Flight fare prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel('Data_Train.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>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>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",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>18/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>20:20</td>\n",
       "      <td>22:55</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>17:15</td>\n",
       "      <td>19:50</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4804</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>10631</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>15/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BLR → COK</td>\n",
       "      <td>02:00</td>\n",
       "      <td>07:45</td>\n",
       "      <td>5h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10641</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>MAA → CCU</td>\n",
       "      <td>07:55</td>\n",
       "      <td>10:15</td>\n",
       "      <td>2h 20m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10649</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>1/03/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>21:25</td>\n",
       "      <td>00:05 02 Mar</td>\n",
       "      <td>2h 40m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10664</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>9/04/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>18:55</td>\n",
       "      <td>21:50</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10665</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>15:15</td>\n",
       "      <td>17:45</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2053 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Airline Date_of_Journey    Source Destination            Route Dep_Time  \\\n",
       "0      IndiGo      24/03/2019  Banglore   New Delhi        BLR → DEL    22:20   \n",
       "3      IndiGo      12/05/2019   Kolkata    Banglore  CCU → NAG → BLR    18:05   \n",
       "4      IndiGo      01/03/2019  Banglore   New Delhi  BLR → NAG → DEL    16:50   \n",
       "11     IndiGo      18/04/2019   Kolkata    Banglore        CCU → BLR    20:20   \n",
       "14     IndiGo      24/04/2019   Kolkata    Banglore        CCU → BLR    17:15   \n",
       "...       ...             ...       ...         ...              ...      ...   \n",
       "10631  IndiGo      15/05/2019     Delhi      Cochin  DEL → BLR → COK    02:00   \n",
       "10641  IndiGo       6/03/2019   Chennai     Kolkata        MAA → CCU    07:55   \n",
       "10649  IndiGo       1/03/2019   Kolkata    Banglore        CCU → BLR    21:25   \n",
       "10664  IndiGo       9/04/2019  Banglore       Delhi        BLR → DEL    18:55   \n",
       "10665  IndiGo      27/04/2019   Kolkata    Banglore        CCU → BLR    15:15   \n",
       "\n",
       "       Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0      01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "3             23:30   5h 25m      1 stop         No info   6218  \n",
       "4             21:35   4h 45m      1 stop         No info  13302  \n",
       "11            22:55   2h 35m    non-stop         No info   4174  \n",
       "14            19:50   2h 35m    non-stop         No info   4804  \n",
       "...             ...      ...         ...             ...    ...  \n",
       "10631         07:45   5h 45m      1 stop         No info   6037  \n",
       "10641         10:15   2h 20m    non-stop         No info   7295  \n",
       "10649  00:05 02 Mar   2h 40m    non-stop         No info   4778  \n",
       "10664         21:50   2h 55m    non-stop         No info   4823  \n",
       "10665         17:45   2h 30m    non-stop         No info   4804  \n",
       "\n",
       "[2053 rows x 11 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################################################################################################\n",
    "# IndiGo Analysis\n",
    "################################################################################################################\n",
    "df_indigo=df[df['Airline'] == 'IndiGo']\n",
    "df_indigo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pooja\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3065: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n",
      "<ipython-input-18-b10a35dfbe06>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_indigo['year'] = df_indigo['year'].astype(int)\n",
      "<ipython-input-18-b10a35dfbe06>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_indigo['day'] = df_indigo['day'].astype(int)\n",
      "<ipython-input-18-b10a35dfbe06>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_indigo['month'] = df_indigo['month'].str.lstrip('0')\n"
     ]
    }
   ],
   "source": [
    "df_indigo[[\"day\", \"month\", \"year\"]] = df_indigo[\"Date_of_Journey\"].str.split(\"/\", expand = True)\n",
    "df_indigo['year'] = df_indigo['year'].astype(int)\n",
    "df_indigo['day'] = df_indigo['day'].astype(int)\n",
    "df_indigo['month'] = df_indigo['month'].str.lstrip('0')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-21-246d05d5200b>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_indigo['month'] = df_indigo['month'].astype(int)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</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",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>18/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>20:20</td>\n",
       "      <td>22:55</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4174</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>17:15</td>\n",
       "      <td>19:50</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4804</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10631</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>15/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BLR → COK</td>\n",
       "      <td>02:00</td>\n",
       "      <td>07:45</td>\n",
       "      <td>5h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6037</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10641</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>MAA → CCU</td>\n",
       "      <td>07:55</td>\n",
       "      <td>10:15</td>\n",
       "      <td>2h 20m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7295</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10649</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>1/03/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>21:25</td>\n",
       "      <td>00:05 02 Mar</td>\n",
       "      <td>2h 40m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4778</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10664</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>9/04/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>18:55</td>\n",
       "      <td>21:50</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4823</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10665</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>15:15</td>\n",
       "      <td>17:45</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4804</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2053 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Airline Date_of_Journey    Source Destination            Route Dep_Time  \\\n",
       "0      IndiGo      24/03/2019  Banglore   New Delhi        BLR → DEL    22:20   \n",
       "3      IndiGo      12/05/2019   Kolkata    Banglore  CCU → NAG → BLR    18:05   \n",
       "4      IndiGo      01/03/2019  Banglore   New Delhi  BLR → NAG → DEL    16:50   \n",
       "11     IndiGo      18/04/2019   Kolkata    Banglore        CCU → BLR    20:20   \n",
       "14     IndiGo      24/04/2019   Kolkata    Banglore        CCU → BLR    17:15   \n",
       "...       ...             ...       ...         ...              ...      ...   \n",
       "10631  IndiGo      15/05/2019     Delhi      Cochin  DEL → BLR → COK    02:00   \n",
       "10641  IndiGo       6/03/2019   Chennai     Kolkata        MAA → CCU    07:55   \n",
       "10649  IndiGo       1/03/2019   Kolkata    Banglore        CCU → BLR    21:25   \n",
       "10664  IndiGo       9/04/2019  Banglore       Delhi        BLR → DEL    18:55   \n",
       "10665  IndiGo      27/04/2019   Kolkata    Banglore        CCU → BLR    15:15   \n",
       "\n",
       "       Arrival_Time Duration Total_Stops Additional_Info  Price  day  month  \\\n",
       "0      01:10 22 Mar   2h 50m    non-stop         No info   3897   24      3   \n",
       "3             23:30   5h 25m      1 stop         No info   6218   12      5   \n",
       "4             21:35   4h 45m      1 stop         No info  13302    1      3   \n",
       "11            22:55   2h 35m    non-stop         No info   4174   18      4   \n",
       "14            19:50   2h 35m    non-stop         No info   4804   24      4   \n",
       "...             ...      ...         ...             ...    ...  ...    ...   \n",
       "10631         07:45   5h 45m      1 stop         No info   6037   15      5   \n",
       "10641         10:15   2h 20m    non-stop         No info   7295    6      3   \n",
       "10649  00:05 02 Mar   2h 40m    non-stop         No info   4778    1      3   \n",
       "10664         21:50   2h 55m    non-stop         No info   4823    9      4   \n",
       "10665         17:45   2h 30m    non-stop         No info   4804   27      4   \n",
       "\n",
       "       year  \n",
       "0      2019  \n",
       "3      2019  \n",
       "4      2019  \n",
       "11     2019  \n",
       "14     2019  \n",
       "...     ...  \n",
       "10631  2019  \n",
       "10641  2019  \n",
       "10649  2019  \n",
       "10664  2019  \n",
       "10665  2019  \n",
       "\n",
       "[2053 rows x 14 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indigo['month'] = df_indigo['month'].astype(int)\n",
    "df_indigo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "#df_indigo_jan = df_indigo[df_indigo['month'] == 3]\n",
    "print(max(df_indigo['month']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(16,6))\n",
    "\n",
    "plt.subplot(1, 2, 1) # row 1, col 2 index 1\n",
    "n, bins, patches = plt.hist(df_twitter.polarity)\n",
    "plt.xticks(bins)\n",
    "plt.ylabel('Number of users')\n",
    "plt.xlabel('Polarity')\n",
    "plt.xticks(rotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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": [
    "#Information about the data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping two null values\n",
    "df.dropna(inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            object\n",
       "Date_of_Journey    object\n",
       "Source             object\n",
       "Destination        object\n",
       "Route              object\n",
       "Dep_Time           object\n",
       "Arrival_Time       object\n",
       "Duration           object\n",
       "Total_Stops        object\n",
       "Additional_Info    object\n",
       "Price               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking duplicate rows\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping 220 duplicate rows\n",
    "df.drop_duplicates(inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Count of each airlines in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "print(df['Airline'].value_counts())\n",
    "df['Airline'].value_counts().plot(kind = 'bar') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jet Airways, Indigo and Air India have the highest number of flights scheduled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Frequency of each Source of the flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delhi       4345\n",
      "Kolkata     2860\n",
      "Banglore    2179\n",
      "Mumbai       697\n",
      "Chennai      381\n",
      "Name: Source, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "print(df['Source'] .value_counts())\n",
    "df['Source'].value_counts().plot(kind = 'bar') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highest number of flights are departing from Delhi, followed by Kolkata and Bangalore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Frequency of the Destination cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cochin       4345\n",
      "Banglore     2860\n",
      "Delhi        1265\n",
      "New Delhi     913\n",
      "Hyderabad     697\n",
      "Kolkata       381\n",
      "Name: Destination, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['Destination'] .value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have two destinations -  Delhi and New Delhi, and in source we have only Delhi. So, let's replace New Delhi as Delhi in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Destination'] = df['Destination'].replace('New Delhi' , 'Delhi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['Destination'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cochin is the most travelled destination, followed by Bangalore annd Delhi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are 5 kinds of stops in different routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEaCAYAAAD9iIezAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUhElEQVR4nO3df7DldX3f8efLBQlVMVAWQneRJXaTFDBi2SEorTViy1qsMIlkNh1lpyWzU4aMtmYms9RpjM5sh5nM2JZYmNJEWJJGZm3NsIUQw6yitVLhghjkV1kFYQOyK62yThwS8N0/zmd7jsvZvfeyy/ney+f5mDlzvt/3+X7Pfd+P8trv/Xy/53tSVUiS+vCqoRuQJM2OoS9JHTH0Jakjhr4kdcTQl6SOGPqS1JEjhm5gPscff3ytWbNm6DYkaVm5++67v1tVK/evL/nQX7NmDXNzc0O3IUnLSpJvT6s7vSNJHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqyJL/cNahWrP5lqFbAOCxKy8YugVJ8khfknpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6siCQj/JY0nuS3JvkrlWOy7JbUkeac/HTmx/RZKdSR5Ocv5E/az2PjuTXJUkh/9XkiQdyGKO9H+xqs6sqnVtfTOwo6rWAjvaOklOAzYApwPrgauTrGj7XANsAta2x/pD/xUkSQt1KNM7FwJb2/JW4KKJ+o1V9VxVPQrsBM5OchJwTFXdUVUF3DCxjyRpBhYa+gX8WZK7k2xqtROr6imA9nxCq68CnpjYd1errWrL+9dfJMmmJHNJ5vbs2bPAFiVJ8zligdudW1VPJjkBuC3JQwfZdto8fR2k/uJi1bXAtQDr1q2buo0kafEWdKRfVU+2593AHwNnA0+3KRva8+62+S7g5IndVwNPtvrqKXVJ0ozMG/pJXpPkdfuWgX8EfAPYDmxsm20EbmrL24ENSY5KciqjE7Z3timgvUnOaVftXDKxjyRpBhYyvXMi8Mft6sojgD+qqj9NchewLcmlwOPAxQBVdX+SbcADwPPA5VX1Qnuvy4DrgaOBW9tDkjQj84Z+VX0LePOU+jPAeQfYZwuwZUp9Djhj8W1Kkg4HP5ErSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHVkwaGfZEWSryW5ua0fl+S2JI+052Mntr0iyc4kDyc5f6J+VpL72mtXJcnh/XUkSQezmCP9DwEPTqxvBnZU1VpgR1snyWnABuB0YD1wdZIVbZ9rgE3A2vZYf0jdS5IWZUGhn2Q1cAHwexPlC4GtbXkrcNFE/caqeq6qHgV2AmcnOQk4pqruqKoCbpjYR5I0Aws90v/3wG8CP5qonVhVTwG05xNafRXwxMR2u1ptVVvevy5JmpF5Qz/Je4DdVXX3At9z2jx9HaQ+7WduSjKXZG7Pnj0L/LGSpPks5Ej/XOC9SR4DbgTemeQPgafblA3teXfbfhdw8sT+q4EnW331lPqLVNW1VbWuqtatXLlyEb+OJOlg5g39qrqiqlZX1RpGJ2g/X1XvB7YDG9tmG4Gb2vJ2YEOSo5KcyuiE7Z1tCmhvknPaVTuXTOwjSZqBIw5h3yuBbUkuBR4HLgaoqvuTbAMeAJ4HLq+qF9o+lwHXA0cDt7aHJGlGFhX6VXU7cHtbfgY47wDbbQG2TKnPAWcstklJ0uHhJ3IlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcO5X76WmbWbL5l6BYAeOzKC4ZuQeqWR/qS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdmTf0k/xEkjuTfD3J/Uk+1urHJbktySPt+diJfa5IsjPJw0nOn6ifleS+9tpVSfLy/FqSpGkWcqT/HPDOqnozcCawPsk5wGZgR1WtBXa0dZKcBmwATgfWA1cnWdHe6xpgE7C2PdYfvl9FkjSfeUO/Rn7QVo9sjwIuBLa2+lbgorZ8IXBjVT1XVY8CO4Gzk5wEHFNVd1RVATdM7CNJmoEFzeknWZHkXmA3cFtVfRU4saqeAmjPJ7TNVwFPTOy+q9VWteX965KkGVlQ6FfVC1V1JrCa0VH7GQfZfNo8fR2k/uI3SDYlmUsyt2fPnoW0KElagEVdvVNV3wNuZzQX/3SbsqE9726b7QJOnthtNfBkq6+eUp/2c66tqnVVtW7lypWLaVGSdBALuXpnZZKfbMtHA+8CHgK2AxvbZhuBm9rydmBDkqOSnMrohO2dbQpob5Jz2lU7l0zsI0magYV8MfpJwNZ2Bc6rgG1VdXOSO4BtSS4FHgcuBqiq+5NsAx4Angcur6oX2ntdBlwPHA3c2h6SpBmZN/Sr6s+Bt0ypPwOcd4B9tgBbptTngIOdD5AkvYz8RK4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6Mm/oJzk5yReSPJjk/iQfavXjktyW5JH2fOzEPlck2Znk4STnT9TPSnJfe+2qJHl5fi1J0jQLOdJ/HviNqvo7wDnA5UlOAzYDO6pqLbCjrdNe2wCcDqwHrk6yor3XNcAmYG17rD+Mv4skaR7zhn5VPVVV97TlvcCDwCrgQmBr22wrcFFbvhC4saqeq6pHgZ3A2UlOAo6pqjuqqoAbJvaRJM3Aoub0k6wB3gJ8FTixqp6C0T8MwAlts1XAExO77Wq1VW15//q0n7MpyVySuT179iymRUnSQSw49JO8FvhvwL+sqmcPtumUWh2k/uJi1bVVta6q1q1cuXKhLUqS5rGg0E9yJKPA/y9V9dlWfrpN2dCed7f6LuDkid1XA0+2+uopdUnSjCzk6p0Avw88WFWfmHhpO7CxLW8Ebpqob0hyVJJTGZ2wvbNNAe1Nck57z0sm9pEkzcARC9jmXOADwH1J7m21fw1cCWxLcinwOHAxQFXdn2Qb8ACjK38ur6oX2n6XAdcDRwO3tockaUbmDf2q+jLT5+MBzjvAPluALVPqc8AZi2lQknT4+IlcSeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1ZN4vRpdeidZsvmXoFgB47MoLhm5BnfFIX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjswb+kk+lWR3km9M1I5LcluSR9rzsROvXZFkZ5KHk5w/UT8ryX3ttauS5PD/OpKkg1nIkf71wPr9apuBHVW1FtjR1klyGrABOL3tc3WSFW2fa4BNwNr22P89JUkvs3lDv6q+BPyf/coXAlvb8lbgoon6jVX1XFU9CuwEzk5yEnBMVd1RVQXcMLGPJGlGXuqc/olV9RRAez6h1VcBT0xst6vVVrXl/etTJdmUZC7J3J49e15ii5Kk/R3uE7nT5unrIPWpquraqlpXVetWrlx52JqTpN691NB/uk3Z0J53t/ou4OSJ7VYDT7b66il1SdIMvdTQ3w5sbMsbgZsm6huSHJXkVEYnbO9sU0B7k5zTrtq5ZGIfSdKMzHtr5SSfBt4BHJ9kF/BR4EpgW5JLgceBiwGq6v4k24AHgOeBy6vqhfZWlzG6Euho4Nb2kCTN0LyhX1W/eoCXzjvA9luALVPqc8AZi+pOknRY+YlcSeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdWTmoZ9kfZKHk+xMsnnWP1+SenbELH9YkhXAfwT+IbALuCvJ9qp6YJZ9SBpbs/mWoVsA4LErLxi6hS7M+kj/bGBnVX2rqv4KuBG4cMY9SFK3UlWz+2HJ+4D1VfVrbf0DwC9U1a/vt90mYFNb/Vng4Zk1Od3xwHcH7mGpcCzGHIsxx2JsqYzFKVW1cv/iTKd3gEypvehfnaq6Frj25W9nYZLMVdW6oftYChyLMcdizLEYW+pjMevpnV3AyRPrq4EnZ9yDJHVr1qF/F7A2yalJXg1sALbPuAdJ6tZMp3eq6vkkvw58DlgBfKqq7p9lDy/RkplqWgIcizHHYsyxGFvSYzHTE7mSpGH5iVxJ6oihL0kdMfQlqSOG/gEk+akk703yT5L81ND9DC3Jq5P8fJI3tSuvupPk3CSvacvvT/KJJKcM3dcQHIux5TYWhv4USX4NuBP4JeB9wP9K8s+H7Wo4SS4AvglcBXwS2Jnk3cN2NYhrgL9M8mbgN4FvAzcM29JgHIuxZTUWXr0zRZKHgbdV1TNt/W8CX6mqnx22s2EkeQh4T1XtbOtvBG6pqp8btrPZSnJPVf3dJL8F/EVV/f6+2tC9zZpjMbbcxmLWt2FYLnYBeyfW9wJPDNTLUrB7X+A33wJ2D9XMgPYmuQL4APD3211jjxy4p6E4FmPLaiw80p8iyQ3Am4CbGN0b6EJG0z3/G6CqPjFcd7OX5BrgFGAbo/G4mNFN8P4nQFV9drjuZqed2/mnwF1V9T+SvAF4R1Ut2T/lXy6OxdhyGwtDf4okHz3Y61X1sVn1shQkue4gL1dVdXO+o/0Hfjajf/zuqqrvDNzSYByLseU0Fob+QSR5HaNQ+8HQvWh47QT/bwGfZ3TH2H8AfLyqPjVoYwNwLMaW21gY+lMkOQP4A+C4VvoucMkyuU/QYZdkNfC7wLmMjmS+DHyoqnYN2tiMeYJ/zLEYW25j4SWb010LfLiqTqmqU4DfAP7zwD0N6TpGd0P9W8Aq4L+3Wm88wT/mWIwtq7HwSH+KJF+vqjfPV+tFknur6sz5aq90nuAfcyzGlttYeMnmdN9K8m8YTfEAvB94dMB+hvbdJO8HPt3WfxV4ZsB+hvLN9tjnpvb8ugF6GZpjMbasxsIj/SmSHAt8DPh7rfQl4Ler6nuDNTWgdgnaJ4G3MjqS+Qrwwap6fNDGBuIJ/jHHYmy5jIVH+tO9q6o+OFlIcjHwmYH6GdrJVfXeyUKSc4GuQn//E/xJuj3B71iMLbex8Eh/imkfoV7KH6t+uTkeI0m+Anykqr7Q1t8B/NuqetuQfQ3BsRhbbmPhkf6EdhOxfwysSnLVxEvHAM8P09VwkrwVeBuwMsmHJ146htHXXfbmNfv+wwaoqtv33V2xQ47F2LIaC0P/xz0JzAHvBe6eqO8F/tUgHQ3r1cBrGf3/ZPKk1LOM7j7aG0/wjzkWY8tqLJzemSLJkVX11235WEZz2n8+cFuDSXJKVX27Lb8KeG1VPTtwWzPnCf4xx2JsuY2FoT9FktsZHe0fAdwL7AG+WFUfPshur1hJ/gj4F8ALjP4Cej3wiar6nUEbm7EkF1fVZ+ar9cCxGFtuY+Encqd7fTuS/SXguqo6C3jXwD0N6bQ2HhcBfwK8gdFtZHtzxQJrPXAsxpbVWDinP90RSU4CfgX4yNDNLAFHJjmSUeh/sqr+Okk3fyJ6gn/MsRhbrmNh6E/3ceBzwJer6q4kPw08MnBPQ/pPwGPA14Evte//7GlO3xP8Y47F2LIcC+f0tWhJAqyoqiV7NPNy8AT/mGMxttzGwjl9LUqSm2ukq8BvbktyTJLjGP3Vc12SJXUzrRlyLMaW1VgY+lqsVUM3MCBP8I85FmPLaiwMfS3W14ZuYECTJ/hvHrqZgTkWY8tqLAz9BUryz4buYSno6ftwp9h3gn+nJ/gdiwnLaiw8kbtASR6vqjcM3ccQ2h01fxs4hdEVX2F0C9mfHrIvSYtn6E9IcqAz7gF+pqqOmmU/S0WShxhdgnY3o0/lArDvO0ElLR9ep//jTgTOB/7vfvUw+uKQXn2/qm4duglJh87Q/3E3M7qZ2L37v9Dux9OrLyT5HeCzwHP7ilV1z3AtSXopnN7RvJJ8YUq5quqdM29mIEl+jtHlql+d/Dq8JOur6k+H62z2kpzN6H//u5KcBqwHHqqqPxm4tcEluaGqLhm6j4Mx9KV5JPkgcDnwIHAm8KGquqm91tU3iCX5KPBuRrMEtwG/ANzO6Lr0z1XVluG6m60k2/cvAb8IfB5g/68YXSoMfc0ryeuBjwJvb6UvAh+vqu8P19XsJLkPeGtV/SDJGuC/An9QVf8hydeq6i3Ddjg7bSzOBI4CvgOsrqpnkxzN6K+gnx+yv1lKcg/wAPB7QDEK/U8DGwCq6ovDdXdgXqevhfgUo5tI/Up7PAtcN2hHs7Vi35ROVT0GvAN4d/uofQbsawjPV9ULVfWXwDf3fZlOVf0Q+NGwrc3cOkZXtH2E0cUOtwM/rKovLtXAB0/kamHeWFW/PLH+sST3DtXMAL6T5Mx9J/jbEf97GP1j+KZBO5u9v0ryN1ron7Wv2P4a7Cr0q+pHwL9L8pn2/DTLIFM90tdC/DDJvq+C2/dhrR8O2M+sXcJoKuP/q6rn2wm7t0/f5RXr7S3w94XePkcCG4dpaVhVtauqLgZuBf5w6H7m45y+5pXkTGAro69JhNHnGDYu5dvHSprO0Ne8khwFvA94I/CTwPcZXbL38SH7krR4S37+SUvCTcD3gHuAvxi2FUmHwiN9zSvJN6rqjKH7kHToPJGrhfhKkt6uUpFekTzS17ySPAD8beBRRvfe2Xdr5W4+iCO9Uhj6mleSU6bVq+rbs+5F0qEx9CWpI87pS1JHDH1J6oihL0kdMfQlqSOGviR15P8BMP4hs1A4G7QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df['Total_Stops'] .value_counts())\n",
    "df['Total_Stops'].value_counts().plot(kind = 'bar') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is only one data point for 4 stops, so we are removing that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Total_Stops']!='4 stops']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Price of each airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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": [
    "sns.barplot(x = 'Airline' , y = 'Price'  , data = df )  \n",
    "plt.xticks(rotation = 90) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison of Price for different Source cities of flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "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": [
    "sns.barplot(x = 'Source' , y = 'Price'  , data = df )  \n",
    "plt.xticks(rotation = 90) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flights from Delhi are the most expensive and flights from Chennai are least expensive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison of Price for different Destination cities of flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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": [
    "sns.barplot(x = 'Destination' , y = 'Price'  , data = df )  \n",
    "plt.xticks(rotation = 90) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flights destinated to Cochin are most expensive and to Kolkata are least expensive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting Date_of_journey,Arrival_Time and Dep_Time into datetime object\n",
    "def change_into_datetime(col):\n",
    "    df[col]=pd.to_datetime(df[col])\n",
    "for i in ['Date_of_Journey','Dep_Time', 'Arrival_Time']:\n",
    "    change_into_datetime(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                    object\n",
       "Date_of_Journey    datetime64[ns]\n",
       "Source                     object\n",
       "Destination                object\n",
       "Route                      object\n",
       "Dep_Time           datetime64[ns]\n",
       "Arrival_Time       datetime64[ns]\n",
       "Duration                   object\n",
       "Total_Stops                object\n",
       "Additional_Info            object\n",
       "Price                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract day and month from Date_of_journey and stored in two other columns\n",
    "df['journey_day']=df['Date_of_Journey'].dt.day\n",
    "df['journey_month']=df['Date_of_Journey'].dt.month\n",
    "df.drop('Date_of_Journey', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From Arrival_time and Dept_time features,we extract hour and minutes and stored in new columns and drop these columns\n",
    "def extract_hour(data,col):\n",
    "    data[col+'_hour']=data[col].dt.hour\n",
    "    \n",
    "def extract_min(data,col):\n",
    "    data[col+'_min']=data[col].dt.minute\n",
    "    \n",
    "def drop_col(data,col):\n",
    "    data.drop(col,axis=1,inplace=True)\n",
    "    \n",
    "#Extracting values from Dep_time\n",
    "extract_hour(df,'Dep_Time')\n",
    "#extracting minutes\n",
    "extract_min(df,'Dep_Time')\n",
    "#drop the column\n",
    "drop_col(df,'Dep_Time')\n",
    "\n",
    "#Extracting values from Arrival_time\n",
    "extract_hour(df,'Arrival_Time')\n",
    "#extracting min\n",
    "extract_min(df,'Arrival_Time')\n",
    "#drop the column\n",
    "drop_col(df,'Arrival_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</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>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</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>6</td>\n",
       "      <td>9</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>5</td>\n",
       "      <td>12</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>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>3</td>\n",
       "      <td>1</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       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       Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            5              1   \n",
       "2     2 stops         No info  13882            6              9   \n",
       "3      1 stop         No info   6218            5             12   \n",
       "4      1 stop         No info  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_min  Arrival_Time_hour  Arrival_Time_min  \n",
       "0             22            20                  1                10  \n",
       "1              5            50                 13                15  \n",
       "2              9            25                  4                25  \n",
       "3             18             5                 23                30  \n",
       "4             16            50                 21                35  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seperating duration hours and minute from Duration\n",
    "duration=list(df['Duration'])\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split(' '))==2:\n",
    "        pass\n",
    "    else:\n",
    "        if 'h' in duration[i]: # Check if duration contains only hour\n",
    "             duration[i]=duration[i] + ' 0m' # Adds 0 minute\n",
    "        else:\n",
    "             duration[i]='0h '+ duration[i]\n",
    "df['Duration']=duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hour(x):\n",
    "    return x.split(' ')[0][0:-1]\n",
    "\n",
    "def minutes(x):\n",
    "    return x.split(' ')[1][0:-1]\n",
    "\n",
    "df['duration_hour']=df['Duration'].apply(hour)\n",
    "df['duration_min']=df['Duration'].apply(minutes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <th>duration_hour</th>\n",
       "      <th>duration_min</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>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>5</td>\n",
       "      <td>1</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>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore       Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore       Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  journey_day  journey_month  Dep_Time_hour  \\\n",
       "0         No info   3897           24              3             22   \n",
       "1         No info   7662            5              1              5   \n",
       "2         No info  13882            6              9              9   \n",
       "3         No info   6218            5             12             18   \n",
       "4         No info  13302            3              1             16   \n",
       "\n",
       "   Dep_Time_min  Arrival_Time_hour  Arrival_Time_min duration_hour  \\\n",
       "0            20                  1                10             2   \n",
       "1            50                 13                15             7   \n",
       "2            25                  4                25            19   \n",
       "3             5                 23                30             5   \n",
       "4            50                 21                35             4   \n",
       "\n",
       "  duration_min  \n",
       "0           50  \n",
       "1           25  \n",
       "2            0  \n",
       "3           25  \n",
       "4           45  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_col(df,'Duration')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline              object\n",
       "Source               object\n",
       "Destination          object\n",
       "Route                object\n",
       "Total_Stops          object\n",
       "Additional_Info      object\n",
       "Price                 int64\n",
       "journey_day           int64\n",
       "journey_month         int64\n",
       "Dep_Time_hour         int64\n",
       "Dep_Time_min          int64\n",
       "Arrival_Time_hour     int64\n",
       "Arrival_Time_min      int64\n",
       "duration_hour         int32\n",
       "duration_min          int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['duration_hour'] = df['duration_hour'].astype(int)\n",
    "df['duration_min'] = df['duration_min'].astype(int)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (20,12))\n",
    "sns.heatmap(df.corr() , annot = True , cmap = 'coolwarm' , vmin = -1 , vmax = 1 , robust = True ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Airline', 'Source', 'Destination', 'Route', 'Total_Stops', 'Additional_Info']"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding Categorical values\n",
    "column=[column for column in df.columns if df[column].dtype=='object']\n",
    "column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Price',\n",
       " 'journey_day',\n",
       " 'journey_month',\n",
       " 'Dep_Time_hour',\n",
       " 'Dep_Time_min',\n",
       " 'Arrival_Time_hour',\n",
       " 'Arrival_Time_min',\n",
       " 'duration_hour',\n",
       " 'duration_min']"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding continuous values\n",
    "continuous_col =[column for column in df.columns if df[column].dtype!='object']\n",
    "continuous_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding to covert Categorical data into some numerical format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using two main Encoding Techniques to covert Categorical data into some numerical format\n",
    "\n",
    "Nominal data - Data that are not in any order - One Hot Encoding\n",
    "\n",
    "Ordinal data - Data are in order - labelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </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>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore       Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore       Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  \n",
       "0         No info  \n",
       "1         No info  \n",
       "2         No info  \n",
       "3         No info  \n",
       "4         No info  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical = df[column]\n",
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3700\n",
       "IndiGo                               2043\n",
       "Air India                            1693\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": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Airline'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Airline vs Price Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Airline', ylabel='Price'>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.xticks(rotation = 90)\n",
    "sns.boxplot(x='Airline',y='Price',data=df.sort_values('Price',ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From graph we can see that Jet Airways have the highest Price and apart from that, almost all other airlines have a similar median."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total_Stops vs Price Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total_Stops', ylabel='Price'>"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(x='Total_Stops',y='Price',data=df.sort_values('Price',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "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>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Trujet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Air India  GoAir  IndiGo  Jet Airways  Jet Airways Business  \\\n",
       "0          0      0       1            0                     0   \n",
       "1          1      0       0            0                     0   \n",
       "2          0      0       0            1                     0   \n",
       "3          0      0       1            0                     0   \n",
       "4          0      0       1            0                     0   \n",
       "\n",
       "   Multiple carriers  Multiple carriers Premium economy  SpiceJet  Trujet  \\\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",
       "   Vistara  Vistara Premium economy  \n",
       "0        0                        0  \n",
       "1        0                        0  \n",
       "2        0                        0  \n",
       "3        0                        0  \n",
       "4        0                        0  "
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "Airline=pd.get_dummies(categorical['Airline'],drop_first=True)\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4345\n",
       "Kolkata     2860\n",
       "Banglore    2178\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Source'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Source vs Price Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Source', ylabel='Price'>"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(x='Source',y='Price',data=df.sort_values('Price',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "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>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>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": [
       "   Chennai  Delhi  Kolkata  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": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#OneHotEncoding of source column\n",
    "source=pd.get_dummies(categorical['Source'],drop_first=True)\n",
    "source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4345\n",
       "Banglore     2860\n",
       "Delhi        2178\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Destination', ylabel='Price'>"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAAHgCAYAAADniEPQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAA9F0lEQVR4nO3df5xkdX3n+9enZ5qfIwLTw68ZdMzOsFk0SJaWxURQAj3QJgb3Rq8k7lqbsIH4MINZd5OrV4LE4F7d3I2bwdVAILF0NeKaZEEvLdMBAbMxYCMwCP6glQlMGJjp4Yf80KFn+nP/qG9DV9PTM1Mz1ae6+vV8POpR9f3WOac+NRyq613fc74nMhNJkiRJknqqLkCSJEmS1BkMiJIkSZIkwIAoSZIkSSoMiJIkSZIkwIAoSZIkSSoMiJIkSZIkABZXXcBc6+vry5UrV1ZdhiRJkiRV4s477xzLzGUzPbfgAuLKlSsZGRmpugxJkiRJqkRE/OOunvMQU0mSJEkSYECUJEmSJBUGREmSJEkSYECUJEmSJBUGREmSJEkSYECUJEmSJBUGREmSJEkSYECUJEmSJBUGREmSJEkSYECUJEmSJBUGREmSJEkS0OaAGBH/ISLui4hvR8RfRsRBEXFkRAxHxAPl/ogpy38gIkYj4nsRcc6U/lMi4t7y3LqIiNJ/YERcW/pvj4iV7Xw/kiRJktTN2hYQI2I5cDHQn5mvARYB5wPvB27KzNXATaVNRJxYnn81cC7wyYhYVDb3KeBCYHW5nVv6LwCeyMxVwMeBj7Xr/UiSJElSt2v3IaaLgYMjYjFwCPAIcB5QL8/XgbeWx+cBX8jM7Zn5IDAKnBoRxwKHZeY3MjOBz0xbZ3JbXwLOmhxdlCRJkiTtnbYFxMz8J+D/BR4CNgNPZeZ64OjM3FyW2QwcVVZZDjw8ZRObSt/y8nh6f9M6mbkDeApY2o73o5caGxtj7dq1bNu2repSJEmSJO0H7TzE9AgaI3yvAo4DDo2IfzPbKjP05Sz9s60zvZYLI2IkIka2bt06e+HaY/V6nQ0bNlCv13e/sCRJkqSO185DTM8GHszMrZk5Dvw18HPAY+WwUcr9lrL8JuD4KeuvoHFI6qbyeHp/0zrlMNaXA49PLyQzr8rM/szsX7Zs2X56ewvb2NgYQ0NDZCZDQ0OOIkqSJEldoJ0B8SHgtIg4pJwXeBbwHeB6oFaWqQHXlcfXA+eXmUlfRWMymjvKYahPR8RpZTvvmrbO5LbeBtxczlNUm9XrdSb/qScmJhxFlCRJkrpAO89BvJ3GxDHfAu4tr3UV8FFgICIeAAZKm8y8D/gicD/wVeA9mbmzbO7dwNU0Jq75ATBU+q8BlkbEKPA+yoyoar/h4WHGx8cBGB8fZ/369RVXJEmSJGlfLW7nxjPzQ8CHpnVvpzGaONPyHwE+MkP/CPCaGfp/Arx93yvV3hoYGOCGG25gfHyc3t5e1qxZU3VJkiRJkvZRuy9zoS5Vq9WYvKJIT08PtVptN2tIkiRJ6nQGRLWkr6+PwcFBIoLBwUGWLvXqIpIkSdJ819ZDTNXdarUaGzdudPRQkiRJ6hIGRLWsr6+PK664ouoyJEmSJO0nHmIqSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkwoAoSZIkSQIMiJIkSZKkom0BMSL+eUTcPeX2o4j4nYg4MiKGI+KBcn/ElHU+EBGjEfG9iDhnSv8pEXFveW5dRETpPzAiri39t0fEyna9H0mSJEnqdm0LiJn5vcw8OTNPBk4BngP+Bng/cFNmrgZuKm0i4kTgfODVwLnAJyNiUdncp4ALgdXldm7pvwB4IjNXAR8HPtau9yNJkiRJ3W6uDjE9C/hBZv4jcB5QL/114K3l8XnAFzJze2Y+CIwCp0bEscBhmfmNzEzgM9PWmdzWl4CzJkcXJUmSJEl7Z64C4vnAX5bHR2fmZoByf1TpXw48PGWdTaVveXk8vb9pnczcATwFLG1D/ZIkSZLU9doeECPiAOCXgf+5u0Vn6MtZ+mdbZ3oNF0bESESMbN26dTdlSJIkSdLCNBcjiIPAtzLzsdJ+rBw2SrnfUvo3AcdPWW8F8EjpXzFDf9M6EbEYeDnw+PQCMvOqzOzPzP5ly5btlzclSZIkSd1mLgLir/Li4aUA1wO18rgGXDel//wyM+mraExGc0c5DPXpiDitnF/4rmnrTG7rbcDN5TxFSZIkSdJeWtzOjUfEIcAAcNGU7o8CX4yIC4CHgLcDZOZ9EfFF4H5gB/CezNxZ1nk38GngYGCo3ACuAT4bEaM0Rg7Pb+f7kSRJkqRuFgttwK2/vz9HRkaqLkOSJEmSKhERd2Zm/0zPzdUsppIkSZKkDmdAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkTtg7GxMdauXcu2bduqLkWSJEnSfmBAVMvq9TobNmygXq9XXYokSZKk/cCAqJaMjY0xNDREZjI0NOQooiRJktQFDIhqSb1eJzMBmJiYcBRRkiRJ6gIGRLVkeHiY8fFxAMbHx1m/fn3FFUmSJEnaVwZEtWRgYIDe3l4Aent7WbNmTcUVSZIkSdpXBkS1pFarEREA9PT0UKvVKq5IkiRJ0r4yIKolfX19DA4OEhEMDg6ydOnSqkuSJEmStI8WV12A5q9arcbGjRsdPZQkSZK6hAFRLevr6+OKK66ougxJkiRJ+4mHmEqSJEmSAAOiJEmSJKloa0CMiMMj4ksR8d2I+E5EvD4ijoyI4Yh4oNwfMWX5D0TEaER8LyLOmdJ/SkTcW55bF2X6zIg4MCKuLf23R8TKdr4fSZIkSepm7R5B/BPgq5n508Brge8A7wduyszVwE2lTUScCJwPvBo4F/hkRCwq2/kUcCGwutzOLf0XAE9k5irg48DH2vx+JEmSJKlrtS0gRsRhwBnANQCZ+XxmPgmcB9TLYnXgreXxecAXMnN7Zj4IjAKnRsSxwGGZ+Y3MTOAz09aZ3NaXgLMmRxclSZIkSXunnSOIPwVsBf4iIu6KiKsj4lDg6MzcDFDujyrLLwcenrL+ptK3vDye3t+0TmbuAJ4CvCCfJEmSJLWgnQFxMfAvgU9l5s8Cz1IOJ92FmUb+cpb+2dZp3nDEhRExEhEjW7dunb1qSZIkSVqg2hkQNwGbMvP20v4SjcD4WDlslHK/Zcryx09ZfwXwSOlfMUN/0zoRsRh4OfD49EIy86rM7M/M/mXLlu2HtyZJkiRJ3adtATEzHwUejoh/XrrOAu4Hrgdqpa8GXFceXw+cX2YmfRWNyWjuKIehPh0Rp5XzC981bZ3Jbb0NuLmcpyhJkiRJ2kuL27z9tcDnIuIA4IfAr9MIpV+MiAuAh4C3A2TmfRHxRRohcgfwnszcWbbzbuDTwMHAULlBYwKcz0bEKI2Rw/Pb/H4kSZIkqWvFQhtw6+/vz5GRkarLkCRJkqRKRMSdmdk/03Ptvg6iJEmSJGmeMCBKkiRJkgADoiRJkiSpMCBKkiRJkgADoiRJkiSpMCCqZWNjY6xdu5Zt27ZVXYokSZKk/cCAqJbV63U2bNhAvV6vuhRJkiRJ+4EBUS0ZGxtjaGiIzGRoaMhRREmSJKkLGBDVknq9TmYCMDEx4SiiJEmS1AUMiGrJ8PAw4+PjAIyPj7N+/fqKK5IkSZK0rwyIasnAwAC9vb0A9Pb2smbNmoorkiRJkrSvDIhqSa1WIyIA6OnpoVarVVyRJEmSpH1lQFRL+vr6GBwcJCIYHBxk6dKlVZckSZIkaR8trroAzV+1Wo2NGzc6eihJkiR1CQOiWtbX18cVV1xRdRmSJEmS9hMPMZUkSZIkAQZESZIkSVJhQFTL7rjjDt70pjdx5513Vl2KJEmSpP3AgKiWXXbZZUxMTPD7v//7VZciSZIkaT8wIKold9xxB8888wwAzzzzjKOIkiRJUhcwIKoll112WVPbUURJkiRp/jMgqiWTo4e7akuSJEmafwyIasmSJUtmbUuSJEmafwyIasn0Q0z/8A//sJpCJEmSJO03BkS15NRTT31h1HDJkiWccsopFVckSZIkaV8ZENWyyy67jJ6eHkcPJUmSpC6xuOoCNH+deuqp3HLLLVWXIUmSJGk/cQRRkiRJkgQYECVJkiRJhQFRkiRJkgQYELUPxsbGWLt2Ldu2bau6FKmJ+6YkSVJrDIhqWb1eZ8OGDdTr9apLkZq4b0qSJLXGgKiWjI2NMTQ0RGYyNDTkSI06hvumJElS6wyIakm9XiczAZiYmHCkRh3DfVOSJKl1BkS1ZHh4mPHxcQDGx8dZv359xRVJDe6bkiRJrTMgqiUDAwP09vYC0Nvby5o1ayquSGpw35QkSWqdAVEtqdVqRAQAPT091Gq1iiuSGtw3JUmSWmdAVEv6+voYHBwkIhgcHGTp0qVVlyQB7puSJEn7YnHVBWj+qtVqbNy40REadRz3TUmSpNbE5Gx/C0V/f3+OjIxUXYYkSZIkVSIi7szM/pme8xBTSZIkSRJgQJQkSZIkFW0NiBGxMSLujYi7I2Kk9B0ZEcMR8UC5P2LK8h+IiNGI+F5EnDOl/5SyndGIWBdlisKIODAiri39t0fEyna+H0mSJEnqZnMxgnhmZp485RjX9wM3ZeZq4KbSJiJOBM4HXg2cC3wyIhaVdT4FXAisLrdzS/8FwBOZuQr4OPCxOXg/kiRJktSVqjjE9DygXh7XgbdO6f9CZm7PzAeBUeDUiDgWOCwzv5GNGXU+M22dyW19CThrcnRRkiRJkrR32h0QE1gfEXdGxIWl7+jM3AxQ7o8q/cuBh6esu6n0LS+Pp/c3rZOZO4CnAC96JkmSJEktaPd1EH8+Mx+JiKOA4Yj47izLzjTyl7P0z7ZO84Yb4fRCgFe84hWzVyxJkiRJC1RbRxAz85FyvwX4G+BU4LFy2CjlfktZfBNw/JTVVwCPlP4VM/Q3rRMRi4GXA4/PUMdVmdmfmf3Lli3bP29OkiRJkrpM2wJiRBwaES+bfAysAb4NXA/UymI14Lry+Hrg/DIz6atoTEZzRzkM9emIOK2cX/iuaetMbuttwM3lPEVJkiRJ0l5q5yGmRwN/U+aMWQx8PjO/GhHfBL4YERcADwFvB8jM+yLii8D9wA7gPZm5s2zr3cCngYOBoXIDuAb4bESM0hg5PL+N70eSJEmSulostAG3/v7+HBkZqboMSZIkSapERNw55TKETaq4zIUkSZIkqQMZECVJkiRJgAFRkiRJklQYECVJkiRJgAFRkiRJklQYECVJkiRJgAFRkiRJklQYECVJkiRJgAFRkiRJklQYECV1nbGxMdauXcu2bduqLkWSJGleMSBK6jr1ep0NGzZQr9erLkWSJGleMSBK6ipjY2MMDQ2RmQwNDTmKKEmStBcMiJK6Sr1eJzMBmJiYcBRRkiRpLxgQJXWV4eFhxsfHARgfH2f9+vUVVyRJkjR/GBAldZWBgQF6e3sB6O3tZc2aNRVXJEmSNH8YECV1lVqtRkQA0NPTQ61Wq7giSZKk+cOAKKmr9PX1MTg4SEQwODjI0qVLqy5JkiRp3lhcdQGStL/VajU2btzo6KEkSdJeMiBK6jp9fX1cccUVVZchSZI073iIqSRJkiQJMCBKkiRJkgoDoiRJkiQJMCBKkiRJkgoDoiRJkiQJMCBKkiRJkgoDoiRJkiQJMCBKkiRJkgoDoiRJkiQJMCBKkiRJkgoDoiRJkiQJMCBKkiRJkgoDoiRJkiQJMCBKkiRJkoo9CogRcUJE3BQR3y7tkyLikvaWJkmSJEmaS3s6gvhnwAeAcYDM3ACc366iJEmSJElzb08D4iGZece0vh37uxhJkiRJUnX2NCCORcQ/AxIgIt4GbG5bVZIkSZKkObd4D5d7D3AV8NMR8U/Ag8C/aVtVkiRJkqQ5t0cBMTN/CJwdEYcCPZn5dHvLkiRJkiTNtT2dxfQ/R8ThmflsZj4dEUdExOXtLk6SJEmSNHf29BzEwcx8crKRmU8Ab25LRZIkSZKkSuxpQFwUEQdONiLiYODAWZaXJEmSJM0zexoQ/wdwU0RcEBG/AQwD9T1ZMSIWRcRdEfGV0j4yIoYj4oFyf8SUZT8QEaMR8b2IOGdK/ykRcW95bl1EROk/MCKuLf23R8TKPXw/kiRJkqRp9iggZuZ/AT4C/Avg1cAflr498V7gO1Pa7wduyszVwE2lTUScCJxftn8u8MmIWFTW+RRwIbC63M4t/RcAT2TmKuDjwMf2sCZJkiRJ0jR7OoJIZg5l5n/KzP+YmTfuyToRsQL4ReDqKd3n8eLoYx1465T+L2Tm9sx8EBgFTo2IY4HDMvMbmZnAZ6atM7mtLwFnTY4uSpIkSZL2zqwBMSL+rtw/HRE/mnJ7OiJ+tAfb/2/A7wETU/qOzszNAOX+qNK/HHh4ynKbSt/y8nh6f9M6mbkDeApYugd1SZIkSZKmmTUgZuYbyv3LMvOwKbeXZeZhs60bEb8EbMnMO/ewlplG/nKW/tnWmV7LhRExEhEjW7du3cNyJEmSJGlh2e0hphHRExHfbmHbPw/8ckRsBL4A/EJE/A/gsXLYKOV+S1l+E3D8lPVXAI+U/hUz9DetExGLgZcDj08vJDOvysz+zOxftmxZC29FkiRJkrrfbgNiZk4A90TEK/Zmw5n5gcxckZkraUw+c3Nm/hvgeqBWFqsB15XH1wPnl5lJX0VjMpo7ymGoT0fEaeX8wndNW2dyW28rr/GSEURJkiRJ0u4t3sPljgXui4g7gGcnOzPzl1t4zY8CX4yIC4CHgLeXbd0XEV8E7gd2AO/JzJ1lnXcDnwYOBobKDeAa4LMRMUpj5PD8FuqRJEmSJAGxJwNuEfHGmfoz89b9XlGb9ff358jISNVlSJIkSVIlIuLOzOyf6blZRxAj4iDgt4BVwL3ANWW2UEmSJElSl9ndOYh1oJ9GOBwE/mvbK5IkSZIkVWJ35yCemJk/AxAR1wB3tL8kSZIkSVIVdjeCOD75wENLJUmSJKm77W4E8bUR8aPyOICDSzuAzMzD2lqdJEmSJGnOzBoQM3PRXBUiSZIkSarW7g4xlaR5Z2xsjLVr17Jt27aqS5EkSZpXDIiSuk69XmfDhg3U6/WqS5EkSZpXDIiSusrY2BhDQ0NkJkNDQ44iSpIk7QUDoqSuUq/XyUwAJiYmHEWUJEnaCwZEtez73/8+g4ODjI6OVl2K9ILh4WHGxxtX6BkfH2f9+vUVVyRJkjR/GBDVsssvv5xnn32WD3/4w1WXIr1gYGCA3t5eAHp7e1mzZk3FFUmSJM0fBkS15Pvf/z4bN24EYOPGjY4iqmPUajUiAoCenh5qtVrFFUmSJM0fBkS15PLLL29qO4qoTtHX18fg4CARweDgIEuXLq26JEmSpHljcdUFaH6aHD3cVVuqUq1WY+PGjY4eSpIk7SVHENWSlStXztqWqtTX18cVV1zh6KEkSdJeMiCqJZdccklT+9JLL62oEkmSJEn7iwFRLTnyyCNfmAgkIjjiiCMqrkiSJEnSvjIgqiX1ep2ensbu09PT48XI1VHGxsZYu3Yt27Ztq7oUSZKkecWAqJYMDw+zc+dOAHbu3OnFyNVRrrzySu655x6uvPLKqkuRJEmaVwyIaokXI1enGhsbe+EHixtvvNFRREmSpL1gQFRLvBi5OtWVV15JZgKQmY4iSpIk7QUDolrixcjVqYaHh5vaHv4sSZK05xZXXYDmLy9Grk40OXq4q7YkSZJ2zYColk1ejFzqJD09PS9MoDTZliRJ0p7xm5OkrnL22Wc3tQcGBiqqRJIkaf4xIErqKhdddFHTNTovuuiiiiuSJEmaPwyIkrpKX1/fC6OGa9ascQIlSZKkveA5iJK6zkUXXcSjjz7q6KEkSdJeMiBK6jpOoCRJktQaDzGVJEmSJAEGREmSJElSYUBUy6688krOOOMMrrnmmqpLkaR5Y2xsjLVr17Jt27aqS5Ek6SUMiGrZ5z73OQDq9XrFlUjS/FGv19mwYYOfnZKkjmRAVEuuvPLKprajiJK0e2NjYwwNDZGZDA0NOYooSeo4BkS1ZHL0cJK/hEvS7tXrdTITgImJCT87JUkdx4AoSdIcGR4eZnx8HIDx8XHWr19fcUWSJDUzIEqSNEcGBgbo7e0FoLe3lzVr1lRckSRJzQyIask73/nOpnatVquoEkmaP2q1GhEBQE9Pj5+dkqSOY0BUSy666KKm9gUXXFBRJZI0f/T19TE4OEhEMDg4yNKlS6suSZKkJgZEtWxyFNFfwCVpz9VqNU466SQ/OyVJHSkmZ1NbKPr7+3NkZKTqMiRJkiSpEhFxZ2b2z/Rc20YQI+KgiLgjIu6JiPsi4g9K/5ERMRwRD5T7I6as84GIGI2I70XEOVP6T4mIe8tz66KcwBERB0bEtaX/9ohY2a73I0mSJEndrp2HmG4HfiEzXwucDJwbEacB7wduyszVwE2lTUScCJwPvBo4F/hkRCwq2/oUcCGwutzOLf0XAE9k5irg48DH2vh+JEmSJKmrtS0gZsMzpdlbbgmcB0xeGbgOvLU8Pg/4QmZuz8wHgVHg1Ig4FjgsM7+RjeNhPzNtncltfQk4a3J0UdLCNTY2xtq1a9m2bVvVpUiSJM0rbZ2kJiIWRcTdwBZgODNvB47OzM0A5f6osvhy4OEpq28qfcvL4+n9Tetk5g7gKeAlU8JFxIURMRIRI1u3bt1P705Sp6rX62zYsIF6vb77hSVJkvSCtgbEzNyZmScDK2iMBr5mlsVnGvnLWfpnW2d6HVdlZn9m9i9btmw3VUuaz8bGxhgaGiIzGRoachRRkiRpL8zJZS4y80ngFhrnDj5WDhul3G8pi20Cjp+y2grgkdK/Yob+pnUiYjHwcuDxdrwHSfNDvV5ncnbmiYkJRxElSZL2QjtnMV0WEYeXxwcDZwPfBa4HJi/+VAOuK4+vB84vM5O+isZkNHeUw1CfjojTyvmF75q2zuS23gbcnAvtuh2SmgwPDzM+Pg7A+Pg469evr7giSZKk+aOdI4jHAl+LiA3AN2mcg/gV4KPAQEQ8AAyUNpl5H/BF4H7gq8B7MnNn2da7gatpTFzzA2Co9F8DLI2IUeB9lBlRJS1cAwMD9Pb2AtDb28uaNWsqrkiSJGn+iIU24Nbf358jIyNVlyGpTcbGxnjHO97B+Pg4BxxwANdeey1Ll75k7ipJkqQFKyLuzMz+mZ6bk3MQ1Z28lIA6UV9fH8uXNyY6Pu644wyHkiRJe8GAqJZdfPHF3HPPPbz3ve+tuhTpBWNjYzzySGMeq0ceecQfMCRJkvaCAVEtGRsbY9OmxuUpH3roIb+Eq2NMncU0M53FVJIkaS8YENWSiy++uKntKKI6hbOYSpIktc6AqJZMjh5OeuihhyqqRGo2MDDAokWLAFi0aJGzmEqSJO0FA6KkrlKr1di5s3GFnJ07d1Kr1XazhiRJkiYZECV1lccff7yp/cQTT1RUiSRJ0vxjQFRLfu7nfq6pfcYZZ1RUidTs8ssvb2p/+MMfrqgSSZKk+ceAqJb8/d//fVP7tttuq6gSqdnGjRtnbUuSJGnXDIiSusrKlStnbUuSJGnXDIiSusoll1zS1L700ksrqkSSJGn+MSBK6ionnHDCC6OGK1euZNWqVdUWJEmSNI8YECV1nUsuuYRDDz3U0UNJkqS9tLjqAjQ/HXfccTzyyCNNbalTnHDCCQwNDVVdhiRJ0rzjCKJaMjUcztSWJEmSNP8YECVJksTY2Bhr165l27ZtVZciqUIGREmSJFGv19mwYQP1er3qUiRVyIAoSZK0wI2NjTE0NERmMjQ05CiitIAZECV1HQ+TkqS9U6/XyUwAJiYmHEWUFjADoloyeZ25XbWlKl155ZXcc889XHnllVWXIknzwvDwMOPj4wCMj4+zfv36iiuSVBUDolpyySWXNLW93pw6xdjYGMPDwwCsX7/eUURJ2gMDAwMsWrQIgEWLFrFmzZqKK5JUFQOiWnLCCSe8MGq4cuVKVq1aVW1BUnHllVcyMTEBNA6TchRRknavVquxc+dOAHbu3EmtVqu4IklVMSCqZZdccgmHHnqoo4fqKH/7t3/b1J4cTZQk7drjjz/e1H7iiScqqkRS1QyIatmRRx7JqlWrOOKII6ouRZIk7YPLL7+8qf3hD3+4okokVc2AqJZ5vSR1ouOOO27WtiTppTZu3DhrW9LCYUBUS8bGxrjhhhvITG644QYnAlHH2LJly6xtSdJLHXLIIbO2JS0cBkS1pF6vvzAd9vPPP+8oojpGb2/vrG1J0ktt37591rakhcOAqJZMvz7SjTfeWFElUrNnnnlm1rYk6aUiYta2pIXDgKiWLF26dNa2VJUlS5bM2pYkvdQb3vCGpvbpp59eUSWSqmZAVEs2b948a1uqyvPPPz9rW5L0UgceeOCsbUkLhwFRLfFQFHWqAw44YNa2JOmlbrvttqb2rbfeWlElkqpmQFRLzjrrrKb22WefXVElUjPPQZSkvXf00UfP2pa0cBgQ1ZIf/OAHTW2vl6ROsXLlylnbkqSXevTRR2dtS1o4DIhqyejoaFP7u9/9bkWVSM0uueSSpvall15aUSWSNH8cc8wxs7YlLRyLqy5AUndZt27dS35AmGs9PT1MTExw4IEHsm7dukprWbVqFRdffHGlNUjS7jz22GOztiUtHI4gSuo6kxPTvPKVr6y4EkmaH9asWdPUPueccyqqRFLVHEGUtF91wmjZZA1Vjx5K0nzxlre8heuuu+6F9i//8i9XWI2kKjmCqJYccsghs7YlSdL88eUvf7mpff3111dUiaSqGRDVkh07dszaliRJ88fw8HBTe/369RVVIqlqBkS1JCJmbUuSpPnj9NNPb2qfccYZFVUiqWoGRLVk+/bts7YlSZIkzT8GREmSpAXu61//elP7tttuq6gSSVVrW0CMiOMj4msR8Z2IuC8i3lv6j4yI4Yh4oNwfMWWdD0TEaER8LyLOmdJ/SkTcW55bF+V4xog4MCKuLf23R8TKdr0fNVu0aNGsbUmSNH8MDAy8cLpIRLzksheSFo52jiDuAP5jZv4L4DTgPRFxIvB+4KbMXA3cVNqU584HXg2cC3wyIiZTx6eAC4HV5XZu6b8AeCIzVwEfBz7WxvejKT74wQ82tS+99NKKKpEkSfvqLW95C5kJQGZ6mQt1lLGxMdauXcu2bduqLmVBaFtAzMzNmfmt8vhp4DvAcuA8oF4WqwNvLY/PA76Qmdsz80FgFDg1Io4FDsvMb2Tjk+sz09aZ3NaXgLMmRxfVXmeffXZT+8wzz6yoEkmStK+8zIU6Wb1eZ8OGDdTr9d0vrH02J+cglkM/fxa4HTg6MzdDI0QCR5XFlgMPT1ltU+lbXh5P729aJzN3AE8BS2d4/QsjYiQiRrZu3bqf3tXCNjY21tT2Fx1Jkuavr371q03toaGhiiqRmo2NjTE0NERmMjQ05HfOOdD2gBgRS4C/An4nM38026Iz9OUs/bOt09yReVVm9mdm/7Jly3ZXsvbAunXrZm1LkqT5Y+fOnbO2parU6/UXDn+emJhwFHEOtDUgRkQvjXD4ucz869L9WDlslHK/pfRvAo6fsvoK4JHSv2KG/qZ1ImIx8HLg8f3/TjTdLbfc0tT+2te+Vk0hkiRpn+3YsWPWtlSV4eFhxsfHARgfH2f9+vUVV9T92jmLaQDXAN/JzD+e8tT1QK08rgHXTek/v8xM+ioak9HcUQ5DfToiTivbfNe0dSa39Tbg5pz8iUGSJEl7ZMmSJbO2paoMDAzQ29sLQG9vrzPszoF2jiD+PPBvgV+IiLvL7c3AR4GBiHgAGChtMvM+4IvA/cBXgfdk5uTxDe8GrqYxcc0PgMkD468BlkbEKPA+yoyokiRJ2nPve9/7mtq/+7u/W1ElUrNarfbCJVh6enqo1Wq7WUP7anG7NpyZf8fM5wgCnLWLdT4CfGSG/hHgNTP0/wR4+z6UKUmStODdc889Te277rrLGcrVEfr6+hgcHOT6669ncHCQpUtfMh+l9rM5mcVU3WflypVN7VWrVlVTiCRJ2mfDw8NNbc/zUiep1WqcdNJJjh7OEQOiWrJ9+/am9rPPPltRJZIkaV+dfvrpTe0zzjijokqkl+rr6+OKK65w9HCOGBDVks2bN8/aliRJkjT/GBAlSZIWuNtuu62pfeutt1ZUiaSqGRDVksnZpHbVliRJ88fRRx89a1vSwtG2WUzV3Y488ki2bdv2Qruvr6/CaiRp99atW8fo6GjVZbBp0yYAVqxYUWkdq1at4uKLL660BnUOTx2RNMmAqJZMDYcAW7duragSSZpffvzjH1ddgvQSPT09s7YlLRwGREnSgtApo2WTdaxbt67iSqQXTf/hwh8ypIXLn4ckSZIkSYABUZIkacGbfn055xaQFi4DoiRJ0gJ3wAEHzNqWtHAYECVJkha46bOWPvLIIxVVIqlqBkRJkqQF7thjj521LWnhMCBKkiQtcDt37mxqT0xMVFSJpKoZENWSc845p6l97rnnVlSJJEnaV1u2bGlqP/bYYxVVIqlqBkRJkqQFLiJmbUtaOAyIasmtt97a1L7llluqKUSSJO2z0047ran9+te/vqJKJFXNgKiW9PT0zNqWJEnzx2GHHTZrW9LC4bd6teS5556btS1JkuaPr3/9603t2267raJKJFVtcdUFaO+tW7eO0dHRqst4iYsvvriS1121alVlry1JUjcYGBjgy1/+MhMTE/T09LBmzZqqS5JUEUcQJUmSFrharfbCpS0mJiao1WoVVySpKo4gzkOdMFr2zne+k4cffviF9vHHH8+6desqrEiSJLXq8ccfb2o/8cQTLF26tKJqJFXJEUS15EMf+lBT+w/+4A8qqkSSJO2r3//9329qf/CDH6yoEklVMyCqJSeccAIHHHAA0Bg9XLVqVcUVSZKkVm3evHnWtqSFw4Colq1cuZKenh5HDyVJkqQuYUBUyw455BBOOukkRw8lSZKkLmFAlCRJkiQBzmIqdY1OvT5mFR544AGgM2b87QReK1TS7kQEmdnUlrQwGRClLjE6Osr3v/0tXrFkZ9WlVO6A8cbBET/Z+M2KK6neQ88sqroESfPAIYccwrPPPtvUlrQwGRClLvKKJTu5pP+ZqstQB7l8ZEnVJUiaB6aGw5nakhYOz0GUJEla4JYsWTJrW9LC4QiiJKntPEf2RZ4j28xzZDvDM888M2tbqtI73vEONm/ezIoVK/j85z9fdTldz4AoSWq70dFR7rrvLji86ko6wETj7q5/uqvaOjrBk1UXIGk+2Lx5MwCbNm2quJKFwYAoSZobh8PEmyaqrkIdpOcWz3SRNLt3vOMdTe1f+7VfcxSxzfxkliRJktSRJkcPJzmK2H4GREmSJEkSYECUJEmSJBUGREmSJEkd6dhjj21qr1ixoqJKFg4DoiRJkqSOdO211za1naCm/QyIkiRJkjrS2NhYU3vbtm0VVbJwGBAlSZIkdaR6vU5vby8Avb291Ov1iivqfm0LiBHx5xGxJSK+PaXvyIgYjogHyv0RU577QESMRsT3IuKcKf2nRMS95bl1ERGl/8CIuLb03x4RK9v1XiRJkiTNveHhYcbHxwEYHx9n/fr1FVfU/do5gvhp4Nxpfe8HbsrM1cBNpU1EnAicD7y6rPPJiFhU1vkUcCGwutwmt3kB8ERmrgI+Dnysbe9EkiSpi02fCOS4446rqBKp2cDAQNMI4po1ayquqPstbteGM/O2GUb1zgPeVB7XgVuA/6v0fyEztwMPRsQocGpEbAQOy8xvAETEZ4C3AkNlncvKtr4EfCIiIjOzPe9I6mybNm3i2acXcfnIkqpLUQf5x6cXcagXFZY62rp16xgdHa20hscee6yp/eijj3LxxRdXUsuqVasqe211nlqtxtDQEAA9PT3UarWKK+p+c30O4tGZuRmg3B9V+pcDD09ZblPpW14eT+9vWiczdwBPAUvbVrkkSVKXOuKII2ZtS1Xp6+vjzDPPBODMM89k6VK/7rdb20YQ91LM0Jez9M+2zks3HnEhjcNUecUrXtFKfVLHW7FiBT/ZsZlL+p+puhR1kMtHlnCQ14ySOlonjJaNjY3xK7/yK2QmBxxwAFdffbVfxKUFaq5HEB+LiGMByv2W0r8JOH7KciuAR0r/ihn6m9aJiMXAy4HHZ3rRzLwqM/szs3/ZsmX76a1IkiR1h76+Po488kgA3vzmNxsO1THGxsa4+eabAbj55pu9zMUcmOsRxOuBGvDRcn/dlP7PR8QfA8fRmIzmjszcGRFPR8RpwO3Au4Arpm3rG8DbgJs9/1CSOtOmTZvgKei5xasraYonYVN6jmynOOaYY/jJT37iOV7qKPV6nR07dgCNWUzr9Trve9/7Kq6qu7XzMhd/SSO8/fOI2BQRF9AIhgMR8QAwUNpk5n3AF4H7ga8C78nMnWVT7wauBkaBH9CYoAbgGmBpmdDmfZQZUSVJkrT3ent7Wb16taOH6ijr169ncgwoM7nxxhsrrqj7tXMW01/dxVNn7WL5jwAfmaF/BHjNDP0/Ad6+LzVKkubGihUr2BpbmXjTRNWlqIP03NLDiuWeIytp144++mg2btzY1FZ7eayPJEmSpI706KOPztrW/tcps5jOG51wraJO8cADDwCdMftaJ/C6TZIkSfvXMccc0zSCeMwxx1RXzAJhQNxLo6Oj3HXv/UwccmTVpVQunm8cD37nD/wlp+e5GSfQlSRJ0j547LHHZm1r/zMgtmDikCP5yYm/VHUZ6iAH3f+VqkuQJEnqOmvWrOG66657oX3OOedUWM3C4DmIkiRJkjrS6aef3tR+4xvfWFElC4cBUZIkSVJH+sQnPtHU/pM/+ZOKKlk4PMRUkiRJ0oyqnqBx6gQ1k+0qJwVcCJMSOoIoSZIkqSMdeOCBs7a1/zmCKHWRh55ZxOUjS6ouo3KPPdf47evoQ7wo+0PPLOKEqouQJM1bVY+Wff/73+ff//t//0L7U5/6FKtWraqwou5nQJS6hB+WL3q+XKPzoJWrK66keifgviFJmr9OOOEEDjzwQLZv387KlSv9mzYHDIhSl6j6F75OMvlvsW7duoorkSRJ++qVr3wlo6OjXHrppVWXsiB4DqIkSZKkjnXIIYdw0kknOXo4RwyIkiRJkiTAgChJkiRJKjwHcS9t2rSJnuee4qD7v1J1KeogPc9tY9OmHVWXIUmSJO0TRxAlSZIkSYAjiHttxYoVPLZ9MT858ZeqLkUd5KD7v8KKFcdUXYYkaS+tW7eO0dHRqsvoCA+USwQ5K3bDqlWr/LfQgmRAlCRJC9bo6Cjfvftu/InvxcPKnrz77irL6AiPVl2AVCEDoiRJWtCOAS4gqi5DHeQasuoSHN2ewtHtZu0e3TYgSpLmxpPQc4unvvNMuV9SaRWd4UlgedVFSJ1pdHSU++79DocfclTVpVRu4vnGDzj/9INtFVdSvSef29L21zAgSpLazosbv2jyl/DVy1dXXEkHWO6+Ic3m8EOO4syfPr/qMtRBvvbdL7T9NQyIkqS287CgF03+W6xbt67iSiRJeikDYgt6nnvc6yAC8ZMfAZAHHVZxJdXree5xcIoDSZIkzXMGxL3koTAveuCBpwFY/c8MRnCM+4YkSZLmPQPiXvIwqRd5mJQkSZLUXQyIkvarTpiWu5Omw/ZCy5KkVmzatImnnnt6TiYl0fzx5HNbyE0/butrGBAldZ2DDz646hIkSZLmJQOipP3K0TJ1qk4Y3YbOGeF2dFvqbCtWrOCpJ75TdRkd4ZmfPAHAkoOOqLiSzrBixYq2bt+AOA/5JaeZX3IkzSeOcHeWTZs28TRwDVl1Keogm4FnNm2qtAYnv3vRAw88DsDyf7a04kqqt5ylbd83DIhqmV9yJM0n/pAkaT7plM+sThmY6BQLYWDCgDgPdftOKUnSXFmxYgVPjo1xAVF1Keog15Ac3ubD+LTnHJSYWwZESZIkSTNyYGLhMSBKkqQF7VE8BxFgW7n3LK/GPnF41UVIFTEgSpKkBcuJQF60tUw+d/jq1RVXUr3Dcd/QwmVAlCRJC5aHz71o8t9i3bp1FVciqUo9VRcgSZIkSeoMjiBKkiRVqFMuI+D1jSWBAVGSJEl4KQFJDQZESZKkCjlaJqmTeA6iJEmSJAkwIEqSJEmSCgOiJEmSJAnogoAYEedGxPciYjQi3l91PZIkSZI0X83rgBgRi4D/DgwCJwK/GhEnVluVJEmSJM1P8zogAqcCo5n5w8x8HvgCcF7FNUmSJEnSvDTfA+Jy4OEp7U2lT5IkSZK0l+Z7QIwZ+vIlC0VcGBEjETGydevWOShLkiRJkuaf+R4QNwHHT2mvAB6ZvlBmXpWZ/ZnZv2zZsjkrTpIkSZLmk/keEL8JrI6IV0XEAcD5wPUV1yRJkiRJ89LiqgvYF5m5IyJ+G7gRWAT8eWbeV3FZkiRJkjQvzeuACJCZNwA3VF2HJEmSJM138/0QU0mSJEnSfmJAlCRJkiQBBkRJkiRJUmFAlCRJkiQBBkRJkiRJUmFAlCRJkiQBEJlZdQ1zKiK2Av9YdR1dpA8Yq7oIaQbum+pk7p/qVO6b6lTum/vXKzNz2UxPLLiAqP0rIkYys7/qOqTp3DfVydw/1ancN9Wp3DfnjoeYSpIkSZIAA6IkSZIkqTAgal9dVXUB0i64b6qTuX+qU7lvqlO5b84Rz0GUJEmSJAGOIEqSJEmSCgOimkTEzoi4OyLui4h7IuJ9ETHrfhIRKyPi2+Xxv4uIT+xiuRsi4vA2lK0uFRHHRMQXIuIHEXF/2YdO2Mtt3BIRL5n1LCL6I2Ld/qtW3WjKZ+I9EfGtiPi5NrzGmyLiK/t7u+ouEfHMtPYu/97uYv0X/la3o565Xl/zy9T/3hHx5oh4ICJesbvl9/bzsSy/3z+nF5rFVRegjvPjzDwZICKOAj4PvBz40L5uODPfvK/b0MIREQH8DVDPzPNL38nA0cD393X7mTkCjOzrdtT1pn4mngP8P8AbqywoIhZn5o4qa1D3i4hFmbmz6jrUXSLiLOAKYE1mPtSGl3gT8Azw923Y9oLhCKJ2KTO3ABcCvx0NiyLijyLimxGxISIu2sWqx0XEV8uvQ/9lsjMiNkZE35wUr25wJjCemX862ZGZdwN/V/bDb0fEvRHxjsnnI+L3St89EfHRKdt6e0TcERHfj4jTy7Iv/CoZEZdFxJ+X0cYfRsTFc/MWNc8cBjwBEBFLIuKmMqp4b0ScV/pXRsR3IuLPypEY6yPi4PLc68pn5zcm9+HpLxARR0bE/yrL/UNEnFT6L4uIqyJiPfCZiFgWEX9VPo+/GRE/P3f/DOokEfGyiHgwInpL+7Dy97Y3Ik4pn4ffAN4zZZ0Z/56Xz8WvRcTngXtL3/+KiDvL/nzhtNf+r+X/gZsiYlnp+82y3XvKPnpI6X9V2fe/GRF/ODf/Ouok5e/vnwG/mJk/KH3vK3/Pvx0Rv7Ob9V8XEXdFxE9FxFsi4vbS/tuIODoiVgK/BfyHaBz5cfpMy7X7fXYDA6JmlZk/pLGfHAVcADyVma8DXgf8ZkS8aobVTgbeAfwM8I6IOH6OylV3eQ1w5wz9/weNfey1wNnAH0XEsRExCLwV+FeZ+Vrgv0xZZ3Fmngr8DrseDf9p4BzgVOBDk1+2tOAdXL5ofBe4Gpj8YvsT4F9n5r+k8WPGf42IKM+tBv57Zr4aeBL4ldL/F8BvZebrgV2NzPwBcFdmngT838Bnpjx3CnBeZv4a8CfAx8vn8a+U2tTdJvfFuyPibuDDAJn5NHAL8ItlufOBv8rMcRr73MVln5tqtr/npwIfzMwTS/s3MvMUoB+4OCKWlv5DgW+V/wdu5cXP1r/OzNeVz+HvlNeCxj77qfKaj+7rP4bmnQOB64C3ZuZ3ASLiFODXgX8FnEZjP/zZmVaOxmGjf0rjM/CHwN8Bp2XmzwJfAH4vMzeWZT6emSdn5tdnWq6N77FreIip9sTkl541wEkR8bbSfjmNL0LTD/e7KTOfAoiI+4FXAg/PRaFaEN4A/GU59OmxiLiVxhecNwJ/kZnPAWTm41PW+etyfyewchfb/f8yczuwPSK20DiUdVMb6tf8MvUQ09fTGL17DY3Pxf8cEWcAE8ByGvsMwINltBvKPheN869flpmThz19HvilGV7vDZRAmZk3R8TSiHh5ee76zPxxeXw2cOKLmZTDIuJlJSyoO72wL0LjHEQaoQ0aPxD8HvC/aHzh/s2y3xyembeWZT4LDJbHu/p7/jxwR2Y+OOV1L46If10eH1+W20Zjv7+29P8PXvycfU1EXA4cDiwBbiz9P8+LP5Z8FvjYXr17zXfjNA77vAB4b+l7A/A3mfksQET8NXA6cNe0df8FjUtcrMnMR0rfCuDaiDgWOAB4kJnt6XKawhFEzSoiforGL91baHwhWlt+lTk5M1+VmetnWG37lMc78YcIteY+GiMm08UMfZP9u7puz+Q+Odv+6H6rWWXmN4A+YBnwznJ/SvnS/hhwUFl0pn1pV/vtdDMtN7lfPzulrwd4/ZTP4+WGw4UrM/83jR8i3ggsysxvM/tn4mx/z1/YzyLiTTR+jHh9GRG8ixf385eUUe4/Dfx2Zv4MjRHxg2ZYRgvPBPB/Aq+LiP+79O3p5+JmGkdtTB1dvAL4RNnPLmLX++WeLqcpDIjapXI+wZ/S+B8rafwK+O4p5zmcEBGHVlmjutrNwIER8ZuTHRHxOhrngL2jnEOzDDgDuANYD/zGlPNdjqygZnWxiPhpYBGN0ZOXA1syczwizqRxpMQuZeYTwNMRcVrpOn8Xi95GI3xOfjkfy8wfzbDceuC3p9R28h6/EXWrzwB/SeOwUjLzSeCpiHhDef6dU5bd07/nLweeyMznyv5/2pTneoDJEchfo3EoH8DLgM1l21Nf83/z4n4/tV8LRDnC55eAd0bEBTQ+794aEYeU/e9fA1+fYdUnaRxC/Z/L5yI09s1/Ko9rU5Z9msY+yG6W0yz8hVzTHVzObegFdtA4DOSPy3NX0zg871vlXJutNM75kva7zMxyWNN/i4j30/j1cCON8wiXAPfQ+DX69zLzUeCr5UvySEQ8D9xA4xwuaV9MfiZC49fuWmbujIjPAV+OiBHgbuC7e7CtC4A/i4hnaZwz9tQMy1wG/EVEbACeY9dfaC4G/ntZbjGNL1q/tSdvSF3rc8DlNELipF8H/jwinuPFQz1hz/+efxX4rbKffQ/4hynPPQu8OiLupLEvT04Y9vvA7cA/0pjoZvLL+nuBz0fEe4G/au0tar7LzMcj4lwan1m/Q2PE+Y7y9NWZOf3w0sn1HouItwBDEfEbND4r/2dE/BON/XLyHNovA1+KxsRha2dZTrOIxsCQJElqp4hYkpmT1/Z6P3BsZr53N6tJe6ScT3heZv7bqmuRNL85gihJ0tz4xYj4AI2/vf8I/Ltqy1G3iIgraExA4/WGJe0zRxAlSZIkSYCT1EiSJEmSCgOiJEmSJAkwIEqSJEmSCgOiJGnBi4idEXF3RNwXEfdExPsioqW/kVMuAj3Z/vt9qOvfRcRxU9pXR8SJrW5PkqTdcZIaSdKCFxHPZOaS8vgo4PPA/87MD+3LtvZDXbcA/ykzR/bH9iRJ2h1HECVJmiIztwAXAr8dDYsi4o8i4psRsSEiLgKIiGMj4rYy8vjtiDg9Ij4KHFz6PleWm7z24Zsi4paI+FJEfDciPlcuUk5EXFq2/+2IuKq87tuAfuBzZXsHl/X7yzq/GhH3lnU+Nll/RDwTER8pI6H/EBFHz+k/oCRpXjMgSpI0TWb+kMbfyKOAC4CnMvN1wOuA34yIVwG/BtyYmScDrwXuzsz3Az/OzJMz850zbPpngd8BTgR+Cvj50v+JzHxdZr4GOBj4pcz8EjACvLNs78eTGymHnX4M+AXgZOB1EfHW8vShwD9k5muB24Df3A//JJKkBcKAKEnSzKLcrwHeFRF3A7cDS4HVwDeBX4+Iy4Cfycyn92Cbd2TmpsycAO4GVpb+MyPi9oi4l0boe/VutvM64JbM3JqZO4DPAWeU554HvlIe3znlNSRJ2q3FVRcgSVKniYifAnYCW2gExbWZeeMMy50B/CLw2Yj4o8z8zG42vX3K453A4og4CPgk0J+ZD5fAedDuSpzlufF8cYKBnfi3XpK0FxxBlCRpiohYBvwpjcM+E7gReHdE9JbnT4iIQyPilcCWzPwz4BrgX5ZNjE8uu4cmw+BYRCwB3jbluaeBl82wzu3AGyOiLyIWAb8K3LoXrylJ0oz8VVGSpDKxDNAL7AA+C/xxee5qGodpfqtMKrMVeCvwJuB3I2IceAZ4V1n+KmBDRHxrF+chNsnMJyPiz4B7gY00Dl2d9GngTyPix8Drp6yzOSI+AHyNxmjiDZl53d6+aUmSpvMyF5IkSZIkwENMJUmSJEmFAVGSJEmSBBgQJUmSJEmFAVGSJEmSBBgQJUmSJEmFAVGSJEmSBBgQJUmSJEmFAVGSJEmSBMD/D2l7KVgt3mTUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(x='Destination',y='Price',data=df.sort_values('Price',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>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": [
       "   Cochin  Delhi  Hyderabad  Kolkata\n",
       "0       0      1          0        0\n",
       "1       0      0          0        0\n",
       "2       1      0          0        0\n",
       "3       0      0          0        0\n",
       "4       0      1          0        0"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#OneHotEncoding of destination column\n",
    "destination=pd.get_dummies(categorical['Destination'],drop_first=True)\n",
    "destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEL → BOM → COK          2376\n",
       "BLR → DEL                1536\n",
       "CCU → BOM → BLR           979\n",
       "CCU → BLR                 724\n",
       "BOM → HYD                 621\n",
       "                         ... \n",
       "CCU → VTZ → BLR             1\n",
       "CCU → IXZ → MAA → BLR       1\n",
       "BOM → COK → MAA → HYD       1\n",
       "BOM → CCU → HYD             1\n",
       "BOM → BBI → HYD             1\n",
       "Name: Route, Length: 127, dtype: int64"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Route'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoding of route column\n",
    "categorical['Route1']=categorical['Route'].str.split('→').str[0]\n",
    "categorical['Route2']=categorical['Route'].str.split('→').str[1]\n",
    "categorical['Route3']=categorical['Route'].str.split('→').str[2]\n",
    "categorical['Route4']=categorical['Route'].str.split('→').str[3]\n",
    "categorical['Route5']=categorical['Route'].str.split('→').str[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Route1</th>\n",
       "      <th>Route2</th>\n",
       "      <th>Route3</th>\n",
       "      <th>Route4</th>\n",
       "      <th>Route5</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>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>BLR</td>\n",
       "      <td>DEL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>CCU</td>\n",
       "      <td>IXR</td>\n",
       "      <td>BBI</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NaN</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>DEL</td>\n",
       "      <td>LKO</td>\n",
       "      <td>BOM</td>\n",
       "      <td>COK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>CCU</td>\n",
       "      <td>NAG</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NAG</td>\n",
       "      <td>DEL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore       Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore       Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info Route1 Route2 Route3 Route4 Route5  \n",
       "0         No info   BLR     DEL    NaN    NaN    NaN  \n",
       "1         No info   CCU    IXR    BBI     BLR    NaN  \n",
       "2         No info   DEL    LKO    BOM     COK    NaN  \n",
       "3         No info   CCU    NAG     BLR    NaN    NaN  \n",
       "4         No info   BLR    NAG     DEL    NaN    NaN  "
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_col(categorical,'Route')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                0\n",
       "Source                 0\n",
       "Destination            0\n",
       "Total_Stops            0\n",
       "Additional_Info        0\n",
       "Route1                 0\n",
       "Route2                 0\n",
       "Route3              3475\n",
       "Route4              9100\n",
       "Route5             10418\n",
       "dtype: int64"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Source', 'Destination', 'Total_Stops', 'Additional_Info',\n",
       "       'Route1', 'Route2', 'Route3', 'Route4', 'Route5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['Route3', 'Route4', 'Route5']:\n",
    "    categorical[i].fillna('None',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Source             0\n",
       "Destination        0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Route1             0\n",
       "Route2             0\n",
       "Route3             0\n",
       "Route4             0\n",
       "Route5             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airline has total 12 categories\n",
      "Source has total 5 categories\n",
      "Destination has total 5 categories\n",
      "Total_Stops has total 4 categories\n",
      "Additional_Info has total 10 categories\n",
      "Route1 has total 5 categories\n",
      "Route2 has total 45 categories\n",
      "Route3 has total 30 categories\n",
      "Route4 has total 14 categories\n",
      "Route5 has total 5 categories\n"
     ]
    }
   ],
   "source": [
    "for i in categorical.columns:\n",
    "    print('{} has total {} categories'.format(i,len(categorical[i].value_counts())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying label encoder for Routes\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "for i in ['Route1', 'Route2', 'Route3', 'Route4', 'Route5']:\n",
    "    categorical[i]=encoder.fit_transform(categorical[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "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>Route1</th>\n",
       "      <th>Route2</th>\n",
       "      <th>Route3</th>\n",
       "      <th>Route4</th>\n",
       "      <th>Route5</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>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Total_Stops Additional_Info  Route1  \\\n",
       "0       IndiGo  Banglore       Delhi    non-stop         No info       0   \n",
       "1    Air India   Kolkata    Banglore     2 stops         No info       2   \n",
       "2  Jet Airways     Delhi      Cochin     2 stops         No info       3   \n",
       "3       IndiGo   Kolkata    Banglore      1 stop         No info       2   \n",
       "4       IndiGo  Banglore       Delhi      1 stop         No info       0   \n",
       "\n",
       "   Route2  Route3  Route4  Route5  \n",
       "0      13      29      13       4  \n",
       "1      25       1       3       4  \n",
       "2      32       4       5       4  \n",
       "3      34       3      13       4  \n",
       "4      34       8      13       4  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_col(categorical,'Additional_Info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops'], dtype=object)"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding Total stops\n",
    "dict={'non-stop':0, '2 stops':2, '1 stop':1, '3 stops':3, '4 stops':4}\n",
    "categorical['Total_Stops']=categorical['Total_Stops'].map(dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        2\n",
       "2        2\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "10678    0\n",
       "10679    0\n",
       "10680    0\n",
       "10681    0\n",
       "10682    2\n",
       "Name: Total_Stops, Length: 10461, dtype: int64"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['Total_Stops']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_col(categorical,'Source')\n",
    "drop_col(categorical,'Destination')\n",
    "drop_col(categorical,'Airline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df=pd.concat([categorical,Airline,source,destination,df[continuous_col]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Route1</th>\n",
       "      <th>Route2</th>\n",
       "      <th>Route3</th>\n",
       "      <th>Route4</th>\n",
       "      <th>Route5</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>...</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <th>duration_hour</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Route1  Route2  Route3  Route4  Route5  Air India  GoAir  \\\n",
       "0            0       0      13      29      13       4          0      0   \n",
       "1            2       2      25       1       3       4          1      0   \n",
       "2            2       3      32       4       5       4          0      0   \n",
       "3            1       2      34       3      13       4          0      0   \n",
       "4            1       0      34       8      13       4          0      0   \n",
       "\n",
       "   IndiGo  Jet Airways  ...  Kolkata  Price  journey_day  journey_month  \\\n",
       "0       1            0  ...        0   3897           24              3   \n",
       "1       0            0  ...        0   7662            5              1   \n",
       "2       0            1  ...        0  13882            6              9   \n",
       "3       1            0  ...        0   6218            5             12   \n",
       "4       1            0  ...        0  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_min  Arrival_Time_hour  Arrival_Time_min  \\\n",
       "0             22            20                  1                10   \n",
       "1              5            50                 13                15   \n",
       "2              9            25                  4                25   \n",
       "3             18             5                 23                30   \n",
       "4             16            50                 21                35   \n",
       "\n",
       "   duration_hour  duration_min  \n",
       "0              2            50  \n",
       "1              7            25  \n",
       "2             19             0  \n",
       "3              5            25  \n",
       "4              4            45  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\srush\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\srush\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check for outliers\n",
    "def plot(data,col):\n",
    "    fig,(ax1,ax2)=plt.subplots(2,1)\n",
    "    sns.distplot(data[col],ax=ax1)\n",
    "    sns.boxplot(data[col],ax=ax2)\n",
    "plot(final_df,'Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\srush\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\srush\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Replace the outliers with median\n",
    "final_df['Price']=np.where(final_df['Price']>=40000,final_df['Price'].median(),final_df['Price'])\n",
    "plot(final_df,'Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seperate the dataset in X and Y columns\n",
    "X=final_df.drop('Price',axis=1)\n",
    "y=df['Price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.15820193, 2.05111258, 2.741338  , 2.30563629, 1.4026348 ,\n",
       "       1.84405218, 0.75756884, 0.09953419, 0.70236801, 0.90424144,\n",
       "       0.01717366, 0.61530669, 0.01333408, 0.32384899, 0.        ,\n",
       "       0.22839467, 0.00675647, 0.18257167, 1.53916569, 0.87750999,\n",
       "       0.29143031, 1.54157844, 0.72244814, 0.29467019, 0.15408938,\n",
       "       1.05683376, 0.86086876, 1.39009054, 1.17471457, 1.84664181,\n",
       "       1.5355795 , 1.76998673, 1.05675326])"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info_classif(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>2.104280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route1</th>\n",
       "      <td>2.027368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route2</th>\n",
       "      <td>2.795140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route3</th>\n",
       "      <td>2.295441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route4</th>\n",
       "      <td>1.434930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route5</th>\n",
       "      <td>0.867436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air India</th>\n",
       "      <td>0.770308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoAir</th>\n",
       "      <td>0.096600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndiGo</th>\n",
       "      <td>0.657608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways</th>\n",
       "      <td>0.925406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <td>0.000464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers</th>\n",
       "      <td>0.562896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <td>0.008612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpiceJet</th>\n",
       "      <td>0.335500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trujet</th>\n",
       "      <td>0.011974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara</th>\n",
       "      <td>0.242903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>0.166081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1.543837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.892635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.294920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cochin</th>\n",
       "      <td>1.570914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.398974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>0.296737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.156573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi</th>\n",
       "      <td>0.397632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_day</th>\n",
       "      <td>1.049904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_month</th>\n",
       "      <td>0.869132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>1.412896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <td>1.225876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>1.873380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <td>1.527224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_hour</th>\n",
       "      <td>1.776804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_min</th>\n",
       "      <td>1.079921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          0\n",
       "Total_Stops                        2.104280\n",
       "Route1                             2.027368\n",
       "Route2                             2.795140\n",
       "Route3                             2.295441\n",
       "Route4                             1.434930\n",
       "Route5                             0.867436\n",
       "Air India                          0.770308\n",
       "GoAir                              0.096600\n",
       "IndiGo                             0.657608\n",
       "Jet Airways                        0.925406\n",
       "Jet Airways Business               0.000464\n",
       "Multiple carriers                  0.562896\n",
       "Multiple carriers Premium economy  0.008612\n",
       "SpiceJet                           0.335500\n",
       "Trujet                             0.011974\n",
       "Vistara                            0.242903\n",
       "Vistara Premium economy            0.000000\n",
       "Chennai                            0.166081\n",
       "Delhi                              1.543837\n",
       "Kolkata                            0.892635\n",
       "Mumbai                             0.294920\n",
       "Cochin                             1.570914\n",
       "Delhi                              0.398974\n",
       "Hyderabad                          0.296737\n",
       "Kolkata                            0.156573\n",
       "New Delhi                          0.397632\n",
       "journey_day                        1.049904\n",
       "journey_month                      0.869132\n",
       "Dep_Time_hour                      1.412896\n",
       "Dep_Time_min                       1.225876\n",
       "Arrival_Time_hour                  1.873380\n",
       "Arrival_Time_min                   1.527224\n",
       "duration_hour                      1.776804\n",
       "duration_min                       1.079921"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = pd.DataFrame(mutual_info_classif(X,y),index=X.columns)\n",
    "imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Route2</th>\n",
       "      <td>2.795140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route3</th>\n",
       "      <td>2.295441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>2.104280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route1</th>\n",
       "      <td>2.027368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>1.873380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_hour</th>\n",
       "      <td>1.776804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cochin</th>\n",
       "      <td>1.570914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1.543837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <td>1.527224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route4</th>\n",
       "      <td>1.434930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>1.412896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <td>1.225876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_min</th>\n",
       "      <td>1.079921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_day</th>\n",
       "      <td>1.049904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways</th>\n",
       "      <td>0.925406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.892635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_month</th>\n",
       "      <td>0.869132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route5</th>\n",
       "      <td>0.867436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air India</th>\n",
       "      <td>0.770308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndiGo</th>\n",
       "      <td>0.657608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers</th>\n",
       "      <td>0.562896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.398974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi</th>\n",
       "      <td>0.397632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpiceJet</th>\n",
       "      <td>0.335500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>0.296737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.294920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara</th>\n",
       "      <td>0.242903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>0.166081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.156573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoAir</th>\n",
       "      <td>0.096600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trujet</th>\n",
       "      <td>0.011974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <td>0.008612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <td>0.000464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   importance\n",
       "Route2                               2.795140\n",
       "Route3                               2.295441\n",
       "Total_Stops                          2.104280\n",
       "Route1                               2.027368\n",
       "Arrival_Time_hour                    1.873380\n",
       "duration_hour                        1.776804\n",
       "Cochin                               1.570914\n",
       "Delhi                                1.543837\n",
       "Arrival_Time_min                     1.527224\n",
       "Route4                               1.434930\n",
       "Dep_Time_hour                        1.412896\n",
       "Dep_Time_min                         1.225876\n",
       "duration_min                         1.079921\n",
       "journey_day                          1.049904\n",
       "Jet Airways                          0.925406\n",
       "Kolkata                              0.892635\n",
       "journey_month                        0.869132\n",
       "Route5                               0.867436\n",
       "Air India                            0.770308\n",
       "IndiGo                               0.657608\n",
       "Multiple carriers                    0.562896\n",
       "Delhi                                0.398974\n",
       "New Delhi                            0.397632\n",
       "SpiceJet                             0.335500\n",
       "Hyderabad                            0.296737\n",
       "Mumbai                               0.294920\n",
       "Vistara                              0.242903\n",
       "Chennai                              0.166081\n",
       "Kolkata                              0.156573\n",
       "GoAir                                0.096600\n",
       "Trujet                               0.011974\n",
       "Multiple carriers Premium economy    0.008612\n",
       "Jet Airways Business                 0.000464\n",
       "Vistara Premium economy              0.000000"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.columns=['importance']\n",
    "imp.sort_values(by='importance',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the table, few features have approx 0 value of importance, and we can remove it. But for now, we are not removing it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "# spiliting the dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "mmscaler=MinMaxScaler(feature_range=(0,1))\n",
    "X_train=mmscaler.fit_transform(X_train)\n",
    "X_test=mmscaler.fit_transform(X_test)\n",
    "X_train=pd.DataFrame(X_train)\n",
    "X_test=pd.DataFrame(X_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "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>Model Name</th>\n",
       "      <th>Mean_Absolute_Error_MAE</th>\n",
       "      <th>Adj_R_Square</th>\n",
       "      <th>Root_Mean_Squared_Error_RMSE</th>\n",
       "      <th>Mean_Absolute_Percentage_Error_MAPE</th>\n",
       "      <th>Mean_Squared_Error_MSE</th>\n",
       "      <th>Root_Mean_Squared_Log_Error_RMSLE</th>\n",
       "      <th>R2_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Model Name, Mean_Absolute_Error_MAE, Adj_R_Square, Root_Mean_Squared_Error_RMSE, Mean_Absolute_Percentage_Error_MAPE, Mean_Squared_Error_MSE, Root_Mean_Squared_Log_Error_RMSLE, R2_score]\n",
       "Index: []"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a={'Model Name':[], 'Mean_Absolute_Error_MAE':[] ,'Adj_R_Square':[] ,'Root_Mean_Squared_Error_RMSE':[] ,'Mean_Absolute_Percentage_Error_MAPE':[] ,'Mean_Squared_Error_MSE':[] ,'Root_Mean_Squared_Log_Error_RMSLE':[] ,'R2_score':[]}\n",
    "res=pd.DataFrame(a)\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing libraries required for running the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn import linear_model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "import xgboost as xgb\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "dt = DecisionTreeRegressor()\n",
    "bg = BaggingRegressor()\n",
    "ran = RandomForestRegressor()\n",
    "svr = SVR()\n",
    "xgr = xgb.XGBRegressor()\n",
    "knn = KNeighborsRegressor(n_neighbors=5)\n",
    "etr = ExtraTreesRegressor()\n",
    "re=Ridge()\n",
    "lin_lasso=linear_model.Lasso(alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=[lr, dt, bg, ran]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Testing to see perfomrance of each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:  LinearRegression()\n",
      "Model Name:  LinearRegression()\n",
      "Mean Absolute Error (MAE): 1959.198\n",
      "Mean Squared Error (MSE): 7340959.231\n",
      "Root Mean Squared Error (RMSE): 2709.42\n",
      "R2_score: 0.627753\n",
      "Root Mean Squared Log Error (RMSLE): 7.904\n",
      "Mean Absolute Percentage Error (MAPE): 7340959.231\n",
      "Adjusted R Square:  0.626575\n",
      "------------------------------------------------------------------------------------------------------------\n",
      "Model:  DecisionTreeRegressor()\n",
      "Model Name:  DecisionTreeRegressor()\n",
      "Mean Absolute Error (MAE): 1687.937\n",
      "Mean Squared Error (MSE): 6808224.29\n",
      "Root Mean Squared Error (RMSE): 2609.257\n",
      "R2_score: 0.654767\n",
      "Root Mean Squared Log Error (RMSLE): 7.867\n",
      "Mean Absolute Percentage Error (MAPE): 6808224.29\n",
      "Adjusted R Square:  0.653674\n",
      "------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\srush\\AppData\\Local\\Temp\\ipykernel_13120\\3986559112.py:30: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(new_row, ignore_index=True)\n",
      "C:\\Users\\srush\\AppData\\Local\\Temp\\ipykernel_13120\\3986559112.py:30: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(new_row, ignore_index=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:  BaggingRegressor()\n",
      "Model Name:  BaggingRegressor()\n",
      "Mean Absolute Error (MAE): 1307.555\n",
      "Mean Squared Error (MSE): 4203906.195\n",
      "Root Mean Squared Error (RMSE): 2050.343\n",
      "R2_score: 0.786827\n",
      "Root Mean Squared Log Error (RMSLE): 7.626\n",
      "Mean Absolute Percentage Error (MAPE): 4203906.195\n",
      "Adjusted R Square:  0.786152\n",
      "------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\srush\\AppData\\Local\\Temp\\ipykernel_13120\\3986559112.py:30: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(new_row, ignore_index=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:  RandomForestRegressor()\n",
      "Model Name:  RandomForestRegressor()\n",
      "Mean Absolute Error (MAE): 1287.327\n",
      "Mean Squared Error (MSE): 3997281.468\n",
      "Root Mean Squared Error (RMSE): 1999.32\n",
      "R2_score: 0.797305\n",
      "Root Mean Squared Log Error (RMSLE): 7.601\n",
      "Mean Absolute Percentage Error (MAPE): 3997281.468\n",
      "Adjusted R Square:  0.796663\n",
      "------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\srush\\AppData\\Local\\Temp\\ipykernel_13120\\3986559112.py:30: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(new_row, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "for models in m:\n",
    "    \n",
    "    models.fit(X_train, y_train)\n",
    "    y_pred = models.predict(X_test)    \n",
    "    print('Model: ', models)\n",
    "    \n",
    "    # Printing Performance Metrics for each model\n",
    "    print('Model Name: ', models)\n",
    "    print('Mean Absolute Error (MAE):', round(metrics.mean_absolute_error(y_test, y_pred),3))  \n",
    "    print('Mean Squared Error (MSE):', round(metrics.mean_squared_error(y_test, y_pred),3))  \n",
    "    print('Root Mean Squared Error (RMSE):', round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),3))\n",
    "    print('R2_score:', round(metrics.r2_score(y_test, y_pred),6))\n",
    "    print('Root Mean Squared Log Error (RMSLE):', round(np.log(np.sqrt(metrics.mean_squared_error(y_test, y_pred))),3))\n",
    "    print('Mean Absolute Percentage Error (MAPE):', round(metrics.mean_squared_error(y_test, y_pred),3))\n",
    "    r_squared = round(metrics.r2_score(y_test, y_pred),6)\n",
    "    adjusted_r_squared = round(1 - (1-r_squared)*(len(y)-1)/(len(y)-X.shape[1]-1),6)\n",
    "    print('Adjusted R Square: ', adjusted_r_squared)\n",
    "    print('------------------------------------------------------------------------------------------------------------')\n",
    "    \n",
    "    \n",
    "    # Storing the metrics in res\n",
    "    new_row = {'Model Name' : models,\n",
    "               'Mean_Absolute_Error_MAE' : metrics.mean_absolute_error(y_test, y_pred),\n",
    "               'Adj_R_Square' : adjusted_r_squared,\n",
    "               'Root_Mean_Squared_Error_RMSE' : np.sqrt(metrics.mean_squared_error(y_test, y_pred)),\n",
    "               'Mean_Absolute_Percentage_Error_MAPE' : metrics.mean_squared_error(y_test, y_pred),\n",
    "               'Mean_Squared_Error_MSE' : metrics.mean_squared_error(y_test, y_pred),\n",
    "               'Root_Mean_Squared_Log_Error_RMSLE': np.log(np.sqrt(metrics.mean_squared_error(y_test, y_pred))),\n",
    "               'R2_score' : metrics.r2_score(y_test, y_pred)}\n",
    "    res = res.append(new_row, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models and their Performances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "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>Model Name</th>\n",
       "      <th>Mean_Absolute_Error_MAE</th>\n",
       "      <th>Adj_R_Square</th>\n",
       "      <th>Root_Mean_Squared_Error_RMSE</th>\n",
       "      <th>Mean_Absolute_Percentage_Error_MAPE</th>\n",
       "      <th>Mean_Squared_Error_MSE</th>\n",
       "      <th>Root_Mean_Squared_Log_Error_RMSLE</th>\n",
       "      <th>R2_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression()</td>\n",
       "      <td>1959.198280</td>\n",
       "      <td>0.626575</td>\n",
       "      <td>2709.420460</td>\n",
       "      <td>7.340959e+06</td>\n",
       "      <td>7.340959e+06</td>\n",
       "      <td>7.904490</td>\n",
       "      <td>0.627753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeRegressor()</td>\n",
       "      <td>1687.937490</td>\n",
       "      <td>0.653674</td>\n",
       "      <td>2609.257421</td>\n",
       "      <td>6.808224e+06</td>\n",
       "      <td>6.808224e+06</td>\n",
       "      <td>7.866821</td>\n",
       "      <td>0.654767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(DecisionTreeRegressor(random_state=1660288316...</td>\n",
       "      <td>1307.555058</td>\n",
       "      <td>0.786152</td>\n",
       "      <td>2050.342946</td>\n",
       "      <td>4.203906e+06</td>\n",
       "      <td>4.203906e+06</td>\n",
       "      <td>7.625762</td>\n",
       "      <td>0.786827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(DecisionTreeRegressor(max_features='auto', ra...</td>\n",
       "      <td>1287.326793</td>\n",
       "      <td>0.796663</td>\n",
       "      <td>1999.320251</td>\n",
       "      <td>3.997281e+06</td>\n",
       "      <td>3.997281e+06</td>\n",
       "      <td>7.600563</td>\n",
       "      <td>0.797305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Model Name  Mean_Absolute_Error_MAE  \\\n",
       "0                                 LinearRegression()              1959.198280   \n",
       "1                            DecisionTreeRegressor()              1687.937490   \n",
       "2  (DecisionTreeRegressor(random_state=1660288316...              1307.555058   \n",
       "3  (DecisionTreeRegressor(max_features='auto', ra...              1287.326793   \n",
       "\n",
       "   Adj_R_Square  Root_Mean_Squared_Error_RMSE  \\\n",
       "0      0.626575                   2709.420460   \n",
       "1      0.653674                   2609.257421   \n",
       "2      0.786152                   2050.342946   \n",
       "3      0.796663                   1999.320251   \n",
       "\n",
       "   Mean_Absolute_Percentage_Error_MAPE  Mean_Squared_Error_MSE  \\\n",
       "0                         7.340959e+06            7.340959e+06   \n",
       "1                         6.808224e+06            6.808224e+06   \n",
       "2                         4.203906e+06            4.203906e+06   \n",
       "3                         3.997281e+06            3.997281e+06   \n",
       "\n",
       "   Root_Mean_Squared_Log_Error_RMSLE  R2_score  \n",
       "0                           7.904490  0.627753  \n",
       "1                           7.866821  0.654767  \n",
       "2                           7.625762  0.786827  \n",
       "3                           7.600563  0.797305  "
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest Regressor performs the best with 79.7% accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypertuning the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "random_grid = {\n",
    "    'n_estimators' : [100, 120, 150, 180, 200,220],\n",
    "    'max_features':['auto','sqrt'],\n",
    "    'max_depth':[5,10,15,20],\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 180, 'max_features': 'auto', 'max_depth': 15}"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestRegressor()\n",
    "rf_random=RandomizedSearchCV(estimator=rf,param_distributions=random_grid,cv=3,verbose=2,n_jobs=-1,)\n",
    "\n",
    "rf_random.fit(X_train,y_train)\n",
    "\n",
    "# best parameter\n",
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x25e0ff3ad30>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#predicting the values\n",
    "prediction = rf_random.predict(X_test)\n",
    "\n",
    "#distribution plot between actual value and predicted value\n",
    "sns.displot(y_test-prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1248.5249119854698\n",
      "MSE: 3593916.940472411\n",
      "RMSE: 1895.7628914166485\n",
      "R2_score: 0.817759\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))\n",
    "print('R2_score:', round(metrics.r2_score(y_test, prediction),6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After tuning, the accuracy of the model has improved and it is 81.7%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}