Flight Fare Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "813064d3",
   "metadata": {},
   "source": [
    "# Flight Fare Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a685982",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "The objective of the study is to analyse the flight booking dataset obtained from “Ease My Trip” website and to conduct various statistical hypothesis tests in order to get meaningful information from it. The 'Linear Regression' statistical algorithm would be used to train the dataset and predict a continuous target variable. 'Easemytrip' is an internet platform for booking flight tickets, and hence a platform that potential passengers use to buy tickets. A thorough study of the data will aid in the discovery of valuable insights that will be of enormous value to passengers.n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5c104fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6605fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import training dataset\n",
    "train_data = pd.read_excel('Data_Train.xlsx')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3447ec7",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3a9ff2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d53db813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10683.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9087.064121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4611.359167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79512.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price\n",
       "count  10683.000000\n",
       "mean    9087.064121\n",
       "std     4611.359167\n",
       "min     1759.000000\n",
       "25%     5277.000000\n",
       "50%     8372.000000\n",
       "75%    12373.000000\n",
       "max    79512.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e20b7f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (12, 6)\n",
    "\n",
    "sns.heatmap(train_data.isnull(), yticklabels= False, cbar = True, cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c707c98",
   "metadata": {},
   "source": [
    "Few null values present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf6fe6f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "469a104e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc49720e",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d87feaae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1752\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              818\n",
       "Vistara                               479\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Airline'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad602b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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9nSQhAAAZoIkxgKLkjVi8Tm+0be69TY6WaFrFaPXE9w55m1YxWpOjJX2W37iBpt+5EIvFtGHjs9r1+isqryjTlKmHKt69t/c2ZeqhKq8o04aNz/L5Ayh4d955Z8ryurq6PEeCZIwuDQDAwaEGIYCiNa1ijD798ew0+5ak2++h6XeuTJ06XjWXvCftPHXL/pSnaADgwNXU1AyoQZgoR3AYXbqwbdy4Ucccc0zQYQAA0qAGIQAAAJChOXPmaNy4cX3K3vWud9G8OGCJ0aXLysokSWVlZYwuXSDq6+t1xRVXqKGhIehQAABpUIMQoZWqD7vBNDU1qaerI2uDi7y4Y7dKXm8a0P9dKmHrky3T7dLU1KR4V0dWa/01b9+t7e1sFwBAejNnztTatWslSZFIRFdddVXAEUGSqqur1dDQoObmZkWjUS1YsCDokEKvs7NTt912myTp1ltv1fz58zVy5MiAowIApEKCEKEVi8X0/HNrNXPC0Ccph4+RpBFy3V1DzZqRmeNHSIprb/PGtPO9tLNTkkKViIrFYtqwfo2mTEy/XSYdKkmlind3Zu21J08sldStnds3pJ3vtbbwbZdMDTZ4THf33iGbEG9r3qXWlo6UCVoSsgAKQWNjo+699169+uqrvWVjx47V73//e45RBSASiejss8/WzTffrHPOOUeRSCTokELv2muv7e0bMh6P67rrrmPEbwAoUCQIEWozJ4zUlacfFnQYg7r+0VeHnmkYmjJxpC78yOSgwxjUnQ9sDzqEghWLxbR+wzpVTB7dWzZhokkqU3d8b9plyyd7zcJ2tL/Qp7xl+x5JJGQBBC8Wi2n7jh2afMS+5sRtr7yqWCzGMaoAxONx/frXv5Yk/frXv9aHP/xhkoQBYsTvwtfa2qry8vKgwwBQIEgQAgCyqmLyaJ173rFZW9+9P1+ftXVhaPxZKExsl8Ixcfph+sDln+19/Mj/3BpgNEhWX1+vnTt3SpLa29vV0NCgs88+O9igQizdiN9XX311foPBAGvWrNGSJUv03e9+V3Pnzg06HAAFgEFKAACAJO/Pwvnnn9/btxoKA9sFGFpbW5vq6urU0dEhSero6NDy5cvV3t4ecGThNdjI3oz4Hbx4PK7a2lpJUm1tbW8zcADhRg1CFI39GVQkE01NTerp3FvQzXhf2rlXJbszGzQjU/TlBiCV/n8W7rjjDprmFQC2C5CZFStWDEhy9PT0KBaL6ZxzzgkoqnCbM2eOTj311D7NjBnxuzDU19ertbVVktTS0kJtWwCSSBCiiMRiMT3/7GrNGJedP0bTR0kaVSIXz87AI7kw49ASSR3q2LouK+t7+XXvxJkEIYD+aJpXmNguQGaqqqq0fPnyPmVmpqqqqmACgiTpK1/5is4666zex1/+8peDCwaSvNq2P/7xj9Xd3S1J6u7u1o9+9COddtppikajAUcHIEgkCFFUZoyLaOmpY4IOo2h9Z+XurK8zFzU7uzs7C3ogkNd2dGrHG+Gq2Znpdm5qalJX956s9hvYsn23drZl9nkX+udYqAZrmsefhWCxXYDMRaNRnXTSSX1qq5188snsKwF76KGHVFpaqu7ubpWWlurhhx/mIkfAVqxYoc7Ozj5lnZ2d1LYFQIIQwMGJxWLa8NxqVUzIzvqiYySNkXqGGPE2SN57fUttzauzsr6Wnd59ISe2YrGYnlu/RhPLR6Sdb+x4SSpVd7wz7Xz7IzqpVFKXXmtNn3Rsa/VqAxfy51ioaJpXmNguQGYaGxv1yCOPaP36vr8TTz31lNrb20kSBiRxkSO5phoXOYJ3xBFHpCw/8sgj8xwJgEJDghDAQauYIH28ij6xDtQ9seLoGHpi+Qh9qLpwR1F9qL416BCKVlVVlerq6tTVta/LhZKSEprmBYztAmQmFotpy4sv6pi3H9db9tKWzerYs4eEeoC4yFGY7r///pTl9913n0444YT8BgOgoDCKMQAAIReNRvXJT36yT9mFF15IDY+ARaNR1dTUaOTIkZKkkSNHatGiRWwXIIWZs+foym98q/c2c7Y3EAYJ9eBUVVUNGFSJixzBY3RpAIMhQQgAANTT05P2MYLxkY98pLcGTjwe14c//OGAIwKKx9SpU0moByhxkaOsrEySVFZWxkWOApAYXToZo0sXlsQI00C+0cQYAICQa2trGzD65/Lly3XGGWfwRy5gDzzwgCKRiOLxuCKRiB588EE6+EfopBooa8eOHb0jfO/Zs0eS9LkLz+99fq8/uE+iKeuECRM0adKkPutgYKvcq66uVkNDg5qbmxWNRrVgwYKgQ4Kkr371qzrrrLN6f1uuuuqqoEOCb82aNVqyZIm++93vau7cuUGHg5AhQQgAw1AuRpfu7Ooq6H7+2lq79OaucI0unS2NjY0D+onq7u7Wgw8+qAsuuCCgqJDo4D8x2mRnZycd/COUYrGYntv4vMYfNn1f4ajRGj1ltCRpdIplDun3eK+kV9/c3ft416uvSGJgq1yLRCJavHixlixZoiVLlgxocoxgjBw5Up/5zGd0880367Of/WxvVxYIVjweV21trSSptrZWd9xxB/sM8ooEIQAMQ7FYTM89t1rjs5RDGH2I9wcs3p290YmzbfwESXpLr27LzujSu9q9+zD/eTSzoEMINTr4LxyJiy5NTU3qjMf1yP/c2vtc2yuv6s1IpPfiRFguLGRDphezmpqa5OSy+tpOTk1NmV1UYpsenHnz5umuu+5SeXnhDnQWRtXV1Xr729+uY445JuhQ4Kuvr++tGd3e3q6GhgZaDSCvSBACwDA1Pir9wweCjqJ4/eGRoCPIn3e/+936yU9+MqD8Xe96VwDRIIFRjAtHLBbTsxs36NCpUzVSUmdS4nbs1CmSpJdf36U3tm2TFO4LC/sjFotp7YaNGj15avoZJ1ZolKQ9Xd1Ze+1RFd5rNrW/nna+PdvZptlAcrAwkRwsHIlWAx1+9wgdHR20GkDekSBEUdnW9qZWPF+4NZgK3ba2Ts0+POgoABSa3//+94OWf/rTn85zNEhIjC59++23S/JqdDK6dHAOnTpVf3/JorTz/HnZ8rzEMpyMnjxVR/3zJUGHMahNP1sWdAgAQoBWAygEJAgBAAi5zZs3pyzfsmVLfgNBWs5lt4klgOGhsbFRP/3pT4MOo+hdcMEFw7amaHJz/uQBfpIlBvKhSX0waDWAQkCCEEVl6sSxOu3oMUGHUbT+3Lp76JkAhM6nP/1p/eUvf9GYMV5X/7t3eyOCXnJJ4dbqCYO2tjbdeeedfcrq6ur0vve9j1qEAHrFYjG9/Oo2jRg7aeiZkVLXmzsUi8WGbWIsFovp+eebNHPGHI09JKqxh6T+DXn++SZJNKkPQjQaVU1NTW8z47KyMi1atIjfe+QVCUIAAEJuzpw5OvTQQzV95mGSpPXrNujUU0/VnDlzAo4s3FasWNGnJoEkdXV10dwIwACTZh+vmdVfDjqMovVS/beDDiHnZs6YoyuXXJt2nutrv5qnaJBKdXW1Ghoa1NzcrGg0qgULFgQdEkKGBCEAANCsWbPU7fYlo776Vf4kBCG5GdiePXvU09PT5/menh499thjWrlyJc3AAAAYRiKRiBYvXqwlS5ZoyZIlikQiQYeEkMlZgtDMfiTpI5K2O+fm+mUTJf1C0mxJWyR93DnX7j93laSLJcUlfd4591u//GRJyyWNltQo6QvOOWdmoyTdKelkSTskfcI5tyVX7wcAgOGspKTE+wWWdNhhh2nkyJHBBhRSsVhMz2/apJlzZqlkZKnedvzbU873/KZNkmgGBgBAppIvwhWyt7/97aqrqws6jLS4SDk85bIG4XJJ/y0viZdwpaRHnXPXm9mV/uOvmNlxkhZKOl7SYZJ+b2bHOOfikm6RdKmkJ+QlCOdLekheMrHdOXeUmS2UdIOkT+Tw/QAYRMuOt/T0cyVBh1G0Wnb0aOK0oKNAmDU2NqqpqUk9zqutVmIlWrp0KSd/AZk5Z5a+8o2vp53nhq/9Z56iAVAM9u54ORTNZHNl746XpYqjgw4DORaLxfT8s5s0Y9zMoEMZUlydQYcwqJdff0kSFymHo5wlCJ1zfzCz2f2KqyWd5k/XSVoh6St++d3Oub2SNpvZJkmnmNkWSeOccyslyczulHSWvARhtaSr/XXdK+m/zcwcQ/wBALBfYrGY4j3dmjZzam/Zxuc3SOLkL5dS1WTwErVuyATgS5tfVImZli5d2qecpC4QPoU8yumGDRu0d+/e3sejRo3S2972tgAjGkTF0QX9OSJ7ZoybqcX/76qgwyhq333yuqBDQI7kuw/CKc65ZklyzjWb2WS/fLq8GoIJW/2yLn+6f3limZf9dXWb2S5JkyS19n9RM7tUXi1EzZxZ+FcLgGJTMekQnfR2+sg4UJu2xYMOAdC0mVP16Streh/ffn1hN20ZDmKxmNY/v1Hlh++rQjz+MO/U6I3OjrTLRqdPkSS17nmjt6x1a7MkkrpA2Jx55pkFud8/88wzuvLKK/uU7d27VxdccIFOOOGEgKICAAymUAYpsRRlLk15umUGFjr3Q0k/lKTKykpqGAIIhbYdb2nDuqCjKF5tO6TDpg49H3Awyg+fprO/9OmsrOu+G2/PynoAIBtuueWWlOU333yzbrvttjxHAwAYSr4ThK+Z2TS/9uA0Sdv98q2SZiTNd7ikV/3yw1OUJy+z1cxKJY2X1JbL4AEAAICwynYH/01NTdrT3a1NP1uWtXVm257tzWpqaxnQnP9ghKUrgMsvv1xf/vKXB5R/7nOfCyAaAMBQ8p0gbJBUI+l6/74+qfxnZvZf8gYpOVrSU865uJm9YWbvlPSkpAsl3dRvXSslnSvpMfofHP5efj2u76zcHXQYRevl1+Oi++XwmDjpEL3t+KCjKF6vvRJ0BABQWGKxmFav36CSSZOHnjkT4yZKkvZ0F3BXGxMna4+ktS3tWVldzw6vfkQYEoQnnHCCZs2apRdffLG3bPbs2TQvRqC2tTVrxabHgg6jqG1ra9bsw2cFHQZyIGcJQjP7ubwBScrNbKukr8tLDN5jZhdLeknSxyTJObfOzO6R9KykbkmX+yMYS9Jl8kZEHi1vcJKH/PI7JP3EH9CkTd4oyBjGst1xcFNTk3o6d2vmhFFZXW82vbRzr0pGjtGRRx6ZlfUdrcLuyBoAgKANNnjM3u5u/XnZ8rTLvtG8TU0trcN68JiSSZM16iPnBR1G0dr7wM+DDiGvvv/97+uss87qffy9730vsFgAAOnlchTjwc4cTh9k/mskXZOifJWkuSnKO+QnGBEO2e6AeenSpdrbvFFXnn5Y1taZbdc/+qpGTTtS3/nOd4IOBcAwk5wEaWpqUrynu8/AJM0vbdP2Ei/RMZySG8BQYrGY1m3YoNFTKnrLbFJUZZK6etLXdCvzl3lh575eb/a81iIpHDXGgP5Gjx6ts846S/fff7/OPvtsjR49OuiQhr3BLnL09DhdX/vVtMu+9PILKimxYX2RY+rEaTrtqPcFHUZR+8uOJ4MOATlSKIOUAACAPPJG0H1O5dMnavy0sZKkvfF9I+dOnD5BkrT++eckkdxAuIyeUqGjz89O45Tn77o7K+sBitVll12mk08+WaecckrQoYRCLBbTxo2bNH3anN6yqZNnS5K6u9L3yHXYVG+Zt97YdzHklebNkjgPAMKABCEAACFVPn2izr5iftp57rvp4TxFM3xkOpBDU1OTunriWRt9uHVrs3aVbM9oMIXhVBsEQOEjOZhf06fN0b9c+s2srOu/f/gfWVkPgMJHghAAACCLvCaq6zVm6qS085WUj9MoSW90dqSdL1OjJkclSZt3taSdb/e2HZKoDYID82Zri/auXhV0GEWrq7VFqogGHQYAAAOQIARw0Fp2SvfECngEwgLXslOaOC3oKABk05ipk3Tcoo8EHUZKzy5/IOgQAAAAUGBIEAI4KLkYXbq7c7emTBqZ1fVm02s7OlWaxdGlJ05jdGkAQHEYW16hUe+oDDqMorX3peeDDgFFan+6r4jHXdaaBr/SvFmR7QMHLkmlGLqvePn1l/TdJ68LOoyi9vLrL+loHRV0GMgBEoQItZd2dur6R18NOoxBvbSzU0cXeM2yXIwuvXP7Bl34kclZW2e23fnAdk2YzOjSKB6DjWjYFe8cso/B1q1t2hV5c1iPaIjhL9M/1pK3b+zp7sra4CK7X9uuph3tw+bPNYBgxGIxbdiwqXfAkcGUT5wlSeruTj8gSaamVHivt6u9O+1827ZvkVTY3VdwQT47jtZRfJbDFAlChNb+HNSamprU09WhWZPGZOW1X9yxWyUjyoasgXZ0SGuWvdbWqTsf2B50GIN6ra1TEwo3fwkMEIvF9OzGdRo/dWxvWVlFqcpUqr3d6fu/O3Sqd9x75fUXe8t2bXtTUmH/CQCSxWIxrVm/XiMqyoeeecJ4lUrqjPdk5bVLy8vVJWn9jta083W1eM+zXwEYzNTJs/WphV8POoyUfnz3fwYdwpCyXbEBGG5IECK09ucHYunSpeps2ayv/tNxWXnta3/zrEZWzKEGWgqZJkSbmpq0e/dbGjUikrXX3tsV15gxhwyZuJ0wuTgSt7vapT88EnQUxWtXu3TY1KCjyJ7xU8fqvZf8XVbW9cdlz2RlPYVgf2qWZaqpqUkd3V0F29ff7m071NT6ekY12vZHodd+G1FRropzq4MOY1At99YHHQIAoAC0traqvDyDC1pAlpEgBFBQMk3cDtZkMt69V9OnThhy+Ve27VSkdNSAZGCh/8HNVC76huzs2q2J5SOyut5samvt0sgR2esb8rCpxZEIxsGJxWJavf5ZadLYoWfO1LgRkkbojc492VtnNk0co7ckrW55KXvr3EGtUgAADtaaNWu0ZMkSffe739XcuXODDgchQ4IQQFFKlUhcunSpNm54LsM1mI48cvj2I5iLviFfa12vD1UX7tXMh+pbNaV8+G5T5NCksSqtZtCFg9Fdvyqr68t2zc6mpiZ1dXUVdC29rpZWNe3cldWancPlohdQbHJVO7272xVsU95t27eotS2zwUz2R5iOY/F4XLW1tZKk2tpa3XHHHYpEstdaChgKCUIAw0Zyba+mpibt2b1bo0btq/G2d2+XRo/xapgdM/4waoeh6O3PiIZ7uzuy1jR4V/Ob6mhpyvhPQJhO7pEdXs3O52STJmZnheO8GqJdPfHsrC8XJkW1W9Kalteysjq3o00StTqBIMRiMa1fv0kV/oAh2TDh0JmSpO6u7Aw+km3lUe+97tjelbV1trR5/R+H5ThWX1+vnTt3SpLa29vV0NCgs88+O9igECokCAEMG8m15hobG7Vs2TLt2bNbo0aN1N69nRo9ZowuueSS0JxkYPiLxWJat2GNxkwemXa+konSaJWqM55+QJJMjZ5cKqlbm9s3DDnv7u2dksJzco/ssUkTVfqRDwYdRtHqfuC3QYcAhFrFxFn62Jn/HnQYRe2Xjd8KOoS8aWtrU11dnTo6vHO1jo4OLV++XKeddpqi0WjA0SEsSBACGJYSyYhly5bprbfe0iGHHEJyEMPSmMkjdex504IOY1Drf94cdAgAAAAFbcWKFYrH+9Zy7+npUSwW0znnnBNQVAgbEoRAhl7asVvX/ubZrK3rqIqsrAppJJKBsViMJo4AAGRBz47t2vvAz4MOo2j17NguVVAbCEBfVVVVqqurU1fXvibaJSUldImEvCJBCGQg1YG5qalJrmuvZk0el3bZF7e/LhvRd7TcoyoYHTVfsj1YR5i1tXbpofrWoMMYVFtrl6YU7hgqAFD0sn3u0tTUpD3d3Ro9uXBrQe/Z3qzRpaV9zuMOSkWUc8CQaGlt1tNrfh90GEWtpbVZkybPDDqMvIhGo6qpqeltZlxWVqZFixbRvBh5RYIQyMBgI+Z2tW3Vf3ziXWmX/eYvHteIiYczsiqKWqZ/ZpqamtTVvUcVk8dk7bVbtu/WiNLRQ/45m1JO4h0H5q3WdpX8rSnoMIpaT2u7VJHdP3Fvtu5QZPW6rK4zTOKtO6SKKVldZ7Yvui1dulRN7a/rqH++JGvrzLZNP1umI6PjOI8DkHPV1dVqaGhQc3OzotGoFixYEHRICBkShACAIWX6p3Dp0qXa0f6Czj3v2Ky99r0/X69J0SOK/s9ZpiMO74+mpiZ1dHcWdD9/u7d3qqkt8xGPM0GXAQCAQldRPk0nzTsj6DCKWtPLTwQdQl5FIhEtXrxYS5Ys0ZIlSxSJRIIOCSFDghA4CC+2vK5v/uLxIec5amKeAgJQsGKxmNasX60Rk7K40nFSRNLe7r1ZXGl2RSZKXXpL61tWZ2V9XTu8+2wmCA8pj6r0hCw1Hwyp7i3tWV/n2PJJKn3H8Vlfb1h0v7Q16BAysmf7Nm362bKgwxjUnu3bpGj67mQAIFvmzZunu+66S+Xl9JuD/CNBCBygRFPGpqYmue5OzZ6271//luYdstKROvLII3XURJo9Ilxatu/RvT9fn9X1TRom3a+MmCSVV1vQYRS11noXdAgAsiTT86MdO3Zo586dfcr27NkjJ2nkqFFDLt+5d69M0ujRo/uUT5gwQZMmDXHVJjqO8zgckJa2F/XLxm8FHUZRa2l7UZMmHxV0GHlHchBBIUEIHKBEk8ulS5dq08Z+yRAzHXnkkUXfJBLYX4MN6NPdvVdTp41Pu+y25l0qLR01oK/BSVGS7EAYuR1t6n7gt0GHUbTcjras90GYbQfTp+HSpUu1s6NTFyy9ash5f/qd6zShbCTnZcibXJy3eOdTTlMnz876urNh2/YtKi217A3oI2nS5KM4BwyBW265RS+88ELQYRS9I444QpdddtlBrYMEIXCQqqqq1NTUpC3b2jVn+mRtfmW7LDKCHzOE0mAD+ux6/RXVXPKetMvWLfuTxo+bPqz/wL3R+pb0t6CjKG5vtEqqyPJKd7yp7vpVWV5pyOx4M6vbJRej5b61e7dsxIisrjebXFeXDhkzJouj5U7hXAQISLYH9JG886kNGzZldZ3ZRgUJoLiRIAQO0plnnqlYLKb4W+36xuUL9bX/uVuRQ6J0oA8ARSBXtTw6urs0Zmo2O5zMnt3bdqisdERWa3moIrufZbb/XO/PIEFNTU3a092lMVMmZ+W1d7+2XaMz/LwZgGf/vPbyS/rpd67LaL4JR4evmSKGl0yPsU1NTYrHnaZPm5OV132lebMikaFrBo6PUtsPB+Zga70he0gQAgCQJ2V2iLQl6CiKW1mWu3DMVS2PdRuy1w9nLoStlsf+bGdv+23I2mub6HYkF5ITEan6KJT29TE44WgSFyh+mR7Hli5dqo0bs1vTkGMYEA4kCIEs2fzKdn3tf+7W5le266hjhsmICgCyJnc11XZrzOSRWV93tuze3qmy0mw2myz8PikPZuCFrq4udXd3S5J6enoGXbakpESlpaUa0a/JbEaDLoyvKPjPMEiD9aW6t7tbh06bmnbZN5q3aVRpad/v+4SJfN45kIvkPjAcpDreJP/eJP/OJEv8pvT/HTnmUBLsQFiYc+EaDbCystKtWkU/Q8iuxsZG3XTTTerp6ZGZ6fOf/zwnrYBv6dKl2rDxWU2dOsQgJdt26W3HHMcV6v2wdOlSbW7foGPPmxZ0KINa//NmzYm+je16AB5++GHdeOONA8q/9KUvaf78+QFEFF5Lly7Vy6/v0t9fsijtfH9etlwzxo3n+w4AAApZyjY51CAEsuDMM8/UsmXL9NZbb8k5pwceeIAEIeDr3wysublZktOoUSO1d2+nJNO0adP0tmOmc4X6AOze3qn1P28OOoxB7d7eKVGp+oDMnz9ft956q/bs2dNbNnr0aJKDAXlj2zb9ednyIefRuPQXQwAAAAoRCUIgS5Jr4zY1NamtrU0TJ04MMCKgMPRvBrZ06VI9//xGzZg5VS+/tE1HH30MtW0O0P50WL63u0Pjp43Nyuvuan5To0rLMms2HC38JsGFrK6uTh//+Mf7PEb+Jb7DTU1N6ozHNXH6Yb3Ptb3yqkZGIt7+MG4833cAAFC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      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Airline vs Price\n",
    "sns.catplot(y = \"Price\", x = \"Airline\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e519d3a7",
   "metadata": {},
   "source": [
    " From graph we can see that Jet Airways Business have the highest Price.\n",
    " Apart from the first Airline almost all are having similar median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e42bf7b5",
   "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>Destination</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Air India</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GoAir</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GoAir</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GoAir</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GoAir</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>1586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Jet Airways Business</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Jet Airways Business</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>1196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Multiple carriers Premium economy</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Trujet</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Vistara Premium economy</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Vistara Premium economy</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vistara Premium economy</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Airline Destination     0\n",
       "0                            Air Asia    Banglore   150\n",
       "1                            Air Asia      Cochin    80\n",
       "2                            Air Asia       Delhi    71\n",
       "3                            Air Asia   New Delhi    18\n",
       "4                           Air India    Banglore   512\n",
       "5                           Air India      Cochin   747\n",
       "6                           Air India       Delhi   120\n",
       "7                           Air India   Hyderabad   136\n",
       "8                           Air India     Kolkata    25\n",
       "9                           Air India   New Delhi   212\n",
       "10                              GoAir    Banglore    25\n",
       "11                              GoAir      Cochin    76\n",
       "12                              GoAir       Delhi    69\n",
       "13                              GoAir   New Delhi    24\n",
       "14                             IndiGo    Banglore   445\n",
       "15                             IndiGo      Cochin   705\n",
       "16                             IndiGo       Delhi   366\n",
       "17                             IndiGo   Hyderabad   196\n",
       "18                             IndiGo     Kolkata   184\n",
       "19                             IndiGo   New Delhi   157\n",
       "20                        Jet Airways    Banglore  1256\n",
       "21                        Jet Airways      Cochin  1586\n",
       "22                        Jet Airways       Delhi   370\n",
       "23                        Jet Airways   Hyderabad   219\n",
       "24                        Jet Airways   New Delhi   418\n",
       "25               Jet Airways Business      Cochin     2\n",
       "26               Jet Airways Business   New Delhi     4\n",
       "27                  Multiple carriers      Cochin  1196\n",
       "28  Multiple carriers Premium economy      Cochin    13\n",
       "29                           SpiceJet    Banglore   300\n",
       "30                           SpiceJet      Cochin    87\n",
       "31                           SpiceJet       Delhi   137\n",
       "32                           SpiceJet   Hyderabad   122\n",
       "33                           SpiceJet     Kolkata   128\n",
       "34                           SpiceJet   New Delhi    44\n",
       "35                             Trujet   Hyderabad     1\n",
       "36                            Vistara    Banglore   183\n",
       "37                            Vistara      Cochin    45\n",
       "38                            Vistara       Delhi   131\n",
       "39                            Vistara   Hyderabad    23\n",
       "40                            Vistara     Kolkata    43\n",
       "41                            Vistara   New Delhi    54\n",
       "42            Vistara Premium economy       Delhi     1\n",
       "43            Vistara Premium economy     Kolkata     1\n",
       "44            Vistara Premium economy   New Delhi     1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Airline','Destination']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13b0ff32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Price vs Destination\n",
    "sns.catplot(y = \"Price\", x = \"Destination\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16cf4160",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing Date Format\n",
    "train_data['day'] = train_data['Date_of_Journey'].apply(lambda x:x.split('/')[0])\n",
    "train_data['month'] = train_data['Date_of_Journey'].apply(lambda x:x.split('/')[1])\n",
    "train_data['year'] = train_data['Date_of_Journey'].apply(lambda x:x.split('/')[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0b4a077",
   "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",
       "      <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>03</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>05</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>06</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>05</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>01</td>\n",
       "      <td>03</td>\n",
       "      <td>2019</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 day  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  24   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662   1   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882   9   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  12   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  01   \n",
       "\n",
       "  month  year  \n",
       "0    03  2019  \n",
       "1    05  2019  \n",
       "2    06  2019  \n",
       "3    05  2019  \n",
       "4    03  2019  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f1c7b0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['day'] = train_data['day'].astype(int)\n",
    "train_data['month'] = train_data['month'].astype(int)\n",
    "train_data['year'] = train_data['year'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b46cdde2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 14 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",
      " 11  day              10683 non-null  int32 \n",
      " 12  month            10683 non-null  int32 \n",
      " 13  year             10683 non-null  int32 \n",
      "dtypes: int32(3), int64(1), object(10)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0a31d2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop('Date_of_Journey',axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da27bf63",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Arrival_Time'] = train_data['Arrival_Time'].apply(lambda x:x.split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f770ca79",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Arrival_hour'] = train_data['Arrival_Time'].apply(lambda x:x.split(':')[0])\n",
    "train_data['Arrival_minutes'] = train_data['Arrival_Time'].apply(lambda x:x.split(':')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "34fed699",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Arrival_hour'] = train_data['Arrival_hour'].astype(int)\n",
    "train_data['Arrival_minutes'] = train_data['Arrival_minutes'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c3d70dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop('Arrival_Time',axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d775dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\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>Dep_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",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>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",
       "      <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>05:50</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</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>09:25</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</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>18:05</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",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</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",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    22:20   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL    16:50   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  day  month  year  Arrival_hour  \\\n",
       "0    non-stop         No info   3897   24      3  2019             1   \n",
       "1     2 stops         No info   7662    1      5  2019            13   \n",
       "2     2 stops         No info  13882    9      6  2019             4   \n",
       "3      1 stop         No info   6218   12      5  2019            23   \n",
       "4      1 stop         No info  13302    1      3  2019            21   \n",
       "\n",
       "   Arrival_minutes  \n",
       "0               10  \n",
       "1               15  \n",
       "2               25  \n",
       "3               30  \n",
       "4               35  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "33b674ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['Arrival_Time'] = data['Arrival_Time'].apply(lambda x:x.split(' ')[0])\n",
    "\n",
    "train_data['Dept_hour'] = train_data['Dep_Time'].apply(lambda x:x.split(':')[0])\n",
    "train_data['Dept_minutes'] = train_data['Dep_Time'].apply(lambda x:x.split(':')[1])\n",
    "\n",
    "train_data['Dept_hour'] = train_data['Dept_hour'].astype(int)\n",
    "train_data['Dept_minutes'] = train_data['Dept_minutes'].astype(int)\n",
    "\n",
    "train_data.drop('Dep_Time',axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf37030e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minutes</th>\n",
       "      <th>Dept_hour</th>\n",
       "      <th>Dept_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>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",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>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",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  day  month  year  Arrival_hour  \\\n",
       "0    non-stop         No info   3897   24      3  2019             1   \n",
       "1     2 stops         No info   7662    1      5  2019            13   \n",
       "2     2 stops         No info  13882    9      6  2019             4   \n",
       "3      1 stop         No info   6218   12      5  2019            23   \n",
       "4      1 stop         No info  13302    1      3  2019            21   \n",
       "\n",
       "   Arrival_minutes  Dept_hour  Dept_minutes  \n",
       "0               10         22            20  \n",
       "1               15          5            50  \n",
       "2               25          9            25  \n",
       "3               30         18             5  \n",
       "4               35         16            50  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c548400f",
   "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>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minutes</th>\n",
       "      <th>Dept_hour</th>\n",
       "      <th>Dept_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>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",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>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",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  day  month  year  Arrival_hour  \\\n",
       "0    non-stop         No info   3897   24      3  2019             1   \n",
       "1     2 stops         No info   7662    1      5  2019            13   \n",
       "2     2 stops         No info  13882    9      6  2019             4   \n",
       "3      1 stop         No info   6218   12      5  2019            23   \n",
       "4      1 stop         No info  13302    1      3  2019            21   \n",
       "\n",
       "   Arrival_minutes  Dept_hour  Dept_minutes  \n",
       "0               10         22            20  \n",
       "1               15          5            50  \n",
       "2               25          9            25  \n",
       "3               30         18             5  \n",
       "4               35         16            50  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "edf36482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEL → BOM → COK          2376\n",
       "BLR → DEL                1552\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: 128, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Route'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "160467cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', nan, '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e6742987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Price vs Total Stops\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Total_Stops\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8ae92863",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Graph of Total_Stops vs Price\n",
    "# sns.catplot(y='Price', x='Total_Stops', train_data=train_data.sort_values('Price', ascending=False), kind='boxen', height=6, aspect=3)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f5580771",
   "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>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minutes</th>\n",
       "      <th>Dept_hour</th>\n",
       "      <th>Dept_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9039</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23h 40m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No info</td>\n",
       "      <td>7480</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline Source Destination Route Duration Total_Stops Additional_Info  \\\n",
       "9039  Air India  Delhi      Cochin   NaN  23h 40m         NaN         No info   \n",
       "\n",
       "      Price  day  month  year  Arrival_hour  Arrival_minutes  Dept_hour  \\\n",
       "9039   7480    6      5  2019             9               25          9   \n",
       "\n",
       "      Dept_minutes  \n",
       "9039            45  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[train_data['Total_Stops'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a7ec50",
   "metadata": {},
   "source": [
    "Only one nan value is present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3f13764b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instead of Focusing on 'Route' we will focus on 'Total_Stops' coz its easy and the ticket price would drastically change \n",
    "# if there are multiple stops.\n",
    "# we replace nan value with one coz from Delhi to Cochin there would atleast be 1 stop and only 1 nan value is present so it would not get affect a lot\n",
    "\n",
    "train_data['Total_Stops'] = train_data['Total_Stops'].map({'non-stop':0, '2 stops':1, '1 stop' : 2, '3 stops' : 3, '4 stops' : 4, 'nan' : 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "39ccad02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info\n",
    "# Route and Total_Stops are related to each other\n",
    "\n",
    "train_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6b238228",
   "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>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minutes</th>\n",
       "      <th>Dept_hour</th>\n",
       "      <th>Dept_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>19h</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Duration  Total_Stops  Price  day  month  \\\n",
       "0       IndiGo  Banglore   New Delhi   2h 50m          0.0   3897   24      3   \n",
       "1    Air India   Kolkata    Banglore   7h 25m          1.0   7662    1      5   \n",
       "2  Jet Airways     Delhi      Cochin      19h          1.0  13882    9      6   \n",
       "3       IndiGo   Kolkata    Banglore   5h 25m          2.0   6218   12      5   \n",
       "4       IndiGo  Banglore   New Delhi   4h 45m          2.0  13302    1      3   \n",
       "\n",
       "   year  Arrival_hour  Arrival_minutes  Dept_hour  Dept_minutes  \n",
       "0  2019             1               10         22            20  \n",
       "1  2019            13               15          5            50  \n",
       "2  2019             4               25          9            25  \n",
       "3  2019            23               30         18             5  \n",
       "4  2019            21               35         16            50  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "764ede2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
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       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minutes</th>\n",
       "      <th>Dept_hour</th>\n",
       "      <th>Dept_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>19h</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Duration  Total_Stops  Price  day  month  \\\n",
       "0       IndiGo  Banglore   New Delhi   2h 50m          0.0   3897   24      3   \n",
       "1    Air India   Kolkata    Banglore   7h 25m          1.0   7662    1      5   \n",
       "2  Jet Airways     Delhi      Cochin      19h          1.0  13882    9      6   \n",
       "3       IndiGo   Kolkata    Banglore   5h 25m          2.0   6218   12      5   \n",
       "4       IndiGo  Banglore   New Delhi   4h 45m          2.0  13302    1      3   \n",
       "\n",
       "   year  Arrival_hour  Arrival_minutes  Dept_hour  Dept_minutes  \n",
       "0  2019             1               10         22            20  \n",
       "1  2019            13               15          5            50  \n",
       "2  2019             4               25          9            25  \n",
       "3  2019            23               30         18             5  \n",
       "4  2019            21               35         16            50  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "97cee721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline             object\n",
       "Source              object\n",
       "Destination         object\n",
       "Duration            object\n",
       "Total_Stops        float64\n",
       "Price                int64\n",
       "day                  int32\n",
       "month                int32\n",
       "year                 int32\n",
       "Arrival_hour         int32\n",
       "Arrival_minutes      int32\n",
       "Dept_hour            int32\n",
       "Dept_minutes         int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "020bc27e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing Duration to minutes\n",
    "\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute #why strip is used here?\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fdd63996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
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       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minutes</th>\n",
       "      <th>Dept_hour</th>\n",
       "      <th>Dept_minutes</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
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       "      <td>22</td>\n",
       "      <td>20</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>7h 25m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</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>19h</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</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>5h 25m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Duration  Total_Stops  Price  day  month  \\\n",
       "0       IndiGo  Banglore   New Delhi   2h 50m          0.0   3897   24      3   \n",
       "1    Air India   Kolkata    Banglore   7h 25m          1.0   7662    1      5   \n",
       "2  Jet Airways     Delhi      Cochin      19h          1.0  13882    9      6   \n",
       "3       IndiGo   Kolkata    Banglore   5h 25m          2.0   6218   12      5   \n",
       "4       IndiGo  Banglore   New Delhi   4h 45m          2.0  13302    1      3   \n",
       "\n",
       "   year  Arrival_hour  Arrival_minutes  Dept_hour  Dept_minutes  \\\n",
       "0  2019             1               10         22            20   \n",
       "1  2019            13               15          5            50   \n",
       "2  2019             4               25          9            25   \n",
       "3  2019            23               30         18             5   \n",
       "4  2019            21               35         16            50   \n",
       "\n",
       "   Duration_hours  Duration_minutes  \n",
       "0               2                50  \n",
       "1               7                25  \n",
       "2              19                 0  \n",
       "3               5                25  \n",
       "4               4                45  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adding columns 'Duration_hours' and 'Duration_minutes' into dataset 'train_data'\n",
    "\n",
    "train_data['Duration_hours'] = duration_hours\n",
    "train_data['Duration_minutes'] = duration_mins\n",
    "\n",
    "#get first 5 rows\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2a4e742a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline             0\n",
       "Source              0\n",
       "Destination         0\n",
       "Duration            0\n",
       "Total_Stops         1\n",
       "Price               0\n",
       "day                 0\n",
       "month               0\n",
       "year                0\n",
       "Arrival_hour        0\n",
       "Arrival_minutes     0\n",
       "Dept_hour           0\n",
       "Dept_minutes        0\n",
       "Duration_hours      0\n",
       "Duration_minutes    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7af6c2d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(subset = ['Total_Stops'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "279340a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline             0\n",
       "Source              0\n",
       "Destination         0\n",
       "Duration            0\n",
       "Total_Stops         0\n",
       "Price               0\n",
       "day                 0\n",
       "month               0\n",
       "year                0\n",
       "Arrival_hour        0\n",
       "Arrival_minutes     0\n",
       "Dept_hour           0\n",
       "Dept_minutes        0\n",
       "Duration_hours      0\n",
       "Duration_minutes    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "edcc8060",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10682 entries, 0 to 10682\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Airline           10682 non-null  object \n",
      " 1   Source            10682 non-null  object \n",
      " 2   Destination       10682 non-null  object \n",
      " 3   Duration          10682 non-null  object \n",
      " 4   Total_Stops       10682 non-null  float64\n",
      " 5   Price             10682 non-null  int64  \n",
      " 6   day               10682 non-null  int32  \n",
      " 7   month             10682 non-null  int32  \n",
      " 8   year              10682 non-null  int32  \n",
      " 9   Arrival_hour      10682 non-null  int32  \n",
      " 10  Arrival_minutes   10682 non-null  int32  \n",
      " 11  Dept_hour         10682 non-null  int32  \n",
      " 12  Dept_minutes      10682 non-null  int32  \n",
      " 13  Duration_hours    10682 non-null  int64  \n",
      " 14  Duration_minutes  10682 non-null  int64  \n",
      "dtypes: float64(1), int32(7), int64(3), object(4)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0c6e0a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop('Duration',axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dbae1a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minutes</th>\n",
       "      <th>Dept_hour</th>\n",
       "      <th>Dept_minutes</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</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>1.0</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</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>1.0</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</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>2.0</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  day  month  year  \\\n",
       "0       IndiGo  Banglore   New Delhi          0.0   3897   24      3  2019   \n",
       "1    Air India   Kolkata    Banglore          1.0   7662    1      5  2019   \n",
       "2  Jet Airways     Delhi      Cochin          1.0  13882    9      6  2019   \n",
       "3       IndiGo   Kolkata    Banglore          2.0   6218   12      5  2019   \n",
       "4       IndiGo  Banglore   New Delhi          2.0  13302    1      3  2019   \n",
       "\n",
       "   Arrival_hour  Arrival_minutes  Dept_hour  Dept_minutes  Duration_hours  \\\n",
       "0             1               10         22            20               2   \n",
       "1            13               15          5            50               7   \n",
       "2             4               25          9            25              19   \n",
       "3            23               30         18             5               5   \n",
       "4            21               35         16            50               4   \n",
       "\n",
       "   Duration_minutes  \n",
       "0                50  \n",
       "1                25  \n",
       "2                 0  \n",
       "3                25  \n",
       "4                45  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e1f84bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IndiGo', 'Air India', 'Jet Airways', 'SpiceJet',\n",
       "       'Multiple carriers', 'GoAir', 'Vistara', 'Air Asia',\n",
       "       'Vistara Premium economy', 'Jet Airways Business',\n",
       "       'Multiple carriers Premium economy', 'Trujet'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Airline'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b217d3a",
   "metadata": {},
   "source": [
    "## Apply various encoding methods for Airline, Source and Destination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4b45e10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \n",
       "0               0                0                                0  \n",
       "1               0                0                                0  \n",
       "2               0                0                                0  \n",
       "3               0                0                                0  \n",
       "4               0                0                                0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Airline is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Airline = train_data[[\"Airline\"]]\n",
    "Airline = pd.get_dummies(Airline, drop_first= True)\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "57628144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4536\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b8d379de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRpTBUrsFJKjwHnjhDfBVw8SptrgWtHiG8EXjtCvJesQJM0PXV3dzvRhSRJmjLymKZdkiaks7OTVatWsWnTprxTkSRJmhALLElVqVgs0tLSQkqJlpYWisVi3ilJkiSNywJLUlVqa2tj9+7dAPT09NDW1pZzRpIkSeOzwJJUdXbt2kVrayu9vb0A9Pb20traSk+Pd2GQJEnVzQJLUtXp6Oigv79/SKy/v59CoZBTRpIkSRNjgSWp6jQ2NlJTM/TtqaamhsbGxpwykiRJmhgLLElVp76+nubmZurq6gCoq6ujubmZ+vr6nDOTJEkamwWWpKrU1NT0UkFVX19PU1NTzhlJkiSNzwJLUlWqra1l3bp1RATXXHMNtbUVuy+6JElS2fiNRVLVOvfcc9mwYQMNDQ15pyJJkjQh9mBJqmoWV5IkaSqZUIEVEa+IiI6IeCh7viIiPlbZ1CRJkiRpaploD9bngHXAIYCU0iZgdaWSkiRJkqSpaKIF1vyU0n3DYsVyJyNJkiRJU9lEC6ydEfEzQAKIiFXA9oplJUmSJElT0ERnEfwwsB54VUQ8AzwOvK9iWUmSJEnSFDShAiul9BjQGBHHADUppb2VTUuSJEmSpp6JziLYEhELUkr7Ukp7I2JhRPx5pZOTJEmSpKlkotdgvT2l9PzAk5TSbuAdFclIkiRJkqaoiRZYsyJi7sCTiJgHzB1j/ZdExKyIuD8ivpE9r4+IOyOiK3tcOGjddRGxNSIejYhLBsUviIgHs9euj4jI4nMj4tYsfm9ELJvg8UiSJElS2U20wPoK0BERV0XEbwJ3AjdPsO3vAg8Pen410JFSWg50ZM+JiHMo3VvrNcClwI0RMStr81lgDbA8+7s0i18F7E4pnQ18GrhugjlJkiRJUtlNqMBKKf0FcC3wakoF0Cez2JgiYilwGdA6KHw5Py3ObgbeNSh+S0rpQErpcWArcGFELAaOTyl9N6WUgC8NazOwrQ3AxQO9W5IkSZI02SY6TTsppduB2w9z+38L/AFw3KDYySml7dk2t0fEoiy+BPjPQetty2KHsuXh8YE2T2fbKkbEHuBEYOfgJCJiDaUeME4//fTDPARJkiRJmpgxe7Ai4p7scW9E/GTQ396I+Mk4bX8Z2JFS+v4Ecxmp5ymNER+rzdBASutTSitTSisbGhommI4kSZIkHZ4xe7BSSm/IHo8ba71RvB54Z0S8A6gDjo+IrwDPRcTirPdqMbAjW38bcNqg9kuBZ7P40hHig9tsi4ha4ASg5whylSRJkqSjNu41WBFRExEPHe6GU0rrUkpLU0rLKE1ecVdK6X1AO3BlttqVwNez5XZgdTYz4JmUJrO4LxtOuDciLsqur3r/sDYD21qV7eNlPViSpq7u7u68U5AkSZqwcQuslFI/0BkR5bp46VPAWyOiC3hr9pyU0mbgNmALcAfw4ZRSX9bmQ5QmytgK/IifXgt2E3BiRGwFfo9sRkJJ00NnZyerVq1i06ZNeaciSZI0IROd5GIxsDki7gP2DQRTSu+cSOOU0t3A3dnyLuDiUda7ltJshcPjG4HXjhDvBd4zkRwkTS3FYpGWlhZSSrS0tPCVr3yF2toJz8sjSZKUi4l+W/lERbOQpGHa2trYvXs3AD09PbS1tXHFFVfknJUkSdLYxiywIqIO+G3gbOBB4KaUUnEyEpM0c+3atYvW1lZ6e3sB6O3tpbW1lcbGRurr63POTpIkaXTjXYN1M7CSUnH1duCvK56RpBmvo6OD/v7+IbH+/n4KhUJOGUmSJE3MeAXWOSml96WU/helWfreOAk5SZrhGhsbqakZ+vZUU1NDY2NjThlJkiRNzHgF1qGBBYcGSpos9fX1NDc3U1dXB0BdXR3Nzc0OD5QkSVVvvALr3Ij4Sfa3F1gxsBwRP5mMBJW/9vZ2urq6ANi5c2fO2WimaGpqYs6cOQDMmTOHpqamnDOSJEka35iTXKSUZk1WIqpehUKBvv5E3bz5L83qJlVaX18fL7zwAgAvvPACfX19TtMuSZKq3rg3GpYATj19GaeevizvNDSDfOITn3hpoov+/n4+8QnvFiFJkqqfBZakqrN161buueeeIbF77rmHxx57LKeMJEmSJsYCS1LV+fznPz9ivLW1dZIzkSRJOjwWWJKqTnNz82HFJUmSqoUFlqSqc9ZZZ/GGN7xhSOwNb3gDZ511Vk4Zaabp7u7OOwVJ0hRlgSWpKv3pn/7pS8uzZs0a8lyqpM7OTlatWsWmTZvyTkWSNAVZYEmqSnPnzmXJkiUArF27lrlz5+ackWaCYrFIS0sLKSVaWlooFot5pyRJmmIssCRVrYaGBl7xilfw7ne/O+9UNEO0tbW9dL+/np4e2tracs5IkjTVWGBJqmrz58/POwXNELt27aK1tZXe3l4Aent7aW1tpaenJ+fMJElTiQWWJElAR0fHSze3HtDf30+hUMgpI0nSVGSBJUkS0NjYSE3N0I/FmpoaGhsbc8pIkjQVVazAioi6iLgvIjojYnNEfCKL10fEnRHRlT0uHNRmXURsjYhHI+KSQfELIuLB7LXrIyKy+NyIuDWL3xsRyyp1PJKk6a2+vp7m5mbq6uoAqKuro7m5mfr6+pwzkyRNJZXswToAvCWldC5wHnBpRFwEXA10pJSWAx3ZcyLiHGA18BrgUuDGiJiVbeuzwBpgefZ3aRa/CtidUjob+DRwXQWPR5I0zTU1Nb1UUNXX19PU1JRzRpKkqaZiBVYqeSF7Ojv7S8DlwM1Z/GbgXdny5cAtKaUDKaXHga3AhRGxGDg+pfTdlFICvjSszcC2NgAXD/RuSZJ0uGpra1m3bh0RwTXXXENtbW3eKUmSppiKXoMVEbMi4gFgB3BnSule4OSU0naA7HFRtvoS4OlBzbdlsSXZ8vD4kDYppSKwBzhxhDzWRMTGiNjY3d1dpqOTJE1H5557Lhs2bGDFihV5pyJJmoIqWmCllPpSSucBSyn1Rr12jNVH6nlKY8THajM8j/UppZUppZUNDQ3jZC1Jmun8rJAkHalJmUUwpfQ8cDela6eey4b9kT3uyFbbBpw2qNlS4NksvnSE+JA2EVELnAB4wxJJkiRJuajkLIINEbEgW54HNAKPAO3AldlqVwJfz5bbgdXZzIBnUprM4r5sGOHeiLgou77q/cPaDGxrFXBXdp2WJEmSJE26Sl69uxi4OZsJsAa4LaX0jYj4LnBbRFwFPAW8ByCltDkibgO2AEXgwymlvmxbHwK+CMwDbs/+AG4CvhwRWyn1XK2u4PFIkiRJ0pgqVmCllDYB548Q3wVcPEqba4FrR4hvBF52/VZKqZesQFNltLe309XVRV9/qWPw4IFe2tvbeec735lzZpIkSVL1mZRrsDR1FQoFin39nLT4NE5afBqz58ylUCjknZYkSZJUlbzBh8bVcOrpXPGhPwDgts/+Rc7ZaKYY6D1duHBh3qlIkiRNmAWWRtTe3k6hUKCrq4tDff0vFVY7nn2K3c/VsHbtWhobGx0qqIopFArs27cv7zQkSZIOiwWWRlQoFNj88KMsPLl0T+eDxdJ8IwsWlZ5vfvhRAAssSZLKpLu723uwSdOABZZGtfDkJVz8G2tHfK3jy9dPcjaSJE1fnZ2drF27lhtuuIEVK1bknY6ko+AkF5IkSTkqFou0tLSQUqKlpYVisZh3SpKOgj1YAn56zdWArq4uDhb7Ru2p2v3cNvbtmsXatT/t4fKaLEmSDl9bWxu7du0CYOfOnbS1tXHFFVfknJWkI2WBJaB0zdWDWx7h2JMWA1B7wiJqgQPFvhHXn39iab3Hd+wB4IWd2wGvyZIk6XDs2rWLz33ucxw8eBCAgwcP8rnPfY7Gxkbq6+tzzk7SkbDA0kuOPWkx57/rt46o7f3/+rkyZyNJ0vTX0dHBgQMHhsQOHDhAoVCwF0uaorwGS5IkKScXXHDBiPGVK1dOciaSysUCS5IkKSd33HHHiPFvfetbk5yJpHKxwJIkScrJo48+elhxSdXPAkuSJCknr371q0eMv/KVr5zkTCSViwWWJElSTubMmTNi/JJLLpnkTCSViwWWJElSTu68806OmT+fFa9+NSte/WqOmT8fgI0bN+acmaQjZYElqSrt3LkTgP3799Pe3p5zNpJUGQsXLuRnzjiDv/zYx/jLj32MnznjDCKCxsbGvFOTdIQssCRVpd27d3PMvLnMmzubQqGQdzqSVBGzZ89+WWzx4sXeZFiawrzRsKSqddbSRXmnIEmTrqGhIe8UJB0Fe7AkSZKqSETknYJmkO7u7rxTmHYqVmBFxGkR8e8R8XBEbI6I383i9RFxZ0R0ZY8LB7VZFxFbI+LRiLhkUPyCiHgwe+36yN55ImJuRNyaxe+NiGWVOh5JkiRpOuns7GTVqlVs2rQp71SmlUr2YBWB/5FSejVwEfDhiDgHuBroSCktBzqy52SvrQZeA1wK3BgRs7JtfRZYAyzP/i7N4lcBu1NKZwOfBq6r4PFImiTt7e3s37+fx7bt4LFtO+jq6nKiC0mSyqhYLNLS0kJKiZaWForFYt4pTRsVK7BSSttTSj/IlvcCDwNLgMuBm7PVbgbelS1fDtySUjqQUnoc2ApcGBGLgeNTSt9NKSXgS8PaDGxrA3Bx2K8uTXmFQoF5c2Zx5ikLOPOUBdB30IkuJEkqo7a2Nnbv3g1AT08PbW1tOWc0fUzKJBfZ0L3zgXuBk1NK26FUhEXEwFXsS4D/HNRsWxY7lC0Pjw+0eTrbVjEi9gAnAjuH7X8NpR4wTj/99LIdl6TKOXNxPZ9a83YArl5/e87ZSFJ5tbe3UygU6OrqgpT4n3/+5wD86MknIYK1a9fS2NjIO9/5zpwz1XS0a9cuWltb6e3tBaC3t5fW1lYaGxudwbIMKj7JRUQcC/wL8NGU0k/GWnWEWBojPlaboYGU1qeUVqaUVjozjyRpPF70rUorFAr88JFHWNbQwLJFi+jbv5++/ftZtmgRyxoa+OEjj9hzr4rp6Oigv79/SKy/v99zrkwq2oMVEbMpFVf/mFIa6Hd8LiIWZ71Xi4EdWXwbcNqg5kuBZ7P40hHig9tsi4ha4ASgpyIHI0maETo7O1m7di033HADK1asyDsdTWNnnnIKn2xuHvG1P25tneRsNJM0NjbSOuwcq6mp8QbXZVLJWQQDuAl4OKX0N4NeageuzJavBL4+KL46mxnwTEqTWdyXDSfcGxEXZdt8/7A2A9taBdyVXaclaQpqb29n7dq1dHV18dj2Hq5efztXr7+dx7b30NXVxdq1a53sQhXlRd+SZoL6+nqam5upq6sDoK6ujubmZocHlkklhwi+HvgN4C0R8UD29w7gU8BbI6ILeGv2nJTSZuA2YAtwB/DhlFJftq0PAa2UJr74ETBwQcZNwIkRsRX4PbIZCSVNTYVCga6HH+LME+dy1qLjoO8Q9B3irEXHceaJc+l6+CGHL6ii2traXhoeuGPHDi/6ljRtNTU1vVRQ1dfX09TUlHNG00fFhgimlO5h5GukAC4epc21wLUjxDcCrx0h3gu85yjSlFRlzjz5eK5938+N+NoffeXeSc5GM8muXbtYv379S71WxWKR9evXe9G3pGmptraWdevWsXbtWq655hpqaydl7rsZwf+SM8TAbEWj6erqovdQH/f/6+eOaPsv7NxO154drF27dtR1nA1JUjXr6Ojg4MGDQ2IHD5ZuEXDFFVfklJUkVc65557Lhg0bcBK48rLAmiEKhQKbNj/M3IUnj7zCMScyBzhY7Bv59XHMWbCIfuDRZ0eeY+TA7ucALLA0xPDCv6uri1Q8MGpP1WPP/YTY1TWkkLdwV7mcffbZI8aXL18+yZlI0uSxuCo/C6wZZO7Ckzn94t/IZd9PdXw5l/2quhUKBX645UHOPGkeAMtOCKCOVDw44vpnnli6GPfQjq0APL5zP2DhrvLYsGHDiPGvfvWrnH/++ZOcjaabkX9QKo46W+Dj27cT3d3+oCRNQRZYknJ15knz+GTTK4+o7R+3PVrmbDSTve1tb+Oee+55WfySSy7JIRtNN6UflLZwenY932nHHQdAX3aj1+FOX7gQgN4f/xiAp3pKI0QssKTqZ4ElqWImcu1fOrT/iAulx3e+SOzp8to/lcW3v/3tEePf+ta3eNOb3jTJ2Wg6Or2+nj+67JePqO213/xGmbORVCkWWJIqplAo8MPNnSyrnz3i62ccC1BL6ht5SOB4li2sBQ5xcPuWEV9/oucQ4C++mpjm5mbuuece6uaXhqL2vtj7UlySpImywJJUUcvqZ/PxSxflsu+P37Ejl/1qanrooYdeFjv77LM566yzcshGkjRVWWBJGtV4Q/zG09XVRTp4KLdC54meQ8QLYw8hHI9DDKef4ef1zp072b17N/v3lyZNOdB74KXXHnvsMd7+9rezcOFCTjrppJfinheSpNFYYEkaVWmI3wOcccJo9wwf2+nzgHmQ+g6VN7EJOuMEgBc5sK3ziNo/uScBDjGcaiZy7d++F/dRO2foR2DN7BpqqHnZ+geKB/hx94/5cXdpsoHiwSJdXV1j7sMCTJJmLgssSWM644TgT9448jVU092ffSefwnCmK0fP6b4X91Ez++XF0oCa2TX0p/4j2n7N7Br2H9rPpi2bRny9/1D/uAXYWCzOJGlqs8CSJFWVQqFA55ZOauuP8CPqOJh9XH4/CsxiFgc5yOYfbz7stsWeImCvqSRNZRZYU0Q5ftE9cLCY2w1/D+x+jq59u7wW5ggc7f/7o9HV1UU6kGZsT86Tzydi/9Fdw3U0Zuo5D1BbX8uCdyzIO41J9/y/PZ93CpKko2SBNUUUCgUeeGgLs45rOLINzFlAzIGDh/rKm9gExbEn0Qs8+GT3EbXv21tqNxO/bBYKBX740A84/djJ/3932mxgNqTipO+6Kpx+LMDz9D7xvUnf91MvzAJm5jkvSdJUZoE1hcw6roFjfm5V3mnkYt+9G/JOIVenH9vHx1a+kHcamkR/vvHYXPefd89p8WBxRvbmFHcV6dqbX68pzOyeU0kqBwssSdLLFAoF7t98PyzIYefzS38Hj/AG1FPaAniBF7j/mfvz2f/zpQcLrMM3kdkr+w8e5NpvfuOItv/krl3U7N07ZvFtcSxVBwssSdLIFkD/m49spj1NTTV3jz7zosZWKBR49KGHWHrsyL3Pp86eDbNn09fbe0TbX3rMMQDse+KJEV/f9kJplIMFlgYb7b5/Y/G+f0fPAkuSJKkMlh57LP/jdRfksu+//sH3c9mvqluhUODRR37IaUvOAGD+3OOYf8px47Z7cW/phutPP/MkYOF+uCywJEmSpGnqtCVn8D9/54+PqO1ffuaTZc5mZqjYWICI+HxE7IiIhwbF6iPizojoyh4XDnptXURsjYhHI+KSQfELIuLB7LXrIyKy+NyIuDWL3xsRyyp1LJIkSZI0EZUcbP1F4NJhsauBjpTScqAje05EnAOsBl6TtbkxImZlbT4LrAGWZ38D27wK2J1SOhv4NHBdxY5EkiRJkiagYkMEU0r/Z4RepcuBN2fLNwN3A3+YxW9JKR0AHo+IrcCFEfEEcHxK6bsAEfEl4F3A7Vmbj2fb2gB8JiIipZQqc0RSfnb85AB3P5HPPcyUjx0/OcDpeSchSZIO22RPF3RySmk7QPa4KIsvAZ4etN62LLYkWx4eH9ImpVQE9gAnjrTTiFgTERsjYmN395Hd6FaSJEmSxlMtk1zECLE0RnysNi8PprQeWA+wcuVKe7g05Sw6fi5vXnYo7zQ0ie7ZOTfvFOjd00vq8i1zJok98dOfMSVJR2Sye7Cei4jFANnjjiy+DTht0HpLgWez+NIR4kPaREQtcALQU7HMJUmSJGkck92D1Q5cCXwqe/z6oPg/RcTfAKdSmszivpRSX0TsjYiLgHuB9wM3DNvWd4FVwF2Vvv5qvLu0V1JXVxd9Bw6x794Nuew/b317u+nqen7MO9hXkjfZ00xUd0Id/cu90fBMUvOMNxqWpKNVsQIrIv6Z0oQWJ0XENuBPKRVWt0XEVcBTwHsAUkqbI+I2YAtQBD6cUhq4ov9DlGYknEdpcovbs/hNwJezCTF6KM1CWFGFQoH7H9xC//z6Su/q5WqOg3nQV5yhEx3Mq+cnwPd/9ONJ33XNi6WOUQssSZIkjaeSswj+2igvXTzK+tcC144Q3wi8doR4L1mBNpn659fTe84vT/ZulaO6Ld/IOwVJUoUd7SiVrq4u+nt7+esffL+MWU3ctr17qenqOuKRHo7UmJrGO2+7urro70tHfMPgp595kppZMep55XkzsmqZ5EKSJCk3hUKBRzZtYsmcOUfU/hSA2bPp7+0ta14Tders2dDXx95HHjnsts8cPAg4UmMqKhQKPPLwo5zSsHTE1xsWngrAwQPFI9r+ySeVZr15fue+l7324+7SRN+eNy9ngSVNAU+9MIs/33hs3mloEj31wixekXcS0gyzZM4cPrz41LzTmHR/v/3Z8VdSRZSj5zSvO8CmVNr/0VwfP117wCywpCrX2NiY2767urpIB/ZxxoKR7oow/T35fCLmHsPy5csnfd+vIN//95KkyisUCmzZ/AgNJyw+ovYL5i2CeXCo98h6qI7GScefAkD3tj1H1L57z3ZgevaAWWBJVe6d73xnbm8+a9eu5cC2Tv7kjbNz2X/e/uw7h5i7dDnXX3993qlIkqaphhMW0/Sm5rzTmHRt/9GadwoVY4F1mA7s3c2hZ7bknYYmUezdTTa6fkZ6ck/iz74zM29y/OSexCtGHtYuSZI0IgssSaM62iFqXV1d7H9xH3W1+Qwx7C0m5s0/8iF+r1jqMD1pMuV/v8kDM/J6pGcOHGDWUV5Lc7Sm67U4mpkssA7T3OMWkpack3camkRz9zyWdwq5OdrhiROZPjYd2s+ZJ80/ou0/vvNFYva8MQsoP7SlqaNQKPDIAw/kMmagIXvsy2kWwDydArBvH88/8EAu+x+4w2Ue79V5FvVQ+hw8dKBvWg+XG03389t5fv+OaVnYW2BJqpjxCrS1a9fywy0PHsUeguXLvUaqYp6Hmrtr8s5Ck+l5YEm+KZwCXMXMnFhnprqJnKbBo1TUb37wYRbMX5TL/ufX1MO8I59GfSo7YV7pZ41nfrQrl/0//+IOoDKFvQXWYap5sccbz84wNS/2MJOvwaqk4cPvdu7cye7du9m/fz/9/f3UROlLVn9K1NTUMG/ePBYuXMhJJ50EwCsWOYSvUvKevfLFgy9Se+LM+4gq7ioyf878XGauBGCJ/6Y08yyYv4hfetXqvNPQJPv3R26p2LZn3qfXUcj7C8e+A4eYdVzD+CtPQ317uzlm7uycvnSc4heOChmth6u9vZ2/+qu/on/QzT1mzZrF7bffPpnpzWh5z17ZuaUzl33nLrBXVpKmOAusw5D3F44HHprZsxf6pWNmO3ToEN/85je57LLL8k5FFVaOyVX2vbiPmtn5DG/sP9TPMUc6ucop9iBJk23P3h5++OwDeaehSbZnbw9LOLEi27bAmiLK84XjRWpq55Qpo8PTXzzIMfOPZthLg186ZpAbb7xxxPgNN9xggTUDVHpylYGhqAMOHTpEsVikv79/xPUjgtmzZzN79k/vBzd4qOpInFxFmjr2HdzDlmf+X95paJLtO3hkN0ieCAusKWIyZnPb9+KLzJo994i233fowLgFlF84NFEf+chHuO6660aMS+M50vfLiy++mEOHXn7Pt9ra2lxnGZtpdvX28r28k9Ck2gUsyGnfef94O/wHn8M18APRaEb74ehw1dSMPCKgtrZ2yI9Ph2u8H6sqrVL//y2wZojxvnAML8AG/sEfOnSIQ4cOkQZdCzNnzhxmz579sn8UFlAql8suu4zrr7+e/fv3vxSbN2+evVeqqA9+8IN85jOfGTEuaXrK8/KPyTDW97uDBw++bP05c+awaNEiv98dpRj8xXkmWLlyZdq4cWPeaUgax/PPPz/kDb29vZ0FCxbkl5BmhLe85S1Dfg2ura3lrrvuyjGjmWXt2rW53QdL+fkx8KrzzvM660nU09PD6tWr6R1037e6ujpuueUW6uvrc8xsyhnxnhL2YEmqSgsWLODcc8+ls7OT888/3+JKk+KrX/0q7373u4c81+TJe7beF/ftY27MvHtwHUiJ+ccc4cQsZbCA/IfqzTT19fU0NzfT2tpKb28vdXV1NDc3W1yViT1YkqraV7/6Vd7znvfknYZmkA984AP86Ec/Yvny5dx00015p6NJMt61yuMZ71qa4ff3O1yD7wc4mqO5nsVhYDNPsVjkfe97H88++yynnnoqX/nKV6itte/lMI34D9oCS5KkYb7whS/wgQ98IO80NI2Mdi3Mvn37Rm2zdOlSr4VRRXV2drJ27VpuuOEGVqxYkXc6U5EFFlhgSZKk6vHNb35zxFlT//AP/9CJfTQpuru7aWhoyDuNqWrEAiufuzCWUURcGhGPRsTWiLg673wkSZIm6rLLLnvZsD9nTdVksrgqvyldYEXELODvgbcD5wC/FhHn5JuVJEnSxN16661jPpc0tUzpAgu4ENiaUnospXQQuAW4POecJEmSJmxg1lTAWVOlaWCqTxWyBHh60PNtwM8NXyki1gBrAE4//fTJyUySJGmCbrjhBmdNlaaJqd6DNdKFZS+btSOltD6ltDKltNJxppIkqRpZXEnTw1QvsLYBpw16vhR4NqdcJEmSJM1wU73A+h6wPCLOjIg5wGqgPeecJEmSJM1QU/oarJRSMSJ+B/gWMAv4fEppc85pSZIkSZqhpnSBBZBS+jfg3/LOQ5IkSZIipZfNCTGtRUQ38GTeeUxRJwE7805CM47nnfLgeac8eN4pD553R25nSunS4cEZV2DpyEXExpTSyrzz0Mzieac8eN4pD553yoPnXflN9UkuJEmSJKlqWGBJkiRJUplYYOlwrM87Ac1InnfKg+ed8uB5pzx43pWZ12BJkiRJUpnYgyVJkiRJZWKBJUmSJEllYoE1TUVEX0Q8EBGdEfGDiPiFCuzjzRHxjXJvV9PboHNzc3Z+/l5EjPleFBHLIuKhbPm/RcRnRlnv3yJiQQXS1hQSES8MWn5HRHRFxOnjrX+472nZ+mV/b9XUEhEpIr486HltRHSX6/MxIj4eEb9/mG3+Xzn2reoVEadExC0R8aOI2JJ9/q3J83tZRLRGxDl57b+a1OadgCpmf0rpPICIuAT4/4A35ZlQRNSmlIp55qCqMPjcXAT8E3AC8KdHu+GU0juOdhuaPiLiYuAG4G0ppacqsIs3Ay8Afpmd2fYBr42IeSml/cBbgWfyTCilZOE/jUVEAF8Dbk4prc5i5wG/kmdeKaXmPPdfTezBmhmOB3YDRMSxEdGR9Wo9GBGXZ/FlEfFwRHwu61n4dkTMy1772YjYFBHfjYi/HOhJGCwi6iPiX7P1/jMiVmTxj0fE+oj4NvCliGiIiH+JiO9lf6+fvP8MqjYppR3AGuB3omRWdo59LzuXPjhK01Mj4o6sZ+IvBoIR8UREnDQpyauqRcQbgc8Bl6WUfpTFfi8iHsr+PjpO+5+NiPsj4qyI+JWIuDd7XoiIkyNiGfDbwH/PemTfONJ6lT5OVY3bgcuy5V8D/nngheE9UNn5tyz7eyT71f+hiPjHiGiMiP+bvbddOGj750bEXVn8t7LtjPh5nr32AprOfgk4lFL6h4FASukB4DvAsRGxITu3/jErxoiICyLiPyLi+xHxrYhYnMXvjojrIuK+iPhh9t45MFqkbZTP2s9GxMbs++InBsXvjghvWIwF1nQ2L/vQfwRoBT6ZxXuBd6eUXkfpH+hfD/zjA5YDf59Seg3wPPBfs/gXgN9OKf080DfK/j4B3J9SWgFcA3xp0GsXAJenlH4d+Dvg0ymln82233r0h6qpLKX0GKX3okXAVcCe7Pz4WeC3IuLMEZqdB/wq8F+AX42I0yYpXU0Nc4GvA+9KKT0CpS8XwAeAnwMuonRunT9S4ygN+/sHSu9bjwH3ABellM4HbgH+IKX0RLbOp1NK56WUvjPSehU8RlWXW4DVEVEHrADunWC7syl9Lq4AXgX8OvAG4PcpfZYOWEGpgPt54E8i4lTG/jzX9PZa4PujvHY+8FHgHOAs4PURMZtSb/6qlNIFwOeBawe1qU0pXZi1Gzya5DxG/qz9o5TSSkrn5ZsGflTXTzlEcPoaPAzr5yn1Hr0WCKAlIn4R6AeWAAO/sj6e/QICpX+4y6J0PctxKaWBITD/BPzyCPt7A1lBllK6KyJOjIgTstfas2ETAI3AOYM+A46PiONSSnuP9oA1pQ2cEG8DVkTEquz5CZQK/x8OW78jpbQHICK2AGcAT09GopoSDlEatncV8LtZ7A3A11JK+wAiog14I3D/sLavpnRPmLellJ7NYkuBW7NffOcAj4+y34mup2kmpbQp69X8NeDfDqPp4ymlBwEiYjOl97YUEQ8Cywat9/Xsc3R/RPw7cCHwTUb+PP/x0R6PprT7UkrbACLiAUrn0fOUirI7s+9fs4Dtg9q0ZY/fZ+h5N9pn7RURsYZSHbGYUjG3qRIHM1VZYM0AKaXvZsOmGoB3ZI8XpJQORcQTQF226oFBzfqAefz0i+94Rlpv4CZr+wbFaoCfH1RwaYaLiLMonW87KJ1HH0kpfWvYOsuGNRt+rvpepsH6gSuAQkRck1JqYeLvZdspvSeeDwwUWDcAf5NSao+INwMfH6XtRNfT9NQO/BWla/NOHBQvMnTEUN2g5cHvZf2Dnvcz9H1t+E1LE/BeRv881/S2GVg1ymsjfT4GsDkbiTRWm+Gfpy/bVjaq5PeBn00p7Y6IL+J59zIOEZwBIuJVlH6t2EWpR2BH9mb8S5R+jRhVSmk3sDciLspCq0dZ9f9QerMn+2KxM6X0kxHW+zbwO4NyO2/CB6JpJyIaKA2z+kwq3fX8W8CHsuEMRMQrIuKYPHPU1JRSepFSb/t7I+IqSu9R74qI+dk59W5K1ysM9zyloVgt2XsZlN43ByYtuHLQunuB4wY9H209zQyfB/5soEdqkCeA1wFExOuAkYY9j+fyiKiLiBMpFXDf4zA/zzWt3AXMHbgeD0rXjTL6ZGaPAg3ZiCYiYnZEvOYI9308pR/O92TXmb79CLczrfmr7/Q1L+sahtIvF1emlPoi4h+B/x0RG4EHgEcmsK2rgM9FxD7gbmDPCOt8HPhCRGwCXmT0Lxdrgb/P1qul9KXntydyQJo2Bs7N2ZR+2f0y8DfZa62Uhif8ILuWoBt41+SnqOkgpdQTEZdSep/5KPBF4L7s5daU0vDhgQPtnouIXwFuj4jfpPT+9tWIeAb4T376Bfl/AxuyyQU+MsZ6mgGyYVl/N8JL/wK8P3vf+x4vH/I8EfdRGhJ4OvDJlNKzR/h5rmkgG0b6buBvI+JqStfjPQH86yjrH8yG3l+fXb5RC/wtpZ6ww913Z0Tcn7V9DPi/R3IM012UfjSWRhcRx6aUBu4TczWwOKX0u+M0kyRJkmYce7A0EZdFxDpK58uTwH/LNx1JkiSpOtmDJUmSJEll4iQXkiRJklQmFliSJEmSVCYWWJIkSZJUJhZYkqRpJyL+KCI2R8SmiHggIn4u75wkSTODswhKkqaV7Gaavwy8LqV0ICJOAuYc5TZrU0rFsiQoSZrW7MGSJE03i4GdKaUDACmlndmNWS+OiPsj4sGI+HxEzAWIiCeyIoyIWBkRd2fLH4+I9RHxbeBLEXFyRHwtIjqzv1/I1ntfRNyX9ZT9r4iYlctRS5KqggWWJGm6+TZwWkT8MCJujIg3RUQd8EXgV1NK/4XSCI4PTWBbFwCXp5R+Hbge+I+U0rnA64DNEfFq4FeB16eUzgP6gPeW/YgkSVOGBZYkaVpJKb1AqTBaA3QDtwIfBB5PKf0wW+1m4BcnsLn2lNL+bPktwGezffSllPYAF2f7+l5EPJA9P6tMhyJJmoK8BkuSNO2klPqAu4G7I+JB4MoxVi/y0x8c64a9tm+cXQVwc0pp3ZHkKUmafuzBkiRNKxHxyohYPih0HvAcsCwizs5ivwH8R7b8BKVeKID/OsamO8iGFUbErIg4PoutiohFWbw+Is4ox3FIkqYmCyxJ0nRzLHBzRGyJiE3AOcDVwAeAr2Y9Wv3AP2TrfwL4u4j4DqVrqEbzu8AvZe2/D7wmpbQF+Bjw7Wxfd1KaZEOSNENFSinvHCRJkiRpWrAHS5IkSZLKxAJLkiRJksrEAkuSJEmSysQCS5IkSZLKxAJLkiRJksrEAkuSJEmSysQCS5IkSZLK5P8Hm3/Y6YoKtJIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source vs Price\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Source\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4c3f44b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0               0             0               0              0\n",
       "1               0             0               1              0\n",
       "2               0             1               0              0\n",
       "3               0             0               1              0\n",
       "4               0             0               0              0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Source is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Source = train_data[[\"Source\"]]\n",
    "Source = pd.get_dummies(Source, drop_first= True)\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "77f71fb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b5386471",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As Destination is Nominal Categorical data we will perform OneHotEncoding\n",
    "\n",
    "Destination = train_data[[\"Destination\"]]\n",
    "Destination = pd.get_dummies(Destination, drop_first = True)\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c9ca5d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minutes</th>\n",
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       "      <th>Dept_minutes</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
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       "      <td>Banglore</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</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>1.0</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</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>2.0</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  day  month  year  \\\n",
       "0       IndiGo  Banglore   New Delhi          0.0   3897   24      3  2019   \n",
       "1    Air India   Kolkata    Banglore          1.0   7662    1      5  2019   \n",
       "2  Jet Airways     Delhi      Cochin          1.0  13882    9      6  2019   \n",
       "3       IndiGo   Kolkata    Banglore          2.0   6218   12      5  2019   \n",
       "4       IndiGo  Banglore   New Delhi          2.0  13302    1      3  2019   \n",
       "\n",
       "   Arrival_hour  Arrival_minutes  Dept_hour  Dept_minutes  Duration_hours  \\\n",
       "0             1               10         22            20               2   \n",
       "1            13               15          5            50               7   \n",
       "2             4               25          9            25              19   \n",
       "3            23               30         18             5               5   \n",
       "4            21               35         16            50               4   \n",
       "\n",
       "   Duration_minutes  \n",
       "0                50  \n",
       "1                25  \n",
       "2                 0  \n",
       "3                25  \n",
       "4                45  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3cbf5241",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combining Airline,Source and Destination into train_dataset\n",
    "\n",
    "train_dataset = pd.concat([train_data, Airline, Source, Destination], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "27e6f909",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Airline</th>\n",
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       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Delhi</td>\n",
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       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Price  day  month  year  \\\n",
       "0       IndiGo  Banglore   New Delhi          0.0   3897   24      3  2019   \n",
       "1    Air India   Kolkata    Banglore          1.0   7662    1      5  2019   \n",
       "2  Jet Airways     Delhi      Cochin          1.0  13882    9      6  2019   \n",
       "3       IndiGo   Kolkata    Banglore          2.0   6218   12      5  2019   \n",
       "4       IndiGo  Banglore   New Delhi          2.0  13302    1      3  2019   \n",
       "\n",
       "   Arrival_hour  Arrival_minutes  ...  Airline_Vistara Premium economy  \\\n",
       "0             1               10  ...                                0   \n",
       "1            13               15  ...                                0   \n",
       "2             4               25  ...                                0   \n",
       "3            23               30  ...                                0   \n",
       "4            21               35  ...                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7074af6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset.drop([\"Airline\", \"Source\", \"Destination\",\"year\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e4e40369",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>45</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Price  day  month  Arrival_hour  Arrival_minutes  Dept_hour  \\\n",
       "0          0.0   3897   24      3             1               10         22   \n",
       "1          1.0   7662    1      5            13               15          5   \n",
       "2          1.0  13882    9      6             4               25          9   \n",
       "3          2.0   6218   12      5            23               30         18   \n",
       "4          2.0  13302    1      3            21               35         16   \n",
       "\n",
       "   Dept_minutes  Duration_hours  Duration_minutes  ...  \\\n",
       "0            20               2                50  ...   \n",
       "1            50               7                25  ...   \n",
       "2            25              19                 0  ...   \n",
       "3             5               5                25  ...   \n",
       "4            50               4                45  ...   \n",
       "\n",
       "   Airline_Vistara Premium economy  Source_Chennai  Source_Delhi  \\\n",
       "0                                0               0             0   \n",
       "1                                0               0             0   \n",
       "2                                0               0             1   \n",
       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "\n",
       "   Source_Kolkata  Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0               0              0                   0                  0   \n",
       "1               1              0                   0                  0   \n",
       "2               0              0                   1                  0   \n",
       "3               1              0                   0                  0   \n",
       "4               0              0                   0                  0   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  Destination_New Delhi  \n",
       "0                      0                    0                      1  \n",
       "1                      0                    0                      0  \n",
       "2                      0                    0                      0  \n",
       "3                      0                    0                      0  \n",
       "4                      0                    0                      1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "913e5264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f88db1b4",
   "metadata": {},
   "source": [
    "# Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "617333f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_excel('Test_set.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3e458d7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "648108ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jet Airways                          897\n",
      "IndiGo                               511\n",
      "Air India                            440\n",
      "Multiple carriers                    347\n",
      "SpiceJet                             208\n",
      "Vistara                              129\n",
      "Air Asia                              86\n",
      "GoAir                                 46\n",
      "Multiple carriers Premium economy      3\n",
      "Vistara Premium economy                2\n",
      "Jet Airways Business                   2\n",
      "Name: Airline, dtype: int64 \n",
      "\n",
      "Delhi       1145\n",
      "Kolkata      710\n",
      "Banglore     555\n",
      "Mumbai       186\n",
      "Chennai       75\n",
      "Name: Source, dtype: int64 \n",
      "\n",
      "Cochin       1145\n",
      "Banglore      710\n",
      "Delhi         317\n",
      "New Delhi     238\n",
      "Hyderabad     186\n",
      "Kolkata        75\n",
      "Name: Destination, dtype: int64 \n",
      "\n",
      "2.0    5625\n",
      "0.0    3491\n",
      "1.0    1520\n",
      "3.0      45\n",
      "4.0       1\n",
      "Name: Total_Stops, dtype: int64 \n",
      "\n",
      "No info                         2148\n",
      "In-flight meal not included      444\n",
      "No check-in baggage included      76\n",
      "1 Long layover                     1\n",
      "Business class                     1\n",
      "Change airports                    1\n",
      "Name: Additional_Info, dtype: int64 \n",
      "\n",
      "(2671, 28)\n",
      "Index(['Total_Stops', 'Journey_day', 'Journey_month', 'Arrival_hour',\n",
      "       'Arrival_minute', 'Dep_hour', 'Dep_minute', 'Duration_hours',\n",
      "       'Duration_minutes', 'Air India', 'GoAir', 'IndiGo', 'Jet Airways',\n",
      "       'Jet Airways Business', 'Multiple carriers',\n",
      "       'Multiple carriers Premium economy', 'SpiceJet', 'Vistara',\n",
      "       'Vistara Premium economy', 'Chennai', 'Delhi', 'Kolkata', 'Mumbai',\n",
      "       'Cochin', 'Delhi', 'Hyderabad', 'Kolkata', 'New Delhi'],\n",
      "      dtype='object')\n",
      "Index(['Airline', 'Source', 'Destination', 'Total_Stops', 'Price', 'day',\n",
      "       'month', 'year', 'Arrival_hour', 'Arrival_minutes', 'Dept_hour',\n",
      "       'Dept_minutes', 'Duration_hours', 'Duration_minutes'],\n",
      "      dtype='object')\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>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_minutes</th>\n",
       "      <th>Air India</th>\n",
       "      <th>...</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Arrival_hour  Arrival_minute  \\\n",
       "0            1            6              6             4              25   \n",
       "1            1           12              5            10              20   \n",
       "2            1           21              5            19               0   \n",
       "3            1           21              5            21               0   \n",
       "4            0           24              6             2              45   \n",
       "\n",
       "   Dep_hour  Dep_minute  Duration_hours  Duration_minutes  Air India  ...  \\\n",
       "0        17          30              10                55          0  ...   \n",
       "1         6          20               4                 0          0  ...   \n",
       "2        19          15              23                45          0  ...   \n",
       "3         8           0              13                 0          0  ...   \n",
       "4        23          55               2                50          0  ...   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making changes save as we have done on train dataset \n",
    "\n",
    "test_data.shape\n",
    "\n",
    "#checking number of missing values\n",
    "test_data.isnull().sum()\n",
    "\n",
    "#Route--> null values=1 and Total_Stops--> null values=1, so we can ignore them\n",
    "test_data.dropna(inplace=True)\n",
    "\n",
    "# check again\n",
    "test_data.isnull().sum()\n",
    "\n",
    "#statistical analysis(description)\n",
    "test_data.describe()\n",
    "\n",
    "\n",
    "# EDA\n",
    "\n",
    "#converting to 'datetime'\n",
    "test_data['Date_of_Journey'] = pd.to_datetime(test_data.Date_of_Journey, format='%d/%m/%Y')\n",
    "\n",
    "#adding new columns(Journey_day, Journey_month, Journey_year) out of column(Date_of_Journey)\n",
    "#extracting day\n",
    "test_data['Journey_day'] = test_data['Date_of_Journey'].dt.day\n",
    "#extracting month\n",
    "test_data['Journey_month'] = test_data['Date_of_Journey'].dt.month\n",
    "\n",
    "#drop column 'Date_of_Journey' as it's of no use now\n",
    "test_data.drop(['Date_of_Journey'], axis=1, inplace=True)\n",
    "\n",
    "#converting 'Arrival_Time' to 'datetime'\n",
    "test_data['Arrival_Time'] = pd.to_datetime(test_data.Arrival_Time)\n",
    "\n",
    "#adding new columns(Arrival_hour, Arrival_minute)\n",
    "#extracting Arrival hour\n",
    "test_data['Arrival_hour'] = test_data['Arrival_Time'].dt.hour\n",
    "#extracting Arrival minute\n",
    "test_data['Arrival_minute'] = test_data['Arrival_Time'].dt.minute\n",
    "\n",
    "#drop column 'Arrival_Time' as it's of no use now\n",
    "test_data.drop(['Arrival_Time'], axis=1, inplace=True)\n",
    "\n",
    "#converting 'Dep_Time' to 'datetime'\n",
    "test_data['Dep_Time'] = pd.to_datetime(test_data.Dep_Time)\n",
    "\n",
    "#adding new columns(Dep_hour, Dep_minute)\n",
    "#extracting Depature hour\n",
    "test_data['Dep_hour'] = test_data['Dep_Time'].dt.hour\n",
    "#extracting Departure minute\n",
    "test_data['Dep_minute'] = test_data['Dep_Time'].dt.minute\n",
    "\n",
    "#drop column 'Dep_Time' as it's of no use now\n",
    "test_data.drop(['Dep_Time'], axis=1, inplace=True)\n",
    "\n",
    "# 'Duration'--> Time taken by plane to reach destination.\n",
    "# It is the differnce between Departure time and Arrival time\n",
    "\n",
    "# Assigning and converting Duration column into list to extract values one-by-one in for-loop\n",
    "duration = list(test_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute #why strip is used here?\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration\n",
    "    \n",
    "#adding columns 'Duration_hours' and 'Duration_minutes' into dataset 'train_data'\n",
    "test_data['Duration_hours'] = duration_hours\n",
    "test_data['Duration_minutes'] = duration_mins\n",
    "\n",
    "#drop column 'Duration' as it's of no use now\n",
    "test_data.drop(['Duration'], axis=1, inplace=True)\n",
    "\n",
    "\n",
    "#HANDLING CATEGORICAL DATA\n",
    "\n",
    "#checking distribution of categorical data(Airline)\n",
    "print(test_data.Airline.value_counts(),\"\\n\")\n",
    "\n",
    "#As Airline--> Nominal Categorical Data, so we perform 'OneHotEncoding'\n",
    "#make a dataframe named 'Airline'\n",
    "Airline = test_data['Airline']\n",
    "Airline = pd.get_dummies(Airline, drop_first=True)\n",
    "\n",
    "#checking distribution of categorical data(Source)\n",
    "print(test_data.Source.value_counts(),\"\\n\")\n",
    "\n",
    "#As Source--> Nominal Categorical Data, so we perform 'OneHotEncoding'\n",
    "#make a dataframe named 'Source'\n",
    "Source = test_data['Source']\n",
    "Source = pd.get_dummies(Source, drop_first=True)\n",
    "\n",
    "#checking distribution of categorical data(Destination)\n",
    "print(test_data.Destination.value_counts(),\"\\n\")\n",
    "\n",
    "#As Destination--> Nominal Categorical Data, so we perform 'OneHotEncoding'\n",
    "#make a dataframe named 'Destination'\n",
    "Destination = test_data['Destination']\n",
    "Destination = pd.get_dummies(Destination, drop_first=True)\n",
    "\n",
    "#checking distribution of categorical data(Route)\n",
    "test_data['Route'].value_counts()\n",
    "\n",
    "#as 'Total_Stops' and 'Route' are related(dependent on) to each other, so we can drop 'Route'.\n",
    "test_data.drop(['Route'], axis=1, inplace=True)\n",
    "\n",
    "#checking distribution of categorical data(Total_Stops)\n",
    "print(train_data.Total_Stops.value_counts(),\"\\n\")\n",
    "\n",
    "#As Total_Stops --> Ordinal Categorical Data, so we perform 'LabelEncoding'\n",
    "#we assign values in 'Total_Stops' with corresponding keys.\n",
    "test_data.replace({'Total_Stops':{'non-stop': 0, '1 stop': 1, '2 stops': 2, '3 stops': 3, '4 stops': 4}},inplace=True)\n",
    "test_data.head()\n",
    "\n",
    "#checking distribution of categorical data(Additional_Info)\n",
    "print(test_data.Additional_Info.value_counts(),\"\\n\")\n",
    "\n",
    "#as 80% of information in 'Additional_Info' is 'No info', hence we can drop this column\n",
    "test_data.drop(['Additional_Info'], axis=1, inplace=True)\n",
    "\n",
    "#conactenate dataframe--> (train_data, Airline, Source, Destination)\n",
    "test_data = pd.concat([test_data, Airline, Source, Destination], axis=1)\n",
    "\n",
    "\n",
    "#drop columns 'Airline, Source, Destination' as these are now of no further use\n",
    "test_data.drop(['Airline', 'Source', 'Destination'], axis=1, inplace=True)\n",
    "\n",
    "#shape of dataframe 'train_data'\n",
    "print(test_data.shape)\n",
    "\n",
    "#check which two(30-28=2) columns are missing in 'test_data'\n",
    "print(test_data.columns)\n",
    "print(train_data.columns)\n",
    "\n",
    "\n",
    "#print first 5 rows\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4df83c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_minutes</th>\n",
       "      <th>Air India</th>\n",
       "      <th>...</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Journey_day  Journey_month  Arrival_hour  Arrival_minute  \\\n",
       "0            1            6              6             4              25   \n",
       "1            1           12              5            10              20   \n",
       "2            1           21              5            19               0   \n",
       "3            1           21              5            21               0   \n",
       "4            0           24              6             2              45   \n",
       "\n",
       "   Dep_hour  Dep_minute  Duration_hours  Duration_minutes  Air India  ...  \\\n",
       "0        17          30              10                55          0  ...   \n",
       "1         6          20               4                 0          0  ...   \n",
       "2        19          15              23                45          0  ...   \n",
       "3         8           0              13                 0          0  ...   \n",
       "4        23          55               2                50          0  ...   \n",
       "\n",
       "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
       "0                        0        0      1        0       0       1      0   \n",
       "1                        0        0      0        1       0       0      0   \n",
       "2                        0        0      1        0       0       1      0   \n",
       "3                        0        0      1        0       0       1      0   \n",
       "4                        0        0      0        0       0       0      1   \n",
       "\n",
       "   Hyderabad  Kolkata  New Delhi  \n",
       "0          0        0          0  \n",
       "1          0        0          0  \n",
       "2          0        0          0  \n",
       "3          0        0          0  \n",
       "4          0        0          0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3adfa793",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "767aa387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 30)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ba3b598c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop('year', inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ddda5b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Price', 'day', 'month', 'Arrival_hour',\n",
       "       'Arrival_minutes', 'Dept_hour', 'Dept_minutes', 'Duration_hours',\n",
       "       'Duration_minutes', 'Airline_Air India', 'Airline_GoAir',\n",
       "       'Airline_IndiGo', 'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
       "       'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
       "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
       "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
       "       'Destination_Kolkata', 'Destination_New Delhi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c573db60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3897\n",
       "1     7662\n",
       "2    13882\n",
       "3     6218\n",
       "4    13302\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#divide 'train_data' into 'X=independent feature(columns)' and 'Y=dependent/target feature'\n",
    "X = train_dataset.drop(['Price'], axis=1)\n",
    "Y = train_dataset['Price']\n",
    "\n",
    "#print X and Y\n",
    "X.head()\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1f79b72b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find relation between independent and dependent attributes\n",
    "#heatmap ************(learn it)\n",
    "\n",
    "plt.figure(figsize=(18,18))\n",
    "sns.heatmap(train_data.corr(), annot = True, cmap = \"magma\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9fed5154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extracting important features using 'ExtraTreesRegressor'\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "select = ExtraTreesRegressor()\n",
    "select.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b4b20f72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.92462286e-01 1.44802504e-01 5.29647747e-02 2.78621516e-02\n",
      " 1.95457825e-02 2.37053974e-02 2.12640869e-02 1.36869922e-01\n",
      " 1.75916454e-02 1.41059266e-02 2.17473589e-03 2.24854782e-02\n",
      " 1.42440673e-01 6.79792742e-02 1.55927948e-02 7.93057163e-04\n",
      " 3.98838869e-03 1.04448770e-04 4.65942149e-03 8.79558701e-05\n",
      " 4.05685966e-04 1.25669574e-02 3.14458905e-03 7.56812091e-03\n",
      " 1.26987684e-02 1.81480778e-02 8.42220838e-03 4.29560652e-04\n",
      " 2.51353254e-02]\n"
     ]
    }
   ],
   "source": [
    "#selection_importances_\n",
    "print(select.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e1f25953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of 'feature_importances_' for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "fig_importances = pd.Series(select.feature_importances_, index=X.columns)\n",
    "fig_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233e61f6",
   "metadata": {},
   "source": [
    "# Fitting Model Using Random Forest\n",
    "Split data into train and test so as to predict w.r.t. 'X_test'.\n",
    "\n",
    "If needed do scaling of data a) scaling is not done in Random Forest\n",
    "\n",
    "Import the model(which suits the data most or gives better results)\n",
    "Fit training data into it\n",
    "\n",
    "Predict w.r.t. 'X_test' (compare with 'Y_test')\n",
    "\n",
    "In regression models check RMSE score\n",
    "\n",
    "Plot final graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "197f9e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import 'train_test_split' \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=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "91540272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import 'RandomForestRegressor'\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "#abbrevate\n",
    "RF_reg = RandomForestRegressor()\n",
    "#fit training data into model\n",
    "RF_reg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "51fcf748",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict Y using 'X_test'\n",
    "Y_pred = RF_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3d8ce78b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9534970043331872"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for training set\n",
    "RF_reg.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "32e47b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7947043310520321"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for testing set\n",
    "RF_reg.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e1956197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot between 'Y_test' and 'Y_pred'\n",
    "sns.distplot(Y_test-Y_pred)\n",
    "plt.xlabel('Y_test')\n",
    "plt.ylabel('Y_pred')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9d0dc5c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot between 'Y_test' and 'Y_pred'\n",
    "plt.scatter(Y_test, Y_pred, alpha=0.5)\n",
    "plt.xlabel('Y_test')\n",
    "plt.ylabel('Y_pred')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e79dead9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1183.0233939907127\n",
      "MSE: 4426596.776962555\n",
      "RMSE: 2103.947902625575\n"
     ]
    }
   ],
   "source": [
    "#import metrics\n",
    "from sklearn import metrics\n",
    "\n",
    "# calculate MAE, MSE, RMSE\n",
    "print('MAE:', metrics.mean_absolute_error(Y_test, Y_pred))\n",
    "print('MSE:', metrics.mean_squared_error(Y_test, Y_pred))\n",
    "rmse = np.sqrt(metrics.mean_squared_error(Y_test, Y_pred))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "967e165e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02705937909309705"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate RMSE/(max(DV)-min(DV))\n",
    "rmse/(max(Y)-min(Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cea072b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7947043310520321\n"
     ]
    }
   ],
   "source": [
    "# calculate r2_score\n",
    "r2 = metrics.r2_score(Y_test, Y_pred)\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "455f2d14",
   "metadata": {},
   "source": [
    " ## Hyperparameter Tuning\n",
    "\n",
    "\n",
    "* Choose following method for hyperparameter tuning\n",
    "    1. **RandomizedSearchCV** --> Fast\n",
    "    2. **GridSearchCV**\n",
    "* Assign hyperparameters in form of dictionery\n",
    "* Fit the model\n",
    "* Check best paramters and best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8e91561b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1a383a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "45a1d257",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 5 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, train_test_split\n",
    "rf_random = RandomizedSearchCV(estimator = RF_reg, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8327ea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   7.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   6.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   7.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   7.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   7.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  11.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  10.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  11.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  11.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=  11.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   6.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   4.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   3.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   8.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   6.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  10.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  11.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  10.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   9.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  10.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  10.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=  10.5s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   8.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.5s\n"
     ]
    }
   ],
   "source": [
    "rf_random.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d72fde1",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9ad9fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_random.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64566b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf9bd337",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sns.distplot(Y_test-prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "661cd760",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "plt.scatter(Y_test, prediction, alpha = 0.5)\n",
    "plt.xlabel(\"Y_test\")\n",
    "plt.ylabel(\"Y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eb09b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(Y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(Y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(Y_test, prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc308231",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f35ceb94",
   "metadata": {},
   "source": [
    "# Save model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5b2e670",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('flight_rf.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d03c59b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('flight_rf.pkl','rb')\n",
    "forest = pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ec68f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_prediction = forest.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce069988",
   "metadata": {},
   "outputs": [],
   "source": [
    "# r2-value is increased\n",
    "metrics.r2_score(Y_test, Y_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd3712c4",
   "metadata": {},
   "source": [
    "Conclusion : \n",
    "    The model as performed well when we use Randon Forest Regression giving us a good result of 0.81"
   ]
  }
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