Time\"],axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c6624ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
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       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
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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",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d1b7ab4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formating the Duration to correct format (ex- 2h 10m, 0h 15m, 5h 0m)\n",
    "\n",
    "duration = list(train_data['Duration']) # convert to list \n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split())!=2:\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip()+' 0m'\n",
    "        else:\n",
    "            duration[i]= \"0h \"+duration[i]\n",
    "            \n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep=\"h\")[0]))\n",
    "    duration_mins.append(int(duration[i].split(sep=\"m\")[0].split()[-1]))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b8bf3010",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Duration_hours']= duration_hours\n",
    "train_data['Duration_mins']= duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3a61463",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now Duration is not important so delete it from training data as I extracted useful information from it.\n",
    "\n",
    "train_data.drop([\"Duration\"],axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6df39833",
   "metadata": {},
   "outputs": [
    {
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       "  </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",
       "  </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",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "\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  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4e8a05",
   "metadata": {},
   "source": [
    "## Handling Categorical value\n",
    "\n",
    "    Nominal Categorical data\n",
    "        1. Airline\n",
    "        2. Source\n",
    "        3. Destination\n",
    "      \n",
    "    Ordinal Categorical data\n",
    "        1. Total_stops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7deb3bf6",
   "metadata": {},
   "source": [
    "### Airline Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cdfb1eb6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3700\n",
       "IndiGo                               2043\n",
       "Air India                            1694\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              815\n",
       "Vistara                               478\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking value count of Airline column\n",
    "\n",
    "train_data['Airline'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9e764056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.title('Count of flights with different Airlines')\n",
    "ax=sns.countplot(x = 'Airline', data =train_data)\n",
    "plt.xlabel('Airline')\n",
    "plt.ylabel('Count of flights')\n",
    "plt.xticks(rotation = 90)\n",
    "for p in ax.patches:\n",
    "    ax.annotate(int(p.get_height()), (p.get_x()+0.25, p.get_height()+1), va='bottom',\n",
    "                    color= 'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08865530",
   "metadata": {},
   "source": [
    "Jet Airways Business, Vistara Premium economy, Trujet have actually almost negligible flights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c2f21011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HP/iBrrrqqoCiQYdwOKwzzjhD69ev7yw788wzqU0KAMBRIrEIIOv11kw92tacUP+Jr+7Zr1D9IZqpD5DuI3ZHo61a8bO1nc+/trteIUbsBpCFXnrppS6JK0l64okntGPHDs2YMSOgqCB5fSw+99xzXcqeffZZNTY2klwEAOAokFgEkPW8EaA3a1LRiM6y8eE8ScPVHm3ud/lJRcMlSW827Ogsq63zRo0koZV6kUhEW7c9r4kTR6uwqEBSgaJth7fThInHSpIOvPGq9uw5IIntACA7rFmzJm55RUWFrr766oENBl3QxyIAAOlBYhHAoDCpaIS+8KnUNVO/7V6aqafTxImjVfr59/c7X8XKvwxANACQGqWlpT1qLHaUI1iM2A0AQHoweAsAAACQAjNmzNA555zTpey9730vzaAzQMeI3QUFBZKkgoICRuzOQNu2bQs6BABAkqixiJwXr3++eGpqatTe2pTSAVde3ndQeW/U9OjbL55c7GcumW0TbW1KaS3D2r0HtbeRbQMASM43v/lNLViwQNFoVKFQSFdeeWXQIcFXUlKiyspK1dbWKhwOa/78+UGHhBhr167VihUrdPnll7NtACCLkFhEzotEInrxhU2aNmZon/NNGSFJQ+TaWvucLxnTRg+RFFVzbd9XZ3ftb5GUe/3MRSIRbd1SrQlj+942446VpHxF21pS9trjx+ZLatP+vVv7nO/1htzcNonqbWCdtrbmhJo576k9oPq6JgbWAZA1hg4dqn/7t3/TihUr9MUvflFDh/b9G4aBEwqFtHDhQq1YsUIXXHCBQqFQ0CHB19LSoltuuUWSdPPNN2vu3LnsOwCQJUgsApKmjRmqK847LugwenX9Y68FHUJgJowdqos+Pj7oMHq15sG9QYeQ0SKRiLZs3ayi8cM7y8aMNUkFaktgYJ3C8V6TtX2NL3WW1e09JIlkLoDMU1VVpfvuu091dXUyM91222164IEHtGjRIo5ZGSAajeq3v/2tJOm3v/2tPvaxj5FczBDXXntt5+A60WhU1113na666qqAowIAJILEIgAgrYrGD9eiz7wzZeu77+4tKVsXkO3q6+tVWFgYdBjwRSIR1TU0qPD4aZ1ldbtfVSQSIbGYAdauXav9+/dLkhobG1VZWamFCxcGGxT00ksv9Rj06IknntCOHTvonzSD8HsDoDcM3gIAAPpUX18fdAiIo7q6WkuWLNGmTZuCDgUxCqdM1sKvfaXzVjhlctAhQVJDQ4MqKirU1NQkSWpqatLq1avV2NgYcGRYs2ZN3PKKiooBjgS94fcGQF+osYiskuhgHsmoqalRe0tzRjc33rW/WXkHExtIJBn0UwegP9XV1SorK9OPfvQjzZo1K+hw4ItGoyovL5cklZeXa9WqVTTpBPqwbt26zqa2Hdrb2xWJRHTBBRcEFBUkqbS0tEeNxY5yBC8ajeo73/mOJOk73/mO7rnnHn5vAHRBYhFZJRKJ6MXnN2rqqNT9mE0eJmlYnlw0dYOypNrUY/MkNalp9+aUrfOVN7yTaxKLAHpD8ipz0aQTSE5xcbFWr17dpczMVFxcHExA6DRjxgydc845XZKL733ve2kGnSF+85vf6M0335QkvfHGG/rtb3+rT37ykwFHBSCTkFhE1pk6KqTl54wIOoys98P1B4MOAVks0drDNTU1am07lNJ+Eev2HtT+hsRq8FIr9+iQvMpMvTXpPPfccxUOhwOODshM4XBYZ5xxRpfk1Zlnnsk+kyG+8Y1vaMGCBZ2Pv/71rwcXDDo1NDTo9ttv71K2atUqnX/++ew7ADqRWASQEulqpt7W0pLRIy+/vq9F+97MvWbqkUhEL2yp1tjCIX3Od8xoScpXW7QlZa8dHpcvqVWv1/edrGyo92ohZ/LnmMlIXmUumnQCyWtoaNBzzz3XpezZZ59VY2Mjx7QM8PDDDys/P19tbW3Kz8/XI488woWsDPCb3/xGzrkuZc453XffffrCF74QUFQAMg2JRQApEYlEtPWFjSoak7p1hkdIGiG1R5tTt9IU897v22qo3Ziyddbt9+4zPSE2tnCIPlqSuaMDPryWAUeOBsmrzFVcXKyKigq1th7uwiMvL48mnUAfVqxYoebmrucTzc3N+vnPf65vfetbAUUF6fCFrLa2NklSW1sbF7IyxI4dO+KW79y5c2ADAZDRSCwCSJmiMdKniul/7WjdG4n2PxOQZiSvMlc4HFZpaanuuOMOtbS0aOjQoVq6dCl/wIE+1NfXq2D4cE2bfrjfvl07dzDqfQbgQlbm+sIXvqC//e1vPco///nPBxANgEyVF3QAAAAg84TDYS1ZsqRL2YUXXkjyKkN8/OMf7/wjHo1G9bGPfSzgiIDMNmTIEE2bPkNXfPf7nbdp02doyJC+u/RA+hUXF/cYGIwLWZlhxowZOvvss7uUnXPOOQysA6ALEosAACCuzZs39/kYwXnwwQc7/4iHQiE99NBDAUcEAEemoxZ2QUGBJKmgoIBa2Bnkv/7rv5SX56UN8vLy9M1vfjPgiNAdNa8RNJpCAwCAHl566SU9+eSTXcrWr1+vHTt2UFMhYB39kbW0eIMitbS00B8Zcl68QeT27dvXObL9oUOHJElfuuhwTexmf3CqCy64QGPGjNG4ceN6rDfTB1MbLEpKSlRZWana2lqFw2HNnz8/6JDgGzp0qL74xS9qxYoVuuyyyzR06NCgQ0KM6upqlZWV6Uc/+pFmzZoVdDjIUSQWAWCQS9eI3S2trRk9QEpDfaveOpB7I3anypo1a+KWV1RU6Oqrrx7YYNAF/ZEBPUUiEb2w7UWNPm7y4cJhwzV8wnBJ0vA4y4yMmW6W9NpbB7s8f+C1VyVl/mBqg0EoFNKyZctUVlamsrKyHk2jEaySkhK9613v0sknnxx0KIgRjUZVXl4uSSovL9eqVavYdxAIEosAMMhFIhG98MJGjU5hRabhI70/adG2ltStNMVGj5Gkt/XantSN2H2g0bvPhT+ZpaWlWr9+fdxyBIuBdTJL7MWbmpoatbZHdf+Pf9r5fP3u3TqQF9Ly5ctz5sJEKiV6caympkZOLqWv7eRUU5PYBSq27dGbPXu27rrrLhUWFgYdCuIgqZh51q5d21kru7GxUZWVlVq4cGGwQSEnkVgEgBwwOiz904eDjiL7/enRoCMYOM6l9g86UodRoTOLV1Num0YfN0nDJ4zXcEktMTVKR02aJEl6Yds2SblxYSKVIpGINm3dpuHjJ/Y949giDZN0qLUtZa89rMh7zZrGN/qc79DePZLYtqlAUhFITEe3KE1+lw5NTU10i4LAkFgEAAA9rFy5Mm75bbfdpmuvvXaAo0F3H//4xzu3EaNCB2/0cZP0T//2+T7n+dMt8fcp9G/4+Ik66V/7/nyDtP2XbFsAA4tuUZBJSCwCAIAeZsyYob/97W8aMWK4ps2Ypl07dungwUM64YQTgg4N8kaFNjNJkpnpoYceovkTgF5VVVXpzjvvDDqMQeHCCy8c1LVTY7sfiB0AKVbHYEd0ARAcukVBJiGxiKyzp+EtrXsxc/t1yxZ7Glo0fUrQUQDIVIsWLdJ9992naTOm6crvfkPXffsGbdm8VZ/4xCeCDi3nNTQ0aPXq1Wpr85p8trW16Y477qD5E4BeRSIRvfLaHg05pufI10hc61v7FIlEBnUyLRKJ6MUXazRt6gwdMzKsY0bG/1158cUaSXQBEJSOblE6mkMXFBTQLQoCQ2IRAAD0EA6HNXFi1z7NJk6cyAlrBli3bl2XGgqS1NraSvMnAH0aN/1UTSv5etBhZLVda38QdAgDYtrUGbqirO9uT64v/+YARYPelJSUqLKyUrW1tQqHw5o/f37QISFHkVhE1pk49hidO3NE0GFkvWfqDwYdAoAMV1RUpNb2li6PEYzYpmmHDh1Se3t7l+fb29v1+OOPd47kTfM0AAAGt1AopGXLlqmsrExlZWUKhUJBh4QclbbEopndLunjkvY652b5ZWMl/UrSdEk7JX3KOdfoP3elpEskRSV9xTn3e7/8TEmrJQ2XVCXpq845Z2bDJK2RdKakfZI+7Zzbma73AwBArunow6+3xxg4kUhEL27frmkzjlfe0Hy949R3xZ2vOdqqXTtelkTzNAAAjkbsRb1M9q53vUsVFRVBh9EnLngObumssbha0v/KS/51uELSY865683sCv/xN8zsFEmLJZ0q6ThJfzSzk51zUUk3SbpU0pPyEotzJT0sLwnZ6Jw7ycwWS7pB0qfT+H4A9KNu39t69oW8oMPIenX72jV2UtBRINdVVVWppqZG7a5d1337Br28Y5fyLE9VVVWcGAZk2ozj9Y3vXtXvfDd8+zsDEA2AbNO875WcacqbLs37XpGKZgYdBgZIJBLRi89v19RR04IOpV9RZe4YBK+8sUsSFzwHs7QlFp1zfzKz6d2KSySd609XSFon6Rt++T3OuWZJO8xsu6SzzGynpFHOufWSZGZrJC2Ql1gskXS1v677JP2vmZlzzqXnHQEAkDsikYii7W2aNG2imtuaNHHqeNXu2jPoO63PBPFqSHhJXpdQ0nDXjpeVZ6bly5d3Kae2AJC7Mn2k2K1bt6q5ubnz8bBhw/SOd7wjwIh6UTQz4z9LpNbUUdO07P9dGXQYWe1HT10XdAhIs4HuY3GCc65WkpxztWY23i+fLK9GYofdflmrP929vGOZV/x1tZnZAUnjJNV3f1Ezu1RerUdNm5b5VxuAbFU0bqTOeBd9exyt7XuiQYcASJImTZuoL1xR2vn4tuszu5nNYBGJRLTlxW0qnHK46vLo47xTpjdbmvpdPjx5giSp/tCbnWX1u2slUVsAyFXz5s3L2P3/ueee0xVXXNGlrLm5WRdeeKFOO+20gKICACQqUwZviddpk+ujvK9lehY6d6ukWyVpzpw51GgEAAAZrXDKJC38jy+kbH3333hbytYFAKl00003xS1fsWKFbrnllgGOBgCQrIFOLL5uZpP82oqTJO31y3dLmhoz3xRJr/nlU+KUxy6z28zyJY2W1JDO4AEgWzXse1tbNwcdRfZr2CcdNzHoKAAgM6VjoIOamhodamvT9l+uTOl6U+nQ3lrVNNT16H7gaORS1wWXX365vv71r/co/9KXvhRANACAZA10YrFSUqmk6/37tTHlvzSz/5E3eMtMSU8756Jm9qaZnS3pKUkXSfpZt3Wtl7RI0uP0rwgAAAAEIxKJaOOWrcobN77/mRM1aqwk6VBbBncTMna8DknaVNeYktW17/PqXuRKYvG0007T8ccfr5dffrmzbPr06TSDRkbY01CrddsfDzqMrLanoVbTpxwfdBhIo7QlFs3sbnkDtRSa2W5JV8lLKN5rZpdI2iXpk5LknNtsZvdKel5Sm6TL/RGhJekyeSNMD5c3aMvDfvkqSb/wB3ppkDeqNAAgjrHjRuodpwYdRfZ7/dWgIwAQpN4G1mmORvWnW/quUbf/tVodCu2NW6ttMNVOyxs3XsM+/pmgw8hqzQ/eHXQIA+4nP/mJFixY0Pn4xz/+cWCxAACSk85RoXs7ozivl/mvkXRNnPINkmbFKW+Sn5gEAABHLzZpUlNTo2h7W5cBW2p37dHevPrOxMhgSoYAiYhEItq8datGTDxcIy+vcKyGS2qOtvW57PAJRZKkHQe61mo7uCe3aqcB8QwfPlwLFizQAw88oIULF2r48OFBh5QTertY0t7udH35N/tcdtcrLykvz3pcLBls5wYTx07SuSd9MOgwstrf9j0VdAhIs0wZvAVI2CtvRPXD9QeDDiPrvfJGVDODDgJARvFGI35BhZPHavSkYyRJzdHDoxCPnTxGklR/8HXVv+p1azyY/jwAiRgxcbxOvWhJyta3ec1dKVsXkM0uu+wynXnmmTrrrLOCDiVnRCIRbdu2XZMnzegsmzh+uiSprbXvXsaOm+gt8/abh7speLV2hyTODYBcQ2IRWaW4uDjl66ypqVF7y0FNGzMs5etOlV37m5U3dIROPPHElK1zptLzeQLIboWTx2rhl+f2O9/9P3tkAKIZXBId2KKmpkat7dGUjuRcv7tWB/LiN8PtbrDVNgGQPUgqDrzJk2bo3y/9XkrW9b+3/ndK1gMgu5BYRFaZN29eyv/sLF++XM2123TFeceldL2pdP1jr2nYpBP1wx/+MOhQAABHyGtGu0UjJo7rc768wlEaJunNlqY+50vGsPFhSdKOA3V9zndwzz5J1DbBkXurvk7NGzcEHUZWa62vk4rCQYcBAEBCSCwCSJm6/dK9kQwetTFL1O2Xxk4KOgoA6TBi4jidsvTjQYfRq+dXPxh0CAAAAMgiJBYBpES6mqm3tRzUhHFDU77uVHl9X4vyU9xMfewkmqkDALLTMYVFGvbuOUGHkdWad70YdAgAfK+8sUs/euq6oMPIaq+8sUszdVLQYSCNSCwCSIl0NVPfv3erLvr4+P5nDsiaB/dqzHiaqSP79DYSZGu0JaH+E+t3N+hA6K1BPxokckMy/V82tbWldMCVg3v2qqa+gf4vAaRMMse0aNSlrG/EV2t3KLS350jRvcn0YxoX+lNjpk7isxzkSCwCknbtb9H1j70WdBi92rW/RTNztGns6w0tWvPg3qDD6NXrDS0ak7l5T6BXkUhEz2/brNETj+ksKyjKV4Hy1dzWf99+x04cIUl69Y2XO8sO7HlLEv3zIftEIhFVb92ioUWFfc8YHq18SS3R1HX7kV80Tm2StjbU9zlfS533PPsXgP5EIhFt3bq9c4Tn3hSOPV6S1NbW9wjQiZpQ5L3egca2fufds3enpMw+pqWj4gQwGJFYRM5L9OpJTU2N2lubdPy4ESl77Zf3HVTekIJ+m9HOzNGmsclsm2hrkyaNT922qd17UKEEts2Y8bm5bTA4jJ54jD7w+fekbH1/XvlcytYFDLShRYWa8MmFQYfRq9d/fX/QIQDIIhPHT9fnFl8VdBi9uuOe7wQdAoAUIbGInJfolajly5erpW6Hvvkvp6Tsta/93fMaWjSDZrS9SGbbbNu6ObUvbtKJJ9LEGchViTYjS4bXjLY1owdIObhnn2rq30i4GVuiMr25GwAA2aq+vl6Fhf3UuAfSiMQigKwXr8ZgTU2Nom3NmjxxTL/Lv7pnv0L5w7rUTjx27OCqiXigUfrTo0FHkf0ONErHTQw6CgyESCSijVuel8Yd0//MiRo1RNIQvdlyKHXrTLWxI/S2pI11u1K3zn2pbSKfrqRvS2trRtcKbNlbr5rGAyR9gUEmXce0tjaX0bUC9+zdqfqGxPtjTEQuHs+qq6tVVlamH/3oR5o1a1bQ4SBHkVgEkPXi1Wz0ajG+kOAabFDXTkzXiN0trQc1tnBIytedKg31rRo6JLUjdh83cXAlnNGPcccov4TRbY9W29oNKV2fl/R9QTZubOpWOspLIKey78SUGxfWQUnVda+nbJVuX4OkzO7jDBjsIpGItmzZriK/v8NUGHPsNElSW2tq+k5Mh8Kw93737W1NyfrqGrw+n3PpeBaNRlVeXi5JKi8v16pVqxQKhQKOCrmIxCKAQSk2+VNTU6NDBw9q2LDDSbDm5lYNH+ElnU4efdygThala8Tu1+u36KMlmdvs4uG19ZpQOHgTxr1JZiTI5ramlPaLeKD2LTXV1TC6LdLOxo1V/sc/EnQYWa/twd8HHQIASUVjj9cn530r6DCy2q+rvh90CANu7dq12r9/vySpsbFRlZWVWrgwc/sKxuBFYhHAoBSbTKuqqtJ9992n2tpatbe3Ky8vT5OOO06LFi0isYFBJxKJaPPWao0YP7TP+fLGSsOVr5Zo/yNAJ2r4+HxJbdrRuLXP+Q7ubZGUW7UKAAAAUqWhoUEVFRVqavLO45qamrR69Wqde+65CofDAUeHXENiEcCg15Fk7KjJRU0pDHYjxg/VOz8zKegwerXl7tqgQwAAAMha69atU7Rb9x3t7e2KRCK64IILAooKuYrEIpCEXfsO6trfPZ/S9Z1UlLLVoR/paBIMAAAOa9+3V80P3h10GFmtfd9eqYgaRwB6V1xcrIqKCrW2Hu6jMi8vb1B374TMRWIRSFBvIw+71mYdP35Uv8u/vPcN2ZCuIw+fVMRAEMheDfWtenhtfdBh9KqhvlUTMrcLSGSBt+sblfePmqDDyHrt9Y1S0bSgw8AASNdgYYfa2jR8fObWwj60t1bD8/NTN1hYUZjzwxxTV1+rZ6v/GHQYWa2uvlbjxufOb004HFZpaWlnc+iCggItXbqUZtAIBIlFIEG9jTzc2rBb//3p9/a7/Pd+9YSGjJ2ScwNJYHBK9A9PTU2NWtsOqWj8iJS9dt3egxqSP7zfP3ATCkncA4PVW/X7FNq4Oegwsl60fp9UNCFl60vXYGE1jW/opH/9fErXm0rbf7lSJ4ZHcY4HYECVlJSosrJStbW1CofDmj9/ftAhIUeRWAQAJC3RP4/Lly/XvsaXtOgz70zZa9939xaNC58wKP7AJTqCczJqamrU1NaS0f0YHtzbopqGxEaPTkaq+08dWRhW/mkpqoGUw9p2NgYdAgBktKLCSTpj9vlBh5HVal55MugQBlwoFNKyZctUVlamsrIyhUKhoENCjiKxCByll+ve0Pd+9URC8500dgACApA1IpGIqrds1JBxKVzpKCkkqbmtOYUrTa3QWKlVb2tL3caUrbN1n3dPP6q54ZjCccp/96lBh5H12nbtDjqEhBzau0fbf7ky6DB6dWjvHincf7c4AJBqs2fP1l133aXCQvr/QXBILAJHIbaZZU1NjVxbi6ZPOpwh2Fm7T5Y/VCeeeKJOGkuzTAA9DRknFZZY0GFkvfq1LugQAKRBoudO+/bt0/79+7uUHTp0SE7S0GHD+ly2pblZJmn48OE9nhszZozGjevn6k94FOd4OCp1DS/r11XfDzqMrFbX8LLGjT8p6DACQVIRQSOxCByF2Oagy5cv1/ZtW7rOYKYTTzxxUDTZBI5U3d5Duu/uLf3PmMT6xtEvNZDT3L4GtT34+6DDyHpuX0NK+1hMh6Ppt3H58uXa39SiC5df2ed8d/7wOo0pGMr5GgKRrkGP2tqcJo6fnvJ1p8qevTuVn28pG/Ro3PiTSPDnkJtuukkvvfRS0GEMCieccIIuu+yyo1oHiUUgRYqLi1VTU6Odexo1Y/J47Xh1ryw0hB845LTeRlNva2vWxEmj+11+T+0B5ed3HU19XHhw1f59s/5t6R9BR5H93qyXVJTile57S21rN6R4pTlo31sp3Tbp+hP+9sGDsiFDUr7uVHGtrRo5YkTqRh6WpKIJg+p4CmSjdA16tHXr9pSuMx2ogAEMDiQWgRSZN2+eIpGIom836ruXL9a3f36PQiPD9PeFnNbbaOoH3nhVpZ9/f7/LV6z8i0aPmsxJJwZcupJXTW2tGjExlZ1qptbBPftUkD8kxcmr1H6e6fgTnuhASt42bNOIieNT9toH9+xVQX5+Qp95qgcoygWvv7JLd/7wun7nGTMzN5tQYnBK9JhbU1OjaNRp8qQZKXndV2t3KBRKrBbi6DA1DHHkjraGHVKLxCIAAAEqsJHSzqCjyH4FKe6mMl01SDZvTV23AOmSizVIkhnpfvPWrSl//Vz8zAdCbNIiXh+MkteH4jtmkuDA4JLMMW3bttTWbOR4BuQeEotAiu14da++/fN7tOPVvTrpZDqCA9C79NWKO6gR44emfN2pcnBviwryU92kM/ObyB/NIBStra1qa2uTJLW3t/e6bF5eniQpPz9fQ7o1601oEIrRRRn/OQapt+4dmqNRjTluUp/L7n+tVsNCoZ7f+9FhPvM0SccFAmAwiXfsif0Niv3tidXxG9P9d+XkY0nSA7nInMutURTnzJnjNmygvySkR1VVlX72s5+pvb1dZqavfOUrnNAC3Sxfvlxbtz2viRMT6GNxzwG94+RTuPKdhOXLl2tH41a98zN9JzmCtOXuWs0Iv4PteoQeeeQR3XjjjT3K/+M//kNz584NIKLctnz5cr321pv6p3/7fJ/z/emWlTr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eKe/HtFregfF8865sfcA5d6CXGDqaQk+UdJ6Z9VcF96+S/sfMviLvgNsWZ57HnHMHnHNN/ns5XtLZkk6R9Fcz+7ukUr/8DUlNklaa2QXyDgwdr7PavCuwR9W04QjkwnYPnHNuo7wD5GckVaXhJdY65w455+rlnTR1v4r8YUkX+d/Hp+QlbLtv5w9Kus9fh2K22xRJvzezanknuKfGLFPpnDvUx+PY17/Cf/118n5wp/nPxX5HnpH0OTO7WtJs59yb/b3xDHMk+0VfnnbO7XbOtcv7ozZd3j66wzn3ovN+ke/sZdm+tlvOcc69JelMeX966iT9ysyW+k/fHXN/TuxyZnasvBPv+/31NDnnDiqxY+eRHl8Rh5n93LyaFc/IS2L9QpKcc1vkXdg72bwaF/Pk/cF4Q97x7sO9rPLXzrnoAIQ+WB3N8amTmc32ayTUmNfy4B3+Orb5s1Qopkk74opt+rhYMbUFfYmcZ46W9GvzamzdqK6/GbG/WSbpWjPbKOmPkibLSyBwnpPa85y7/HW9T94fdkmKyktgSNJ58n7TnvHnO0/SCf5zLZIe8aerJf2fc67Vn57ey+v1ZodzrtrfzzfL+9/j+lhXb9vhfHkJwYOS99mb2Wh5/6/+z1+2+77eUePqbzGvFffY7z8Xcc696Zyrk5dY/F3MZ9Cx/EpJX5B30ePT8hIaR3Ku1tv5XiLbt8d/nV5e42j0d0yQ0n9c6K2lTMT/foySdJ1f9rJz7kl/uq9zp2S/j315xjlX67yWLDU6XDO1r+92vH38fEk3d+QJ/M+kv9+x/r7bj0sa5+8jkvRwzD4cUtf9e7r/GfxC0oXm9eV5jrxa5EFI9txqUJ4r53ofOybpOufcLV0KezZViH1unLwf6Vlm5uR90Z2ZfT3O7G93TDjnXjWvqvtceTX7xkr6lLzM9Ztmdq68nfQc59xB85omdzRfXinvStMWeTXd5JzbZmZnyvszcZ2ZPeqc+25vcTvn3vLX+X55Ca02HW4KXxAz3/Vm9pC/3ifN7Hx5icFYzTHTUXnfI5N3sO3xQ2VeNe3z5B3k/13SB51zXzRvEIGPSfq7mZ3unNvXW/wpljPbPQNUSiqXd5VsXEx57PdPivkOJsH189jkXR37fR/rsDjLSd4VtP9xzlX62+jqmOfe7jZv98ex6/6E85u5dRZ63/vY78ifzOyf5O0LvzCzH7r09XOXUke6X/Qj3vFFir+duutru+Uk/0RnnaR1/h/I0o6nYmfrtpj1srq4x85E5unr+IouNsurmS5Jcs5dbl5Tsg3qvf+2ufL+CFWb1zJ3hLyLeA/FmTfR/RDxHc3xabO8fhUj/oXC081rujZcve9z6N0D8i6EnyGvNt2zHc3oJCneeWacdXxP3vZY6B+j1sU8F7uvLJFXI+xM51yrme30H0uc56TyPGeJc25Dt7KmmD/sJqnCOXdlnGVb/USD5NUE6+gCpN3i93vW1/aJ3c/bYx63K/5/57jbwbx+VRM5NsTqeK3Y40tfx4dEYo3Iq+zxC3m1zS5XkudqfZ3v9bd9+/mvk0oPqNsxofsMA3Bc6O19FXck9yXJT4TFrquvc6dkv4+d323zTgpiBxU5ku92vH083nGlv9+xRL/bHeuN3Ye7798dy98hL5neJC+5F69C1ECI3ZaJHPsH5blyrtdY/L2ki+1wX4CTzWy8pDflVSuPZ5GkNc65451z051zU+U1N35/Aq+3XtLX5CWY/izvatyf/edGS2r0D7jvlFcDUJLknHtK3tWdf5V/9cXMjpN00Dl3p7wTmjP6emH/R/X/ybs6IXlVks/0pz8RM9+J/lWRG+T9kel31C/fk5LeZ4f76hphZif7n+1o51yV/95Pj3mdp5xz35ZU77+/gZIz2z0D3C7pu/6fqFg75cfunwDEG1Sor+0hSSXm9TszTt4J/TPdnv+9pMvMbIj/Oieb2chu8zwm6VP+OmRmY/3y0Tr8J75UR+b3kr7s//jKzN4TbyYzO17SXufcbfKaqWT6No11NPtFMrZImmGH+3/p7Up7KrbboGFm7zCz2Cugp8ur6SB5tRY67tfHLue8Wm+7zWyBv55hZjZCvR87Yx3J8RWHPS6pwMwuiykb4d//Sd4fGZnZyfJqDWyVtz983t8Hp8s7nn7Y32ZIv0SPT9dJKjez2IF4Ogb+2CJpesc5lLxm7f8n9Mp5NbLXyTvP6FEzqZfzzO7HodjfjKV9vNxoeb/Treb1b3d8zHOc5wzcec5jkhZ1/O6Y1z/b8f0s05ud6n/7JKq37fCovN/DER3xOq+lUaMd7mcykX29t2N/ov5FXqupNnlNZI/kXK3X870Etm+v/3VSqb9jgjSgx4VkJXJ+laidOvwfv0Rec+KjiSvePv6opC92JO3948qR/I7FfrfPldcv+BuJBuece03Sa5K+Ja9JcibYqf6PLYPyXDknE4v+TtDsnHtUXnv89ebV5LhPXr8Q++Q16d1kPTvO/Iy8tv2xfqOYUd368Gd5fRFsl1ftdawOJ5gekZRvXnXq78lL1MW6V9JfnXON/uPZkp42r2ryf0n6fi+v+UN/no3yqg53VEP+jqSfmNmf5V056PA1/33/Q9IhJVil2HlV8JdKutt/D0/KS0oeK+lBv+z/5I2I2BFXtXnVzP8krx+ZtMqx7Z4RnNdk7CdxnvqNpLH++7hMXp8d3W2U1GZeM8D/iPP80/Jq5Dwp6Xv+j0uslfJOpJ71v2e3qNvVOOfcZknXSPo//zv/P/5TV8trBvFneSceR+J78n7MN/qv/71e5jtX3lXT5+Ql+eN9XpnqaPaLhDmv24VLJT1k3uAIL/cy69U6+u02mBwjqcL8wbPkdVdxtf/cMPM6w/+qDh+XY31WXtOljfJquU/s7djZcWyVpCM8vsLnX5FfIOmfzeuM/2l5zYm+Ia8/sZD/uf5K3m9uSNJHFFM70XldafxF3p9JpFmixyf/AutPJT3s75NPyDv/+r2/js/JO35Vy6uRcXO89aCLu+X1v3VPnOfinWdGJJ1i/iANkn4gr/XHX9V3tzx3SZpjZhvk/Qne0vEE5zkDd57jnHteXgLhUf+36Q/y+os7Eolsn0TF3Q7OuUfk1Wjd4L9OR/PuUnnfz43yLvj11/Kox7Hf+YOyJegzfoxOh5u+Jnuu1tf53rnqe/v2918nlfo6JkgDdFxIVm/nTke4utvknUM8La9S0dG0VOhtH18pr6/Pjf5x5V+P8Hfsanmf4UZ5A5ccyUWOuyS94h8fMkFfxxYnDd5z5Y5Oe3OKmZ0mbwCMHiN7ZSrzRjG70Tn3WNCxZCu2++BhXj8ubznnyoOOBcg25jXXmRPbLOco15d1x1YAyGSc5yCVzBuldrRz7r+DjgVIJfO6FHnOObcq6Fj6Yl6t8Wedc0dTszWj5VyNRTP7oryrGd8KOpZEmNkYM9sm6RDJpSPHdgeA1Mu2YysAALnEzO6XdJGyq0UM0C8z+5ukdyuBQdOCZF5XZuvldWM2aOVkjUUAAAAAAAAARyfnaiwCAAAAAAAAOHokFgEAAAAAAAAkjcQiAAAAAAAAgKSRWAQAAEBKmdlCM3Nm9k7/8XFmdl8v8043s03+9Bwz++lAxgoAAIAjx+AtAAAASCkzu1fSJEmPOeeu7mO+fElTJD3onJs1QOEBAAAgRaixCAAAgJQxs2MkvU/SJZIW+2WxtRKXmtmvzex3kh7ttuy5ZvagP321md1uZuvM7CUz+0rMfBea2dNm9nczu8XMQgP1/gAAAHAYiUUAAACk0gJJjzjntklqMLMz4sxzjqRS59wH+1nXOyV9RNJZkq4ysyFm9i5Jn5b0Pufc6ZKikpakKngAAAAkjsQiAAAAUukzku7xp+/xH3f3B+dcQwLresg51+ycq5e0V9IESedJOlPSM2b2d//xCUcdNQAAAJKWH3QAAAAAGBzMbJykD0qaZWZOUkiSk7Si26xvJ7jK5pjpqLxzV5NU4Zy78ijDBQAAwFGixiIAAABSZZGkNc65451z051zUyXtkDdAS6o8JmmRmY2XJDMba2bHp3D9AAAASBCJRQAAAKTKZyTd363sN5K+maoXcM49L+lbkh41s42S/iBvBGoAAAAMMHPOBR0DAAAAAAAAgCxDjUUAAAAAAAAASSOxCAAAAAAAACBpJBYBAAAAAAAAJI3EIgAAAAAAAICkkVgEAAAAAAAAkDQSiwAAAAAAAACSRmIRAAAAAAAAQNL+P6xwupSm8CGlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Price vs Airline to see individual airline company prices\n",
    "\n",
    "sns.catplot(y=\"Price\",x = \"Airline\", data = train_data.sort_values('Price',ascending=False),kind=\"boxen\", height=6,aspect=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e11b1f5",
   "metadata": {},
   "source": [
    "Clearly Jet Airways Business has the highest Price among all airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "51754c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing Multiple carriers Premium economy,Jet Airways Business, Vistara Premium economy, Trujet to Others\n",
    "\n",
    "train_data[\"Airline\"].replace({'Multiple carriers Premium economy':'Other', 'Jet Airways Business':'Other','Vistara Premium economy':'Other','Trujet':'Other'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5a2d073d",
   "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_Multiple carriers</th>\n",
       "      <th>Airline_Other</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Vistara</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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_Multiple carriers  Airline_Other  Airline_SpiceJet  Airline_Vistara  \n",
       "0                          0              0                 0                0  \n",
       "1                          0              0                 0                0  \n",
       "2                          0              0                 0                0  \n",
       "3                          0              0                 0                0  \n",
       "4                          0              0                 0                0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As airline is nominal categorical data\n",
    "# Using One Hot Encoding for it and making dummy variables for Airline\n",
    "\n",
    "Airline = train_data[['Airline']]\n",
    "Airline = pd.get_dummies(Airline,drop_first=True)\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae299d51",
   "metadata": {},
   "source": [
    "### Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b6390c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4345\n",
       "Kolkata     2860\n",
       "Banglore    2179\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking value count for Source column\n",
    "\n",
    "train_data['Source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f3b9395d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ArFyCRQCWhL1792Z0dDSTk5NJksnJyYyOjmbfvn0D7gwAAGBlEiwCsCTs3Lkz09PTc2rT09MZGxsbUEcAAAArm2ARgCVhZGQkq1bN/dhatWpVRkZGBtQRAADAyiZYBGBJGB4ezpYtW7J27dokydq1a7Nly5YMDw8PuDMAAICVSbAIwJKxadOmrweJw8PD2bRp04A7AgAAWLkEiwAsGUNDQ7nqqqtSVbn66qszNDQ06JYAAABWLH+RAbCkXHrppbn22muzfv36QbcCAACwohmxCMCSI1QEAAAYvL4Gi1V1W1VdX1XXVdWurjZcVR+uqvHu+qxZ219VVTdX1eeq6sWz6s/vjnNzVb25qqqrr6mq93f1T1TVBf18PQAAAADAjMUYsfg9rbXnttY2dvffkGRna+3iJDu7+6mqS5JckeTZSS5P8taqWt3t87YkVya5uLtc3tVfm2R/a+2ZSX47ya8vwusBAAAAgBVvEFOhX5bkmu72NUlePqv+vtbagdbarUluTvKCqjovyRmttY+31lqSdx+1z5FjXZvksiOjGQEAAACA/ul3sNiS/EVVfaqqruxqT26t3Z0k3fW5XX1Dki/N2veOrrahu310fc4+rbWpJPcnOfvoJqrqyqraVVW7JiYmTsgLAwAAAICVrN+rQn9na+2uqjo3yYer6qZjbDvfSMN2jPqx9plbaO3tSd6eJBs3bnzE4wAAAABAb/o6YrG1dld3fU+SP07ygiRf6aY3p7u+p9v8jiRPm7X7+Unu6urnz1Ofs09VDSV5UpJ9/XgtAAAAAMA39C1YrKrTq+qJR24n+b4kn0myI8mru81eneRPuts7klzRrfR8YWYWaflkN136gap6YXf+xB85ap8jx9qc5CPdeRgBAAAAgD7q51ToJyf5424tlaEk/6O19qGq+tskH6iq1yb5YpJXJElr7Yaq+kCSG5NMJXl9a+1wd6zXJXlXknVJPthdkuQdSd5TVTdnZqTiFX18PQAAAABAp2/BYmvtliSXzlPfm+SyR9nnTUneNE99V5Jvnac+mS6YBAAAAAAWT79XhQYAAAAAliHBIgAAAADQM8EiAAAAANAzwSIAAAAA0DPBIgAAAADQM8EiAEvOxMTEoFsAAABY8QSLACwpu3fvzubNm7Nnz55BtwIAALCiCRYBWDKmpqaybdu2tNaybdu2TE1NDbolAACAFUuwCMCSsX379uzfvz9Jsm/fvmzfvn3AHQEAAKxcgkUAloS9e/dmdHQ0k5OTSZLJycmMjo5m3759A+4MAABgZRIsArAk7Ny5M9PT03Nq09PTGRsbG1BHAAAAK5tgEYAlYWRkJKtWzf3YWrVqVUZGRgbUEQAAwMomWARgSRgeHs6WLVuydu3aJMnatWuzZcuWDA8PD7gzAACAlUmwCMCSsWnTpq8HicPDw9m0adOAOwIAAFi5BIsALBlDQ0O56qqrUlW5+uqrMzQ0NOiWAAAAVix/kQGwpFx66aW59tprs379+kG3AgAAsKIZsQjAkiNUBAAAGDzBIgAAAADQM8EiAAAAANAzwSIAAAAA0DPBIgAAAADQM8EiAAAAANAzwSIAAAAA0DPBIgAAAADQM8EiAAAAANAzwSIAAAAA0DPBIgAAAADQM8EiAAAAANCzBQWLVfWsqtpZVZ/p7j+nqn6+v60BAAAAACerhY5Y/L0kVyU5lCSttT1JruhXUwAAAADAyW2hweJprbVPHlWbOtHNAAAAAABLw0KDxXur6puStCSpqs1J7u5bVwAAAADASW1ogdu9Psnbk3xzVd2Z5NYkr+pbVwAAAADASW1BwWJr7ZYkI1V1epJVrbUH+tsWAAAAAHAyW+iq0Nuq6szW2oOttQeq6qyq+tV+NwcAAAAAnJwWeo7F72+t3XfkTmttf5If6EtHAAAAAMBJb6HB4uqqWnPkTlWtS7LmGNsDAAAAAMvYQoPF9ybZWVWvrar/K8mHk1yzkB2ranVVfbqq/rS7P1xVH66q8e76rFnbXlVVN1fV56rqxbPqz6+q67vH3lxV1dXXVNX7u/onquqCBb4eAAAAAOA4LChYbK39RpI3JfmWJM9O8itdbSF+IslnZ91/Q5KdrbWLk+zs7qeqLklyRXf8y5O8tapWd/u8LcmVSS7uLpd39dcm2d9ae2aS307y6wvsCQAAAAA4DgsdsZjW2gdbaz/dWvup1tqfL2Sfqjo/yUuSjM4qvyzfGO14TZKXz6q/r7V2oLV2a5Kbk7ygqs5LckZr7eOttZbk3Uftc+RY1ya57MhoRgAAAACgf44ZLFbVx7rrB6rqq7MuD1TVVxdw/P+c5GeTTM+qPbm1dneSdNfndvUNSb40a7s7utqG7vbR9Tn7tNamktyf5Ox5XseVVbWrqnZNTEwsoG0AAAAA4FiOGSy21r6ru35ia+2MWZcnttbOONa+VfVPk9zTWvvUAnuZb6RhO0b9WPvMLbT29tbaxtbaxvXr1y+wHQAAAADg0TzmVOiqWlVVn3kcx/7OJC+tqtuSvC/JP6mq9yb5Sje9Od31Pd32dyR52qz9z09yV1c/f576nH2qaijJk5Lsexy9AgAAAAA9eMxgsbU2nWR3VT29lwO31q5qrZ3fWrsgM4uyfKS19qokO5K8utvs1Un+pLu9I8kV3UrPF2ZmkZZPdtOlH6iqF3bnT/yRo/Y5cqzN3XM8YsQiAAAAAHBiDS1wu/OS3FBVn0zy4JFia+2lj+M5fy3JB6rqtUm+mOQV3bFuqKoPJLkxyVSS17fWDnf7vC7Ju5KsS/LB7pIk70jynqq6OTMjFa94HP0AAAAAAD1aaLD4xuN5ktbaR5N8tLu9N8llj7Ldm5K8aZ76riTfOk99Ml0wCQAAAAAsnmMGi1W1Nsm/SfLMJNcneUe3+jIAAAAAsII91jkWr0myMTOh4vcn+Y997wgAAAAAOOk91lToS1pr35YkVfWOJJ/sf0sAAAAAwMnusUYsHjpywxRoAE4WExMTg24BAABgxXusYPHSqvpqd3kgyXOO3K6qry5GgwAw2+7du7N58+bs2bNn0K0AAACsaMcMFltrq1trZ3SXJ7bWhmbdPmOxmgSAJJmamsq2bdvSWsu2bdsyNWUwPQAAwKA81ohFADhpbN++Pfv370+S7Nu3L9u3bx9wRwAAACuXYBGAJWHv3r0ZHR3N5ORkkmRycjKjo6PZt2/fgDsDAABYmQSLACwJO3fuzPT09Jza9PR0xsbGBtQRAADAyiZYBGBJGBkZyapVcz+2Vq1alZGRkQF1BAAAsLIJFgFYEoaHh7Nly5asXbs2SbJ27dps2bIlw8PDA+4MAABgZRIsArBkbNq06etB4vDwcDZt2jTgjgAAAFYuwSIAS8bQ0FCuuuqqVFWuvvrqDA0NDbolAACAFUuwyLKzY8eOjI+PJ0nGx8ezY8eOAXcEnEiXXnpprr322jznOc8ZdCsAAAArmmCRZWdsbCyHp1su+nuX5PB0s2IsLEN79+4ddAsAAAArnmCRZempT78gr/vZX8xTn37BoFsBTrA/+qM/ypVXXpk//uM/HnQrAAAAK5pgEYAl48CBA3nLW96SJHnzm9+cAwcODLgjAACAlUuwCMCS8cY3vjGHDx9Okhw+fDhvfOMbB9wRAADAyiVYBGBJuPnmm/Oxj31sTu1jH/tYbrnllgF1BAAAsLIJFgFYEt75znfOWx8dHV3kTgAAAEgEiwAsEVu2bOmpDgAAQH8JFgFYEi666KKcccYZc2rf9V3flYsuumhAHQEAAKxsgkUAlowLLrjg67dXr16dX/zFXxxcMwAAACucYBGAJWPVqm98bG3dujVr1qwZYDcAAAArm2ARgCXnWc96Vn7wB39w0G0AAACsaIJFAJac0047bdAtAAAArHiCRQAATgoTExODbgEAgB4IFgEAGLjdu3dn8+bN2bNnz6BbAQBggQSLAAAM1NTUVLZt25bWWrZt25apqalBtwQAwAIIFgEAGKjt27dn//79SZJ9+/Zl+/btA+4IAICFECwCADAwe/fuzejoaCYnJ5Mkk5OTGR0dzb59+wbcGQAAj0WwCADAwOzcuTPT09NzatPT0xkbGxtQRwAALJRgEQCAgRkZGcmqVXO/kq5atSojIyMD6ggAgIUSLAIAMDDDw8PZsmVL1q5dmyRZu3ZttmzZkuHh4QF3BgDAYxEsAgAwUJs2bfp6kDg8PJxNmzYNuCMAABZCsAgAwEANDQ3lqquuSlXl6quvztDQ0KBbAgBgAXxrAwBg4C699NJce+21Wb9+/aBbAQBggfo2YrGq1lbVJ6tqd1XdUFVv7OrDVfXhqhrvrs+atc9VVXVzVX2uql48q/78qrq+e+zNVVVdfU1Vvb+rf6KqLujX6wEAoL+EigAAS0s/p0IfSPJPWmuXJnluksur6oVJ3pBkZ2vt4iQ7u/upqkuSXJHk2UkuT/LWqlrdHettSa5McnF3ubyrvzbJ/tbaM5P8dpJf7+PrAQAAAAA6fQsW24yvdXdP6S4tycuSXNPVr0ny8u72y5K8r7V2oLV2a5Kbk7ygqs5LckZr7eOttZbk3Uftc+RY1ya57MhoRgAAAACgf/q6eEtVra6q65Lck+TDrbVPJHlya+3uJOmuz+0235DkS7N2v6OrbehuH12fs09rbSrJ/UnOnqePK6tqV1XtmpiYOEGvDgAAAABWrr4Gi621w6215yY5PzOjD7/1GJvPN9KwHaN+rH2O7uPtrbWNrbWNzt0DAAAAAMevr8HiEa21+5J8NDPnRvxKN7053fU93WZ3JHnarN3OT3JXVz9/nvqcfapqKMmTkuzrx2sAAAAAAL6hn6tCr6+qM7vb65KMJLkpyY4kr+42e3WSP+lu70hyRbfS84WZWaTlk9106Qeq6oXd+RN/5Kh9jhxrc5KPdOdhBAAAAAD6aKiPxz4vyTXdys6rknygtfanVfXxJB+oqtcm+WKSVyRJa+2GqvpAkhuTTCV5fWvtcHes1yV5V5J1ST7YXZLkHUneU1U3Z2ak4hV9fD0AAAAAQKdvwWJrbU+S581T35vkskfZ501J3jRPfVeSR5yfsbU2mS6YBAAAAAAWz6KcYxEAAAAAWF4EiwAAAABAzwSLLCs7duzI+Ph47vribXnbb7wxd33xtoyPj2fr1q3ZsWPHoNsDAAAAWDb6uXgLLLqxsbFMHZ7O+qc+PQcPHc455z0tSfLZmz6XJHnpS186yPYAAAAAlg3BIsvO+qc+Pa983c/OqX3gbb8xoG4AAAAAlidToQFYMu69994kyfj4uNMbAAAADJhgEYAlY//+/Tn9tLVJO5yxsbFBtwMAALCimQrNkrZjx4454cL4+HgOHZ5+xNTne+76YvZ/ZVW2bt2aJBkZGXG+RViivunpGwbdAgAAABEsssSNjY3lhs9+Lmc9eSZoOP3s85IkB6cOz9nuzHNnHr9r39ey/yt3JrGQCwAAAMDxECyy5J315A257F9tXfD2O9/z5j52AwAAALAyOMciAAAAANAzwSIAAAB9MzExMegWAOgTwSIAAAB9sXv37mzevDl79uwZdCsA9IFgEQAAgBNuamoq27ZtS2st27Zty9TU1KBbAuAEs3gLS8KOHTsyNjb2iPr4+HgOTh3uaUGW/V+5Iw/uXZ2tWx+54MvIyIjVogEA4ATYvn179u/fnyTZt29ftm/fnle+8pUD7gqAE0mwyJIwNjaW62+8KU8457w59aEnnZuhJAemDi/4WKedPXOMW++5f079a/fenSSCRQAAOE579+7N6OhoJicnkySTk5MZHR3NyMhIhoeHB9wdACeKYJEl4wnnnJfnvfxf9+34n/6fv9e3YwMAwEqyc+fOTE9Pz6lNT09nbGzMqEWAZcQ5FgEAADihRkZGsmrV3D83V61alZGRkQF1BEA/CBYBAAA4oYaHh/Oa17xmTu01r3mNadAAy4xgEQAAgBNuz549c+5ff/31A+oEgH4RLAIAAHBC3Xzzzfmbv/mbObW//uu/zi233DKgjgDoB8EiAAAAJ9Tv/u7vzlt/61vfusidANBPgkUAAABOqG/6pm/qqQ7A0iRYBAAA4IT6vu/7vnnrL37xixe5EwD6SbAIAADACfWpT30qVTWnVlXZtWvXgDoCoB8EiwAAAJxQz3/+89Nam1NrrWXjxo0D6giAfhAsArAk7NixIw8//HC+cPud+cLtd+bee+8ddEsAwDx27NiRn/u5n5v3sXe9612L2wwAfTU06AYAYCHGxsaybs0puej89bnljnuyf//+QbcEAMxjbGws9993X57zLd8yp/6F22/PxMTEgLoCoB8EiwAsGRedf25+4yd/OD/7n/8gt9y1b9DtAACP4pue8Yz85s///Jzaz/zqr2bVKacMqCMA+sFUaAAAAACgZ4JFAAAAAKBngkUAAAAAoGeCRQAAAACgZ4JFAAAAAKBngkUAAAAAoGeCRQAAAACgZ4JFAE56O3bsyPj4eG6545787H/+g9xyxz15+OGHs3Xr1uzYsWPQ7QEAAKxIfQsWq+ppVfW/q+qzVXVDVf1EVx+uqg9X1Xh3fdasfa6qqpur6nNV9eJZ9edX1fXdY2+uqurqa6rq/V39E1V1Qb9eDwCDMzY2lhw+mAufcmba1Mz1Jc9Yn/Gbbph5DAAAWFQTExODboGTQD9HLE4l+anW2rckeWGS11fVJUnekGRna+3iJDu7++keuyLJs5NcnuStVbW6O9bbklyZ5OLucnlXf22S/a21Zyb57SS/3sfXA8AAXXjecH7tyu+fc7nwvOFBtwUAACvO7t27s3nz5uzZs2fQrTBgfQsWW2t3t9b+rrv9QJLPJtmQ5GVJruk2uybJy7vbL0vyvtbagdbarUluTvKCqjovyRmttY+31lqSdx+1z5FjXZvksiOjGQEAAAA4saamprJt27a01rJt27ZMTU0NuiUGaFHOsdhNUX5ekk8keXJr7e5kJnxMcm632YYkX5q12x1dbUN3++j6nH1aa1NJ7k9y9jzPf2VV7aqqXYbqAgAAADw+27dvz/79+5Mk+/bty/bt2wfcEYM01O8nqKonJPmjJD/ZWvvqMQYUzvdAO0b9WPvMLbT29iRvT5KNGzc+4nEAAAAevx07dnz9vMfj4+NJa/mZX/3VOdt84fbbk6ps3bo1STIyMpKXvvSli94r8Pjt3bs3o6OjmZycTJJMTk5mdHQ0IyMjGR52mqKVqK8jFqvqlMyEiv+9tXYkwv5KN7053fU9Xf2OJE+btfv5Se7q6ufPU5+zT1UNJXlSkn0n/pUAAADwaMbGxvL5m27KofvuywXr1+eCc8/N4YcfnnO54Nxzc8H69Tl03335/E03WYANlqCdO3dmenp6Tm16etr7eQXr24jF7lyH70jy2dbaf5r10I4kr07ya931n8yq/4+q+k9JnpqZRVo+2Vo7XFUPVNULMzOV+keS/JejjvXxJJuTfKQ7DyMAAACL6MKnPCW/smXLgrb9hdHRPncD9MPIyEhGj3r/rlq1KiMjIwPqiEHr54jF70zyr5L8k6q6rrv8QGYCxe+tqvEk39vdT2vthiQfSHJjkg8leX1r7XB3rNclGc3Mgi5fSPLBrv6OJGdX1c1J/n26FaYBAAAAOLGGh4ezZcuWrF27Nkmydu3abNmyxTToFaxvIxZbax/L/OdATJLLHmWfNyV50zz1XUm+dZ76ZJJXHEebAJykjj5XUzt8MG94+wfnbHPL3ftS93zNuZpgmZiYmMj69esH3QYAcAybNm3K9u3bc9ddd2V4eDibNm0adEsM0KKsCg0AvRobG8v4Zz+TqX1fzIVnr8lF5z4xOXxozuWic5+YC89ek6l9X8z4Zz/j3C6whO3evTubN2/Onj17Bt0KAHAMQ0NDueqqq1JVufrqqzM01Pd1gTmJ+X8fgJPWhU8+I2961d9f0LY/995P9LkboF+mpqaybdu2tNaybdu2vPe97/VHCgCcxC699NJce+21ZhpgxCIAAIO1ffv27N+/P0myb9++bN++fcAdAQCPRahIIlgEAGCA9u7dm9HR0UxOTiZJJicnMzo6mn379g24MwAAHotgEQCAgdm5c2emp6fn1Kanp50zFQBgCRAsAgAwMCMjIz3VAQA4eTgrNgOxY8eOnkYijI+PZ/LQ4Xz6f/5e33r62r13Z/z+e7J169YF7zMyMpKXvvSlfesJAJa74eHhbNiwIbfeeuvXaxs2bMjw8PAAuwIAYCEEiwzE2NhY9tzw2aw568kL2+H0s3NqkoNTh/vW06lnnpvpJJ+7a2HndDqw/ytJIliE43CsHxnGx8fTpg4seLXnW77y1dTe8Xl/HPAjAJy8br755jmhYpLceuutueWWW3LRRRcNqCsAABZCsMjArDnryXn6Zf9q0G08bl/c+Z5BtwBL3tjYWD5/4/W58Jx1j3jsgidVkrVpUwcXdKwLz16bJDl0z81z6rfe+3ASPwLAyeqd73znvPXR0dFs27ZtkbsBHsuj/Sg484PgVH5hdHRBx7n17rtTExN+EARY4gSLAAzUheesy69s+nt9O/4vbP9c344NHL8tW7bkYx/72Lx14OQz86PgjXn6UacreNoTn5gkOdyt8P5Ynn7WWUmSyS9/eU79i92K8IJFgKVBsAgAwMAcvSI0cPJ7+vBwfu4l/7Qvx37T//rTvhwXgP4QLAJwwjyehZnaoYf7Oqrw1nsfSt0//7kXH40pWLB4jkyFPu3005IkDz34UBJToQEAlgLBIgAnzNjYWD5/w+5cMHzKgrZ/xhOSZCjt8MLOo/h4XHDWUJJDOXj3jQva/rZ9h5KYggWL5ZnPfObMVOg2t24qNADAyU+wCMAJdcHwKfmly88ddBuP2y996J5BtwArynXXXZe1p63Nec84L3fefmeS5Lu+67usCA0AsASsGnQDAACsbBuesSH/98//39nwjA1Jkl/8xV8ccEcAACyEYBEAgIHYsWNHxsfHc+ftd+atv/rW3Hn7namq/MzP/Ex27Ngx6PYAAHgMpkIDALBoZi/yND4+nocefiinrjk1d9x2R5Lk1LWnZvee3RkfH//6dhZUAgA4OQkWAVaIXldsfjzGx8fTDh5a0ucpvG3fodTXeltF+vEQlLCcHevfm/Hx8Tz00EMZWjPzNXTo1KFMt+k52wydOpSDhw/mhptuyNSBqTkh42zeRwAAgyVYBFghZlZsvi7PeFL17Tmevi7JuqQdPtS35+i3ZzwpSR7KgTt29+05br9/ZvlbgQhLRa8/TIyPj+fBhx7M0Knzf9VcferqtNbmfWy+bQ9MHchnPvuZOfWpg48eOB6LMBIA4MQRLAKsIM94UuU//MNTBt3GivfLf7V0g1dWprGxsey5cU/WrV+3sB3OTE4/8/S+9rQma5Ik4xPjC97n4YmHkwj1AQBOFMEiAMAStlinOVguxsf7e6oDIyIBgJVEsAgAsISNjY1l9427MzTcx691T0xWZ3UOHj7Yv+dYBKuHV+dgDuaGL9/Ql+NP7ZtKYkQkALByCBYBAJa4oeGhnPkDZw66jRXvvj+7b9AtAAAsKsEij7BYU6oOHJzKF3e+p6/P008H9n8l4w/utXIsAAAAsCIJFnmEsbGxXPeZG7P6iev79ySnnpk6NTl46HD/nqPP6gnnZDLJ9bdP9O05Dj8wc2zB4uAtRuDeb+Pj42kHmoVDTgK339dSD/f3PG+LxY8fAACwcgkWmdfqJ67P6X9/86DbWPEe/MS1g26BztjYWD7/mb/L05+wdMPwp52S5JSkTQ26E57+hCS5L5O3/e2gWzkuX/za6iR+/AAAgJVKsAiwQE9/wuH8/MavDboNOGn86q4nDLoFAABggASLAMCKtVxOczB1cMrCISeBqb1TGX/AaQ4AgJVDsAgArFhjY2P59A2fTs4cdCfH4bSZy8HDBwfdCWcmX8vX8uk7Pz3oTo7PfTNXgsXl7/H8uDI+Pp7pgwfzpv/1p33p6fa9e7PqgQd6DuiF4QCDIVgEAFa2M5Pp754edBdw0lj10VWDboFFMjY2ls995jM5/wkLP7XFU085JTnllByenOxLT+effnqS5MHbblvwPnd8beZUNYJFgMUnWAQAAFihzn/CE/JT3/78QbdxXP7j331q0C3AkvdoI5jvvffe7N+/v6djnXXWWTnnnHMeUTeyeHkSLAIAAACsYGNjY/ncTZ/P0zY8Y079tDVPzGlPeWLPx3vogQNz7n/pztuTGFm8HAkWAQAAAFa4p214Rn7m3/5CX479m2/5lb4cl8FzAhUAAAAAoGeCRQAAAACgZ4JFAAAAAKBngkUAAAAAoGd9Cxar6p1VdU9VfWZWbbiqPlxV4931WbMeu6qqbq6qz1XVi2fVn19V13ePvbmqqquvqar3d/VPVNUF/XotAAAAAMBc/VwV+l1J3pLk3bNqb0iys7X2a1X1hu7+/6+qLklyRZJnJ3lqkrGqelZr7XCStyW5Msn/SfJnSS5P8sEkr02yv7X2zKq6IsmvJ/mhPr4eYIW756sH8tHbDg+6DThp3PPVA3n6oJsAAAAGpm8jFltr/2+SfUeVX5bkmu72NUlePqv+vtbagdbarUluTvKCqjovyRmttY+31lpmQsqXz3Osa5NcdmQ0IwAAAADQX/0csTifJ7fW7k6S1trdVXVuV9+QmRGJR9zR1Q51t4+uH9nnS92xpqrq/iRnJ7n36CetqiszM+oxT3+6sRXA43PuGWvy3RccGnQbcNL42L1rBt0CAAAwQCfL4i3zjTRsx6gfa59HFlt7e2ttY2tt4/r16x9niwAAAADAEYs9YvErVXVeN1rxvCT3dPU7kjxt1nbnJ7mrq58/T332PndU1VCSJ+WRU68BAI5p8v7JtPF5f5uEFanur2/MEQIAOIbFHrG4I8mru9uvTvIns+pXdCs9X5jk4iSf7KZNP1BVL+zOn/gjR+1z5Fibk3ykOw8jAAAAANBnfRuxWFV/kOS7k5xTVXck+cUkv5bkA1X12iRfTPKKJGmt3VBVH0hyY5KpJK/vVoROktdlZoXpdZlZDfqDXf0dSd5TVTdnZqTiFf16LQDA8rX2SWszffH0oNuAk8aqO0+WsyUBACe7vgWLrbUffpSHLnuU7d+U5E3z1Hcl+dZ56pPpgsmTyY4dOzI2NjboNo7L+Ph4Dh84lAc/ce2gW1nxDj8wkfHx+7J169ZBt3JcRkZG8tKXvnTQbQAAAAAn0GKfY3HZGxsby6evvzHTpw0PupXHb9UTk3XJ4anDj70t/bVuOF9N8qkvfHnQnTxuqx6aOfWpYBEAAACWF8FiH0yfNpzJS/7poNuAk8LaG/900C0AAAAAfSBYBAAAOAn1+zRL4+PjmZ6czH/8u0/17TkWwx0PPJBV4+N9PX2QU/sAzE+wCAAAcBIaGxvLTXv2ZMOpp/bl+E9JklNOyfTkZF+Ov1ieesopyeHDeeCmm/py/DsPHkzi1D4sHY/nR4nx8fFMH275zbf8Sl96+tKdt2fV6ur5BwCh/slPsAgAAHCS2nDqqXn9eU8ddBsr2n+9+65BtwA9GRsby02f/Vyesv78Be+z/qyZf2cOHpjqS09PPmdDkuS+ex9c8D5fnrgjiVD/ZCdYBAAAAFhGnrL+/Gx5xb8bdBvHZfQPf3vQLbAAgkWABfri11bnV3c9YdBtwEnji19bnWcNugkAAGBgBIsACzAyMjLoFo7b+Ph42oEH84wza9CtrHi339dSa07PxRdfPOhWjsuzsjzeGwAAi2kxFmaaOjS95Ef83X3PHZnYv8rCTCc5wSLAArz0pS9d8h84W7duzYE7duc//MNTBt3KivfLf3Uoa86/OG9+85sH3QoAAItsbGwsN95wU9Y/6by+HP/Mdecm65JDk/05X+JiOeeMpyRJJu64vy/Hn7j/7iTO4Xi8BIsAAAAAi2j9k87Lpn+8ZdBtrGjb/3J00C0sC4JFgBXk9vtbfvmvDg26jRXv9vtbnrXwRfoAAABOSoLFPjjwwP4cuvPGQbcBJ4V6YH+Spwy6DbI458IbHx9PO/hQLhheutOtb9t3KHXqaX09/+GzznduQgAAYOkTLAKsEItxnsitW7fm4N035pcuP7evz9NPv/She3Lqec5/CAAA8FgEi32w5olnpW24ZNBtwElhzf23DLoFFtlt+w7llz50z6DbeNxu23coz+rPebQBWCT9XnF1sYyPj+fwgQP5r3ffNehWVrQ7DxzI6vHxvq5Mu1isgAucaIJFAE6YXqf3jo+Ppx16OBeec1qfOkpuvfeh1CnrFjy1+VnnmaYMsNSNjY3lpuuuW/InY1nfXR+enBxoHyvdU5LkwQdz33XXDbiT4/Pl7lqwCJxIgkUATphep1tv3bo1n7/x+j52lCSViy82tZljuC9Z9dFVg+4CTh73Jdkw6CaO31OSvDY16DbgpPGOtEG3cNyW02jkQwcOW5V4wCbuuzv3PXyP0cjHSbAIwMAca2Tg+Ph4Hn7owaw9dWEfVZMHp7LutNMfMTLxWecagcijWw7/bYyPj+ehgw9l6Gxf6wZtau9UTuvz4k+LYsPyeG8Ay8/Y2FhuuP6zOfO0pXs+7yQ5bdVwsi45eGBq0K2saE9aNzMu/M4v7B1wJ8fnvodmTkMlWARgxTnWCMfZv0jPhIwPZe2auR9bkwemsu60b/wR77xB9GoxFjXqt61bt+aGL9+QM3/gzEG3suLd92f35eKnGCEN0E9nnnZuvuebrxh0G3DS+N83vW+gzy9Y7INVD+3L2hv/dNBtwElh1UP7kiV/hiMGYXbgs2PHjrztbW/Lww8/lHVrTs3DBw5merrl277t2/LiF794yQdDAAAAS5Fg8QRbDtNGxsfH8+CBQ1n9xPWPvTF9dfiBiZy+5pQlPqXqKcvifcFgHQkOf+u3fisPHzj49fpXv/pVoSIAAMCACBZPsOUyper62ydy+t/fPOhWVrwHP3FtLn7GelOqIMmGDTMrCUxPf+PE47fffns+/elP53nPe96g2oKTwtS+qdz3Z/cNuo0Vb2rflEH6AMCKIlhkXocfmMiDn7h20G2seIcfmEhi5CgkyW/+5m/OW/+N3/iN/MEf/MEidwMnj8UYFT4+Pp4HH3owq05Z2qtnTx+azunzLPJ0wjxlecxeATiZ3f/Avnz+rusG3QacNO5/YF825OyBPb9gkUdYvD9QHsqqoVP7/lz9Mj11MKef1u+VH9f7AwU6w8PDueuuu+atw0q2GLMlZi+mtBD33ntv9u/fP+9jhw4dytTUzCqYrbW01ubdrqpSVUmSoaGhnHLKKY/Y5qyzzso555yz4L4SizwBLHUPHrw/N975N4NuA04aDx68f6DPL1jkEU7GP1COBJGrT1nTt54OHzrQc1DojxNYPD/90z+d17zmNY+o/9RP/dTiNwMrTD+/G/zCL/xC/vIv/zKrVs2Mhpyenk6SvP71r88rX/nKvjwnAEvTchl0MT4+noceeiinrO7f37c8tkOHD+S0vg8WWhyDfG8IFhmIXv9AebQg8ugREQ8//HCmp6dTNXeqVmvTWbVqVdatW5fk0Uc4CArh5HXRRRflRS96UT7+8Y9/vfaiF70oF1100QC7Ao7X3/zNzKiTI4HiEf/tv/03wSLHZe/kZP520E3ASWRvkjMH3cRxWg5rGiS9D7Tp1bFmDjya2TMKZjv683mhjvxgONujzUA4lsczO6EXMoDjJ1hkSVjoB8iOHTvyvve9L/fcc0+mpqYyNDSUc889N+ecc45/MGAZ+OVf/uV83/d9X1prqar88i//8qBbAo7Tj/3Yj+Utb3nLvHUAWI6WUkB6dAh66NCh3HjjjXNOZVJVueSSS74eGvrbe2URLLKsLKV/oIHerVmzJlu3bs3v/M7v5Cd/8iezZo3pI7DUvfKVr8zv/u7vzhklMTQ0ZLQix+3stWvzHalBtwEnjT2Z/5y2cCxH/439gQ98IOPj4zl48ODXa6ecckq+53u+x2f3CiVYBGBJ+ef//J/n2c9+dr75m7950K0AJ8gf/uEf5gd/8Afn3Ifj9eUk7xCkwNd9OUt/KjSDNzIyktHR0Tm1VatWLZvzX9I7wSIAS45QEZaXs88+O9/0Td+UL3zhC7n44otz9tlnD7ollrjl8gfu+Ph4Dj/0UDYYoT9Qdx44kNXLYIGHM7N83hsMzvDwcLZs2ZLR0dFMTk5m7dq12bJlS4aHhwfdGgNSs+fFrwQbN25su3btGnQbAAAc5fd///fzoz/6o4NuA04aW7duzU179mTDqacOupUV7c6DB/PNz3lO3vzmNw+6FTgpTE1N5VWvelXuuuuuPPWpT8173/veDA0Zt7YCzHt+Ef/PAwBwUhAqwlz9Hl02Pj6ehx98MGtWr+7r8/TbgcOHs+700/s2ovCbY6QfzDY0NJSrrroqW7duzdVXXy1UXOGMWAQAAFiBjl7tdSHuvffe7N+//xH1Q4cOfX0RptZajvV35qpVq5LMhBNHVpGd7ayzzso555zTU19WoYXFNzExkfXr1w+6DRbPvCMWBYsAAACcUJdffnkeeuihR9RPO+20fOhDHxpARwAcp3mDxVWL3QUAAADL24//+I/3VAdgaRIsAgAAcEK95CUvybp16+bU1q1bl5e85CUD6giAfhAsAgAAcMK9//3vP+Z9AJY+wSIAAAAn3JlnnplLL700SfK85z0vZ5555mAbAuCEW/KLt1TV5Ul+J8nqJKOttV871vYWbwEAAFg8f/iHf5hXvOIVg24DgOOz/BZvqarVSf5rku9PckmSH66qSwbbFQAAAEcIFQGWryUdLCZ5QZKbW2u3tNYOJnlfkpcNuCcAAAAAWPaWerC4IcmXZt2/o6vNUVVXVtWuqto1MTGxaM0BAAAAwHK11IPF+eZ3P+Kkka21t7fWNrbWNq5fv34R2gIAAACA5W2pB4t3JHnarPvnJ7lrQL0AAAAAwIqx1IPFv01ycVVdWFWnJrkiyY4B9wQAAAAAy97QoBs4Hq21qar6t0n+PMnqJO9srd0w4LYAAAAAYNlb0sFikrTW/izJnw26DwAAAABYSZb6VGgAAAAAYAAEiwAAAABAzwSLAAAAAEDPBIsAAAAAQM+qtTboHhZVVU0kuX3QfbBozkly76CbAE44721Ynry3YXny3oblyXt7Zbm3tXb50cUVFyyyslTVrtbaxkH3AZxY3tuwPHlvw/LkvQ3Lk/c2ianQAAAAAMDjIFgEAAAAAHomWGS5e/ugGwD6wnsblifvbVievLdhefLexjkWAQAAAIDeGbEIAAAAAPRMsAgAAAAA9EywyEmhqg5X1XVVtbuq/q6q/kEfnuO7q+pPT/RxgeM369+AG7p/B/59VR3zM6qqLqiqz3S3X1NVb3mU7f6sqs7sQ9vALFX1tVm3f6Cqxqvq6Y+1fa+fz932J/x7AjBXVbWqes+s+0NVNXGivk9X1S9V1U/3uM/fnIjnhpWsqp5SVe+rqi9U1Y3dd+UrB/m3clWNVtUlg3p+js/QoBuAzsOttecmSVW9OMn/P8k/HmRDVTXUWpsaZA+wgsz+N+DcJP8jyZOS/OLxHri19gPHewxg4arqsiT/Jcn3tda+2Ien+O4kX0siYID+ejDJt1bVutbaw0m+N8mdg2yoteZHBTgOVVVJ/jjJNa21K7rac5P8s0H21VrbMsjn5/gYscjJ6Iwk+5Okqp5QVTu7UYzXV9XLuvoFVfXZqvq9boTTX1TVuu6x76iqPVX18ar6zSMjmmarquGq+p/ddv+nqp7T1X+pqt5eVX+R5N1Vtb6q/qiq/ra7fOfi/c8AK1Nr7Z4kVyb5tzVjdfde/tvuPftjj7LrU6vqQ90oqd84Uqyq26rqnEVpHla4qvqHSX4vyUtaa1/oav++qj7TXX7yMfb/jqr6dFVdVFX/rKo+0d0fq6onV9UFSf5Nkn/XjXL+h/Nt1+/XCSvIB5O8pLv9w0n+4MgDR4847N7jF3SXm7oRSJ+pqv9eVSNV9dfdZ/QLZh3/0qr6SFf/191x5v3+3z32tQDH43uSHGqt/e6RQmvtuiR/leQJVXVt9/79710Imap6flX9ZVV9qqr+vKrO6+ofrapfr6pPVtXnu+8AR2YSbX+U7+Vvq6pd3d/wb5xV/2hVbVyc/wk40QSLnCzWdX8g3JRkNMmvdPXJJD/YWvv2zPwj+B+P/AOX5OIk/7W19uwk9yX5513995P8m9bai5IcfpTne2OST7fWnpPk6iTvnvXY85O8rLX2L5L8TpLfbq19R3f80eN/qcBjaa3dkpnPqHOTvDbJ/d378DuS/OuqunCe3Z6b5IeSfFuSH6qqpy1Su8CMNUn+JMnLW2s3JTN/jCT50SR/P8kLM/P+fd58O9fM9Obfzcxn8C1JPpbkha215yV5X5Kfba3d1m3z262157bW/mq+7fr4GmGleV+SK6pqbZLnJPnEAvd7Zma+Rz8nyTcn+RdJvivJT2fmu/cRz8lMcPmiJP+hqp6aY3//B47Ptyb51KM89rwkP5nkkiQXJfnOqjolM7MQNrfWnp/knUneNGufodbaC7r9Zs80em7m/17+c621jZl57//jIwN8WNpMheZkMXsa5IsyM1rwW5NUkm1V9Y+STCfZkOTISIRbu19Xkpl/HC+omfOoPbG1dmR61P9I8k/neb7vShdEttY+UlVnV9WTusd2dNM9kmQkySWzvsucUVVPbK09cLwvGHhMR95435fkOVW1ubv/pMz8sPD5o7bf2Vq7P0mq6sYkz0jypcVoFEiSHMrM9OTXJvmJrvZdSf64tfZgklTV9iT/MMmnj9r3W5K8PTPTp+/qaucneX83MuLUJLc+yvMudDugR621Pd1I4R9O8mc97Hpra+36JKmqGzLzGd2q6vokF8za7k+6790PV9X/TvKCJP8r83////Lxvh7gmD7ZWrsjSarqusy8V+/LTBj54e5v4tVJ7p61z/bu+lOZ+95+tO/lr6yqKzOTRZ2XmRBzTz9eDItHsMhJp7X28W7a4vokP9BdP7+1dqiqbkuyttv0wKzdDidZl28EEY9lvu1ad/3grNqqJC+aFTQCi6CqLsrM+/qezLxff7y19udHbXPBUbsd/W+CzzhYXNNJXplkrKqubq1ty8I/l+/OzOf785IcCRb/S5L/1FrbUVXfneSXHmXfhW4HPD47kvxWZs5vevas+lTmzoBbO+v27M/k6Vn3pzP387llrpbkX+bRv/8Dx+eGJJsf5bH5vktXkhu62YDH2ufo796POFY34+ink3xHa21/Vb0r3tvLgqnQnHSq6psz80vI3syMTLqn+1LxPZn5peNRtdb2J3mgql7Yla54lE3/38x8aUn3R8i9rbWvzrPdXyT5t7N6e+6CXwjwuFTV+sxMdXxLa60l+fMkr+umYqSqnlVVpw+yR2B+rbWHMjNT4F9W1Wsz83n78qo6rXvf/mBmzuN0tPsyMx1yW/e5nMx8BziyUMSrZ237QJInzrr/aNsBJ8Y7k/zykRGIs9yW5NuTpKq+Pcl8pyl5LC+rqrVVdXZmgsu/TY/f/4GefCTJmiPnNE1mzm+cR1849XNJ1nezClNVp1TVsx/nc5+RmUE893fnQ/7+x3kcTjJGc3CyWNcNt05mfhV5dWvtcFX99yT/T1XtSnJdkpsWcKzXJvm9qnowyUeT3D/PNr+U5Perak+Sh/Lof4hsTfJfu+2GMvMH0r9ZyAsCenLk34BTMjMC4j1J/lP32Ghmplb8XXeOpYkkL1/8FoGFaK3tq6rLM/OZ+ZNJ3pXkk93Do621o6dBH9nvK1X1z5J8sKr+r8x8Vv9hVd2Z5P/kG6HF/5Pk2m5Bhx8/xnbACdBNjfydeR76oyQ/0n1+/20eeYqShfhkZqY+Pz3Jr7TW7nqc3/+BBehOSfCDSf5zVb0hM+c0vS3J/3yU7Q92pyN6c3fqsKEk/zkzIx97fe7dVfXpbt9bkvz143kNnHxqZjAILB9V9YTW2te6229Icl5r7SceYzcAAAAAemDEIsvRS6rqqsz89317ktcMth0AAACA5ceIRQAAAACgZxZvAQAAAAB6JlgEAAAAAHomWAQAAAAAeiZYBADghKiqn6uqG6pqT1VdV1V/f9A9AQDQP1aFBgDguFXVi5L80yTf3lo7UFXnJDn1OI851FqbOiENAgBwwhmxCADAiXBekntbaweSpLV2b2vtrqq6rKo+XVXXV9U7q2pNklTVbV34mKraWFUf7W7/UlW9var+Ism7q+rJVfXHVbW7u/yDbrtXVdUnu5GR/62qVg/kVQMArGCCRQAAToS/SPK0qvp8Vb21qv5xVa1N8q4kP9Ra+7bMzJZ53QKO9fwkL2ut/Yskb07yl621S5N8e5IbqupbkvxQku9srT03yeEk//KEvyIAAI5JsAgAwHFrrX0tM4HglUkmkrw/yY8lubW19vlus2uS/KMFHG5Ha+3h7vY/SfK27jkOt9buT3JZ91x/W1XXdfcvOkEvBQCABXKORQAATojW2uEkH03y0aq6Psmrj7H5VL7xI/faox578DGeqpJc01q76vH0CQDAiWHEIgAAx62q/l5VXTyr9NwkX0lyQVU9s6v9qyR/2d2+LTOjDpPknx/j0DvTTZ+uqtVVdUZX21xV53b14ap6xol4HQAALJxgEQCAE+EJSa6pqhurak+SS5K8IcmPJvnDbgTjdJLf7bZ/Y5Lfqaq/ysw5Eh/NTyT5nm7/TyV5dmvtxiQ/n+Qvuuf6cGYWjwEAYBFVa23QPQAAAAAAS4wRiwAAAABAzwSLAAAAAEDPBIsAAAAAQM8EiwAAAABAzwSLAAAAAEDPBIsAAAAAQM8EiwAAAABAz/4/4nZB/MjgHyYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Price vs Source to see individual Sources prices\n",
    "\n",
    "sns.catplot(y=\"Price\",x = \"Source\", data = train_data.sort_values('Price',ascending=False),kind=\"boxen\", height=6,aspect=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab70c05",
   "metadata": {},
   "source": [
    "Every Cities has almost similar price but there are some outlier also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd8dbef3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0               0             0               0              0\n",
       "1               0             0               1              0\n",
       "2               0             1               0              0\n",
       "3               0             0               1              0\n",
       "4               0             0               0              0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is nominal categorical data\n",
    "# Using One Hot Encoding for it and making dummy variables for Source\n",
    "\n",
    "Source = train_data[['Source']]\n",
    "Source = pd.get_dummies(Source,drop_first=True)\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ffa4bf5",
   "metadata": {},
   "source": [
    "### Destination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "14d720a0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4345\n",
       "Banglore     2860\n",
       "Delhi        1265\n",
       "New Delhi     914\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking value count for Destination\n",
    "\n",
    "train_data['Destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bd72c937",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Destination'].replace({'New Delhi':'Delhi'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ab302bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yJVgEYFHYtWtXxsbGMjU1lSSZmprK2NhYdu/ePeDOAAAAlifBIgCLwrZt2zIzM3NIbWZmJuPj4wPqCAAAYHkTLAKwKIyOjmZo6NC3raGhoYyOjg6oIwAAgOVNsAjAojAyMpKNGzdm1apVSZJVq1Zl48aNGRkZGXBnAAAAy5NgEYBFY/369V8NEkdGRrJ+/foBdwQAALB8CRYBWDSGh4dz1VVXpapy9dVXZ3h4eNAtAQAALFv+IgNgUbn00ktzzTXXZM2aNYNuBQAAYFkzYhGARUeoCAAAMHh9DRar6nNVdX1VXVdV27vaSFV9qKomusuz5mx/VVXdXFWfqaoXzak/uzvOzVX1xqqqrr6yqt7X1a+tqgv6+XwAAAAAgFkLMWLx21trz2ytretuvzbJttbaxUm2dbdTVZckuTzJ05NcluTNVbWi2+ctSa5McnH3c1lXvyLJntbaU5P8dpI3LMDzAQAAAIBlbxBToV+a5J3d9Xcmedmc+ntba3tba7cmuTnJc6vqvCSnt9Y+2lprSd512D4Hj3VNkhceHM0IAAAAAPRPv4PFluSvq+rjVXVlV3t8a+2uJOkuz+3qa5N8Yc6+t3e1td31w+uH7NNam05yX5KzD2+iqq6squ1VtX1ycvK4PDEAAAAAWM76vSr0N7fW7qyqc5N8qKpuOsK28400bEeoH2mfQwutvS3J25Jk3bp1D7sfAAAAAOhNX0csttbu7C7vTvKnSZ6b5Evd9OZ0l3d3m9+e5Elzdj8/yZ1d/fx56ofsU1XDSc5IsrsfzwUAAAAA+Jq+BYtVdWpVnXbwepLvTPKpJFuTvLLb7JVJ/ry7vjXJ5d1KzxdmdpGWj3XTpe+vqud150/8ocP2OXisDUn+pjsPIwAAAADQR/2cCv34JH/araUynOQPW2sfrKp/SvL+qroiyeeTvDxJWms3VNX7k9yYZDrJa1prB7pjvTrJO5KsTvKB7idJ3p7k3VV1c2ZHKl7ex+cDAAAAAHT6Fiy21m5Jcuk89V1JXvgI+7w+yevnqW9P8g3z1KfSBZMAAAAAwMLp96rQAAAAAMASJFgEAAAAAHomWAQAAAAAeiZYBAAAAAB6JlgEAAAAAHomWARg0ZmcnBx0CwAAAMueYBGARWXHjh3ZsGFDdu7cOehWAAAAljXBIgCLxvT0dDZv3pzWWjZv3pzp6elBtwQAALBsCRYBWDS2bNmSPXv2JEl2796dLVu2DLgjAACA5UuwCMCisGvXroyNjWVqaipJMjU1lbGxsezevXvAnQEAACxPgkUAFoVt27ZlZmbmkNrMzEzGx8cH1BEAAMDyJlgEYFEYHR3N0NChb1tDQ0MZHR0dUEcAAADLm2ARgEVhZGQkGzduzKpVq5Ikq1atysaNGzMyMjLgzgAAAJYnwSIAi8b69eu/GiSOjIxk/fr1A+4IAABg+RIsArBoDA8P56qrrkpV5eqrr87w8PCgWwIAAFi2/EUGwKJy6aWX5pprrsmaNWsG3QoAAMCyZsQiAIuOUBEAAGDwBIsAAAAAQM8EiwAAAABAzwSLAAAAAEDPBIsAAAAAQM8EiwAAAABAzwSLAAAAAEDPBIsAAAAAQM8EiwAAAABAzwSLAAAAAEDPBIsAAAAAQM8EiwAAAABAz44qWKyqp1XVtqr6VHf7GVX1i/1tDQAAAAA4UR3tiMXfTXJVkv1J0lrbmeTyfjUFAAAAAJzYjjZYPKW19rHDatPHuxkAAAAAYHE42mDxnqr6uiQtSapqQ5K7+tYVAAAAAHBCGz7K7V6T5G1Jvr6q7khya5If7FtXAAAAAMAJ7aiCxdbaLUlGq+rUJEOttfv72xYAAAAAcCI72lWhN1fVma21B1pr91fVWVX16/1uDgAAAAA4MR3tORa/q7V278EbrbU9Sb67Lx0BAAAAACe8ow0WV1TVyoM3qmp1kpVH2B4AAAAAWMKONlh8T5JtVXVFVf2HJB9K8s6j2bGqVlTVJ6vqL7rbI1X1oaqa6C7PmrPtVVV1c1V9pqpeNKf+7Kq6vrvvjVVVXX1lVb2vq19bVRcc5fMBAAAAAI7BUQWLrbXfTPL6JP8yydOT/FpXOxo/meTTc26/Nsm21trFSbZ1t1NVlyS5vDv+ZUneXFUrun3ekuTKJBd3P5d19SuS7GmtPTXJbyd5w1H2BAAAAAAcg6MdsZjW2gdaaz/bWvuZ1tpfHc0+VXV+khcnGZtTfmm+NtrxnUleNqf+3tba3tbarUluTvLcqjovyemttY+21lqSdx22z8FjXZPkhQdHMwIAAAAA/XPEYLGqPtJd3l9VX57zc39Vffkojv/fkvx8kpk5tce31u5Kku7y3K6+NskX5mx3e1db210/vH7IPq216ST3JTl7nudxZVVtr6rtk5OTR9E2AAAAAHAkRwwWW2sv6C5Pa62dPufntNba6Ufat6q+J8ndrbWPH2Uv8400bEeoH2mfQwutva21tq61tm7NmjVH2Q4AAAAA8EgedSp0VQ1V1acew7G/OclLqupzSd6b5N9U1XuSfKmb3pzu8u5u+9uTPGnO/ucnubOrnz9P/ZB9qmo4yRlJdj+GXgEAAACAHjxqsNham0myo6qe3MuBW2tXtdbOb61dkNlFWf6mtfaDSbYmeWW32SuT/Hl3fWuSy7uVni/M7CItH+umS99fVc/rzp/4Q4ftc/BYG7rHeNiIRQAAAADg+Bo+yu3OS3JDVX0syQMHi621lzyGx/yNJO+vqiuSfD7Jy7tj3VBV709yY5LpJK9prR3o9nl1knckWZ3kA91Pkrw9ybur6ubMjlS8/DH0AwAAAAD06GiDxV85lgdprX04yYe767uSvPARtnt9ktfPU9+e5BvmqU+lCyYBAAAAgIVzxGCxqlYl+bEkT01yfZK3d6svAwAAAADL2KOdY/GdSdZlNlT8riT/pe8dAQAAAAAnvEebCn1Ja+0bk6Sq3p7kY/1vCQAAAAA40T3aiMX9B6+YAg3AiWJycnLQLQAAACx7jxYsXlpVX+5+7k/yjIPXq+rLC9EgAMy1Y8eObNiwITt37hx0KwAAAMvaEYPF1tqK1trp3c9prbXhOddPX6gmASBJpqens3nz5rTWsnnz5kxPG0wPAAAwKI82YhEAThhbtmzJnj17kiS7d+/Oli1bBtwRAADA8iVYBGBR2LVrV8bGxjI1NZUkmZqaytjYWHbv3j3gzgAAAJYnwSIAi8K2bdsyMzNzSG1mZibj4+MD6ggAAGB5EywCsCiMjo5maOjQt62hoaGMjo4OqCMAAIDlTbAIwKIwMjKSjRs3ZtWqVUmSVatWZePGjRkZGRlwZwAAAMuTYBGARWP9+vVfDRJHRkayfv36AXcEAACwfAkWAVg0hoeHc9VVV6WqcvXVV2d4eHjQLQEAACxbgkWWnK1bt2ZiYiJJMjExka1btw64I+B4uvTSS3PNNdfkGc94xqBbAQAAWNYEiyw54+PjOTDTctG/uCQHZpoVY2EJ2rVr16BbAAAAWPYEiyxJT3zyBXn1z78uT3zyBYNuBTjO/uRP/iRXXnll/vRP/3TQrQAAACxrgkUAFo29e/fmTW96U5LkjW98Y/bu3TvgjgAAAJYvwSIAi8av/Mqv5MCBA0mSAwcO5Fd+5VcG3BEAAMDyJVgEYFG4+eab85GPfOSQ2kc+8pHccsstA+oIAABgeRMsArAo/N7v/d689bGxsQXuBAAAgESwCMAisXHjxp7qAAAA9JdgEYBF4aKLLsrpp59+SO0FL3hBLrroogF1BAAAsLwJFgFYNC644IKvXl+xYkVe97rXDa4ZAACAZU6wCMCiMTT0tbetTZs2ZeXKlQPsBgAAYHkTLAKw6DztaU/L937v9w66DQAAgGVNsAjAonPKKacMugUAAIBlT7AIAMAJYXJyctAtAADQA8EiAAADt2PHjmzYsCE7d+4cdCsAABwlwSIAAAM1PT2dzZs3p7WWzZs3Z3p6etAtAQBwFASLAAAM1JYtW7Jnz54kye7du7Nly5YBdwQAwNEQLAIAMDC7du3K2NhYpqamkiRTU1MZGxvL7t27B9wZAACPRrAIAMDAbNu2LTMzM4fUZmZmMj4+PqCOAAA4WoJFAAAGZnR0NENDh34kHRoayujo6IA6AgDgaAkWAQAYmJGRkWzcuDGrVq1KkqxatSobN27MyMjIgDsDAODRCBYBABio9evXfzVIHBkZyfr16wfcEQAAR0OwCADAQA0PD+eqq65KVeXqq6/O8PDwoFsCAOAo+NQGAMDAXXrppbnmmmuyZs2aQbcCAMBR6tuIxapaVVUfq6odVXVDVf1KVx+pqg9V1UR3edacfa6qqpur6jNV9aI59WdX1fXdfW+squrqK6vqfV392qq6oF/PBwCA/hIqAgAsLv2cCr03yb9prV2a5JlJLquq5yV5bZJtrbWLk2zrbqeqLklyeZKnJ7ksyZurakV3rLckuTLJxd3PZV39iiR7WmtPTfLbSd7Qx+cDAAAAAHT6Fiy2WV/pbp7U/bQkL03yzq7+ziQv666/NMl7W2t7W2u3Jrk5yXOr6rwkp7fWPtpaa0neddg+B491TZIXHhzNCAAAAAD0T18Xb6mqFVV1XZK7k3yotXZtkse31u5Kku7y3G7ztUm+MGf327va2u764fVD9mmtTSe5L8nZ8/RxZVVtr6rtk5OTx+nZAQAAAMDy1ddgsbV2oLX2zCTnZ3b04TccYfP5Rhq2I9SPtM/hfbyttbautbbOuXsAAAAA4Nj1NVg8qLV2b5IPZ/bciF/qpjenu7y72+z2JE+as9v5Se7s6ufPUz9kn6oaTnJGkt39eA4AAAAAwNf0c1XoNVV1Znd9dZLRJDcl2Zrkld1mr0zy5931rUku71Z6vjCzi7R8rJsufX9VPa87f+IPHbbPwWNtSPI33XkYAQAAAIA+Gu7jsc9L8s5uZeehJO9vrf1FVX00yfur6ookn0/y8iRprd1QVe9PcmOS6SSvaa0d6I716iTvSLI6yQe6nyR5e5J3V9XNmR2peHkfnw8AAAAA0OlbsNha25nkWfPUdyV54SPs8/okr5+nvj3Jw87P2FqbShdMAgAAAAALZ0HOsQgAAAAALC2CRQAAAACgZ4JFlpStW7dmYmIid37+c3nLb/5K7vz85zIxMZFNmzZl69atg24PAAAAYMno5+ItsODGx8czfWAma5745OzbfyDnnPekJMmnb/pMkuQlL3nJINsDAAAAWDIEiyw5a5745Lzi1T9/SO39b/nNAXUDAAAAsDSZCg3AonHPPfckSSYmJpzeAAAAYMAEiwAsGnv27Mmpp6xK2oGMj48Puh0AAIBlzVRoFrWtW7ceEi5MTExk/4GZh019vvvOz2fPl4ayadOmJMno6KjzLcIi9XVPXjvoFgAAAIhgkUVufHw8N3z6Mznr8bNBw6lnn5ck2Td94JDtzjx39v47d38le750RxILuQAAAAAcC8Eii95Zj1+bF/77TUe9/bZ3v7GP3QAAAAAsD86xCAAAAAD0TLAIAABA30xOTg66BQD6RLAIAABAX+zYsSMbNmzIzp07B90KAH0gWAQAAOC4m56ezubNm9Nay+bNmzM9PT3olgA4zizewqKwdevWjI+PP6w+MTGRfdMHelqQZc+Xbs8Du1Zk06aHL/gyOjpqtWgAADgOtmzZkj179iRJdu/enS1btuQVr3jFgLsC4HgSLLIojI+P5/obb8rjzjnvkPrwGedmOMne6QNHfaxTzp49xq1333dI/Sv33JUkgkUAADhGu3btytjYWKamppIkU1NTGRsby+joaEZGRgbcHQDHi2CRReNx55yXZ73sR/p2/E/+2e/27dgAALCcbNu2LTMzM4fUZmZmMj4+btQiwBLiHIsAAAAcV6OjoxkaOvTPzaGhoYyOjg6oIwD6QbAIAADAcTUyMpJXvepVh9Re9apXmQYNsMQIFgEAADjudu7cecjt66+/fkCdANAvgkUAAACOq5tvvjn/8A//cEjt7//+73PLLbcMqCMA+kGwCAAAwHH11re+dd76m9/85gXuBIB+EiwCAABwXH3d131dT3UAFifBIgAAAMfVd37nd85bf9GLXrTAnQDQT4JFAAAAjquPf/zjqapDalWV7du3D6gjAPpBsAgAAMBx9exnPzuttUNqrbWsW7duQB0B0A+CRQAWha1bt+ahhx7KZ2+7I5+97Y7cc889g24JAJjH1q1b8wu/8Avz3veOd7xjYZsBoK+GB90AAByN8fHxrF55Ui46f01uuf3u7NmzZ9AtAQDzGB8fz3333ptn/Mt/eUj9s7fdlsnJyQF1BUA/CBYBWDQuOv/c/OZPfX9+/r/9UW65c/eg2wEAHsHXPeUp+a1f/MVDaj/367+eoZNOGlBHAPSDqdAAAAAAQM8EiwAAAABAzwSLAAAAAEDPBIsAAAAAQM8EiwAAAABAzwSLAAAAAEDPBIsAAAAAQM8EiwCc8LZu3ZqJiYnccvvd+fn/9ke55fa789BDD2XTpk3ZunXroNsDAABYlvoWLFbVk6rqf1fVp6vqhqr6ya4+UlUfqqqJ7vKsOftcVVU3V9VnqupFc+rPrqrru/veWFXV1VdW1fu6+rVVdUG/ng8AgzM+Pp4c2JcLn3Bm2vTs5SVPWZOJm26YvQ8AAFhQk5OTg26BE0A/RyxOJ/mZ1tq/TPK8JK+pqkuSvDbJttbaxUm2dbfT3Xd5kqcnuSzJm6tqRXestyS5MsnF3c9lXf2KJHtaa09N8ttJ3tDH5wPAAF143kh+48rvOuTnwvNGBt0WAAAsOzt27MiGDRuyc+fOQbfCgPUtWGyt3dVa+0R3/f4kn06yNslLk7yz2+ydSV7WXX9pkve21va21m5NcnOS51bVeUlOb619tLXWkrzrsH0OHuuaJC88OJoRAAAAgONreno6mzdvTmstmzdvzvT09KBbYoAW5ByL3RTlZyW5NsnjW2t3JbPhY5Jzu83WJvnCnN1u72pru+uH1w/Zp7U2neS+JGfP8/hXVtX2qtpuqC4AAADAY7Nly5bs2bMnSbJ79+5s2bJlwB0xSMP9foCqelySP0nyU621Lx9hQOF8d7Qj1I+0z6GF1t6W5G1Jsm7duofdDwAAwGO3devWr573eGJiImktP/frv37INp+97bakKps2bUqSjI6O5iUvecmC9wo8drt27crY2FimpqaSJFNTUxkbG8vo6GhGRpymaDnq64jFqjops6HiH7TWDkbYX+qmN6e7vLur357kSXN2Pz/JnV39/Hnqh+xTVcNJzkiy+/g/EwAAAB7J+Ph4/vmmm7L/3ntzwZo1ueDcc3PgoYcO+bng3HNzwZo12X/vvfnnm26yABssQtu2bcvMzMwhtZmZGa/nZaxvIxa7cx2+PcmnW2v/dc5dW5O8MslvdJd/Pqf+h1X1X5M8MbOLtHystXagqu6vqudldir1DyX574cd66NJNiT5m+48jAAAACygC5/whPzaxo1Hte0vjY31uRugH0ZHRzN22Ot3aGgoo6OjA+qIQevniMVvTvLvk/ybqrqu+/nuzAaK31FVE0m+o7ud1toNSd6f5MYkH0zymtbage5Yr04yltkFXT6b5ANd/e1Jzq6qm5P8dLoVpgEAAAA4vkZGRrJx48asWrUqSbJq1aps3LjRNOhlrG8jFltrH8n850BMkhc+wj6vT/L6eerbk3zDPPWpJC8/hjYBOEEdfq6mdmBfXvu2DxyyzS137U7d/RXnaoIlYnJyMmvWrBl0GwDAEaxfvz5btmzJnXfemZGRkaxfv37QLTFAC7IqNAD0anx8PBOf/lSmd38+F569Mhede1pyYP8hPxede1ouPHtlpnd/PhOf/pRzu8AitmPHjmzYsCE7d+4cdCsAwBEMDw/nqquuSlXl6quvzvBw39cF5gTm/z4AJ6wLH396Xv+D33RU2/7Ce67tczdAv0xPT2fz5s1prWXz5s15z3ve448UADiBXXrppbnmmmvMNMCIRQAABmvLli3Zs2dPkmT37t3ZsmXLgDsCAB6NUJFEsAgAwADt2rUrY2NjmZqaSpJMTU1lbGwsu3fvHnBnAAA8GsEiAAADs23btszMzBxSm5mZcc5UAIBFQLAIAMDAjI6O9lQHAODE4azYDMTWrVt7GokwMTGRqf0H8sk/+92+9fSVe+7KxH13Z9OmTUe9z+joaF7ykpf0rScAWOpGRkaydu3a3HrrrV+trV27NiMjIwPsCgCAoyFYZCDGx8ez84ZPZ+VZjz+6HU49Oycn2Td9oG89nXzmuZlJ8pk7j+6cTnv3fClJBItwDI70JcPExETa9N6jXu35li99ObVrYt4vB3wJACeum2+++ZBQMUluvfXW3HLLLbnooosG1BUAAEdDsMjArDzr8XnyC//9oNt4zD6/7d2DbgEWvfHx8fzzjdfnwnNWP+y+C86oJKvSpvcd1bEuPHtVkmT/3TcfUr/1noeS+BIATlS/93u/N299bGwsmzdvXuBugEfzSF8Kzn4hOJ1fGhs7quPcetddqclJXwgCLHKCRQAG6sJzVufX1v+Lvh3/l7Z8pm/HBo7dxo0b85GPfGTeOnDimf1S8MY8+bDTFTzptNOSJAe6Fd4fzZPPOitJMvXFLx5S/3y3IrxgEWBxECwCADAwh68IDZz4njwykl948ff05div/19/0ZfjAtAfgkUAjpvHsjBT2/9QX0cV3nrPg6n75j/34iMxBQsWzsGp0KecekqS5MEHHkxiKjQAwGIgWATguBkfH88/37AjF4ycdFTbP+VxSTKcduDozqP4WFxw1nCS/dl3141Htf3ndu9PYgoWLJSnPvWps1Oh26F1U6EBAE58gkUAjqsLRk7KL1927qDbeMx++YN3D7oFWFauu+66rDplVc57ynm547Y7kiQveMELrAgNALAIDA26AQAAlre1T1mb/+cX/5+sfcraJMnrXve6AXcEAMDRECwCADAQW7duzcTERO647Y68+dffnDtuuyNVlZ/7uZ/L1q1bB90eAACPwlRoAAAWzNxFniYmJvLgQw/m5JUn5/bP3Z4kOXnVydmxc0cmJia+up0FlQAATkyCRYBlotcVmx+LiYmJtH37F/V5Cj+3e3/qK72tIv1YCEpYyo70+2ZiYiIPPvhghlfOfgwdPnk4M23mkG2GTx7OvgP7csNNN2R67/QhIeNcXkcAAIMlWARYJmZXbL4uTzmj+vYYT16dZHXSDuzv22P021POSJIHs/f2HX17jNvum13+ViDCYtHrFxMTExN54MEHMnzy/B81V5y8Iq21ee+bb9u903vzqU9/6pD69L5HDhyPRBgJAHD8CBYBlpGnnFH5T99y0qDbWPZ+9e8Wb/DK8jQ+Pp6dN+7M6jWrj26HM5NTzzy1rz2tzMokycTkxFHv89DkQ0mE+gAAx4tgEQBgEVuo0xwsFRMT/T3VgRGRAMByIlgEAFjExsfHs+PGHRke6ePHutOSFVmRfQf29e8xFsCKkRXZl3254Ys39OX407unkxgRCQAsH4JFAIBFbnhkOGd+95mDbmPZu/cv7x10CwAAC0qwyMMs1JSqvfum8/lt7+7r4/TT3j1fysQDu6wcCwAAACxLgkUeZnx8PNd96sasOG1N/x7k5DNTJyf79h/o32P0WT3unEwluf62yb49xoH7Z48tWBy8hQjc+21iYiJtb7NwyAngtntb6qH+nudtofjyAwAAli/BIvNacdqanPpNGwbdxrL3wLXXDLoFOuPj4/nnT30iT37c4g3Dn3RSkpOSNj3oTnjy45Lk3kx97p8G3cox+fxXViTx5QcAACxXgkWAo/Tkxx3IL677yqDbgBPGr29/3KBbAAAABkiwCAAsW0vlNAfT+6YtHHICmN41nYn7neYAAFg+BIsAwLI1Pj6eT97wyeTMQXdyDE6Z/dl3YN+gO+HM5Cv5Sj55xycH3cmxuXf2QrC49D2WL1cmJiYys29fXv+//qIvPd22a1eG7r+/54BeGA4wGIJFAGB5OzOZ+baZQXcBJ4yhDw8NugUWyPj4eD7zqU/l/Mcd/aktnnjSSclJJ+XA1FRfejr/1FOTJA987nNHvc/tX5k9VY1gEWDhCRYBAACWqfMf97j8zL969qDbOCb/5RMfH3QLsOg90gjme+65J3v27OnpWGeddVbOOeech9WNLF6aBIsAAAAAy9j4+Hg+c9M/50lrn3JI/ZSVp+WUJ5zW8/EevH/vIbe/cMdtSYwsXooEiwAAAADL3JPWPiU/9+O/1Jdj/9abfq0vx2XwnEAFAAAAAOiZYBEAAAAA6JlgEQAAAADomWARAAAAAOhZ34LFqvq9qrq7qj41pzZSVR+qqonu8qw5911VVTdX1Weq6kVz6s+uquu7+95YVdXVV1bV+7r6tVV1Qb+eCwAAAABwqH6uCv2OJG9K8q45tdcm2dZa+42qem13+/9XVZckuTzJ05M8Mcl4VT2ttXYgyVuSXJnkH5P8ZZLLknwgyRVJ9rTWnlpVlyd5Q5Lv6+PzAZa5u7+8Nx/+3IFBtwEnjLu/vDdPHnQTAADAwPRtxGJr7f8k2X1Y+aVJ3tldf2eSl82pv7e1tre1dmuSm5M8t6rOS3J6a+2jrbWW2ZDyZfMc65okLzw4mhEAAAAA6K9+jlicz+Nba3clSWvtrqo6t6uvzeyIxINu72r7u+uH1w/u84XuWNNVdV+Ss5Pcc/iDVtWVmR31mCc/2dgK4LE59/SV+bYL9g+6DThhfOSelYNuAQAAGKATZfGW+UYatiPUj7TPw4utva21tq61tm7NmjWPsUUAAAAA4KCFHrH4pao6rxuteF6Su7v67UmeNGe785Pc2dXPn6c+d5/bq2o4yRl5+NRrAIAjmrpvKm1i3u8mYVmq++prc4QAAI5goUcsbk3yyu76K5P8+Zz65d1KzxcmuTjJx7pp0/dX1fO68yf+0GH7HDzWhiR/052HEQAAAADos76NWKyqP0rybUnOqarbk7wuyW8keX9VXZHk80leniSttRuq6v1JbkwyneQ13YrQSfLqzK4wvTqzq0F/oKu/Pcm7q+rmzI5UvLxfzwUAWLpWnbEqMxfPDLoNOGEM3XGinC0JADjR9S1YbK19/yPc9cJH2P71SV4/T317km+Ypz6VLpg8kWzdujXj4+ODbuOYTExM5MDe/Xng2msG3cqyd+D+yUxM3JtNmzYNupVjMjo6mpe85CWDbgMAAAA4jhb6HItL3vj4eD55/Y2ZOWVk0K08dkOnJauTA9MHHn1b+mv1SL6c5OOf/eKgO3nMhh6cPfWpYBEAAACWFsFiH8ycMpKpS75n0G3ACWHVjX8x6BYAAACAPhAsAgAAnID6fZqliYmJzExN5b984uN9e4yFcPv992doYqKvpw9yah+A+QkWAQAATkDj4+O5aefOrD355L4c/wlJctJJmZma6svxF8oTTzopOXAg9990U1+Of8e+fUmc2ofF47F8KTExMZGZAy2/9aZf60tPX7jjtgytqJ6/ABDqn/gEiwAAACeotSefnNec98RBt7Gs/Y+77hx0C9CT8fHx3PTpz+QJa84/6n3WnDX7e2bf3um+9PT4c9YmSe6954Gj3ueLk7cnEeqf6ASLAAAAAEvIE9acn40v/4+DbuOYjP3xbw+6BY6CYBHgKH3+Kyvy69sfN+g24ITx+a+syNMG3QQAADAwgkWAozA6OjroFo7ZxMRE2t4H8pQza9CtLHu33dtSK0/NxRdfPOhWjsnTsjReGwAAC2khFmaa3j+z6Ef83XX37ZncM2RhphOcYBHgKLzkJS9Z9G84mzZtyt7bd+Q/fctJg25l2fvVv9ufledfnDe+8Y2DbgUAgAU2Pj6eG2+4KWvOOK8vxz9z9bnJ6mT/VH/Ol7hQzjn9CUmSydvv68vxJ++7K4lzOB4rwSIAAADAAlpzxnlZ/60bB93Gsrblb8cG3cKSIFgEWEZuu6/lV/9u/6DbWPZuu6/laUe/SB8AAMAJSbDYB3vv35P9d9w46DbghFD370nyhEG3QRbmXHgTExNp+x7MBSOLd7r153bvT518Sl/Pf/i0852bEAAAWPwEiwDLxEKcJ3LTpk3Zd9eN+eXLzu3r4/TTL3/w7px8nvMfAgAAPBrBYh+sPO2stLWXDLoNOCGsvO+WQbfAAvvc7v355Q/ePeg2HrPP7d6fp/XnPNoALJB+r7i6UCYmJnJg7978j7vuHHQry9ode/dmxcREX1emXShWwAWON8EiAMdNr9N7JyYm0vY/lAvPOaVPHSW33vNg6qTVRz21+WnnmaYMsNiNj4/npuuuW/QnY1nTXR6YmhpoH8vdE5LkgQdy73XXDbiTY/PF7lKwCBxPgkUAjptep1tv2rQp/3zj9X3sKEkqF19sajNHcG8y9OGhQXcBJ457k6wddBPH7glJrkgNug04Ybw9bdAtHLOlNBp5/94DViUesMl778q9D91tNPIxEiwCMDBHGhk4MTGRhx58IKtOPrq3qql901l9yqkPG5n4tHONQOSRLYV/GxMTE3lw34MZPtvHukGb3jWdU/q8+NOCWLs0XhvA0jM+Pp4brv90zjxl8Z7PO0lOGRpJVif79k4PupVl7YzVs+PC7/jsrgF3cmzufXD2NFSCRQCWnSONcJz7jfRsyPhgVq089G1rau90Vp/ytT/inTeIXi3Eokb9tmnTptzwxRty5nefOehWlr17//LeXPwEI6QB+unMU87Nt3/95YNuA04Y//um9w708QWLfTD04O6suvEvBt0GnBCGHtydLPozHDEIcwOfrVu35i1veUseeujBrF55ch7auy8zMy3f+I3fmBe96EWLPhgCAABYjASLx9lSmDYyMTGRB/buz4rT1jz6xvTVgfsnc+rKkxb5lKonLInXBYN1MDj8z//5P+ehvfu+Wv/yl78sVAQAABgQweJxtlSmVF1/22RO/aYNg25l2Xvg2mty8VPWmFIFSdaunV1JYGbmaycev+222/LJT34yz3rWswbVFpwQpndP596/vHfQbSx707unDdIHAJYVwSLzOnD/ZB649ppBt7HsHbh/MomRo5Akv/VbvzVv/Td/8zfzR3/0RwvcDZw4FmJU+MTERB548IEMnbS4V8+e2T+TU+dZ5Om4ecLSmL0CcCK77/7d+ec7rxt0G3DCuO/+3Vmbswf2+IJFHmbh/kB5MEPDJ/f9sfplZnpfTj2l3ys/rvEHCnRGRkZy5513zluH5WwhZkvMXUzpaNxzzz3Zs2fPvPft378/09Ozq2C21tJam3e7qkpVJUmGh4dz0kknPWybs846K+ecc85R95VY5AlgsXtg33258Y5/GHQbcMJ4YN99A318wSIPcyL+gXIwiFxx0sq+9XRg/96eg0J/nMDC+dmf/dm86lWvelj9Z37mZxa+GVhm+vnZ4Jd+6Zfyt3/7txkamh0NOTMzkyR5zWtek1e84hV9eUwAFqelMuhiYmIiDz74YE5a0b+/b3l0+w/szSl9Hyy0MAb52hAsMhC9/oHySEHk4SMiHnrooczMzKTq0Klarc1kaGgoq1evTvLIIxwEhXDiuuiii/L85z8/H/3oR79ae/7zn5+LLrpogF0Bx+of/mF21MnBQPGg//k//6dgkWOya2oq/zToJuAEsivJmYNu4hgthTUNkt4H2vTqSDMHHsncGQVzHf7+fLQOfmE41yPNQDiSxzI7oRcygGMnWGRRONo3kK1bt+a9731v7r777kxPT2d4eDjnnntuzjnnHL8wYAn41V/91Xznd35nWmupqvzqr/7qoFsCjtGP/uiP5k1vetO8dQBYihZTQHp4CLp///7ceOONh5zKpKpyySWXfDU09Lf38iJYZElZTL+ggd6tXLkymzZtyu/8zu/kp37qp7JypekjsNi94hWvyFvf+tZDRkkMDw8brcgxO3vVqjwnNeg24ISxM/Of0xaO5PC/sd///vdnYmIi+/bt+2rtpJNOyrd/+7d7716mBIsALCr/9t/+2zz96U/P13/91w+6FeA4+eM//uN87/d+7yG34Vh9McnbBSnwVV/M4p8KzeCNjo5mbGzskNrQ0NCSOf8lvRMsArDoCBVhaTn77LPzdV/3dfnsZz+biy++OGefffagW2KRWyp/4E5MTOTAgw9mrRH6A3XH3r1ZsQQWeDgzS+e1weCMjIxk48aNGRsby9TUVFatWpWNGzdmZGRk0K0xIDV3XvxysG7durZ9+/ZBtwEAwGF+//d/Pz/8wz886DbghLFp06bctHNn1p588qBbWdbu2LcvX/+MZ+SNb3zjoFuBE8L09HR+8Ad/MHfeeWee+MQn5j3veU+Gh41bWwbmPb+I//MAAJwQhIpwqH6PLpuYmMhDDzyQlStW9PVx+m3vgQNZfeqpfRtR+PUx0g/mGh4ezlVXXZVNmzbl6quvFiouc0YsAgAALEOHr/Z6NO65557s2bPnYfX9+/d/dRGm1lqO9Hfm0NBQktlw4uAqsnOdddZZOeecc3rqyyq0sPAmJyezZs2aQbfBwpl3xKJgEQAAgOPqsssuy4MPPviw+imnnJIPfvCDA+gIgGM0b7A4tNBdAAAAsLT9xE/8RE91ABYnwSIAAADH1Ytf/OKsXr36kNrq1avz4he/eEAdAdAPgkUAAACOu/e9731HvA3A4idYBAAA4Lg788wzc+mllyZJnvWsZ+XMM88cbEMAHHeLfvGWqrosye8kWZFkrLX2G0fa3uItAAAAC+eP//iP8/KXv3zQbQBwbJbe4i1VtSLJ/0jyXUkuSfL9VXXJYLsCAADgIKEiwNK1qIPFJM9NcnNr7ZbW2r4k703y0gH3BAAAAABL3mIPFtcm+cKc27d3tUNU1ZVVtb2qtk9OTi5YcwAAAACwVC32YHG++d0PO2lka+1trbV1rbV1a9asWYC2AAAAAGBpW+zB4u1JnjTn9vlJ7hxQLwAAAACwbCz2YPGfklxcVRdW1clJLk+ydcA9AQAAAMCSNzzoBo5Fa226qn48yV8lWZHk91prNwy4LQAAAABY8hZ1sJgkrbW/TPKXg+4DAAAAAJaTxT4VGgAAAAAYAMEiAAAAANAzwSIAAAAA0DPBIgAAAADQs2qtDbqHBVVVk0luG3QfLJhzktwz6CaA485rG5Ymr21Ymry2YWny2l5e7mmtXXZ4cdkFiywvVbW9tbZu0H0Ax5fXNixNXtuwNHltw9LktU1iKjQAAAAA8BgIFgEAAACAngkWWereNugGgL7w2oalyWsbliavbViavLZxjkUAAAAAoHdGLAIAAAAAPRMsAgAAAAA9EyyyqFTVgaq6rqpuqKodVfXTVXXEf8dVdUFVfaq7/qqqetMjbPeXVXVmH9oGDlNVT6iq91bVZ6vqxu7197Qej/Hhqlo3T31dVb3x+HULzGfOe/KOqvpEVf1ffXiMb6uqvzjexwVmVdVXDrv9iJ+VH2H/r37O7kc/C70/LCdzXy9V9d1VNVFVT3607Xt9b+62P+6fEThxDA+6AejRQ621ZyZJVZ2b5A+TnJHkdcd64Nbadx/rMYBHV1WV5E+TvLO1dnlXe2aSxyf552M9fmtte5Ltx3oc4FHNfU9+UZL/f5JvHWRDVTXcWpseZA/AI6uqFa21A4PuA/iaqnphkv+e5Dtba5/vw0N8W5KvJPmHPhybE4ARiyxarbW7k1yZ5Mdr1oqq+q2q+qeq2llVP/oIuz6xqj7YfSPzmweLVfW5qjpnQZqH5e3bk+xvrb31YKG1dl2Sj3Sv4U9V1fVV9X0H76+qn+9qO6rqN+Yc6+VV9bGq+ueq+pZu269+i1pVv1xVv9eNbrylqjYtzFOEZef0JHuSpKoeV1XbulGM11fVS7v6BVX16ar63W7mwV9X1eruvud0790fPfh74PAHqKqRqvqzbrt/rKpndPVfrqq3VdVfJ3lXVa2pqj/pPg/8U1V988L9Z4CloapOq6pbq+qk7vbp3Wflk6rq2d378UeTvGbOPvN+Fu/el/93Vf1hkuu72p9V1ce73wVXHvbY/6X7/bGtqtZ0tR/pjruje32f0tUv7H5v/FNV/drC/NeBpaP7/Py7SV7cWvtsV/vp7vP4p6rqpx5l/+dU1Ser6qKq+r+r6tru9nhVPb6qLkjyY0n+Y83OcviW+bbr9/OkvwSLLGqttVsy++/43CRXJLmvtfacJM9J8iNVdeE8uz0zyfcl+cYk31dVT1qgdoFZ35Dk4/PU12f29XlpktEkv1VV51XVdyV5WZJvaq1dmuQ35+wz3Fp7bpKfyiOPXP76JC9K8twkrzv4RxJwzFZ3fyTclGQsycE/6qeSfG9r7V9l9ouE/1JV1d13cZL/0Vp7epJ7k/zbrv77SX6stfb8JI80mulXknyytfaMJFcnedec+56d5KWttX+X5HeS/Hb3eeDfdr0B8zv4Or6uqq5L8qtJ0lq7P8mHk7y42+7yJH/SWtuf2dfrpu71OteRPos/N8kvtNYu6W7/h9bas5OsS7Kpqs7u6qcm+UT3++Nv87X39i2tted0nwM+3T1WMvt6f0v3mF881v8YsMysTPLnSV7WWrspSarq2Ul+OMk3JXleZl/Hz5pv55qd3vzWzL7/3pLkI0me11p7VpL3Jvn51trnum1+u7X2zNba3823XR+fIwvAVGiWgoN/rHxnkmdU1Ybu9hmZ/QPm8KmV21pr9yVJVd2Y5ClJvrAQjQJH9IIkf9RNkfpSVf1tZv8w+dYkv99aezBJWmu75+yzpbv8eJILHuG4/6u1tjfJ3qq6O7NTrm/vQ/+w3MydCv38zI4W/IbMvi9vrqp/nWQmydrMvu6S5NZuhHLSvW5r9vzGp7XWDk6R+sMk3zPP470gXRDZWvubqjq7qs7o7tvaWnuouz6a5JKvZZk5vapO64IS4FBffR0ns+dYzGzYl8yG8j+f5M8yGzT8SPeaO7O19rfdNu9O8l3d9Uf6LL4vycdaa7fOedxNVfW93fUnddvtyuzvjPd19ffka+/z31BVv57kzCSPS/JXXf2b87UvKN6d5A09PXtY3vZndnryFUl+squ9IMmfttYeSJKq2pLkW5J88rB9/2WSt2V2+vSdXe38JO+rqvOSnJzk1szvaLdjkTBikUWtqi7K7MiGuzP7h8xPdN+EPLO1dmFr7a/n2W3vnOsHImCHhXZDZkcXHa7mqR2st0e47+Dr+UivZa956LPW2keTnJNkTZIf6C6f3QUWX0qyqtt0vtfjI732Dzffdgd/NzwwpzaU5PlzPg+sFSpC71prf5/Z8P9bk6xorX0qR35PPtJn8a++Rqvq2zL7BcDzuxGIn8zXfkc8rI3u8h1Jfry19o2ZHb28ap5tgN7MJHlFkudU1dVd7Wjfk+/K7AyFuaMZ/3uSN3Wv0x/NI7+uj3Y7FgnBIotWd86Vt2b2l1LL7DeXr55zLpinVdWpg+wRmNffJFlZVT9ysFBVz8ns+dm+rztH05ok/zrJx5L8dZL/MOd8SiMD6Bk4gqr6+iQrMjvi6Iwkd7fW9lfVt2d2ZsAjaq3tSXJ/VT2vK13+CJv+n8yGlgeDiXtaa1+eZ7u/TvLjc3p75lE/EeBw70ryR5md/pzW2r1J7quqF3T3/8CcbY/2s/gZSfa01h7sfnc8b859Q0kOjnj8d5mdMpkkpyW5qzv23Mf8+3ztd8bcOnAUuhlB35PkB6rqisy+176sqk7pXr/fm+Tv5tn13syeKmFz956czL627+iuv3LOtvdn9jWcR9mORcqoDRab1d35X05KMp3ZKQ//tbtvLLNTIT/RnctpMrPnZQNOIK211k1/+m9V9drMftv5ucyeJ/FxSXZkdvTBz7fWvpjkg10wsL2q9iX5y8yeXw0YrIPvycnsCIdXttYOVNUfJPl/q2p7kuuS3HQUx7oiye9W1QOZPa/bffNs88tJfr+qdiZ5MI/8x8imJP+j2244s38k/djRPCHgYf4gya9nNlw86IeT/F5VPZivTUlOjv6z+AeT/Fj3Gv1Mkn+cc98DSZ5eVR/P7O+Bgwu5/VKSa5PcltkFYA6GFD+Z5A+r6ieT/Mlje4qwvLXWdlfVZZl9v/ypzI4Q/lh391hr7fBp0Af3+1JV/d9JPlBV/yGz79N/XFV3ZPZ1ffAcq/9vkmtqdjG3nzjCdixSNTvQCwAABqOqHtda+0p3/bVJzmut/eSj7Ab0WXe+xJe21v79oHsB4MRkxCIAAIP24qq6KrOfTW9L8qrBtgNU1X/P7MIs3z3oXgA4cRmxCAAAAAD0zOItAAAAAEDPBIsAAAAAQM8EiwAAAABAzwSLAADMq6oOVNV1VXVDVe2oqp+uqsf0+bGqrj7s9j8cQ1+vqqonzrk9VlWXPNbjAQDw2Fi8BQCAeVXVV1prj+uun5vkD5P8fWvtdcdyrOPQ14eT/GxrbfvxOB4AAI+NEYsAADyq1trdSa5M8uM1a0VV/VZV/VNV7ayqH02Sqjqvqv5PN9LxU1X1LVX1G0lWd7U/6Lb7Snf5bVX14aq6pqpuqqo/qKrq7vtP3fE/VVVv6x53Q5J1Sf6gO97qbv913T7fX1XXd/u84WD/VfWVqnp9N/LyH6vq8Qv6HxAAYAkSLAIAcFRaa7dk9vPjuUmuSHJfa+05SZ6T5Eeq6sIk/y7JX7XWnpnk0iTXtdZem+Sh1tozW2s/MM+hn5Xkp5JckuSiJN/c1d/UWntOa+0bkqxO8j2ttWuSbE/yA93xHjp4kG569BuS/Jskz0zynKp6WXf3qUn+sbV2aZL/k+RHjsN/EgCAZU2wCABAL6q7/M4kP1RV1yW5NsnZSS5O8k9JfriqfjnJN7bW7j+KY36stXZ7a20myXVJLujq315V11bV9ZkNC5/+KMd5TpIPt9YmW2vTSf4gyb/u7tuX5C+66x+f8xgAADxGw4NuAACAxaGqLkpyIMndmQ0Yf6K19lfzbPevk7w4ybur6rdaa+96lEPvnXP9QJLhqlqV5M1J1rXWvtAFlasercUj3Le/fe3k4gficzAAwDEzYhEAgEdVVWuSvDWz05Nbkr9K8uqqOqm7/2lVdWpVPSXJ3a21303y9iT/qjvE/oPbHqWDIeI9VfW4JBvm3Hd/ktPm2efaJN9aVedU1Yok35/kb3t4TAAAeuCbWgAAHsnqbqrzSUmmk7w7yX/t7hvL7HTiT3SLrUwmeVmSb0vyc1W1P8lXkvxQt/3bkuysqk88wnkWD9Fau7eqfjfJ9Uk+l9kp1ge9I8lbq+qhJM+fs89dVXVVkv+d2dGLf9la+/NenzQAAEenvjYjBAAAAADg6JgKDQAAAAD0TLAIAAAAAPRMsAgAAAAA9EywCAAAAAD0TLAIAAAAAPRMsAgAAAAA9EywCAAAAAD07P8D+9LhqUAOKVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Price vs Destination to see individual Destination prices\n",
    "\n",
    "sns.catplot(y=\"Price\",x = \"Destination\", data = train_data.sort_values('Price',ascending=False),kind=\"boxen\", height=6,aspect=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c070282b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  1                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  1                      0   \n",
       "\n",
       "   Destination_Kolkata  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is nominal categorical data\n",
    "# Using One Hot Encoding for it and making dummy variables for Destination\n",
    "\n",
    "Destination = train_data[['Destination']]\n",
    "Destination = pd.get_dummies(Destination,drop_first=True)\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38ca605c",
   "metadata": {},
   "source": [
    "### Route, Additional_Info, Total_Stops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4fd3f044",
   "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: 10462, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Route\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "573444d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No info                         8182\n",
       "In-flight meal not included     1926\n",
       "No check-in baggage included     318\n",
       "1 Long layover                    19\n",
       "Change airports                    7\n",
       "Business class                     4\n",
       "No Info                            3\n",
       "Red-eye flight                     1\n",
       "1 Short layover                    1\n",
       "2 Long layover                     1\n",
       "Name: Additional_Info, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking value count for Additional_Info\n",
    "\n",
    "train_data['Additional_Info'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a491c36d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Additional_Info\"] = train_data[\"Additional_Info\"].replace({'No Info': 'No info'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "864d1a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info\n",
    "\n",
    "train_data[\"Additional_Info\"].replace({'Change airports':'Other', 'Business class':'Other','1 Short layover':'Other','Red-eye flight':'Other','2 Long layover':'Other',   }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e2920e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label encode and hot encode categorical columns\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "train_data[\"Additional_Info\"]= label_encoder.fit_transform(train_data[\"Additional_Info\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dc3ba0ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3\n",
       "1        3\n",
       "2        3\n",
       "3        3\n",
       "4        3\n",
       "        ..\n",
       "10678    3\n",
       "10679    3\n",
       "10680    3\n",
       "10681    3\n",
       "10682    3\n",
       "Name: Additional_Info, Length: 10462, dtype: int32"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Additional_Info\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1ed0bbd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      5625\n",
       "non-stop    3475\n",
       "2 stops     1318\n",
       "3 stops       43\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking value count for Total_Stops\n",
    "\n",
    "train_data['Total_Stops'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "de7daf9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now since Additional_Info contains almost 80%  of No info\n",
    "\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "# Deleting Route\n",
    "\n",
    "train_data.drop(['Route'],axis =1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "edae8bf2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Price vs Total_Stops to see individual Total_Stops prices\n",
    "\n",
    "sns.catplot(y=\"Price\",x = \"Total_Stops\", data = train_data.sort_values('Price',ascending=True),kind=\"boxen\", height=6,aspect=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859b5e10",
   "metadata": {},
   "source": [
    "As Total_Stops is increasing, Price is also increasing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "02e09f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total_Stops is ordinal categorical type\n",
    "\n",
    "train_data.replace({'non-stop':0, '1 stop':1, '2 stops':2, '3 stops':3, '4 stops':4}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03fcf493",
   "metadata": {},
   "source": [
    "Concatinating the Airline, Source and Destination to train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "345606b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = pd.concat([train_data, Airline, Source, Destination], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "194edca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Other</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Vistara</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>3</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>1</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>3</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>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",
       "    </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>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Additional_Info  Price  \\\n",
       "0       IndiGo  Banglore       Delhi            0                3   3897   \n",
       "1    Air India   Kolkata    Banglore            2                3   7662   \n",
       "2  Jet Airways     Delhi      Cochin            2                3  13882   \n",
       "3       IndiGo   Kolkata    Banglore            1                3   6218   \n",
       "4       IndiGo  Banglore       Delhi            1                3  13302   \n",
       "\n",
       "   Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
       "0           24              3        22       20             1           10   \n",
       "1            1              5         5       50            13           15   \n",
       "2            9              6         9       25             4           25   \n",
       "3           12              5        18        5            23           30   \n",
       "4            1              3        16       50            21           35   \n",
       "\n",
       "   Duration_hours  Duration_mins  Airline_Air India  Airline_GoAir  \\\n",
       "0               2             50                  0              0   \n",
       "1               7             25                  1              0   \n",
       "2              19              0                  0              0   \n",
       "3               5             25                  0              0   \n",
       "4               4             45                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Multiple carriers  \\\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_Other  Airline_SpiceJet  Airline_Vistara  Source_Chennai  \\\n",
       "0              0                 0                0               0   \n",
       "1              0                 0                0               0   \n",
       "2              0                 0                0               0   \n",
       "3              0                 0                0               0   \n",
       "4              0                 0                0               0   \n",
       "\n",
       "   Source_Delhi  Source_Kolkata  Source_Mumbai  Destination_Cochin  \\\n",
       "0             0               0              0                   0   \n",
       "1             0               1              0                   0   \n",
       "2             1               0              0                   1   \n",
       "3             0               1              0                   0   \n",
       "4             0               0              0                   0   \n",
       "\n",
       "   Destination_Delhi  Destination_Hyderabad  Destination_Kolkata  \n",
       "0                  1                      0                    0  \n",
       "1                  0                      0                    0  \n",
       "2                  0                      0                    0  \n",
       "3                  0                      0                    0  \n",
       "4                  1                      0                    0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9cc0f2cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting Airline, Source and Destination from data_train as I extracted useful information from it.\n",
    "\n",
    "data_train.drop([\"Airline\",\"Source\",\"Destination\"],axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ca6a1b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
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       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Other</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Vistara</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",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</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",
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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>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</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>3</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>1</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>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0            0                3   3897           24              3        22   \n",
       "1            2                3   7662            1              5         5   \n",
       "2            2                3  13882            9              6         9   \n",
       "3            1                3   6218           12              5        18   \n",
       "4            1                3  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0       20             1           10               2             50   \n",
       "1       50            13           15               7             25   \n",
       "2       25             4           25              19              0   \n",
       "3        5            23           30               5             25   \n",
       "4       50            21           35               4             45   \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_Multiple carriers  Airline_Other  Airline_SpiceJet  \\\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  Source_Chennai  Source_Delhi  Source_Kolkata  \\\n",
       "0                0               0             0               0   \n",
       "1                0               0             0               1   \n",
       "2                0               0             1               0   \n",
       "3                0               0             0               1   \n",
       "4                0               0             0               0   \n",
       "\n",
       "   Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0              0                   0                  1   \n",
       "1              0                   0                  0   \n",
       "2              0                   1                  0   \n",
       "3              0                   0                  0   \n",
       "4              0                   0                  1   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  \n",
       "0                      0                    0  \n",
       "1                      0                    0  \n",
       "2                      0                    0  \n",
       "3                      0                    0  \n",
       "4                      0                    0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c4d278c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10462, 27)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef8d72b",
   "metadata": {},
   "source": [
    "# Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "75c4996c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel(r\"Test_set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5d6f8003",
   "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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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "40477b55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2671, 10)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98778a95",
   "metadata": {},
   "source": [
    "Perfoming same operation to Test dataset also. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "db8a13e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data info\n",
      "\n",
      "\n",
      "\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",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Null value\n",
      "\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",
      "\n",
      "\n",
      "\n",
      "Exploratory Data Analysis\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(\"Test data info\")\n",
    "print(\"\\n\\n\")\n",
    "print(\"--\"*40)\n",
    "print(test_data.info())\n",
    "print(\"\\n\\n\")\n",
    "\n",
    "print(\"--\"*40)\n",
    "test_data.dropna(inplace=True)\n",
    "print(\"Null value\")\n",
    "print(\"\\n\")\n",
    "print(test_data.isnull().sum())\n",
    "print(\"\\n\\n\")\n",
    "\n",
    "print(\"Exploratory Data Analysis\")\n",
    "print(\"\\n\")\n",
    "print(\"--\"*40)\n",
    "\n",
    "# Date of Journey\n",
    "\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",
    "# Depature Time\n",
    "\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",
    "\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",
    "\n",
    "duration = list(test_data['Duration'])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split())!=2:\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip()+' 0m'\n",
    "        else:\n",
    "            duration[i]= \"0h \"+duration[i]\n",
    "            \n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep=\"h\")[0]))\n",
    "    duration_mins.append(int(duration[i].split(sep=\"m\")[0].split()[-1]))\n",
    "test_data['Duration_hours']= duration_hours\n",
    "test_data['Duration_mins']= duration_mins\n",
    "\n",
    "\n",
    "# Airline \n",
    "\n",
    "Airline = test_data[['Airline']]\n",
    "Airline = pd.get_dummies(Airline,drop_first=True)\n",
    "test_data[\"Airline\"].replace({'Multiple carriers Premium economy':'Other', 'Jet Airways Business':'Other','Vistara Premium economy':'Other','Trujet':'Other'}, inplace=True)\n",
    "# Source \n",
    "\n",
    "Source = test_data[['Source']]\n",
    "Source = pd.get_dummies(Source,drop_first=True)\n",
    "\n",
    "# Destination \n",
    "test_data['Destination'].replace({'New Delhi':'Delhi'},inplace=True)\n",
    "Destination = test_data[['Destination']]\n",
    "Destination = pd.get_dummies(Destination,drop_first=True)\n",
    "\n",
    "# Additional Info\n",
    "\n",
    "test_data[\"Additional_Info\"].replace({'Change airports':'Other', 'Business class':'Other','1 Short layover':'Other','Red-eye flight':'Other','2 Long layover':'Other',   }, inplace=True)\n",
    "test_data[\"Additional_Info\"]= label_encoder.fit_transform(test_data[\"Additional_Info\"])\n",
    "# now since Additional Info is contains almost 80% no info\n",
    "# and Route and Total stops are related to each other\n",
    "# droping Route\n",
    "\n",
    "test_data.drop(['Route'],axis =1 , inplace = True)\n",
    "\n",
    "# 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",
    "data_test = pd.concat([test_data, Airline, Source, Destination], axis=1)\n",
    "\n",
    "data_test.drop([\"Airline\",\"Source\",\"Destination\",\"Duration\"],axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0c969674",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Additional_Info  Journey_day  Journey_month  Dep_hour  \\\n",
       "0            1                3            6              6        17   \n",
       "1            1                3           12              5         6   \n",
       "2            1                1           21              5        19   \n",
       "3            1                3           21              5         8   \n",
       "4            0                3           24              6        23   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0       30             4           25              10             55   \n",
       "1       20            10           20               4              0   \n",
       "2       15            19            0              23             45   \n",
       "3        0            21            0              13              0   \n",
       "4       55             2           45               2             50   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               0                    1   \n",
       "1                  0              0               1                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               0                    0   \n",
       "4                  0              0               0                    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                          1   \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_Vistara  Airline_Vistara Premium economy  Source_Chennai  \\\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_Delhi  Source_Kolkata  Source_Mumbai  Destination_Cochin  \\\n",
       "0             1               0              0                   1   \n",
       "1             0               1              0                   0   \n",
       "2             1               0              0                   1   \n",
       "3             1               0              0                   1   \n",
       "4             0               0              0                   0   \n",
       "\n",
       "   Destination_Delhi  Destination_Hyderabad  Destination_Kolkata  \n",
       "0                  0                      0                    0  \n",
       "1                  0                      0                    0  \n",
       "2                  0                      0                    0  \n",
       "3                  0                      0                    0  \n",
       "4                  1                      0                    0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b9f05c7",
   "metadata": {},
   "source": [
    "# Train and Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3e913f82",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Additional_Info  Journey_day  Journey_month  Dep_hour  \\\n",
       "0            1                3            6              6        17   \n",
       "1            1                3           12              5         6   \n",
       "2            1                1           21              5        19   \n",
       "3            1                3           21              5         8   \n",
       "4            0                3           24              6        23   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0       30             4           25              10             55   \n",
       "1       20            10           20               4              0   \n",
       "2       15            19            0              23             45   \n",
       "3        0            21            0              13              0   \n",
       "4       55             2           45               2             50   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               0                    1   \n",
       "1                  0              0               1                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               0                    0   \n",
       "4                  0              0               0                    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                          1   \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_Vistara  Airline_Vistara Premium economy  Source_Chennai  \\\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_Delhi  Source_Kolkata  Source_Mumbai  Destination_Cochin  \\\n",
       "0             1               0              0                   1   \n",
       "1             0               1              0                   0   \n",
       "2             1               0              0                   1   \n",
       "3             1               0              0                   1   \n",
       "4             0               0              0                   0   \n",
       "\n",
       "   Destination_Delhi  Destination_Hyderabad  Destination_Kolkata  \n",
       "0                  0                      0                    0  \n",
       "1                  0                      0                    0  \n",
       "2                  0                      0                    0  \n",
       "3                  0                      0                    0  \n",
       "4                  1                      0                    0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a7a98139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\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>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Other</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Vistara</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</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>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0            0                3   3897           24              3        22   \n",
       "1            2                3   7662            1              5         5   \n",
       "2            2                3  13882            9              6         9   \n",
       "3            1                3   6218           12              5        18   \n",
       "4            1                3  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0       20             1           10               2             50   \n",
       "1       50            13           15               7             25   \n",
       "2       25             4           25              19              0   \n",
       "3        5            23           30               5             25   \n",
       "4       50            21           35               4             45   \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_Multiple carriers  Airline_Other  Airline_SpiceJet  \\\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  Source_Chennai  Source_Delhi  Source_Kolkata  \\\n",
       "0                0               0             0               0   \n",
       "1                0               0             0               1   \n",
       "2                0               0             1               0   \n",
       "3                0               0             0               1   \n",
       "4                0               0             0               0   \n",
       "\n",
       "   Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0              0                   0                  1   \n",
       "1              0                   0                  0   \n",
       "2              0                   1                  0   \n",
       "3              0                   0                  0   \n",
       "4              0                   0                  1   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  \n",
       "0                      0                    0  \n",
       "1                      0                    0  \n",
       "2                      0                    0  \n",
       "3                      0                    0  \n",
       "4                      0                    0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "58574f2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10462, 27)\n",
      "********************\n",
      "(2671, 28)\n"
     ]
    }
   ],
   "source": [
    "print(data_train.shape)\n",
    "print(\"*\"*20)\n",
    "print(data_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a6addf",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "be3e92a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Additional_Info', 'Price', 'Journey_day',\n",
       "       'Journey_month', 'Dep_hour', 'Dep_min', 'Arrival_hour', 'Arrival_min',\n",
       "       'Duration_hours', 'Duration_mins', 'Airline_Air India', 'Airline_GoAir',\n",
       "       'Airline_IndiGo', 'Airline_Jet Airways', 'Airline_Multiple carriers',\n",
       "       'Airline_Other', 'Airline_SpiceJet', 'Airline_Vistara',\n",
       "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
       "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
       "       'Destination_Kolkata'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6e8df321",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Other</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Vistara</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</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>1</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>3</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</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>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Additional_Info  Journey_day  Journey_month  Dep_hour  \\\n",
       "0            0                3           24              3        22   \n",
       "1            2                3            1              5         5   \n",
       "2            2                3            9              6         9   \n",
       "3            1                3           12              5        18   \n",
       "4            1                3            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
       "0       20             1           10               2             50   \n",
       "1       50            13           15               7             25   \n",
       "2       25             4           25              19              0   \n",
       "3        5            23           30               5             25   \n",
       "4       50            21           35               4             45   \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_Multiple carriers  Airline_Other  Airline_SpiceJet  \\\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  Source_Chennai  Source_Delhi  Source_Kolkata  \\\n",
       "0                0               0             0               0   \n",
       "1                0               0             0               1   \n",
       "2                0               0             1               0   \n",
       "3                0               0             0               1   \n",
       "4                0               0             0               0   \n",
       "\n",
       "   Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0              0                   0                  1   \n",
       "1              0                   0                  0   \n",
       "2              0                   1                  0   \n",
       "3              0                   0                  0   \n",
       "4              0                   0                  1   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  \n",
       "0                      0                    0  \n",
       "1                      0                    0  \n",
       "2                      0                    0  \n",
       "3                      0                    0  \n",
       "4                      0                    0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_train.loc[:,['Total_Stops', 'Additional_Info', 'Journey_day',\n",
    "       'Journey_month', 'Dep_hour', 'Dep_min', 'Arrival_hour', 'Arrival_min',\n",
    "       'Duration_hours', 'Duration_mins', 'Airline_Air India', 'Airline_GoAir',\n",
    "       'Airline_IndiGo', 'Airline_Jet Airways', 'Airline_Multiple carriers',\n",
    "       'Airline_Other', 'Airline_SpiceJet', 'Airline_Vistara',\n",
    "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
    "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
    "       'Destination_Kolkata']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8e95034a",
   "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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_train.iloc[:,2]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f8e3d357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking Correlation\n",
    "\n",
    "plt.figure(figsize=(18,18))\n",
    "sns.heatmap(train_data.corr(),annot=True, cmap=\"RdYlGn\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5567c20e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking all important feature using ExtraTreesRegressor\n",
    "\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "be1c013f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.21953317 0.09203377 0.11416885 0.05395388 0.02196411 0.02041831\n",
      " 0.02456397 0.01677449 0.11790549 0.01524867 0.01493952 0.00175461\n",
      " 0.01926223 0.13720943 0.01817867 0.02847726 0.004886   0.00464698\n",
      " 0.00061598 0.01225112 0.0052537  0.0060664  0.01386823 0.02685319\n",
      " 0.0085459  0.00062608]\n"
     ]
    }
   ],
   "source": [
    "print(selection.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c5274a87",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting important feature \n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index = X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78183e73",
   "metadata": {},
   "source": [
    "Clearly Total_Stops is the most important feature after that Airline_Jet Airways, Journey_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e58639b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spiltting the Train data \n",
    "\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": 68,
   "id": "0579d6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Random Forest Regressor \n",
    "\n",
    "reg_rf = RandomForestRegressor()\n",
    "reg_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "163be9de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the X_test\n",
    "\n",
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "30600ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9814515118049302"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "798725bb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8943932748955341"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0716ba65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(y_test-y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "11115e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c53d4e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 681.7979191409006\n",
      "MSE: 2201929.405793863\n",
      "RMSE: 1483.8899574408686\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": 75,
   "id": "e02aa714",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8943932748955341"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "07603771",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open a file, where you ant to store the data\n",
    "file = open('flight_rf.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(reg_rf, file)"
   ]
  }
 ],
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