FLIGHT-PRICE-PREDICTION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from tqdm import tqdm\n",
    "import pickle\n",
    "\n",
    "from IPython.display import clear_output\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Loading & Pre-Processing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    <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>10678</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>9/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU ? BLR</td>\n",
       "      <td>19:55</td>\n",
       "      <td>22:25</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10679</th>\n",
       "      <td>Air India</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU ? BLR</td>\n",
       "      <td>20:45</td>\n",
       "      <td>23:20</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10680</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR ? DEL</td>\n",
       "      <td>08:20</td>\n",
       "      <td>11:20</td>\n",
       "      <td>3h</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10681</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR ? DEL</td>\n",
       "      <td>11:30</td>\n",
       "      <td>14:10</td>\n",
       "      <td>2h 40m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10682</th>\n",
       "      <td>Air India</td>\n",
       "      <td>9/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL ? GOI ? BOM ? COK</td>\n",
       "      <td>10:55</td>\n",
       "      <td>19:15</td>\n",
       "      <td>8h 20m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>11753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10683 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Airline Date_of_Journey    Source Destination  \\\n",
       "0           IndiGo      24/03/2019  Banglore   New Delhi   \n",
       "1        Air India       1/05/2019   Kolkata    Banglore   \n",
       "2      Jet Airways       9/06/2019     Delhi      Cochin   \n",
       "3           IndiGo      12/05/2019   Kolkata    Banglore   \n",
       "4           IndiGo      01/03/2019  Banglore   New Delhi   \n",
       "...            ...             ...       ...         ...   \n",
       "10678     Air Asia       9/04/2019   Kolkata    Banglore   \n",
       "10679    Air India      27/04/2019   Kolkata    Banglore   \n",
       "10680  Jet Airways      27/04/2019  Banglore       Delhi   \n",
       "10681      Vistara      01/03/2019  Banglore   New Delhi   \n",
       "10682    Air India       9/05/2019     Delhi      Cochin   \n",
       "\n",
       "                       Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
       "0                  BLR ? DEL    22:20  01:10 22 Mar   2h 50m    non-stop   \n",
       "1      CCU ? IXR ? BBI ? BLR    05:50         13:15   7h 25m     2 stops   \n",
       "2      DEL ? LKO ? BOM ? COK    09:25  04:25 10 Jun      19h     2 stops   \n",
       "3            CCU ? NAG ? BLR    18:05         23:30   5h 25m      1 stop   \n",
       "4            BLR ? NAG ? DEL    16:50         21:35   4h 45m      1 stop   \n",
       "...                      ...      ...           ...      ...         ...   \n",
       "10678              CCU ? BLR    19:55         22:25   2h 30m    non-stop   \n",
       "10679              CCU ? BLR    20:45         23:20   2h 35m    non-stop   \n",
       "10680              BLR ? DEL    08:20         11:20       3h    non-stop   \n",
       "10681              BLR ? DEL    11:30         14:10   2h 40m    non-stop   \n",
       "10682  DEL ? GOI ? BOM ? COK    10:55         19:15   8h 20m     2 stops   \n",
       "\n",
       "      Additional_Info  Price  \n",
       "0             No info   3897  \n",
       "1             No info   7662  \n",
       "2             No info  13882  \n",
       "3             No info   6218  \n",
       "4             No info  13302  \n",
       "...               ...    ...  \n",
       "10678         No info   4107  \n",
       "10679         No info   4145  \n",
       "10680         No info   7229  \n",
       "10681         No info  12648  \n",
       "10682         No info  11753  \n",
       "\n",
       "[10683 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline = pd.read_csv(\"https://raw.githubusercontent.com/vcos611/PRML/main/bonus_project/dataset/airline.csv\")\n",
    "airline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "airline.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping rows with missing values\n",
    "airline.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seeing Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AIRLINE :-\n",
      "Jet Airways (3849)\n",
      "IndiGo (2053)\n",
      "Air India (1751)\n",
      "Multiple carriers (1196)\n",
      "SpiceJet (818)\n",
      "Vistara (479)\n",
      "Air Asia (319)\n",
      "GoAir (194)\n",
      "Multiple carriers Premium economy (13)\n",
      "Jet Airways Business (6)\n",
      "Vistara Premium economy (3)\n",
      "Trujet (1)\n",
      "\n",
      "--------------------------------------------------\n",
      "SOURCE :-\n",
      "Delhi (4536)\n",
      "Kolkata (2871)\n",
      "Banglore (2197)\n",
      "Mumbai (697)\n",
      "Chennai (381)\n",
      "\n",
      "--------------------------------------------------\n",
      "DESTINATION :-\n",
      "Cochin (4536)\n",
      "Banglore (2871)\n",
      "Delhi (1265)\n",
      "New Delhi (932)\n",
      "Hyderabad (697)\n",
      "Kolkata (381)\n",
      "\n",
      "--------------------------------------------------\n",
      "TOTAL_STOPS :-\n",
      "1 stop (5625)\n",
      "non-stop (3491)\n",
      "2 stops (1520)\n",
      "3 stops (45)\n",
      "4 stops (1)\n",
      "\n",
      "--------------------------------------------------\n",
      "ADDITIONAL_INFO :-\n",
      "No info (8344)\n",
      "In-flight meal not included (1982)\n",
      "No check-in baggage included (320)\n",
      "1 Long layover (19)\n",
      "Change airports (7)\n",
      "Business class (4)\n",
      "No Info (3)\n",
      "1 Short layover (1)\n",
      "Red-eye flight (1)\n",
      "2 Long layover (1)\n",
      "\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# viewing the distirbution of the data\n",
    "cols1 = ['Airline','Source','Destination','Total_Stops','Additional_Info']\n",
    "fig, ax = plt.subplots(1,len(cols1), figsize=(25,3))\n",
    "for i in range(len(cols1)):\n",
    "    freq = dict(airline[cols1[i]].value_counts())\n",
    "    print(cols1[i].upper(),\":-\")\n",
    "    for key in freq:\n",
    "        print(\"{} ({})\".format(key,freq[key]))\n",
    "    print(\"\\n\",\"-\"*50,sep='')\n",
    "    count, label = freq.values(), freq.keys()\n",
    "    ax[i].pie(count,labels=label,labeldistance=1.2,autopct='%1.1f%%')\n",
    "    ax[i].set_title(cols1[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encode\n",
    "airline['Airline'] = airline['Airline'].astype('category').cat.codes\n",
    "airline['Source'] = airline['Source'].astype('category').cat.codes\n",
    "airline['Destination'] = airline['Destination'].astype('category').cat.codes\n",
    "\n",
    "# One Hot Encode\n",
    "airline = pd.get_dummies(airline, columns=['Additional_Info'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Encodin all rows: 100%|██████████| 10682/10682 [00:08<00:00, 1272.84it/s]\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>Price</th>\n",
       "      <th>...</th>\n",
       "      <th>Additional_Info_Business class</th>\n",
       "      <th>Additional_Info_Change airports</th>\n",
       "      <th>Additional_Info_In-flight meal not included</th>\n",
       "      <th>Additional_Info_No Info</th>\n",
       "      <th>Additional_Info_No check-in baggage included</th>\n",
       "      <th>Additional_Info_No info</th>\n",
       "      <th>Additional_Info_Red-eye flight</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>3</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR ? DEL</td>\n",
       "      <td>1340</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU ? IXR ? BBI ? BLR</td>\n",
       "      <td>350</td>\n",
       "      <td>13:15</td>\n",
       "      <td>445</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>DEL ? LKO ? BOM ? COK</td>\n",
       "      <td>565</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>1140</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU ? NAG ? BLR</td>\n",
       "      <td>1085</td>\n",
       "      <td>23:30</td>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR ? NAG ? DEL</td>\n",
       "      <td>1010</td>\n",
       "      <td>21:35</td>\n",
       "      <td>285</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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",
       "      <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>10678</th>\n",
       "      <td>0</td>\n",
       "      <td>9/04/2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU ? BLR</td>\n",
       "      <td>1195</td>\n",
       "      <td>22:25</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>4107</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10679</th>\n",
       "      <td>1</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU ? BLR</td>\n",
       "      <td>1245</td>\n",
       "      <td>23:20</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4145</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10680</th>\n",
       "      <td>4</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>BLR ? DEL</td>\n",
       "      <td>500</td>\n",
       "      <td>11:20</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>7229</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10681</th>\n",
       "      <td>10</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR ? DEL</td>\n",
       "      <td>690</td>\n",
       "      <td>14:10</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>12648</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10682</th>\n",
       "      <td>1</td>\n",
       "      <td>9/05/2019</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>DEL ? GOI ? BOM ? COK</td>\n",
       "      <td>655</td>\n",
       "      <td>19:15</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>11753</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10682 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey  Source  Destination                  Route  \\\n",
       "0            3      24/03/2019       0            5              BLR ? DEL   \n",
       "1            1       1/05/2019       3            0  CCU ? IXR ? BBI ? BLR   \n",
       "2            4       9/06/2019       2            1  DEL ? LKO ? BOM ? COK   \n",
       "3            3      12/05/2019       3            0        CCU ? NAG ? BLR   \n",
       "4            3      01/03/2019       0            5        BLR ? NAG ? DEL   \n",
       "...        ...             ...     ...          ...                    ...   \n",
       "10678        0       9/04/2019       3            0              CCU ? BLR   \n",
       "10679        1      27/04/2019       3            0              CCU ? BLR   \n",
       "10680        4      27/04/2019       0            2              BLR ? DEL   \n",
       "10681       10      01/03/2019       0            5              BLR ? DEL   \n",
       "10682        1       9/05/2019       2            1  DEL ? GOI ? BOM ? COK   \n",
       "\n",
       "      Dep_Time  Arrival_Time Duration Total_Stops  Price  ...  \\\n",
       "0         1340  01:10 22 Mar      170           0   3897  ...   \n",
       "1          350         13:15      445           2   7662  ...   \n",
       "2          565  04:25 10 Jun     1140           2  13882  ...   \n",
       "3         1085         23:30      325           1   6218  ...   \n",
       "4         1010         21:35      285           1  13302  ...   \n",
       "...        ...           ...      ...         ...    ...  ...   \n",
       "10678     1195         22:25      150           0   4107  ...   \n",
       "10679     1245         23:20      155           0   4145  ...   \n",
       "10680      500         11:20      180           0   7229  ...   \n",
       "10681      690         14:10      160           0  12648  ...   \n",
       "10682      655         19:15      500           2  11753  ...   \n",
       "\n",
       "       Additional_Info_Business class  Additional_Info_Change airports  \\\n",
       "0                                   0                                0   \n",
       "1                                   0                                0   \n",
       "2                                   0                                0   \n",
       "3                                   0                                0   \n",
       "4                                   0                                0   \n",
       "...                               ...                              ...   \n",
       "10678                               0                                0   \n",
       "10679                               0                                0   \n",
       "10680                               0                                0   \n",
       "10681                               0                                0   \n",
       "10682                               0                                0   \n",
       "\n",
       "       Additional_Info_In-flight meal not included  Additional_Info_No Info  \\\n",
       "0                                                0                        0   \n",
       "1                                                0                        0   \n",
       "2                                                0                        0   \n",
       "3                                                0                        0   \n",
       "4                                                0                        0   \n",
       "...                                            ...                      ...   \n",
       "10678                                            0                        0   \n",
       "10679                                            0                        0   \n",
       "10680                                            0                        0   \n",
       "10681                                            0                        0   \n",
       "10682                                            0                        0   \n",
       "\n",
       "       Additional_Info_No check-in baggage included  Additional_Info_No info  \\\n",
       "0                                                 0                        1   \n",
       "1                                                 0                        1   \n",
       "2                                                 0                        1   \n",
       "3                                                 0                        1   \n",
       "4                                                 0                        1   \n",
       "...                                             ...                      ...   \n",
       "10678                                             0                        1   \n",
       "10679                                             0                        1   \n",
       "10680                                             0                        1   \n",
       "10681                                             0                        1   \n",
       "10682                                             0                        1   \n",
       "\n",
       "       Additional_Info_Red-eye flight  Day  Month  Year  \n",
       "0                                   0   24      3  2019  \n",
       "1                                   0    1      5  2019  \n",
       "2                                   0    9      6  2019  \n",
       "3                                   0   12      5  2019  \n",
       "4                                   0    1      3  2019  \n",
       "...                               ...  ...    ...   ...  \n",
       "10678                               0    9      4  2019  \n",
       "10679                               0   27      4  2019  \n",
       "10680                               0   27      4  2019  \n",
       "10681                               0    1      3  2019  \n",
       "10682                               0    9      5  2019  \n",
       "\n",
       "[10682 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day, month, year = [],[],[]\n",
    "\n",
    "for i in tqdm(airline.index, desc=\"Encodin all rows\"):\n",
    "\n",
    "    # encoding Total Stops\n",
    "    if airline.loc[i,'Total_Stops'] == 'non-stop':\n",
    "        airline.loc[i,'Total_Stops'] = 0\n",
    "    else:\n",
    "        airline.loc[i,'Total_Stops'] = int(airline.loc[i,'Total_Stops'].split()[0])\n",
    "\n",
    "    # encdoing Date of Journey\n",
    "    date = airline.loc[i,'Date_of_Journey'].split('/')\n",
    "    day.append(int(date[0]))\n",
    "    month.append(int(date[1]))\n",
    "    year.append(int(date[2]))\n",
    "\n",
    "    # encoding Time\n",
    "    h,m = airline.loc[i,'Dep_Time'].split(':')\n",
    "    airline.loc[i,'Dep_Time'] = int(h)*60 + int(m)\n",
    "\n",
    "    # encoding Duration\n",
    "    dur = airline.loc[i,'Duration'].split()\n",
    "    if len(dur)==1:\n",
    "        airline.loc[i,'Duration'] = int(dur[0][:-1])*60\n",
    "    else:\n",
    "        airline.loc[i,'Duration'] = int(dur[0][:-1])*60 + int(dur[1][:-1])\n",
    "\n",
    "\n",
    "airline['Day'] = day\n",
    "airline['Month'] = month\n",
    "airline['Year'] = year\n",
    "\n",
    "airline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
       "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops', 'Price',\n",
       "       'Additional_Info_1 Long layover', 'Additional_Info_1 Short layover',\n",
       "       'Additional_Info_2 Long layover', 'Additional_Info_Business class',\n",
       "       'Additional_Info_Change airports',\n",
       "       'Additional_Info_In-flight meal not included',\n",
       "       'Additional_Info_No Info',\n",
       "       'Additional_Info_No check-in baggage included',\n",
       "       'Additional_Info_No info', 'Additional_Info_Red-eye flight', 'Day',\n",
       "       'Month', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Dep_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>1 Long layover</th>\n",
       "      <th>1 Short layover</th>\n",
       "      <th>2 Long layover</th>\n",
       "      <th>Business class</th>\n",
       "      <th>Change airports</th>\n",
       "      <th>In-flight meal not included</th>\n",
       "      <th>No check-in baggage included</th>\n",
       "      <th>Red-eye flight</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1340</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>350</td>\n",
       "      <td>445</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>565</td>\n",
       "      <td>1140</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1085</td>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1010</td>\n",
       "      <td>285</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10678</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1195</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>4107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10679</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1245</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10680</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>7229</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10681</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>690</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>12648</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10682</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>655</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>11753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10682 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline  Source  Destination Dep_Time Duration Total_Stops  Price  \\\n",
       "0            3       0            5     1340      170           0   3897   \n",
       "1            1       3            0      350      445           2   7662   \n",
       "2            4       2            1      565     1140           2  13882   \n",
       "3            3       3            0     1085      325           1   6218   \n",
       "4            3       0            5     1010      285           1  13302   \n",
       "...        ...     ...          ...      ...      ...         ...    ...   \n",
       "10678        0       3            0     1195      150           0   4107   \n",
       "10679        1       3            0     1245      155           0   4145   \n",
       "10680        4       0            2      500      180           0   7229   \n",
       "10681       10       0            5      690      160           0  12648   \n",
       "10682        1       2            1      655      500           2  11753   \n",
       "\n",
       "       1 Long layover  1 Short layover  2 Long layover  Business class  \\\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",
       "10678               0                0               0               0   \n",
       "10679               0                0               0               0   \n",
       "10680               0                0               0               0   \n",
       "10681               0                0               0               0   \n",
       "10682               0                0               0               0   \n",
       "\n",
       "       Change airports  In-flight meal not included  \\\n",
       "0                    0                            0   \n",
       "1                    0                            0   \n",
       "2                    0                            0   \n",
       "3                    0                            0   \n",
       "4                    0                            0   \n",
       "...                ...                          ...   \n",
       "10678                0                            0   \n",
       "10679                0                            0   \n",
       "10680                0                            0   \n",
       "10681                0                            0   \n",
       "10682                0                            0   \n",
       "\n",
       "       No check-in baggage included  Red-eye flight  Day  Month  \n",
       "0                                 0               0   24      3  \n",
       "1                                 0               0    1      5  \n",
       "2                                 0               0    9      6  \n",
       "3                                 0               0   12      5  \n",
       "4                                 0               0    1      3  \n",
       "...                             ...             ...  ...    ...  \n",
       "10678                             0               0    9      4  \n",
       "10679                             0               0   27      4  \n",
       "10680                             0               0   27      4  \n",
       "10681                             0               0    1      3  \n",
       "10682                             0               0    9      5  \n",
       "\n",
       "[10682 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping useless columns\n",
    "airline.drop(['Date_of_Journey','Route','Arrival_Time','Additional_Info_No Info','Additional_Info_No info','Year'],axis=1,inplace=True)\n",
    "rename_maps = {key:key.replace(\"Additional_Info_\",\"\") for key in airline.columns if \"Additional_Info\" in key}\n",
    "airline.rename(columns=rename_maps,inplace=True)\n",
    "airline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting data into train and test\n",
    "train_test = airline.sample(frac=0.8,random_state=200)\n",
    "valid = airline.drop(train_test.index)\n",
    "test = train_test.sample(frac=0.125,random_state=200)\n",
    "train = train_test.drop(test.index)\n",
    "\n",
    "target = 'Price'\n",
    "features = [col for col in train.columns if col != target]\n",
    "x_train, y_train = train[features], train[target]\n",
    "x_test, y_test = test[features], test[target]\n",
    "x_valid, y_valid = valid[features], valid[target]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)\n",
    "x_valid = scaler.transform(x_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Training and Testing Models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.svm import LinearSVR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    'Decision Tree': DecisionTreeRegressor(),\n",
    "    'Random Forest': RandomForestRegressor(),\n",
    "    'XGBoost': XGBRegressor(),\n",
    "    'LightGBM': LGBMRegressor(),\n",
    "    'KNN': KNeighborsRegressor(),\n",
    "    'Linear Regression': LinearRegression(),\n",
    "    'Logistic Regression': LogisticRegression(),\n",
    "    'SVR': SVR(),\n",
    "    'Linear SVR': LinearSVR()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying all models: 100%|██████████| 9/9 [01:05<00:00,  7.31s/it]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "r2_, r2train, mse_, mse_train = {},{},{},{}\n",
    "for mod in tqdm(models, desc='Trying all models'):\n",
    "    regres = models[mod]\n",
    "    regres.fit(x_train, y_train)\n",
    "    y_pred = regres.predict(x_valid)\n",
    "    y_pred_train = regres.predict(x_train)\n",
    "    r2_[mod] = r2_score(y_valid, y_pred)\n",
    "    r2train[mod] = r2_score(y_train, y_pred_train)\n",
    "    mse_[mod] = mse(y_valid, y_pred)\n",
    "    mse_train[mod] = mse(y_train, y_pred_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabulating the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model                         R2 Score                      MSE                                     \n",
      "                              Training    Validation        Training            Validation          \n",
      "\n",
      "Decision Tree                 0.9963      0.79              75288.5600          4663934.59          \n",
      "Random Forest                 0.9784      0.84              441517.2471         3572653.10          \n",
      "XGBoost                       0.9619      0.86              780860.6267         3224506.35          \n",
      "LightGBM                      0.9064      0.85              1916199.0771        3371929.49          \n",
      "KNN                           0.8432      0.76              3210392.4841        5290864.31          \n",
      "Linear Regression             0.4909      0.44              10421827.9526       12602985.56         \n",
      "Logistic Regression           0.4440      0.42              11381439.8538       13053869.61         \n",
      "SVR                           0.0527      0.04              19392609.5502       21591013.88         \n",
      "Linear SVR                    -0.2415     -0.26             25416002.9360       28273989.00         \n"
     ]
    }
   ],
   "source": [
    "print(\"{:<30}{:<30}{:<40}\".format(\"Model\", \"R2 Score\", \"MSE\"))\n",
    "print(\"{:<30}{:<12}{:<18}{:<20}{:<20}\".format(\"\",\"Training\", \"Validation\", \"Training\", \"Validation\"))\n",
    "print()\n",
    "for mod in models:\n",
    "    print(\"{:<30}{:<12.4f}{:<18.2f}{:<20.4f}{:<20.2f}\".format(mod, r2train[mod], r2_[mod], mse_train[mod], mse_[mod]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting bar graph\n",
    "xaxis = np.arange(len(models))\n",
    "plt.bar(xaxis+0.2, list(r2_.values()), label='Validation', width=0.4)\n",
    "plt.bar(xaxis-0.2, list(r2train.values()), label='Training', width=0.4)\n",
    "plt.ylabel('R2 Score')\n",
    "plt.title('Evaluating Models')\n",
    "plt.xticks(xaxis, list(r2_.keys()), rotation=70)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Parameter Tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation R2 Score after tuning: 0.8498032791383738\n",
      "Best Parameters for random forest:\n",
      " {'max_depth': 32, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 800}\n"
     ]
    }
   ],
   "source": [
    "# optimising parameters of Random Forest\n",
    "from sklearn.experimental import enable_halving_search_cv\n",
    "from sklearn.model_selection import HalvingGridSearchCV\n",
    "regres = RandomForestRegressor()\n",
    "param_grid = {\n",
    "    'n_estimators': [100,200,400,800],\n",
    "    'max_depth': [2,4,8,16,32],\n",
    "    'min_samples_split': [2,4,8],\n",
    "    'min_samples_leaf': [1,2,4],\n",
    "}\n",
    "hgs = HalvingGridSearchCV(\n",
    "    regres,\n",
    "    param_grid,\n",
    "    cv=5,\n",
    "    scoring='r2',\n",
    "    n_jobs=-1,\n",
    "    verbose=2\n",
    ")\n",
    "hgs.fit(x_train, y_train)\n",
    "clear_output(wait=True)\n",
    "\n",
    "RFR = hgs.best_estimator_\n",
    "y_pred = RFR.predict(x_valid)\n",
    "print(\"Validation R2 Score after tuning:\", r2_score(y_valid, y_pred))\n",
    "print('Best Parameters for random forest:\\n',hgs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation R2 Score after tuning: 0.8679032998242611\n",
      "Best Parameters for XG Boost\n",
      " {'learning_rate': 0.2, 'max_depth': 8, 'n_estimators': 100, 'subsample': 0.6}\n"
     ]
    }
   ],
   "source": [
    "# optimising parameters of XGBoost\n",
    "regres = XGBRegressor()\n",
    "param_grid = {\n",
    "    'n_estimators': [100,400,1000],\n",
    "    'max_depth': [2,4,8,16],\n",
    "    'learning_rate': [0.01,0.1,0.2,0.3],\n",
    "    'subsample': [0.6,0.8,1]\n",
    "}\n",
    "hgs = HalvingGridSearchCV(\n",
    "    regres,\n",
    "    param_grid,\n",
    "    cv=5,\n",
    "    scoring='r2',\n",
    "    n_jobs=-1,\n",
    "    verbose=2\n",
    ")\n",
    "hgs.fit(x_train, y_train)\n",
    "clear_output(wait=True)\n",
    "\n",
    "XG = hgs.best_estimator_\n",
    "y_pred = XG.predict(x_valid)\n",
    "print(\"Validation R2 Score after tuning:\", r2_score(y_valid, y_pred))\n",
    "print('Best Parameters for XG Boost\\n',hgs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation R2 Score after tuning: 0.847723898832112\n",
      "Best Parameters for LightGBM\n",
      " {'learning_rate': 0.01, 'max_depth': 8, 'n_estimators': 1000, 'subsample': 0.8}\n"
     ]
    }
   ],
   "source": [
    "# optimising parameters of LightGBM\n",
    "regres = LGBMRegressor()\n",
    "param_grid = {\n",
    "    'n_estimators': [100,400,1000],\n",
    "    'max_depth': [2,4,8,16],\n",
    "    'learning_rate': [0.01,0.1,0.2,0.3],\n",
    "    'subsample': [0.6,0.8,1]\n",
    "}\n",
    "hgs = HalvingGridSearchCV(\n",
    "    regres,\n",
    "    param_grid,\n",
    "    cv=5,\n",
    "    scoring='r2',\n",
    "    n_jobs=-1,\n",
    "    verbose=2\n",
    ")\n",
    "hgs.fit(x_train, y_train)\n",
    "clear_output(wait=True)\n",
    "\n",
    "LGB = hgs.best_estimator_\n",
    "y_pred = LGB.predict(x_valid)\n",
    "print(\"Validation R2 Score after tuning:\", r2_score(y_valid, y_pred))\n",
    "print(\"Best Parameters for LightGBM\\n\",hgs.best_params_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Making Ensemble**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.average_ensemble at 0x22c49475070>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class average_ensemble():\n",
    "    def __init__(self, models):\n",
    "        self.models = models\n",
    "        self.weights = None\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        for model in self.models:\n",
    "            model.fit(X, y)\n",
    "        return self\n",
    "    \n",
    "    def predict(self, X):\n",
    "        predictions = np.array([model.predict(X) for model in self.models]).T\n",
    "        if self.weights is None:\n",
    "            self.weights = np.repeat(1/len(self.models), len(self.models))\n",
    "        weighted_predictions = np.average(predictions, axis=1, weights=self.weights)\n",
    "        return weighted_predictions\n",
    "\n",
    "model = average_ensemble([RFR, XG, LGB])\n",
    "X = np.vstack((x_train,x_valid))\n",
    "y = np.hstack((y_train,y_valid))\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Evaluation on Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing all models: 100%|██████████| 3/3 [00:00<00:00, 12.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Test Results (on untouched Test Data):\n",
      "\n",
      "Model                         R2 Score       MSE            \n",
      "\n",
      "Random Forest                 0.8770         2998604.73     \n",
      "XGBoost                       0.9249         1831359.50     \n",
      "LightGBM                      0.8630         3340695.09     \n",
      "\n",
      "Ensemble                      0.9012         2408355.93     \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing on final Test data\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "models = {\n",
    "    'Random Forest' : RFR,\n",
    "    'XGBoost' : XG,\n",
    "    'LightGBM' : LGB,\n",
    "}\n",
    "\n",
    "r2_, mse_ = {},{}\n",
    "for mod in tqdm(models, desc='Testing all models'):\n",
    "    y_pred = models[mod].predict(x_test)\n",
    "    r2_[mod] = r2_score(y_test, y_pred)\n",
    "    mse_[mod] = mse(y_test, y_pred)\n",
    "\n",
    "print(\"Final Test Results (on untouched Test Data):\\n\")\n",
    "print(\"{:<30}{:<15}{:<15}\".format(\"Model\", \"R2 Score\", \"MSE\"))\n",
    "print()\n",
    "for mod in models:\n",
    "    print(\"{:<30}{:<15.4f}{:<15.2f}\".format(mod, r2_[mod], mse_[mod]))\n",
    "print()\n",
    "y_pred = model.predict(x_test)\n",
    "print(\"{:<30}{:<15.4f}{:<15.2f}\".format(\"Ensemble\", r2_score(y_test, y_pred), mse(y_test, y_pred)))\n",
    "r2_['Ensemble'] = r2_score(y_test, y_pred)\n",
    "\n",
    "# plotting bar graph\n",
    "xaxis = np.arange(len(r2_))\n",
    "plt.bar(xaxis, list(r2_.values()),color='red')\n",
    "plt.ylabel('R2 Score')\n",
    "plt.title('Evaluating Final Models')\n",
    "plt.xticks(xaxis, list(r2_.keys()), rotation=70)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Saving the Model for Deployment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the entire data for training\n",
    "model = average_ensemble([RFR, XG, LGB])\n",
    "X = airline[features]\n",
    "y = airline[target]\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "model.fit(X, y)\n",
    "\n",
    "# saving the scaler\n",
    "with open('scaler.pkl', 'wb') as f:\n",
    "    pickle.dump(scaler, f)\n",
    "\n",
    "# saving the model\n",
    "with open('model.pkl', 'wb') as f:\n",
    "    pickle.dump(model, f)"
   ]
  }
 ],
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  "interpreter": {
   "hash": "0f59e86a17c2977396b1008acaa9267b8d672dc8cc29e7647aed620e86351051"
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  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "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": "3.8.10"
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 },
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}