ML_Flight_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a46b8b2c",
   "metadata": {},
   "source": [
    "#### importing all the necessary libraries !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9bc16c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17cbdf6d",
   "metadata": {},
   "source": [
    "#### Importing dataset\n",
    "    1.Since data is in form of excel file we have to use pandas read_excel to load the data\n",
    "    2.After loading it is important to check null values in a column or a row\n",
    "    3.If it is present then following can be done,\n",
    "        a.Filling NaN values with mean, median and mode using fillna() method\n",
    "        b.If Less missing values, we can drop it as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8655a7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.read_excel('Data_Train.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4af752aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>09:00</td>\n",
       "      <td>11:25</td>\n",
       "      <td>2h 25m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>18:55</td>\n",
       "      <td>10:25 13 Mar</td>\n",
       "      <td>15h 30m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11087</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",
       "5     SpiceJet      24/06/2019   Kolkata    Banglore              CCU → BLR   \n",
       "6  Jet Airways      12/03/2019  Banglore   New Delhi        BLR → BOM → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop                      No info   \n",
       "1    05:50         13:15   7h 25m     2 stops                      No info   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops                      No info   \n",
       "3    18:05         23:30   5h 25m      1 stop                      No info   \n",
       "4    16:50         21:35   4h 45m      1 stop                      No info   \n",
       "5    09:00         11:25   2h 25m    non-stop                      No info   \n",
       "6    18:55  10:25 13 Mar  15h 30m      1 stop  In-flight meal not included   \n",
       "\n",
       "   Price  \n",
       "0   3897  \n",
       "1   7662  \n",
       "2  13882  \n",
       "3   6218  \n",
       "4  13302  \n",
       "5   3873  \n",
       "6  11087  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dda2961e",
   "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": "2b27c166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "171487eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Route              1\n",
       "Total_Stops        1\n",
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d57f98b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9039</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>09:45</td>\n",
       "      <td>09:25 07 May</td>\n",
       "      <td>23h 40m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No info</td>\n",
       "      <td>7480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline Date_of_Journey Source Destination Route Dep_Time  \\\n",
       "9039  Air India       6/05/2019  Delhi      Cochin   NaN    09:45   \n",
       "\n",
       "      Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "9039  09:25 07 May  23h 40m         NaN         No info   7480  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### getting all the rows where we have missing value\n",
    "train_data[train_data['Total_Stops'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fefda914",
   "metadata": {},
   "source": [
    "#### as we have 1 missing value , I can directly drop these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6df2912",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6fd3d49e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a9f60a",
   "metadata": {},
   "source": [
    "## Pre-process & Perform Featurization of \"Date_of_Journey\"\n",
    "    ie pre-process it & extract day,month,year from \"Date_of_Journey\" feature.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34648527",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=train_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cac7f3ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0     IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1  Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e321d17e",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c47ae740",
   "metadata": {},
   "source": [
    "we can see that Date_of_Journey is a object data type,\n",
    "Therefore, we have to convert this datatype into timestamp bcz our \n",
    "model will not be able to understand these string values,it just understand Time-stamp..\n",
    "\n",
    "For this we require pandas to_datetime to convert object data type to datetime dtype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8825899e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_into_datetime(col):\n",
    "    data[col]=pd.to_datetime(data[col], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "674605ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
       "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops',\n",
       "       'Additional_Info', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d8b558a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in ['Date_of_Journey','Dep_Time', 'Arrival_Time']:\n",
    "    change_into_datetime(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c4119e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                    object\n",
       "Date_of_Journey    datetime64[ns]\n",
       "Source                     object\n",
       "Destination                object\n",
       "Route                      object\n",
       "Dep_Time           datetime64[ns]\n",
       "Arrival_Time       datetime64[ns]\n",
       "Duration                   object\n",
       "Total_Stops                object\n",
       "Additional_Info            object\n",
       "Price                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "883f00d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-03-01 00:00:00')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date_of_Journey'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f96b7c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-06-27 00:00:00')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date_of_Journey'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2afc92b",
   "metadata": {},
   "source": [
    "**It means our data belongs to 2019 year only, hence extracting year feature & consider this as a input to my machine learning model makes no sense !\n",
    "But if we have more than 1 year  , then of-course it may impact !**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e492e500",
   "metadata": {},
   "source": [
    "## lets do Feature Engineering of \"Date_of_Journey\" & fetch day,month,year !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a19d931",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['journey_day']=data['Date_of_Journey'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2953c1b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['journey_month']=data['Date_of_Journey'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b0d9342",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['journey_year']=data['Date_of_Journey'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7759f5e7",
   "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>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>journey_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2023-01-06 22:20:00</td>\n",
       "      <td>2023-03-22 01:10:00</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2023-01-06 05:50:00</td>\n",
       "      <td>2023-01-06 13:15:00</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0     IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
       "1  Air India      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "\n",
       "             Dep_Time        Arrival_Time Duration Total_Stops  \\\n",
       "0 2023-01-06 22:20:00 2023-03-22 01:10:00   2h 50m    non-stop   \n",
       "1 2023-01-06 05:50:00 2023-01-06 13:15:00   7h 25m     2 stops   \n",
       "\n",
       "  Additional_Info  Price  journey_day  journey_month  journey_year  \n",
       "0         No info   3897           24              3          2019  \n",
       "1         No info   7662            1              5          2019  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0d988395",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Date_of_Journey',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0d7fbc13",
   "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>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>journey_year</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>2023-01-06 22:20:00</td>\n",
       "      <td>2023-03-22 01:10:00</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2023-01-06 05:50:00</td>\n",
       "      <td>2023-01-06 13:15:00</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Airline    Source Destination                  Route            Dep_Time  \\\n",
       "0     IndiGo  Banglore   New Delhi              BLR → DEL 2023-01-06 22:20:00   \n",
       "1  Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR 2023-01-06 05:50:00   \n",
       "\n",
       "         Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0 2023-03-22 01:10:00   2h 50m    non-stop         No info   3897   \n",
       "1 2023-01-06 13:15:00   7h 25m     2 stops         No info   7662   \n",
       "\n",
       "   journey_day  journey_month  journey_year  \n",
       "0           24              3          2019  \n",
       "1            1              5          2019  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d03969",
   "metadata": {},
   "source": [
    "## Lets try to clean Dep_Time & Arrival_Time & featurize it..¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "419c541b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hour_min(df,col):\n",
    "    df[col+'_hour']=df[col].dt.hour\n",
    "    df[col+'_minute']=df[col].dt.minute\n",
    "    df.drop(col,axis=1,inplace=True)\n",
    "    return df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9661c843",
   "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>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>journey_year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</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>2023-03-22 01:10:00</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>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>2023-01-06 13:15:00</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>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Airline    Source Destination                  Route        Arrival_Time  \\\n",
       "0     IndiGo  Banglore   New Delhi              BLR → DEL 2023-03-22 01:10:00   \n",
       "1  Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR 2023-01-06 13:15:00   \n",
       "\n",
       "  Duration Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
       "0   2h 50m    non-stop         No info   3897           24              3   \n",
       "1   7h 25m     2 stops         No info   7662            1              5   \n",
       "\n",
       "   journey_year  Dep_Time_hour  Dep_Time_minute  \n",
       "0          2019             22               20  \n",
       "1          2019              5               50  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Departure time is when a plane leaves the gate\n",
    "\n",
    "extract_hour_min(data,'Dep_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0819bb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>journey_year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</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>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</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",
       "\n",
       "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            1              5   \n",
       "\n",
       "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0          2019             22               20                  1   \n",
       "1          2019              5               50                 13   \n",
       "\n",
       "   Arrival_Time_minute  \n",
       "0                   10  \n",
       "1                   15  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### lets Featurize 'Arrival_Time' !\n",
    "extract_hour_min(data,'Arrival_Time')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67776691",
   "metadata": {},
   "source": [
    "## lets analyse when will most of the flights will take-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9b0413a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Converting the flight Dep_Time into proper time i.e. mid_night, morning, afternoon and evening.\n",
    "\n",
    "def flight_dep_time(x):\n",
    "    '''\n",
    "    This function takes the flight Departure time \n",
    "    and convert into appropriate format.\n",
    "    '''\n",
    "    if ( x> 4) and (x<=8 ):\n",
    "        return 'Early mrng'\n",
    "    \n",
    "    elif ( x>8 ) and (x<=12 ):\n",
    "        return 'Morning'\n",
    "    \n",
    "    elif ( x>12 ) and (x<=16 ):\n",
    "        return 'Noon'\n",
    "    \n",
    "    elif ( x>16 ) and (x<=20 ):\n",
    "        return 'Evening'\n",
    "    \n",
    "    elif ( x>20 ) and (x<=24 ):\n",
    "        return 'Night'\n",
    "    else:\n",
    "        return 'Late night'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2569f96b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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44AOFhITYIy5ut1tVq1aVy+XSyJEjNXHiRDVo0EANGjTQxIkTVa1aNSUnJ9u1AwYM0OjRo1WrVi2Fh4crNTVV8fHx9tVKjRs3Vvfu3TVw4EDNnDlTkvToo48qMTGRK5MAAICkUoaYGTNmSJI6dOjgtX7OnDnq16+fJGnMmDEqLCzUY489pry8PLVu3VqrVq1SSEiIXZ+WlqbAwEA98MADKiwsVOfOnTV37lwFBATYNYsWLdKIESPsq5iSkpI0ffr0q9lHAABQAbksy7KcbuJayM/Pl9vtlsfj8emppXpjV/jsva6VA5PvdbqFK8Kx9A0TjqNkxrEE4LzSfH7z7CQAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADBSoNMNAEB5UW/sCqdbuKwDk+91ugWg3GAkBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYKRSh5iPP/5Yv/3tbxUTEyOXy6V//OMfXtv79esnl8vltbRp08arpqioSMOHD1ft2rVVvXp1JSUl6dtvv/WqycvLU0pKitxut9xut1JSUnT8+PFS7yAAAKiYSh1iTp48qebNm2v69OkXrenevbtycnLsZeXKlV7bR44cqWXLlmnx4sXauHGjCgoKlJiYqOLiYrsmOTlZWVlZSk9PV3p6urKyspSSklLadgEAQAUVWNovSEhIUEJCwiVrgoODFRUVdcFtHo9Hs2fP1oIFC9SlSxdJ0sKFCxUbG6s1a9aoW7du2r17t9LT07V582a1bt1akjRr1iy1bdtWe/bsUaNGjUrbNgAAqGCuyZyYjz76SBEREWrYsKEGDhyoI0eO2NsyMzN1+vRpde3a1V4XExOjuLg4ZWRkSJI2bdokt9ttBxhJatOmjdxut10DAAB+3Uo9EnM5CQkJ+v3vf6+6detq//79evbZZ9WpUydlZmYqODhYubm5CgoKUlhYmNfXRUZGKjc3V5KUm5uriIiIEu8dERFh15yvqKhIRUVF9uv8/Hwf7hUAAChvfB5ievfubf87Li5OLVu2VN26dbVixQr17Nnzol9nWZZcLpf9+pf/vljNL02aNEkTJkwoQ+cAAMAk1/wS6+joaNWtW1d79+6VJEVFRenUqVPKy8vzqjty5IgiIyPtmsOHD5d4r6NHj9o15xs3bpw8Ho+9ZGdn+3hPAABAeXLNQ8yxY8eUnZ2t6OhoSVKLFi1UuXJlrV692q7JycnRzp071a5dO0lS27Zt5fF4tHXrVrtmy5Yt8ng8ds35goODFRoa6rUAAICKq9SnkwoKCrRv3z779f79+5WVlaXw8HCFh4dr/Pjx6tWrl6Kjo3XgwAE9/fTTql27tu6//35Jktvt1oABAzR69GjVqlVL4eHhSk1NVXx8vH21UuPGjdW9e3cNHDhQM2fOlCQ9+uijSkxM5MokAAAg6SpCzLZt29SxY0f79ahRoyRJDz30kGbMmKEdO3Zo/vz5On78uKKjo9WxY0ctWbJEISEh9tekpaUpMDBQDzzwgAoLC9W5c2fNnTtXAQEBds2iRYs0YsQI+yqmpKSkS96bBgAA/LqUOsR06NBBlmVddPu//vWvy75HlSpVNG3aNE2bNu2iNeHh4Vq4cGFp2wMAAL8SPDsJAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMFFjaL/j444/12muvKTMzUzk5OVq2bJnuu+8+e7tlWZowYYLeeust5eXlqXXr1vrzn/+spk2b2jVFRUVKTU3Vu+++q8LCQnXu3FlvvvmmbrjhBrsmLy9PI0aM0PLlyyVJSUlJmjZtmmrWrHn1ewsA8It6Y1c43cJlHZh8r9MtoIxKPRJz8uRJNW/eXNOnT7/g9ldffVVTpkzR9OnT9cknnygqKkr33HOPTpw4YdeMHDlSy5Yt0+LFi7Vx40YVFBQoMTFRxcXFdk1ycrKysrKUnp6u9PR0ZWVlKSUl5Sp2EQAAVESlHolJSEhQQkLCBbdZlqWpU6fqmWeeUc+ePSVJ8+bNU2RkpN555x0NGjRIHo9Hs2fP1oIFC9SlSxdJ0sKFCxUbG6s1a9aoW7du2r17t9LT07V582a1bt1akjRr1iy1bdtWe/bsUaNGja52fwEAQAXh0zkx+/fvV25urrp27WqvCw4O1t13362MjAxJUmZmpk6fPu1VExMTo7i4OLtm06ZNcrvddoCRpDZt2sjtdts1AADg163UIzGXkpubK0mKjIz0Wh8ZGamDBw/aNUFBQQoLCytRc+7rc3NzFRERUeL9IyIi7JrzFRUVqaioyH6dn59/9TsCAADKvWtydZLL5fJ6bVlWiXXnO7/mQvWXep9JkybJ7XbbS2xs7FV0DgAATOHTEBMVFSVJJUZLjhw5Yo/OREVF6dSpU8rLy7tkzeHDh0u8/9GjR0uM8pwzbtw4eTwee8nOzi7z/gAAgPLLpyGmfv36ioqK0urVq+11p06d0oYNG9SuXTtJUosWLVS5cmWvmpycHO3cudOuadu2rTwej7Zu3WrXbNmyRR6Px645X3BwsEJDQ70WAABQcZV6TkxBQYH27dtnv96/f7+ysrIUHh6uOnXqaOTIkZo4caIaNGigBg0aaOLEiapWrZqSk5MlSW63WwMGDNDo0aNVq1YthYeHKzU1VfHx8fbVSo0bN1b37t01cOBAzZw5U5L06KOPKjExkSuTAACApKsIMdu2bVPHjh3t16NGjZIkPfTQQ5o7d67GjBmjwsJCPfbYY/bN7latWqWQkBD7a9LS0hQYGKgHHnjAvtnd3LlzFRAQYNcsWrRII0aMsK9iSkpKuui9aQAAwK9PqUNMhw4dZFnWRbe7XC6NHz9e48ePv2hNlSpVNG3aNE2bNu2iNeHh4Vq4cGFp2wMAAL8SPDsJAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABG8nmIGT9+vFwul9cSFRVlb7csS+PHj1dMTIyqVq2qDh066IsvvvB6j6KiIg0fPly1a9dW9erVlZSUpG+//dbXrQIAAINdk5GYpk2bKicnx1527Nhhb3v11Vc1ZcoUTZ8+XZ988omioqJ0zz336MSJE3bNyJEjtWzZMi1evFgbN25UQUGBEhMTVVxcfC3aBQAABgq8Jm8aGOg1+nKOZVmaOnWqnnnmGfXs2VOSNG/ePEVGRuqdd97RoEGD5PF4NHv2bC1YsEBdunSRJC1cuFCxsbFas2aNunXrdi1aBgAAhrkmIzF79+5VTEyM6tevrz/84Q/65ptvJEn79+9Xbm6uunbtatcGBwfr7rvvVkZGhiQpMzNTp0+f9qqJiYlRXFycXXMhRUVFys/P91oAAEDF5fMQ07p1a82fP1//+te/NGvWLOXm5qpdu3Y6duyYcnNzJUmRkZFeXxMZGWlvy83NVVBQkMLCwi5acyGTJk2S2+22l9jYWB/vGQAAKE98HmISEhLUq1cvxcfHq0uXLlqxYoWkn08bneNyuby+xrKsEuvOd7macePGyePx2Et2dnYZ9gIAAJR31/wS6+rVqys+Pl579+6158mcP6Jy5MgRe3QmKipKp06dUl5e3kVrLiQ4OFihoaFeCwAAqLiueYgpKirS7t27FR0drfr16ysqKkqrV6+2t586dUobNmxQu3btJEktWrRQ5cqVvWpycnK0c+dOuwYAAMDnVyelpqbqt7/9rerUqaMjR47opZdeUn5+vh566CG5XC6NHDlSEydOVIMGDdSgQQNNnDhR1apVU3JysiTJ7XZrwIABGj16tGrVqqXw8HClpqbap6cAAPi1qDd2hdMtXJEDk+915Pv6PMR8++23evDBB/Xf//5X1113ndq0aaPNmzerbt26kqQxY8aosLBQjz32mPLy8tS6dWutWrVKISEh9nukpaUpMDBQDzzwgAoLC9W5c2fNnTtXAQEBvm4XAAAYyuchZvHixZfc7nK5NH78eI0fP/6iNVWqVNG0adM0bdo0H3cHAAAqCp6dBAAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARir3IebNN99U/fr1VaVKFbVo0UL//ve/nW4JAACUA+U6xCxZskQjR47UM888o+3bt+vOO+9UQkKCDh065HRrAADAYeU6xEyZMkUDBgzQI488osaNG2vq1KmKjY3VjBkznG4NAAA4LNDpBi7m1KlTyszM1NixY73Wd+3aVRkZGSXqi4qKVFRUZL/2eDySpPz8fJ/2dbboR5++37Xg632+VjiWvmHCcZQ4lr5iwnGUOJa+YsJxlHx7LM+9l2VZly+2yqnvvvvOkmT95z//8Vr/8ssvWw0bNixR//zzz1uSWFhYWFhYWCrAkp2dfdmsUG5HYs5xuVxery3LKrFOksaNG6dRo0bZr8+ePasffvhBtWrVumB9eZGfn6/Y2FhlZ2crNDTU6XaMxXH0HY6l73AsfYPj6DsmHEvLsnTixAnFxMRctrbchpjatWsrICBAubm5XuuPHDmiyMjIEvXBwcEKDg72WlezZs1r2aJPhYaGltsfKJNwHH2HY+k7HEvf4Dj6Tnk/lm63+4rqyu3E3qCgILVo0UKrV6/2Wr969Wq1a9fOoa4AAEB5UW5HYiRp1KhRSklJUcuWLdW2bVu99dZbOnTokAYPHux0awAAwGHlOsT07t1bx44d0wsvvKCcnBzFxcVp5cqVqlu3rtOt+UxwcLCef/75EqfCUDocR9/hWPoOx9I3OI6+U9GOpcuyruQaJgAAgPKl3M6JAQAAuBRCDAAAMBIhBgAAGIkQAwBABXXo0KEL3r7fsqwK8TBlJvYCAFBBBQQEKCcnRxEREV7rjx07poiICBUXFzvUmW+U60usK6KwsLALPgbB5XKpSpUquvnmm9WvXz89/PDDDnQHAKhILvaonoKCAlWpUsWBjnyLEONnzz33nF5++WUlJCSoVatWsixLn3zyidLT0zV06FDt379fQ4YM0ZkzZzRw4ECn2wUAx1T0UYRr6dyzBF0ul5599llVq1bN3lZcXKwtW7bolltucag73yHE+NnGjRv10ksvlbjr8MyZM7Vq1Sq9//77atasmf70pz8RYq7An/70pwuu/+XI1l133aWAgAA/d2YWRgh95/Dhw0pNTdXatWt15MiREvMR+OC9cheb7VBUVKSgoCA/d2OW7du3S/r5GO7YscPreAUFBal58+ZKTU11qj2fYU6Mn9WoUUNZWVm6+eabvdbv27dPt9xyiwoKCvT111+rWbNmOnnypENdmqN+/fo6evSofvzxR4WFhcmyLB0/flzVqlVTjRo1dOTIEd14441av369YmNjnW633EpLS7voCOETTzyh/fv3a8GCBZo2bRrh+jISEhJ06NAhDRs2TNHR0SXC4e9+9zuHOjPHuT9OnnjiCb344ouqUaOGva24uFgff/yxDhw4YH9Q4+IefvhhvfHGG+X6YY9lYsGvYmNjrSlTppRYP2XKFCs2NtayLMv67LPPrMjISH+3ZqR33nnH6tChg7Vv3z573d69e61OnTpZixcvtrKzs6327dtbvXr1crDL8q9nz57WjBkzSqz/y1/+YvXs2dOyLMv605/+ZMXFxfm7NePUqFHD2r59u9NtGK1evXpWvXr1LJfLZcXGxtqv69WrZzVs2NDq2rWrtXnzZqfbRDnASIyfzZo1S0OGDFGPHj3UqlUruVwubd26VStXrtRf/vIXDRgwQK+//rq2bt2qJUuWON1uuXfTTTfp/fffL3Fud/v27erVq5e++eYbZWRkqFevXsrJyXGmSQMwQug7TZo00aJFi3Trrbc63YrxOnbsqKVLlyosLMzpVox18uRJTZ482T69efbsWa/t33zzjUOd+QZzYvxs4MCBatKkiaZPn66lS5fKsiz95je/0YYNG9SuXTtJ0ujRox3u0hw5OTk6c+ZMifVnzpxRbm6uJCkmJkYnTpzwd2tGCQ8P14cffqgnnnjCa/2HH36o8PBwST//ZxgSEuJEe0aZOnWqxo4dq5kzZ6pevXpOt2O09evXO92C8R555BFt2LBBKSkpFzy9aTpCjAPat2+v9u3bO91GhdCxY0cNGjRIf/3rX+2/fLdv364hQ4aoU6dOkqQdO3aofv36TrZZ7j377LMaMmSI1q9ff8ERQklavXq17r77boc7Lf969+6tH3/8UTfddJOqVaumypUre23/4YcfHOrMPMXFxZo7d+5FRxHWrVvnUGfm+L//+z+tWLGiwn7mEGIccPbsWe3bt++Cv5R33XWXQ12Zafbs2UpJSVGLFi3sD4szZ86oc+fOmj17tqSfT5W8/vrrTrZZ7jFC6DtTp051uoUK4/HHH9fcuXN17733Ki4ursKNIvhDWFiYPZpaETEnxs82b96s5ORkHTx4sMTlgy6Xi8svr9KXX36pr776yv7wbdSokdMtASij2rVra/78+erRo4fTrRhr4cKF+uCDDzRv3jyve8VUFIQYP7vlllvUsGFDTZgw4YLnJ91ut0Od4deOEULfKS4u1j/+8Q/t3r1bLpdLTZo0UVJSEvcrKqWYmBh99NFHatiwodOtGOXWW2/1+mzZt2+fLMtSvXr1Spze/PTTT/3dnk9xOsnP9u7dq7///e8lrgLB1eGcuW8wQug7+/btU48ePfTdd9+pUaNGsixLX331lWJjY7VixQrddNNNTrdojNGjR+uNN97Q9OnTOZVUCvfdd5/TLfgNIzF+1qlTJ40ZM0bdu3d3upUKYdiwYfY58wuNbKWlpTnUmVkYIfSdHj16yLIsLVq0yJ6LcOzYMf3xj39UpUqVtGLFCoc7LN969uzp9XrdunUKDw9X06ZNS4wiLF261J+toRwixPjZsmXL9P/+3//Tk08+qfj4+BK/lM2aNXOoMzNxztw3qlevrs8++4wRQh+oXr26Nm/erPj4eK/1n332mdq3b6+CggKHOjNDaR5tMWfOnGvYCUzA6SQ/69WrlySpf//+9jqXy2U/aZRh+9IJCgrig9cHWrdurX379nEsfSA4OPiC9yUqKCjgeT9XgGDiWxX9uWiEGD/bv3+/0y1UKJwz943hw4dr9OjRys3NZYSwjBITE/Xoo49q9uzZatWqlSRpy5YtGjx4sJKSkhzuDr82zz333EWfizZ06FDt379fQ4YM0ZkzZ4x8Lhqnk/zo9OnTatSokf75z3+qSZMmTrdTIdx///1av34958zLqFKlSiXWMUJ4dY4fP66HHnpIH374ode9i5KSkjR37lzmF5XC+VfZnHP+KELHjh0d6M4MvXr10j333KPBgwd7rZ85c6ZWrVql999/X9OmTdNbb72lHTt2ONTl1SPE+Nn111+vNWvWqHHjxk63UiFcbgiUoekrc/DgwUtur1u3rp86qTj27t2r3bt3S/r5eUqcqiu9cePGacaMGYqPj7dHEbZt26bPP/9c/fr1065du7R27VotXbqUp4NfREV/Lhqnk/xs+PDheuWVV/TXv/5VgYEc/rIipPgGIcX3GjRoYH9wcKrz6vz3v//V6NGj9eyzz3qtf+mll3Tw4EGtWrVKzz//vF588UVCzEVU9OeiMRLjZ/fff7/Wrl2rGjVqKD4+XtWrV/fazukP+Mvy5cuVkJCgypUra/ny5ZesZS5H6cyfP1+vvfaa9u7dK0lq2LChnnzySaWkpDjcmVncbrcyMzMvOIrQokULeTweffnll7r99tt5yOtFzJo1S0OGDFGPHj0u+Fy0AQMG6PXXX9fWrVu1ZMkSp9stNYYC/KxmzZr2FUq4OrfddpvWrl2rsLCwi54zP8f0u1FeS/fdd59yc3MVERFxyZtjMSemdKZMmaJnn31Ww4YNU/v27WVZlv7zn/9o8ODB+u9//1viL2JcXJUqVZSRkVEixGRkZKhKlSqSfr7TdHBwsBPtGaGiPxeNEONnnP4ou9/97nf2f1q/pjtT+tov7258/p2OcfWmTZumGTNmqG/fvva63/3ud2ratKnGjx9PiCmF4cOHa/DgwcrMzNTtt99ujyL89a9/1dNPPy1J+te//mU/wR4X1r59+wr7FGtOJwGAD1WpUkU7d+4sMXqwd+9excfH66effnKoMzMtWrRI06dP1549eyRJjRo10vDhw5WcnCxJKiwstK9Wws/y8/MVGhpq//tSztWZihDjZ8eOHdNzzz2n9evXX/BZPz/88INDnZnt1KlTFzyederUcagj86xdu/aiz6B6++23HerKPHFxcUpOTrZHCs556aWXtGTJEiMvY4VZAgIClJOTo4iICFWqVOmCp9wryu0TOJ3kZ3/84x/19ddfa8CAAYqMjOSqhTL66quvNGDAAGVkZHitryi/oP4yYcIEvfDCC2rZsuUFn52EKzdhwgT17t1bH3/8sdq3by+Xy6WNGzdq7dq1+tvf/uZ0e/gVOPe8KUlav369w91cW4zE+FlISIg2btyo5s2bO91KhdC+fXsFBgZq7NixF/zw5ThfmejoaL366qtcPeMjmZmZmjJlir788ktZlqUmTZpo9OjRzN24AuHh4frqq69Uu3bti94y/xxGrsFIjJ/95je/UWFhodNtVBhZWVnKzMzUb37zG6dbMdqpU6fsKxVQdi1atNCiRYucbsNIaWlp9j1L0tLSGBX0gePHj2vr1q0XPFX8ywnoJmIkxs8++eQTjR07Vs8995zi4uJK3Cbf9ElW/nb77bcrLS1Nd9xxh9OtGO2pp55SjRo1StxUDFfuYnMPfsnlcunMmTN+6gj4+aZ2ffr0sW9o98ufUZfLZfxoFiMxflazZk15PB516tTJaz1zOK7OK6+8ojFjxmjixIkXfHAhofDK/PTTT3rrrbe0Zs0aNWvWrMRxnDJlikOdmWPZsmUX3ZaRkaFp06aJvxmvDIHQd0aPHq3+/ftr4sSJqlatmtPt+BwjMX7WqlUrBQYG6vHHH7/gxN67777boc7MdO7BhecfR0Jh6VzqAXoul0vr1q3zYzcVx5dffqlx48bZfw2/+OKLXDF3BT744IOLbvtlIOTU/OVVr15dO3bs0I033uh0K9cEIzF+tnPnTm3fvl2NGjVyupUKoaLPvPeH4uJijR8/XvHx8fYVDSib77//Xs8//7zmzZunbt26afv27YqPj3e6LWNc6DlIFwqEuLxu3bpp27ZthBj4RsuWLZWdnU2I8RFGrsouICBA3bp10+7duwkxZeTxeDRx4kRNmzZNt9xyi9auXas777zT6baMRiAsm3vvvVdPPvmkdu3adcFT7qY/F43TSX723nvvafz48XryyScv+APVrFkzhzoz17///W/NnDlT33zzjd577z1df/31WrBggerXr8+E3yt0++23a/LkyercubPTrRjr1Vdf1SuvvKKoqChNnDiRpyqX0fmB8JVXXiEQXoVzp9wvpCKccifE+NmFfqBcLhdzOK7S+++/r5SUFPXp00cLFizQrl27dOONN+rNN9/UP//5T61cudLpFo2watUqPfXUU3rxxRfVokWLEk9XZ4L05VWqVElVq1ZVly5dFBAQcNE6nlR/eQRCXClCjJ8dPHjwktvr1q3rp04qhltvvVVPPPGE+vbtq5CQEH322We68cYblZWVpe7duys3N9fpFo3wy3D9y0nShOsr169fvyu6pwkPgb08AiGuFHNi/IyQ4lt79uzRXXfdVWJ9aGiojh8/7v+GDMUE6bKbO3eu0y1UGH379uUmd7gihBgYLTo6Wvv27VO9evW81m/cuLHCzsa/FpggjfKEQIgrRYiB0QYNGqTHH39cb7/9tlwul77//ntt2rRJqampeu6555xuzyjHjx/X7NmztXv3brlcLjVp0kT9+/eX2+12ujUAuCDmxMB4zzzzjNLS0vTTTz9JkoKDg5Wamsp9JEph27Zt6tatm6pWrapWrVrJsixt27ZNhYWFWrVqlW677TanWwSAEggxqBB+/PFH7dq1S2fPnlWTJk1Uo0YNp1syyp133qmbb75Zs2bNUmDgzwO0Z86c0SOPPKJvvvlGH3/8scMdArhaX3/9tebMmaOvv/5ab7zxhiIiIpSenq7Y2Fg1bdrU6fbK5OIXkOOa6NevHx8IPjRv3jydPHlS1apVU8uWLdWqVSsCzFXYtm2bnnrqKTvASFJgYKDGjBmjbdu2OdgZgLLYsGGD4uPjtWXLFi1dulQFBQWSpM8//1zPP/+8w92VHSHGz06cOKGuXbuqQYMGmjhxor777junWzJaamqqIiIi9Ic//EH//Oc/eSDcVQoNDdWhQ4dKrM/OzlZISIgDHQHwhbFjx+qll17S6tWrFRQUZK/v2LGjNm3a5GBnvkGI8bP3339f3333nYYNG6b33ntP9erVU0JCgv7+97/r9OnTTrdnnJycHC1ZskQBAQH6wx/+oOjoaD322GPKyMhwujWj9O7dWwMGDNCSJUuUnZ2tb7/9VosXL9YjjzyiBx980On2AFylHTt26P777y+x/rrrrtOxY8cc6Mi3CDEOqFWrlh5//HFt375dW7du1c0336yUlBTFxMToiSee0N69e51u0RiBgYFKTEzUokWLdOTIEU2dOlUHDx5Ux44dddNNNzndnjH+93//Vz179lTfvn1Vr1491alTR/369dP//M//6JVXXnG6PQBXqWbNmsrJySmxfvv27br++usd6Mi3CDEOysnJ0apVq7Rq1SoFBASoR48e+uKLL9SkSROlpaU53Z5xqlWrpm7duikhIUENGjTQgQMHnG7JGEFBQXrjjTeUl5enrKwsZWVl6YcfflBaWpqCg4Odbg/AVUpOTtZTTz2l3NxcuVwunT17Vv/5z3+Umpqqvn37Ot1emXF1kp+dPn1ay5cv15w5c7Rq1So1a9ZMjzzyiPr06WPPPVi8eLGGDBmivLw8h7s1w48//qhly5Zp0aJFWrNmjWJjY/Xggw+qT58+aty4sdPtlWv9+/e/orq33377GncC4Fo4ffq0+vXrp8WLF8uyLAUGBqq4uFjJycmaO3fuJR/rYAJCjJ/Vrl1bZ8+e1YMPPqiBAwfqlltuKVGTl5en2267Tfv37/d/g4Z58MEH9eGHH6patWr6/e9/rz59+qhdu3ZOt2WMSpUqqW7durr11lt1qf8Kli1b5seuAPjaN998o08//VRnz57VrbfeqgYNGjjdkk8QYvxswYIF+v3vf68qVao43UqFkJycrD59+qhbt25elwfjyjz22GNavHix6tSpo/79++uPf/yjwsPDnW4LgI+88MILSk1NVbVq1bzWFxYW6rXXXjP+zuaEGBipR48eevfdd+1b4r/88ssaOnSoatasKUk6duyY7rzzTu3atcvBLs1QVFSkpUuX6u2331ZGRobuvfdeDRgwQF27duUhfIDhAgIClJOTo4iICK/1x44dU0REhPFPqCfE+EHPnj2vuJZHy1+Z838xQ0NDlZWVZT/08fDhw4qJiTH+F9TfDh48qLlz52r+/Pk6ffq0du3axc0DAYNVqlRJhw8f1nXXXee1ft26derdu7eOHj3qUGe+wfi7H/AAPd87P3uTxX3D5XLJ5XLJsiydPXvW6XYAXKWwsDD797lhw4Zeo6rFxcUqKCjQ4MGDHezQNwgxfjBnzhxJP3/QHjp0SNddd12J85OAU355Omnjxo1KTEzU9OnT1b17d1WqxF0YABNNnTpVlmWpf//+mjBhgtcf00FBQapXr57atm3rYIe+QYjxI8uy1KBBA33xxRcVZma4U879hXH+OpTOLyf2Pvzww1q8eLFq1arldFsAyuihhx6SJNWvX1/t2rVT5cqVHe7o2mBOjJ81bdpUs2fPVps2bZxuxWiVKlVSQkKCfSO2Dz/8UJ06dVL16tUl/Ty6kJ6ezpyYy6hUqZLq1KmjW2+99ZIhkLlagPkKCwtLPN4mNDTUoW58gxDjZytWrNDkyZM1Y8YMxcXFOd2OsR5++OErqjt3Kg8X1q9fvysaweI4Amb68ccfNWbMGP3tb3+74LOSTP9DjxDjZ2FhYfrxxx915swZBQUFqWrVql7bf/jhB4c6AwBUNEOHDtX69ev1wgsvqG/fvvrzn/+s7777TjNnztTkyZPVp08fp1ssE0KMn82bN++S28+dxwQAoKzq1Kmj+fPnq0OHDgoNDdWnn36qm2++WQsWLNC7776rlStXOt1imRBiAACooGrUqKEvvvhCdevW1Q033KClS5eqVatW2r9/v+Lj41VQUOB0i2XC9ZMOKiwsVH5+vtcCAICv3HjjjTpw4IAkqUmTJvrb3/4m6eeLIc7d4dxkhBg/O3nypIYNG6aIiAjVqFFDYWFhXgsAAL7y8MMP67PPPpMkjRs3Tm+++aaCg4P1xBNP6Mknn3S4u7LjdJKfVfRJVgCA8uvQoUPatm2bbrrpJjVv3tzpdsqMEONnFX2SFQCg/MvOztbzzz+vt99+2+lWyoTTSX72ww8/qH79+pJ+vsnQuUuq77jjDn388cdOtgYA+JX44YcfLnu1rAkIMX5W0SdZAQDgL4QYP6vok6wAAPAX5sQ4rKJNsgIAlH+fffaZbrvtNuMfO8BTrB1Wp04d1alTx+k2AAAVSM+ePS+5/fjx4/5p5BrjdJKf9OjRQx6Px3798ssve/0QHTt2TE2aNHGgMwBAReN2uy+51K1bV3379nW6zTLjdJKfBAQEKCcnRxEREZJ+vjIpKytLN954oyTp8OHDiomJMX5oDwAAf2Ekxk/Oz4pkRwAAyoYQAwAAjESI8ROXyyWXy1ViHQAAuDpcneQnlmWpX79+Cg4OliT99NNPGjx4sKpXry5JKioqcrI9AACMw8ReP3n44YevqG7OnDnXuBMAACoGQgwAADASc2IAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJH+P6bIqp3hQib9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Dep_Time_hour'].apply(flight_dep_time).value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ed40c400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Dep_Time_hour'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Dep_Time_hour'].apply(flight_dep_time).value_counts().plot.pie(figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b9e3a7",
   "metadata": {},
   "source": [
    "## lets use Cufflinks & plotly to make your visuals more interactive !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3f7cf3d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## !pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1f0d807e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## !pip install cufflinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "710519c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Lets use Plotly interactive plots directly with Pandas dataframes, but First u need below set-up !\n",
    "\n",
    "import plotly\n",
    "import cufflinks as cf\n",
    "from cufflinks.offline import go_offline\n",
    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "886e8212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "34dcc26f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "marker": {
          "color": "rgba(255, 153, 51, 0.6)",
          "line": {
           "color": "rgba(255, 153, 51, 1.0)",
           "width": 1
          }
         },
         "name": "Dep_Time_hour",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "Early mrng",
          "Evening",
          "Morning",
          "Noon",
          "Night",
          "Late night"
         ],
         "y": [
          2880,
          2357,
          2209,
          1731,
          1040,
          465
         ]
        }
       ],
       "layout": {
        "legend": {
         "bgcolor": "#F5F6F9",
         "font": {
          "color": "#4D5663"
         }
        },
        "paper_bgcolor": "#F5F6F9",
        "plot_bgcolor": "#F5F6F9",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
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       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
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       "      <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",
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       "      <td>22</td>\n",
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       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
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       "      <td>7h 25m</td>\n",
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       "      <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",
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       "      <td>9</td>\n",
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       "      <td>4</td>\n",
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       "      <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",
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       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
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       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
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       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
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       "      <td>SpiceJet</td>\n",
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       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
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       "      <td>9</td>\n",
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       "      <td>11</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
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       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>15h 30m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11087</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>18</td>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>21h 5m</td>\n",
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       "      <td>No info</td>\n",
       "      <td>22270</td>\n",
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       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
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       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11087</td>\n",
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       "      <td>8</td>\n",
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       "      <td>10</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
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       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>8625</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
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      "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",
       "5           SpiceJet   Kolkata    Banglore              CCU → BLR   2h 25m   \n",
       "6        Jet Airways  Banglore   New Delhi        BLR → BOM → DEL  15h 30m   \n",
       "7        Jet Airways  Banglore   New Delhi        BLR → BOM → DEL   21h 5m   \n",
       "8        Jet Airways  Banglore   New Delhi        BLR → BOM → DEL  25h 30m   \n",
       "9  Multiple carriers     Delhi      Cochin        DEL → BOM → COK   7h 50m   \n",
       "\n",
       "  Total_Stops              Additional_Info  Price  journey_day  journey_month  \\\n",
       "0    non-stop                      No info   3897           24              3   \n",
       "1     2 stops                      No info   7662            1              5   \n",
       "2     2 stops                      No info  13882            9              6   \n",
       "3      1 stop                      No info   6218           12              5   \n",
       "4      1 stop                      No info  13302            1              3   \n",
       "5    non-stop                      No info   3873           24              6   \n",
       "6      1 stop  In-flight meal not included  11087           12              3   \n",
       "7      1 stop                      No info  22270            1              3   \n",
       "8      1 stop  In-flight meal not included  11087           12              3   \n",
       "9      1 stop                      No info   8625           27              5   \n",
       "\n",
       "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0          2019             22               20                  1   \n",
       "1          2019              5               50                 13   \n",
       "2          2019              9               25                  4   \n",
       "3          2019             18                5                 23   \n",
       "4          2019             16               50                 21   \n",
       "5          2019              9                0                 11   \n",
       "6          2019             18               55                 10   \n",
       "7          2019              8                0                  5   \n",
       "8          2019              8               55                 10   \n",
       "9          2019             11               25                 19   \n",
       "\n",
       "   Arrival_Time_minute  \n",
       "0                   10  \n",
       "1                   15  \n",
       "2                   25  \n",
       "3                   30  \n",
       "4                   35  \n",
       "5                   25  \n",
       "6                   25  \n",
       "7                    5  \n",
       "8                   25  \n",
       "9                   15  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da831dd",
   "metadata": {},
   "source": [
    "## Pre-process Duration Feature & extract meaningful features "
   ]
  },
  {
   "cell_type": "raw",
   "id": "d008db5e",
   "metadata": {},
   "source": [
    "Lets Apply pre-processing on duration column,\n",
    "-->> Once we pre-processed our Duration feature , lets featurize this feature & extract Duration hours and minute from duration..\n",
    "\n",
    "\n",
    "-->> As my ML model is not able to understand this duration as it contains string values , thats why we have to tell our\n",
    "Ml Model that this is Duration_hour & this Duration_is minute.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "613c4872",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_duration(x):\n",
    "    if 'h' not in x:\n",
    "        x='0h '+x\n",
    "    elif 'm' not in x:\n",
    "        x=x+' 0m'\n",
    "    return x\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a5c54d74",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Duration']=data['Duration'].apply(preprocess_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d9a22fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2h 50m\n",
       "1        7h 25m\n",
       "2        19h 0m\n",
       "3        5h 25m\n",
       "4        4h 45m\n",
       "          ...  \n",
       "10678    2h 30m\n",
       "10679    2h 35m\n",
       "10680     3h 0m\n",
       "10681    2h 40m\n",
       "10682    8h 20m\n",
       "Name: Duration, Length: 10682, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5a740019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2h'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Duration'][0].split(' ')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a2d7009b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(data['Duration'][0].split(' ')[0][0:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "717d643e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(data['Duration'][0].split(' ')[1][0:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "426c5fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Duration_hours']=data['Duration'].apply(lambda x:int(x.split(' ')[0][0:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "df3ab960",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Duration_mins']=data['Duration'].apply(lambda x:int(x.split(' ')[1][0:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d4a56d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>journey_year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>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>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>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>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h 0m</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>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route 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 0m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            1              5   \n",
       "2     2 stops         No info  13882            9              6   \n",
       "\n",
       "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0          2019             22               20                  1   \n",
       "1          2019              5               50                 13   \n",
       "2          2019              9               25                  4   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_mins  \n",
       "0                   10               2             50  \n",
       "1                   15               7             25  \n",
       "2                   25              19              0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201c1efb",
   "metadata": {},
   "source": [
    "## Lets Analyse whether Duration impacts on Price or not ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "19e174e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### eval is a in-built function of python which evaluates the “String” like a python expression and returns the result as an integer.\n",
    "eval('2*60+50*1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f7b6e5f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Duration_total_mins']=data['Duration'].str.replace('h','*60').str.replace(' ','+').str.replace('m','*1').apply(eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "38578903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>journey_year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Duration_total_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>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>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>170</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>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>445</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",
       "\n",
       "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            1              5   \n",
       "\n",
       "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0          2019             22               20                  1   \n",
       "1          2019              5               50                 13   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_mins  Duration_total_mins  \n",
       "0                   10               2             50                  170  \n",
       "1                   15               7             25                  445  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ec5aec1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a41b139eb0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### It Plot data and regression model fits across a FacetGrid.. (combination of 'regplot` and :class:`FacetGrid)\n",
    "#### its a extended form of scatter plot..\n",
    "\n",
    "sns.lmplot(x='Duration_total_mins',y='Price',data=data)\n",
    "\n",
    "\n",
    "## Conclusion-->> pretty clear that As the duration of minutes increases Flight price also increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc409779",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4171ab52",
   "metadata": {},
   "source": [
    "## which city has maximum final destination of flights ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5fb6fc1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Destination'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "23f0dd4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Destination'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c31277c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Destination'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Destination'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42081fe2",
   "metadata": {},
   "source": [
    "**Inference->> \n",
    "Final destination of majority of flights is Cochin. There are two values for Delhi destination which needs to be corrected,**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fda6268",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "98c5f454",
   "metadata": {},
   "source": [
    "## Lets Perform Exploratory Data Analysis(Bivariate Analysis) to come up with some business insights\n",
    "    Problem Statement-->> on which route Jet Airways is extremely used???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e3295092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    BLR → DEL\n",
       "1        CCU → IXR → BBI → BLR\n",
       "2        DEL → LKO → BOM → COK\n",
       "3              CCU → NAG → BLR\n",
       "4              BLR → NAG → DEL\n",
       "                 ...          \n",
       "10678                CCU → BLR\n",
       "10679                CCU → BLR\n",
       "10680                BLR → DEL\n",
       "10681                BLR → DEL\n",
       "10682    DEL → GOI → BOM → COK\n",
       "Name: Route, Length: 10682, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Route']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cf6bbffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Route\n",
       "CCU → BOM → BLR          930\n",
       "DEL → BOM → COK          875\n",
       "BLR → BOM → DEL          385\n",
       "BLR → DEL                382\n",
       "CCU → DEL → BLR          300\n",
       "BOM → HYD                207\n",
       "DEL → JAI → BOM → COK    207\n",
       "DEL → AMD → BOM → COK    141\n",
       "DEL → IDR → BOM → COK     86\n",
       "DEL → NAG → BOM → COK     61\n",
       "DEL → ATQ → BOM → COK     38\n",
       "DEL → COK                 34\n",
       "DEL → BHO → BOM → COK     29\n",
       "DEL → BDQ → BOM → COK     28\n",
       "DEL → LKO → BOM → COK     25\n",
       "DEL → JDH → BOM → COK     23\n",
       "CCU → GAU → BLR           22\n",
       "DEL → MAA → BOM → COK     16\n",
       "DEL → IXC → BOM → COK     13\n",
       "BLR → MAA → DEL           10\n",
       "BLR → BDQ → DEL            8\n",
       "DEL → UDR → BOM → COK      7\n",
       "BOM → DEL → HYD            5\n",
       "CCU → BOM → PNQ → BLR      4\n",
       "BLR → BOM → JDH → DEL      3\n",
       "DEL → DED → BOM → COK      2\n",
       "BOM → BDQ → DEL → HYD      2\n",
       "DEL → CCU → BOM → COK      1\n",
       "BOM → VNS → DEL → HYD      1\n",
       "BOM → UDR → DEL → HYD      1\n",
       "BOM → JDH → DEL → HYD      1\n",
       "BOM → IDR → DEL → HYD      1\n",
       "BOM → DED → DEL → HYD      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Airline']=='Jet Airways'].groupby('Route').size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bde5d36c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "673fe808",
   "metadata": {},
   "source": [
    "## Airline vs Price Analysis\n",
    "    ie finding price distribution & 5-point summary of each Airline.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8b531f53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
       " [Text(0, 0, 'IndiGo'),\n",
       "  Text(1, 0, 'Air India'),\n",
       "  Text(2, 0, 'Jet Airways'),\n",
       "  Text(3, 0, 'SpiceJet'),\n",
       "  Text(4, 0, 'Multiple carriers'),\n",
       "  Text(5, 0, 'GoAir'),\n",
       "  Text(6, 0, 'Vistara'),\n",
       "  Text(7, 0, 'Air Asia'),\n",
       "  Text(8, 0, 'Vistara Premium economy'),\n",
       "  Text(9, 0, 'Jet Airways Business'),\n",
       "  Text(10, 0, 'Multiple carriers Premium economy'),\n",
       "  Text(11, 0, 'Trujet')])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.boxplot(y='Price',x='Airline',data=data)\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "cf8fed04",
   "metadata": {},
   "source": [
    "Conclusion--> From graph we can see that Jet Airways Business have the highest Price., Apart from the first Airline almost all are having similar median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fd40f764",
   "metadata": {},
   "outputs": [],
   "source": [
    "### when we need boxplot + distribution both , its good to consider violinplot.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3c09b542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
       " [Text(0, 0, 'IndiGo'),\n",
       "  Text(1, 0, 'Air India'),\n",
       "  Text(2, 0, 'Jet Airways'),\n",
       "  Text(3, 0, 'SpiceJet'),\n",
       "  Text(4, 0, 'Multiple carriers'),\n",
       "  Text(5, 0, 'GoAir'),\n",
       "  Text(6, 0, 'Vistara'),\n",
       "  Text(7, 0, 'Air Asia'),\n",
       "  Text(8, 0, 'Vistara Premium economy'),\n",
       "  Text(9, 0, 'Jet Airways Business'),\n",
       "  Text(10, 0, 'Multiple carriers Premium economy'),\n",
       "  Text(11, 0, 'Trujet')])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.violinplot(y='Price',x='Airline',data=data)\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48288ff2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "56714455",
   "metadata": {},
   "source": [
    "## Lets Perform Feature-Encoding on Data !\n",
    "    Applying one-hot on data !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4392df0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## but lets remove some of the un-necessary features !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "32a21c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>journey_year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Duration_total_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>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>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>170</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>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>445</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 0m</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>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1140</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>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>325</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 0m   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            1              5   \n",
       "2     2 stops         No info  13882            9              6   \n",
       "3      1 stop         No info   6218           12              5   \n",
       "\n",
       "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0          2019             22               20                  1   \n",
       "1          2019              5               50                 13   \n",
       "2          2019              9               25                  4   \n",
       "3          2019             18                5                 23   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_mins  Duration_total_mins  \n",
       "0                   10               2             50                  170  \n",
       "1                   15               7             25                  445  \n",
       "2                   25              19              0                 1140  \n",
       "3                   30               5             25                  325  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "468ebc64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No info                         78.11\n",
       "In-flight meal not included     18.55\n",
       "No check-in baggage included     3.00\n",
       "1 Long layover                   0.18\n",
       "Change airports                  0.07\n",
       "Business class                   0.04\n",
       "No Info                          0.03\n",
       "1 Short layover                  0.01\n",
       "Red-eye flight                   0.01\n",
       "2 Long layover                   0.01\n",
       "Name: Additional_Info, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(data['Additional_Info'].value_counts()/len(data)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7b1aad58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional_Info contains almost 80% no_info,so we can drop this column\n",
    "# we can drop Route as well as we have pre-process that column\n",
    "## lets drop Duration_total_mins as we have already extracted \"Duration_hours\" & \"Duration_mins\"\n",
    "\n",
    "data.drop(columns=['Additional_Info','Route','Duration_total_mins','journey_year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "68906bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Source', 'Destination', 'Duration', 'Total_Stops', 'Price',\n",
       "       'journey_day', 'journey_month', 'Dep_Time_hour', 'Dep_Time_minute',\n",
       "       'Arrival_Time_hour', 'Arrival_Time_minute', 'Duration_hours',\n",
       "       'Duration_mins'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "007279b1",
   "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>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>19h 0m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Duration Total_Stops  Price  journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi   2h 50m    non-stop   3897           24   \n",
       "1    Air India   Kolkata    Banglore   7h 25m     2 stops   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin   19h 0m     2 stops  13882            9   \n",
       "3       IndiGo   Kolkata    Banglore   5h 25m      1 stop   6218           12   \n",
       "\n",
       "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0              3             22               20                  1   \n",
       "1              5              5               50                 13   \n",
       "2              6              9               25                  4   \n",
       "3              5             18                5                 23   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_mins  \n",
       "0                   10               2             50  \n",
       "1                   15               7             25  \n",
       "2                   25              19              0  \n",
       "3                   30               5             25  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9277f93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f8203480",
   "metadata": {},
   "source": [
    "## Lets separate categorical data & numerical data !\n",
    "    categorical data are those whose data-type is 'object'\n",
    "    Numerical data are those whose data-type is either int of float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2d9623cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col=[col for col in data.columns if data[col].dtype=='object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "42d346e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_col=[col for col in data.columns if data[col].dtype!='object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "aa183b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Airline', 'Source', 'Destination', 'Duration', 'Total_Stops']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b397b79d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "450d154d",
   "metadata": {},
   "source": [
    "## Handling Categorical Data\n",
    "    We are using 2 basic Encoding Techniques to convert Categorical data into some numerical format\n",
    "    if data belongs to Nominal data (ie data is not in any order) -->> OneHotEncoder is used in this case\n",
    "    if data belongs to Ordinal data (ie data is in order ) -->>       LabelEncoder is used in this case\n",
    "\n",
    "    But in real-world , it is not necessary that we have to always One-hot or label , hence we will discuss more interesting approaches to do this !"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a70872b",
   "metadata": {},
   "source": [
    "### Lets apply one-hot encoding on 'Source' feature !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eafc90e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### One-hot from scratch :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e19d367b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banglore', 'Kolkata', 'Delhi', 'Chennai', 'Mumbai'], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Source'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5de2f1d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Banglore\n",
       "1         Kolkata\n",
       "2           Delhi\n",
       "3         Kolkata\n",
       "4        Banglore\n",
       "           ...   \n",
       "10678     Kolkata\n",
       "10679     Kolkata\n",
       "10680    Banglore\n",
       "10681    Banglore\n",
       "10682       Delhi\n",
       "Name: Source, Length: 10682, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a3d557d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        1\n",
       "        ..\n",
       "10678    0\n",
       "10679    0\n",
       "10680    1\n",
       "10681    1\n",
       "10682    0\n",
       "Name: Source, Length: 10682, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Source'].apply(lambda x: 1 if x=='Banglore' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3f912fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for category in data['Source'].unique():\n",
    "    data['Source_'+category]=data['Source'].apply(lambda x: 1 if x==category else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fd5c28c9",
   "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>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Source_Banglore</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Mumbai</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>non-stop</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <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>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>19h 0m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Duration Total_Stops  Price  journey_day  \\\n",
       "0       IndiGo  Banglore   New Delhi   2h 50m    non-stop   3897           24   \n",
       "1    Air India   Kolkata    Banglore   7h 25m     2 stops   7662            1   \n",
       "2  Jet Airways     Delhi      Cochin   19h 0m     2 stops  13882            9   \n",
       "\n",
       "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0              3             22               20                  1   \n",
       "1              5              5               50                 13   \n",
       "2              6              9               25                  4   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
       "0                   10               2             50                1   \n",
       "1                   15               7             25                0   \n",
       "2                   25              19              0                0   \n",
       "\n",
       "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
       "0               0             0               0              0  \n",
       "1               1             0               0              0  \n",
       "2               0             1               0              0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad129020",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bdd19969",
   "metadata": {},
   "source": [
    "## Performing Target Guided Mean Encoding !\n",
    "    ofcourse we can use One-hot , but if we have more sub-categories , it creates curse of dimensionality in ML..\n",
    "    lets use Target Guided Mean Encoding in order to get rid of this.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8a3d47b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "airlines=data.groupby(['Airline'])['Price'].mean().sort_values().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3566764f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Trujet', 'SpiceJet', 'Air Asia', 'IndiGo', 'GoAir', 'Vistara',\n",
       "       'Vistara Premium economy', 'Air India', 'Multiple carriers',\n",
       "       'Multiple carriers Premium economy', 'Jet Airways',\n",
       "       'Jet Airways Business'],\n",
       "      dtype='object', name='Airline')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "df525826",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict1={key:index for index,key in enumerate(airlines,0)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2495bb45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Trujet': 0,\n",
       " 'SpiceJet': 1,\n",
       " 'Air Asia': 2,\n",
       " 'IndiGo': 3,\n",
       " 'GoAir': 4,\n",
       " 'Vistara': 5,\n",
       " 'Vistara Premium economy': 6,\n",
       " 'Air India': 7,\n",
       " 'Multiple carriers': 8,\n",
       " 'Multiple carriers Premium economy': 9,\n",
       " 'Jet Airways': 10,\n",
       " 'Jet Airways Business': 11}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fc1e756e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Airline']=data['Airline'].map(dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "204d42d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3\n",
       "1         7\n",
       "2        10\n",
       "3         3\n",
       "4         3\n",
       "         ..\n",
       "10678     2\n",
       "10679     7\n",
       "10680    10\n",
       "10681     5\n",
       "10682     7\n",
       "Name: Airline, Length: 10682, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Airline']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a478b7df",
   "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>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Source_Banglore</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <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>7</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline    Source Destination Duration Total_Stops  Price  journey_day  \\\n",
       "0        3  Banglore   New Delhi   2h 50m    non-stop   3897           24   \n",
       "1        7   Kolkata    Banglore   7h 25m     2 stops   7662            1   \n",
       "\n",
       "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0              3             22               20                  1   \n",
       "1              5              5               50                 13   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
       "0                   10               2             50                1   \n",
       "1                   15               7             25                0   \n",
       "\n",
       "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
       "0               0             0               0              0  \n",
       "1               1             0               0              0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e1bcfa9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a95f51e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Destination'].unique()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4b0a2780",
   "metadata": {},
   "source": [
    "Note: till now , Delhi (Capital of India) has one Airport & its second Airport is yet to build in Greater Noida (Jewar)\n",
    "      which is part of NCR , so we will consider New Delhi & Delhi as same ...\n",
    "\n",
    "\n",
    "      but in future , these conditions may change.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cb3d6032",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Destination'].replace('New Delhi','Delhi',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "58d2daf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Hyderabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Destination'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4e4bedb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dest=data.groupby(['Destination'])['Price'].mean().sort_values().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "37e57fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Kolkata', 'Hyderabad', 'Delhi', 'Banglore', 'Cochin'], dtype='object', name='Destination')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fa14cdfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict2={key:index for index,key in enumerate(dest,0)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a18e8dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Kolkata': 0, 'Hyderabad': 1, 'Delhi': 2, 'Banglore': 3, 'Cochin': 4}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "63bbe479",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Destination']=data['Destination'].map(dict2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "421332ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2\n",
       "1        3\n",
       "2        4\n",
       "3        3\n",
       "4        2\n",
       "        ..\n",
       "10678    3\n",
       "10679    3\n",
       "10680    2\n",
       "10681    2\n",
       "10682    4\n",
       "Name: Destination, Length: 10682, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Destination']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b32e3ad2",
   "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>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Source_Banglore</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <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>7</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>3</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline    Source  Destination Duration Total_Stops  Price  journey_day  \\\n",
       "0        3  Banglore            2   2h 50m    non-stop   3897           24   \n",
       "1        7   Kolkata            3   7h 25m     2 stops   7662            1   \n",
       "\n",
       "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0              3             22               20                  1   \n",
       "1              5              5               50                 13   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
       "0                   10               2             50                1   \n",
       "1                   15               7             25                0   \n",
       "\n",
       "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
       "0               0             0               0              0  \n",
       "1               1             0               0              0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80f30cff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "828c5a2d",
   "metadata": {},
   "source": [
    "### Perform Manual Encoding on Total_stops feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "08f2b7f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dd2f8473",
   "metadata": {},
   "outputs": [],
   "source": [
    "stops={'non-stop':0, '2 stops':2, '1 stop':1, '3 stops':3, '4 stops':4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b878e20d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Total_Stops']=data['Total_Stops'].map(stops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "adbf4ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        2\n",
       "2        2\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "10678    0\n",
       "10679    0\n",
       "10680    0\n",
       "10681    0\n",
       "10682    2\n",
       "Name: Total_Stops, Length: 10682, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Total_Stops']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2d2b078",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "78c31588",
   "metadata": {},
   "source": [
    "## Performing Outlier Detection !\n",
    "    Here the list of data visualization plots to spot the outliers.\n",
    "1. Box and whisker plot (box plot).\n",
    "2. Scatter plot.\n",
    "3. Histogram.\n",
    "4. Distribution Plot.\n",
    "5. QQ plot"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d821ee89",
   "metadata": {},
   "source": [
    "CAUSE FOR OUTLIERS\n",
    "* Data Entry Errors:- Human errors such as errors caused during data collection, recording, or entry can cause outliers in data.\n",
    "* Measurement Error:- It is the most common source of outliers. This is caused when the measurement instrument used turns out to be faulty.\n",
    "* Natural Outlier:- When an outlier is not artificial (due to error), it is a natural outlier. Most of real world data belong to this category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a42563a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(df,col):\n",
    "    fig,(ax1,ax2,ax3)=plt.subplots(3,1)\n",
    "    sns.histplot(df[col],ax=ax1,kde=True)\n",
    "    sns.boxplot(data=df,x=col,ax=ax2)\n",
    "    sns.histplot(df[col],ax=ax3,kde=False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "3762b075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data,'Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d023b303",
   "metadata": {},
   "source": [
    "### getting a high level over-view of various ways to deal with outliers:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ef6c527f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Price']=np.where(data['Price']>=35000,data['Price'].median(),data['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "cdf46cf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7W9TW1uKpp56yDoS4a9cuaLVa6z7eeOMNKJVK3H///daBED/44AP4+HjXY+HX3oiqvPkBKK+biEtH0hDV9yFrm96epNTdXRcVCIVCwtcnipBrqEKVqREzR0TDT+1YXxBC4IP083jpi5OwCOCO6yPw1tybHN6+mSRJuGVQKGoaGnGqsBI7sgvx85tioNf5duWw2pQw4V5URN4ARb8bIfm0/gi+cvCt+NdPZwv9VD4YEO6PoZFa9A32g4LFEBHJWJcKoOb7f5o1NDTg2LFjyMnJsZsktT2bN29ud70kSVi5ciVWrlzZZhtfX1+89dZbeOuttxz+XE/UfCNq85e0sa4RP/vFHAx48hFrG2dNUurOhkRq4afywRfZhSisqMMnh/Nx5/WRHW5nrGvA77Ycx47spiJz7s2xeOnehC6dQQKa+u7k6yJRYzIjv7QG23+4jPtGxSDYwek72nvySgoIg/Kmn8Fn7K/RXJqFBagRE9wHEVoN/NQ+kADUNVjw97+uwQ33PIKLZTWobTDjxGUjTlw2QuurRGJfHeCrbfUziIi8XZcKoDfeeKPV5StXrkRVVVW3AqL2lVbXw1jXCB+FhNiQPq4Oxy3FhvTB/Ukx+CK7EOU1Dfj82GUokxe0+nh6faMFW7Mu4q29Z3GpvBZKhYTfTb8Oj9wa3+3LRT4KCXclRmFL1kUYKk3457HLuC/JsSlaWnvyyiIEjl0oR/q/S2C2CAhhwbAoHUbGBiGijctbxkP/wD3Ll8NsEbhcXoszxZU4a6hCZV0j0v9dAs3PV2H55zn49e0D2J+ISFZ69B6g+fPn4+abb8Yf//jHntwtXSPvajUAIDbYr9cH/vNkoQEaPHhzP3xz9iqOX6yAcvCtuGP1AdwcH4KhkVqE+Ktx9koVMs+XocjYNLBh3yA/vP3gjbixX3AHe3ecWqnAPSOj8dmRC6iobcDnxy516axLWU090k4Wo/CnQRhjgv1w5M0n8czG/3No++aCOTakDyYMCcePhiocv1iOYiPw0aF8bDpcgHlj+uGZO4Z06n4nIiJP1aPfoBkZGfD17dn7HMhWcwHUmzfVeguVjwITh0bgvqQYmAuOQZKAw3ml+OhQPt7ck4svjxeiyFiHsAANls8Yht2Lx/do8dPMX6PErBv7wk/lg6tV9VBPXYrL5bUObSuEwNGCMmz6rgCFFXVQ+UiYdF0EZt/YFw1X87sUj9JHgWFRgZgzKhb1X/8Jtw4Kg9ki8GFGPib8YR8++S6/20/RERG5uy6dAWqem6uZEAKFhYU4cuRIt0eHprbVNpitZwD6swByWHSQHxr2r8O/jp/B4bxS5JVUo6TKhAHhARgWFYib40N6fS6x4D5q3DcqBluzLqFKp8fP16Xj9V+MwG2Dw9vcprCiFvvPXIGhsmneuthgP9xxfWSPzTkmSRIsxT/i40fH4NuzV/HSFydxuqgSz23LwZ5TBrz28xFePcgmEclblwognU5n87NCocDQoUPx4osv2szLRT0rv6QaAkBogBqBvu438aa76x/m79LCsbkIWr/jEAqhx4L1hzH7pr6YPzYOI2OC4KOQ0Gi2YP+ZK1BNeAKfHbkIAFD7KHDLoFAk9tX12mPstwwKw5eLbsOGb/Pw+tdnsPe0AXf/+V/44Fc398rnERG5WpcKoA0bNvR0HOQA6+WvUJ798VSBvirUf/kKnnh7OzZmnMfWrEvYmnUJgb5KqJUKlNc0oNEi4NPvRgDAsKhAjBsYajP1Rm/xUUh49LYBuHVwGBZuOopcQxXu/0sGFFHX9fpnExE5W7fuAcrMzMTHH3+MTz75BEePHu2pmKg1kgL5JTUAeP+Px2s0YeU9w/G/T4zDXYl6aH2VMNY14mpVPRotAqH+ajSe3I15Y/rhzmGRTil+rnWdPhD/++Q4jB0QgipTI1STFiK/pNqpMRAR9bYu/WY1GAx44IEHsH//fgQFBUEIgYqKCkycOBGbN29GeHjb9zVQ10jhA2FqtMBXpejxAfXINZLigpEUl4QGswVniiqhkCQE+6sQHqBBTOyvELbwSZfFpvNTYePDN+M3fz+GHdlF+PKnwRwjA9n3iMg7dOkM0MKFC2E0GnHixAmUlpairKwMOTk5MBqNWLRoUU/HSAB8YhIBAP1D/TmCr5dR+SiQ0FeHYdGBiNL5dXnwxZ6mUfpgzZwbYb58Eg1mgX8eu4zymnpXh0VE1CO69Jv2q6++wrp163D99ddblw0bNgzvvPMOdu7c2WPB0X8ofiqAePmLnEmtVKBh/7uI0GpQ22DGjuwiNJotrg6LiKjbulQAWSwWqFT2TyGpVCpYLPzl2NMKSmqgCIqGJAFxHK2XnK3R1DSnmsoHV6pM+NfZq66OiIio27pUAE2aNAlPP/00Ll++bF126dIl/OY3v8HkyZN7LDhqsvtUMQCgr84Pml4er4aoNQG+SkwZ1jSn2vGLFcg1VLo4IiKi7unSTdBvv/027r33XvTv3x+xsbGQJAkFBQVITEzExx9/3NMxyt5XOU2TYg4I5+Wv3tTeBKQAUFJa6sRonKO0tBT66PbnJ2s+7v5h/kiKC0Zmfhn2njagb5Af+qid+4QaEVFP6dJvr9jYWGRlZSEtLQ2nT5+GEALDhg3DHXfc0dPxyZ7BWIfv85u+gFpO5Ek9q7UJSK+19O4RTozGOcwWS7vHDNged/KAUOSXVONqVT0O/ngV0xL0vR0iEVGv6NQlsL1792LYsGEwGo0AgDvvvBMLFy7EokWLMHr0aAwfPhz/+te/eiVQufrqRBGEACxXzkHL0Z/JxXwUEu64PhISgDPFldbBOYmIPE2nzgCtWbMGjz32GAIDA+3W6XQ6PP7441i9ejVuu+22HgtQ7nZkFwIAzPmZAKa6NhgP1plLPdS+yEBf3NgvCFkF5dh72oAFY+NcHRIRUad1qgD64Ycf8Nprr7W5fsqUKfjjH//Y7aCoyZVKEw7nNX0pW/I50nZ3dPZSD7Vv7IBQnDVUwVjXiO/Ps3AkIs/TqUtgxcXFrT7+3kypVOLKlSvdDoqa7DpZBIsARsboIKpLXB0OkZXKR4HbhzSN+H60oBySlqO/E5Fn6VQB1LdvX2RnZ7e5/vjx44iKiup2UNTk/35oGmZgeiJzSu5nQJg/+oX0gVkIKEfd7+pwiIg6pVMF0F133YXnn38edXV1dutqa2uxYsUKzJgxo8eCk7MLpTU4dK4UkgTMGMECyJs034/U3ssT7keSJAnjh4RDIQE+sSOw77TB1SERETmsU/cA/c///A+2bt2KIUOGIDU1FUOHDoUkSTh16hTeeecdmM1mPPfcc70Vq6xsyboIABg3MBQxwRz92Zt40/1IIf5q3BDbdEP0i1+cxLhBodAoOVgnEbm/ThVAkZGRSE9Px5NPPolly5ZBCAGg6X+CU6dOxdq1axEZGdkrgcqJxSKsBdB9SbEujoaofTfHhyDzdD7yrgLvf3MeT04Y6OqQiIg61OmBEOPi4rBjxw6UlZXh7NmzEEJg8ODBCA4O7o34ZOm7vFJcKK1FgEaJqcM50By5N43SBw1ZW6G+9Vd4a28ufnZjX+h1vq4Oi4ioXV2aCwwAgoODMXr0aNx8880sfnrY/2Y2nf2ZMSIKfmpeTiD3Zzn3HW7qF4SaejNW7Tzl6nCIiDrU5QKIekdFbYN18MP7RrU/cB+R+xB44Z4ESBLwz2OXOTYQEbk9FkBuZvPhAtQ2mDE0Uoub+vHMGnmOxBgdHhjddM/ain+egNkiXBwREVHbWAC5kQazBR+knwcAPHJbPCRJcm1ARJ20dMpQBPoqcbLQiE2HC1wdDhFRm1gAuZEd2YUorKhDWIAG994Q7epwiDotNECDJVOGAgBe/+o0io32Y4YREbkDFkBuQgiB9d/kAQAWjI3jWCrkseaN6YcRMTpU1jXifz7PsQ6XQUTkTlgAuYmMf5fg+MUKqJUKzB/bz9XhEHWZ0keB134+AkqFhLSTxfjyp5v6iYjcCQsgNyCEwGtfnwEAPDA6FqEBGhdHRNQ910cF4qmJgwA03RBtqOSlMCJyLyyA3MBXOUX44UI5+qh9kDppkKvDIeoR/zVxIK7Ta1FSXY9nNh/jU2FE5FZYALlYo9mCP+xqOvvz6K3xiNByBF3yDhqlD95+8Eb4qXyQ/u8SvL33rKtDIiKyYgHkYp9+fwHnrlQjxF+Nx24f4OpwiHrUoAgtXp6VAAB4c8+P2HeGM8YTkXtgAeRCRRV1eH3naQDAokmDoPVVuTgiop7386QYPDA6FhYBPPVxFn64UO7qkIiIWAC5ihACy/+Zg0pTI0bGBmFBcn9Xh0TUa168NwG3DQ5DbYMZD3/wPc5dqXJ1SEQkcyyAetkNN42CPjrG7tV33L1IO1kMYW7Eub//Hj4KjvpM3kutVGDd/CQk9A1ESXU97v9LBnIuVbg6LCKSMaWrA/B2RUVF+O+Ne22WGWsbsOlwAUyNFowdFIEDnxx3UXREzhOgUeKDX92MX64/jJOFRsz5SwbeXZCE2waHuzo0IpIhngFyskaLBV9mF8LUaEFkoAaj+4e4OiQipwkL0ODvj49F8oBQVNeb8cv3D+OPX59Bg9ni6tCISGZ4BsjJDv54FYZKE3yVCtyVEMVLX+QVSktLoY+OabeNXq/Hsawj0Pqq8MHDo7H88xx8duQi3t53Ft+cvYrf/ywBw6N1ToqYiOSOBZATHS0oQ/ZP9z1MTdAj0I9PfZF3MFssdpd6W3otZZL1vUbpg9d/MRK3DQ7Hs1uzcexCOWa+9Q0eHNMPiyYNRkQgx8Miot7FAshJcosrcTD3KgDgloGh6B/q7+KIiJyrzbNEfYKgSvoFED8aHx8qwGdHLmLOqFg8Pn4AYoL7OD9QIpIFFkBOUFBag69PFAMARsTokBQX7OKIiJyvo7NEF8tq8OkXe4Do6/DRoXx8mH4O5nOHYM75GsJYbG3XfCmNiKg7WAD1MkXUddj+w2WYhcDAcH+MHxIOSbK978eR+ydKSkt7M0wil4sJ7oPizc/hmY8P4XBeKS6U1UI56BYoB92C+DB/3BAbhNhgP7z+0GRXh0pEXoAFUC/6JvcqVBP/C2aLQHyYP6Yl6KGQ7G96duT+iaV3j+itMIncSkxwH8QE90FhRS2+P1+GvKvV1leovxo+g25BXYMZviofV4dKRB6Mj8H3EiEE1h04C0mpRnyYP+5K1EOpYLqJHBWl88M9I6Pxy+Q4jIzRQeUjoaS6Hqpxv8S4V/fiT7vOoLCi1tVhEpGH4jdyL5EkCe/OT0Lj8R0sfoi6IbiPGhOGRuCRW+Jx26AwiKqrKK2ux1t7z+KWV/fioQ2HsTO7EPWNHEuIiBzHb+VepPVVofHYP1n8EPUAjcoHN8UFw7RtOdbNuwk3x4fAIoD9Z67gyU+ykLxqD1764iSOnC+F2SJcHS4RuTneA0REHqW05Cp+NXU0AEDSRsBn0Dj4DExGCYKw/ps8rP8mD6H+aowbFIYx8SEYGROEQREB8FPzniEi+g8WQETkUVp7aMBiEThfUo0fDVU4XVCMEgD/98Nl/N8PlwEAkgTEBvfBkMgADIwIQN8gP+gDfRGl84Ne54tQfzUUHJWdSFZYABGRx1MoJAwID8CA8AD88If52HbwKL7LK8HhvFKcLqpEaXU9CkprUFBag92nDHbbq3wkRAb6IkrnC73Or+lP689NhVK4VsOpa4i8iNcUQGvXrsUf/vAHFBYWYvjw4VizZg1uu+02V4dFRE5WWnIVP7ttpO1CXy0UuihIQdGQAiPhF6LHsFHjUFRRC0OlCQ1mgYtltbhYVgugrNX9CosZqK2AqCmHqC6DqClDgI8Zf1j5O2uhFBag4eP5TnbDTaNQVFTUbhsOnkmt8YoC6O9//zueeeYZrF27Frfccgv+8pe/YPr06Th58iT69evn6vCIyIkcGVfrv2fegMqvQ5p+kBSAnw5Sn2BI/kGQ+oRA8g9GvbIPBo6ehMq6RlTXNwIKH8A/BJJ/CBDetGkdgIWfHrXZt0apgM5PBZ2fCoF+KmiUCqh8ml5qpQRJkmA2C5iFgMXS9KfZ8p+XRQhYBKx/4qc/T546hYbGRgBSU8zCApgbAHMDhLkBvkoFpk+5A74qBfqolfBT+8Bf7YM+aiX8NS3+VCvRR+Nj/bOPygdKn957WEMIAVOjBXUNZtQ2mFFbb0ZNfdP7alMjauvNqK43o7a+ETXXvG/604yan5bX1De1r2swwywEhABKkxciPLxpjDWljwSVQtH0p48Cqp/+/GH3VryR9iO0vsqfXqoWfyqh1ajgq1LYDVRL3ssrCqDVq1fjkUcewaOPPgoAWLNmDb7++musW7cOq1atcnF0RORuHB18dNmTKQCaipGaejOq6hpRZfrpVdeIwwe+RvLEaSg01qKoog4N5qYvekOlCYZKU88GHRjd7mO7DQC2/3TPU1eolQqofRTwUUhQ+UhQXlNIKBUSlNY/m4oMSE2FjUUAZouweW9qNKOuwWItduoazRC99GCeQhuGyrrGdtsoh92BN/fkdrgvpUKyL4x8VdBqmt730SibClkfCeprC1sfBVRKCWofHzTVkRKa66jmckqSpGvet/gTEhotAmaLBQ1mgUaLBY1mgUaLQKPZ8tOfAg0WC8w/LW8qkAXMlqa/h6biGdblQgAKqenysEKSmt5LTQV48/umdbCub1rn4Hq7/cI60G/zX7Vo8Zc+MiYI/cPcZx5Mjy+A6uvrkZmZid/97nc2y6dMmYL09PRWtzGZTDCZ/vPLqaKiaYZ2o9HY4/FZLBbUVVe120YI4VFt3CkWZ7Zxp1h6qo07xdJTbXrrc5QAgpRNL/grASix/fPXsf/AX39qIQEqX0hqP0DtB0ntD6h8UV1nwuyFK5rO9lgEBJq++BQ/fUlKEvD5O79Hnz6+gBCAxYKmrxBL088CAAQqjUb88tk/NX2SJDUVHBaBRtH0hfj5X15HH60O8FFD8lEDSjWgUkNSaoCfXhZJCR+NHySVBvBRN8WraLpkV2dqOqPV24S58aczVyaYTbXQ94275oxN01kbH0VTUfH9zk+xbMnT8FP7oI/aB74qJfqofeCnUlhvWr/v/jmY+9+rYRECDWYLzJamQqHRLH4qJgT2btkIX20QJJUfoPaFpPzpT5VfU25UfpAUCtQDKKkFSpyQBzl6fuYw3D8qtkf32fy93bLYcojwcJcuXRIAxLfffmuz/Pe//70YMmRIq9usWLFCoOnXCl988cUXX3zx5eGvCxcudLp+8PgzQM1aXrcVQrR5LXfZsmVYvHix9WeLxYLS0lKEhobK/vqv0WhEbGwsLly4gMDAQFeH4xaYE1vMhz3mxBbzYY85sdcTORFCoLKyEtHR0Z3e1uMLoLCwMPj4+Ng9BWAwGBAZGdnqNhqNBhqNxmZZUFBQb4XokQIDA/mPtAXmxBbzYY85scV82GNO7HU3JzqdrkvbefwcDWq1GklJSUhLS7NZnpaWhnHjxrkoKiIiInJnHn8GCAAWL16MBQsWYNSoUUhOTsZ7772HgoICPPHEE64OjYiIiNyQVxRAc+bMQUlJCV588UUUFhYiISEBO3bsQFxcnKtD8zgajQYrVqywu0QoZ8yJLebDHnNii/mwx5zYc3VOJCF6a3QGIiIiIvfk8fcAEREREXUWCyAiIiKSHRZAREREJDssgIiIiEh2WAB5mZUrVzZNunfNS6/XW9cLIbBy5UpER0fDz88PEyZMwIkTJ2z2YTKZsHDhQoSFhcHf3x/33HMPLl68aNOmrKwMCxYsgE6ng06nw4IFC1BeXu6MQ+zQwYMHMXPmTERHR0OSJHz++ec2652Zg4KCAsycORP+/v4ICwvDokWLUF9f3xuH3a6OcvLQQw/Z9ZuxY8fatPGmnKxatQqjR4+GVqtFREQEZs2ahTNnzti0kVM/cSQfcusj69atw4gRI6yD9CUnJ2Pnzp3W9XLqH806yonH9ZFOT55Bbm3FihVi+PDhorCw0PoyGAzW9a+++qrQarViy5YtIjs7W8yZM0dERUUJo9FobfPEE0+Ivn37irS0NJGVlSUmTpwoRo4cKRobG61tpk2bJhISEkR6erpIT08XCQkJYsaMGU491rbs2LFDPPfcc2LLli0CgNi2bZvNemfloLGxUSQkJIiJEyeKrKwskZaWJqKjo0Vqamqv56CljnKSkpIipk2bZtNvSkpKbNp4U06mTp0qNmzYIHJycsSxY8fE3XffLfr16yeqqqqsbeTUTxzJh9z6yPbt28WXX34pzpw5I86cOSOeffZZoVKpRE5OjhBCXv2jWUc58bQ+wgLIy6xYsUKMHDmy1XUWi0Xo9Xrx6quvWpfV1dUJnU4n3n33XSGEEOXl5UKlUonNmzdb21y6dEkoFArx1VdfCSGEOHnypAAgDh06ZG2TkZEhAIjTp0/3wlF1Xcsve2fmYMeOHUKhUIhLly5Z23z66adCo9GIioqKXjleR7RVAN17771tbuPtOTEYDAKAOHDggBCC/aRlPoRgHxFCiODgYPG3v/1N9v3jWs05EcLz+ggvgXmh3NxcREdHIz4+Hg888ADOnTsHAMjLy0NRURGmTJlibavRaDB+/Hikp6cDADIzM9HQ0GDTJjo6GgkJCdY2GRkZ0Ol0GDNmjLXN2LFjodPprG3clTNzkJGRgYSEBJtJ+qZOnQqTyYTMzMxePc6u2L9/PyIiIjBkyBA89thjMBgM1nXenpOKigoAQEhICAD2k5b5aCbXPmI2m7F582ZUV1cjOTlZ9v0DsM9JM0/qI14xEjT9x5gxY/Dhhx9iyJAhKC4uxssvv4xx48bhxIkT1gljW04SGxkZifz8fABAUVER1Go1goOD7do0b19UVISIiAi7z46IiLCblNbdODMHRUVFdp8THBwMtVrtdnmaPn067rvvPsTFxSEvLw/Lly/HpEmTkJmZCY1G49U5EUJg8eLFuPXWW5GQkABA3v2ktXwA8uwj2dnZSE5ORl1dHQICArBt2zYMGzbM+kUsx/7RVk4Az+sjLIC8zPTp063vExMTkZycjIEDB2Ljxo3Wm9EkSbLZRghht6yllm1aa+/IftyFs3LgKXmaM2eO9X1CQgJGjRqFuLg4fPnll5g9e3ab23lDTlJTU3H8+HF88803duvk2E/ayocc+8jQoUNx7NgxlJeXY8uWLUhJScGBAwes6+XYP9rKybBhwzyuj/ASmJfz9/dHYmIicnNzrU+DtayQDQaDtZrW6/Wor69HWVlZu22Ki4vtPuvKlSt2Vbm7cWYO9Hq93eeUlZWhoaHB7fMUFRWFuLg45ObmAvDenCxcuBDbt2/Hvn37EBMTY10u137SVj5aI4c+olarMWjQIIwaNQqrVq3CyJEj8eabb8q2fwBt56Q17t5HWAB5OZPJhFOnTiEqKgrx8fHQ6/VIS0uzrq+vr8eBAwcwbtw4AEBSUhJUKpVNm8LCQuTk5FjbJCcno6KiAocPH7a2+e6771BRUWFt466cmYPk5GTk5OSgsLDQ2mbXrl3QaDRISkrq1ePsrpKSEly4cAFRUVEAvC8nQgikpqZi69at2Lt3L+Lj423Wy62fdJSP1nh7H2mNEAImk0l2/aM9zTlpjdv3EYdvlyaPsGTJErF//35x7tw5cejQITFjxgyh1WrF+fPnhRBNj27qdDqxdetWkZ2dLebOndvqo5sxMTFi9+7dIisrS0yaNKnVxxRHjBghMjIyREZGhkhMTHSbx+ArKyvF0aNHxdGjRwUAsXr1anH06FGRn58vhHBeDpof1Zw8ebLIysoSu3fvFjExMS55fLW9nFRWVoolS5aI9PR0kZeXJ/bt2yeSk5NF3759vTYnTz75pNDpdGL//v02j+zW1NRY28ipn3SUDzn2kWXLlomDBw+KvLw8cfz4cfHss88KhUIhdu3aJYSQV/9o1l5OPLGPsADyMs1jUahUKhEdHS1mz54tTpw4YV1vsVjEihUrhF6vFxqNRtx+++0iOzvbZh+1tbUiNTVVhISECD8/PzFjxgxRUFBg06akpETMmzdPaLVaodVqxbx580RZWZkzDrFD+/btEwDsXikpKUII5+YgPz9f3H333cLPz0+EhISI1NRUUVdX15uH36r2clJTUyOmTJkiwsPDhUqlEv369RMpKSl2x+tNOWktFwDEhg0brG3k1E86yocc+8jDDz8s4uLihFqtFuHh4WLy5MnW4kcIefWPZu3lxBP7iCSEEI6fL/JOFosFly9fhlardbubU4mIiKh1QghUVlYiOjoaCkXn7urhU2AALl++jNjYWFeHQURERF1w4cKFDm/cb4kFEACtVgugKYGBgYEujoaIiIgcYTQaERsba/0e7wwWQPjPeALNE7wRERGR5+jK7St8DJ6IiIhkhwUQERERyQ4LICIiIpIdFkBEREQkOyyAiIiISHb4FBi1SQiBuro6V4fRIXHNXDQajcZrB7P09fX12mMjInI2FkDUprq6OkyfPt3VYdBPdu7cCT8/P1eHQUTkFXgJjIiIiGSHZ4DIIVU3zIVQuGl3MTdA+8NmAEDlyAcAH5WLA+o5kqURAcc+dXUYRERex02/0cjdCIXSMwoLH5VnxOkg2c9UTETUS3gJjIiIiGSHBRARERHJDgsgIiIikh0WQERERCQ7LICIiIhIdlgAERERkeywACIiIiLZYQFEREREssMCiIiIiGSHBRARERHJDgsgIiIikh0WQERERCQ7LICIiIhIdlgAERERkeywACIiIiLZYQFEREREssMCiIiIiGRH6eoAvJUQAnV1dQAAX19fSJLk4oiISK74+4jIHs8A9ZK6ujpMnz4d06dPt/7iISJyBf4+IrLHAoiIiIhkhwUQERERyQ4LICIiIpIdFkBEREQkOyyAiIiISHZYABEREZHssAAiIiIi2WEBRERERLLDAoiIiIhkhwUQERERyQ4LICIiIpIdFkBEREQkOyyAiIiISHZYABEREZHssAAiIiIi2WEBRERERLLDAoiIiIhkhwUQEZGMHDp0CHPmzMH69esxZ84cpKenY/369Zg4cSKmTp2KWbNmIT09Henp6dZ2s2bNwqxZs2zeX9smPT291c9quX79+vWYNGkS1q9fb10/YcIETJgwAZMmTbKLq619Nb9v3nbChAnWds3Hcvfdd7e5r2ZTpkyxbu/I8TiiJ/bRVTNnzsSECRMwffp0l8UA2P9dXdtn3IkkhBCuDsLVjEYjdDodKioqEBgY2CP7rK2txfTp0wEAO3fuhJ+fX4/s15muPYbKmxYAPioXR9QGcwO0WR8BcPM4u+KaY/PUfkSud+2/5dDQUJSUlEChUMBisSAkJARlZWW49qsgNDQUAFBSUgJJkqzrrn0fEhICSZJQUlKCsLAwfPzxx/D19bXuo66uDvPnz8fVq1cRFhaGd955B3PnzoXFYoFCocCnn36KRx55BFVVVTaxNsd17T6v3de1sV1r0qRJWLRoEX72s5/ZxdtafAcPHsTzzz9v/dnf3x++vr5tHo8jWh5zV/bRVVlZWVi8eLHNMmfHAMDu70oIgdLSUgBN/eqTTz7p0Xi68/3NM0BERDLS/GVksVisP7f8f3BJSYm1wLh23bXvS0tLrW1KSkqwadMmm3188sknNutTU1Otn2mxWLBw4UK74ufauK7dZ8t9tSx+AGDv3r1Yvnx5q/G2Ft+1xQ8AVFdXt3s8jmgZZ1f20VUtix9XxADY56C5v7kqnvYoXR2At7r2H2FdXZ0LI+k6m7h5otA1vKAfketd23d646S/EAKbNm3ClClTEBMTg4sXL2LTpk3WzxJC4MqVKzbbGAwGh/Y5YsQIm321Jzs726H4Fi1a1KnjcURrx9zZfXTVypUrW13uzBgA+xy05pNPPnFaPB2RZQFkMplgMpmsPxuNxl75jGY/+9nPenz/TmdpBKB2dRTyY2m0vvWKfkReSwiBN998E6+99hrefPPNHtmn2WzGiy++2CP7slgsePPNN/HCCy/g+PHjHbZvPp7XX38dkiQ51LY7++gqk8mE/fv3t7m++bh7Mwag7Ry0ZDabnRKPI2R5CWzVqlXQ6XTWV2xsrKtDIiLyaGazGd9//z2+++47fP/99zCbzd3epxACRqOxR/ZlsVjw/fff48knn3SoffPxFBQUdNi2oKCg1WPuzD66avny5e2ubz7u3owBaDsHrXFGPI6Q5RmgZcuW2VwvNRqNPV4EaTQa6/tt27Y59Sa0nlJXV/efsw4KWXYV17sm757aj8j1amtrMXv27F79DB8fHyQlJWHMmDEYPXo0srKyul24SJIErVaL6urqbu9LoVBg1KhRWLlyJe66664O2zcfT79+/Tps269fv1aPuTP76KqXXnoJU6dObXN983H3ZgxA2zlozc0339zr8ThClt9qGo3GpkDpDdee2vP19fX8p3dcfKpStrytH5HXkiQJTz/9NBQKBZ5++mmkpKR0e58+Pj5YsWIFfvvb33Z7X81x9enTByNGjOjwMljz8Thymaa5bctj7sw+ukqj0WDChAltXgZrPu7evtzUVg5a8vHxcUo8jpDlJTAiIrnqjS8eSZLw4IMPom/fvgCAmJgYPPjgg9bPkiQJ4eHhNttEREQ4tM+kpCSbfbUnMTHRofj+/Oc/d+p4HNHaMXd2H13V1k3QzowBsM9Ba+bNm+e0eDrCAoiISEZCQkIANJ0ZAJrGZmn5hRUaGmodb6e5HWBbPF3bJiwsDA8++KDNPubNm2ez/u2337buS6FQ4K233kJAQIBdfM1trt1ny301v7/WpEmT8NJLL9nE2Py+tfha3lzt7+/f7vE4omWcXdlHV61evdpumbNjAOxz0NzfXBVPe1gAERHJSGpqKiIjIzFv3jxERkZiyZIlmD9/PiRJgkajQVBQEJYsWYIlS5ZY2wUFBSEoKAjz58+3vr+2zW9+8xu7+9N8fX2xePFi6/rmfSkUCutnP/vss9b2CoXCJq5r93ntvhYvXmz93Gs9//zz1hglSYK/vz/mz5/fZny333471Or/PNn63HPPtXs8jmh5zM68Z++mm26CVqsFAPj5+bkkBsD+72rp0qXWPrN48WK3uo+RI0GDI0G3hSNBuwGOBE09wBt+HxG1hiNBExEREXUCCyAiIiKSHRZAREREJDssgIiIiEh2WAARERGR7LAAIiIiItlhAURERESywwKIiIiIZIcFEBEREckOCyAiIiKSHRZAREREJDssgIiIiEh2WAARERGR7LAAIiIiItlhAURERESywwKIiIiIZEfp6gC8la+vL3bu3Gl9T0TkKvx9RGSPBVAvkSQJfn5+rg6DiIi/j4hawUtgREREJDssgIiIiEh2WAARERGR7LAAIiIiItlhAURERESywwKIiIiIZIcFEBEREckOCyAiIiKSHRZAREREJDssgIiIiEh2WAARERGR7LAAIiIiItlhAURERESywwKIiIiIZIcFEBEREckOCyAiIiKSHaWrAyDPIFkaIVwdRFvMDa2/9wKSpdHVIRAReSUWQOSQgGOfujoEh2h/2OzqEIiIyAPwEhgRERHJDs8AUZt8fX2xc+dOV4fRISEETCYTAECj0UCSJBdH1Dt8fX1dHQIRkddgAURtkiQJfn5+rg7DIX369HF1CERE5EF4CYyIiIhkhwUQERERyQ4LICIiIpIdFkBEREQkOyyAiIiISHZYABEREZHssAAiIiIi2eE4QGgaSA8AjEajiyMhIiIiRzV/bzd/j3cGCyAAlZWVAIDY2FgXR0JERESdVVlZCZ1O16ltJNGVssnLWCwWXL58GVqt1munUXCU0WhEbGwsLly4gMDAQFeH4xaYE1vMhz3mxBbzYY85sdcTORFCoLKyEtHR0VAoOndXD88AAVAoFIiJiXF1GG4lMDCQ/0hbYE5sMR/2mBNbzIc95sRed3PS2TM/zXgTNBEREckOCyAiIiKSHRZAZEOj0WDFihXQaDSuDsVtMCe2mA97zIkt5sMec2LP1TnhTdBEREQkOzwDRERERLLDAoiIiIhkhwUQERERyQ4LICIiIpIdFkBEREQkOyyAvMzKlSshSZLNS6/XW9cLIbBy5UpER0fDz88PEyZMwIkTJ2z2YTKZsHDhQoSFhcHf3x/33HMPLl68aNOmrKwMCxYsgE6ng06nw4IFC1BeXu6MQ+zQwYMHMXPmTERHR0OSJHz++ec2652Zg4KCAsycORP+/v4ICwvDokWLUF9f3xuH3a6OcvLQQw/Z9ZuxY8fatPGmnKxatQqjR4+GVqtFREQEZs2ahTNnzti0kVM/cSQfcusj69atw4gRI6yjFCcnJ2Pnzp3W9XLqH806yonH9RFBXmXFihVi+PDhorCw0PoyGAzW9a+++qrQarViy5YtIjs7W8yZM0dERUUJo9FobfPEE0+Ivn37irS0NJGVlSUmTpwoRo4cKRobG61tpk2bJhISEkR6erpIT08XCQkJYsaMGU491rbs2LFDPPfcc2LLli0CgNi2bZvNemfloLGxUSQkJIiJEyeKrKwskZaWJqKjo0Vqamqv56CljnKSkpIipk2bZtNvSkpKbNp4U06mTp0qNmzYIHJycsSxY8fE3XffLfr16yeqqqqsbeTUTxzJh9z6yPbt28WXX34pzpw5I86cOSOeffZZoVKpRE5OjhBCXv2jWUc58bQ+wgLIy6xYsUKMHDmy1XUWi0Xo9Xrx6quvWpfV1dUJnU4n3n33XSGEEOXl5UKlUonNmzdb21y6dEkoFArx1VdfCSGEOHnypAAgDh06ZG2TkZEhAIjTp0/3wlF1Xcsve2fmYMeOHUKhUIhLly5Z23z66adCo9GIioqKXjleR7RVAN17771tbuPtOTEYDAKAOHDggBCC/aRlPoRgHxFCiODgYPG3v/1N9v3jWs05EcLz+ggvgXmh3NxcREdHIz4+Hg888ADOnTsHAMjLy0NRURGmTJlibavRaDB+/Hikp6cDADIzM9HQ0GDTJjo6GgkJCdY2GRkZ0Ol0GDNmjLXN2LFjodPprG3clTNzkJGRgYSEBERHR1vbTJ06FSaTCZmZmb16nF2xf/9+REREYMiQIXjsscdgMBis67w9JxUVFQCAkJAQAOwnLfPRTK59xGw2Y/PmzaiurkZycrLs+wdgn5NmntRHOBu8lxkzZgw+/PBDDBkyBMXFxXj55Zcxbtw4nDhxAkVFRQCAyMhIm20iIyORn58PACgqKoJarUZwcLBdm+bti4qKEBERYffZERER1jbuypk5KCoqsvuc4OBgqNVqt8vT9OnTcd999yEuLg55eXlYvnw5Jk2ahMzMTGg0Gq/OiRACixcvxq233oqEhAQA8u4nreUDkGcfyc7ORnJyMurq6hAQEIBt27Zh2LBh1i9iOfaPtnICeF4fYQHkZaZPn259n5iYiOTkZAwcOBAbN2603owmSZLNNkIIu2UttWzTWntH9uMunJUDT8nTnDlzrO8TEhIwatQoxMXF4csvv8Ts2bPb3M4bcpKamorjx4/jm2++sVsnx37SVj7k2EeGDh2KY8eOoby8HFu2bEFKSgoOHDhgXS/H/tFWToYNG+ZxfYSXwLycv78/EhMTkZuba30arGWFbDAYrNW0Xq9HfX09ysrK2m1TXFxs91lXrlyxq8rdjTNzoNfr7T6nrKwMDQ0Nbp+nqKgoxMXFITc3F4D35mThwoXYvn079u3bh5iYGOtyufaTtvLRGjn0EbVajUGDBmHUqFFYtWoVRo4ciTfffFO2/QNoOyetcfc+wgLIy5lMJpw6dQpRUVGIj4+HXq9HWlqadX19fT0OHDiAcePGAQCSkpKgUqls2hQWFiInJ8faJjk5GRUVFTh8+LC1zXfffYeKigprG3flzBwkJycjJycHhYWF1ja7du2CRqNBUlJSrx5nd5WUlODChQuIiooC4H05EUIgNTUVW7duxd69exEfH2+zXm79pKN8tMbb+0hrhBAwmUyy6x/tac5Ja9y+jzh8uzR5hCVLloj9+/eLc+fOiUOHDokZM2YIrVYrzp8/L4RoenRTp9OJrVu3iuzsbDF37txWH92MiYkRu3fvFllZWWLSpEmtPqY4YsQIkZGRITIyMkRiYqLbPAZfWVkpjh49Ko4ePSoAiNWrV4ujR4+K/Px8IYTzctD8qObkyZNFVlaW2L17t4iJiXHJ46vt5aSyslIsWbJEpKeni7y8PLFv3z6RnJws+vbt67U5efLJJ4VOpxP79++3eWS3pqbG2kZO/aSjfMixjyxbtkwcPHhQ5OXliePHj4tnn31WKBQKsWvXLiGEvPpHs/Zy4ol9hAWQEMJsNosLFy6I8vJyUVFR4dGv2bNni8jISKFUKoVerxczZ84U3333nXV9eXm5+N3vficiIiKEWq0W48aNExkZGTb7KC4uFr/+9a9FcHCw8PX1FVOnThUnTpywaZOXlyfuv/9+ERAQIAICAsT9998v8vPzXX78FRUV4osvvhAA7F5z5851eg5ycnLElClThK+vrwgODha//vWvhcFgcKucFBUViUmTJonQ0FChVCpFTEyMmDt3rt3xelNOWssFALF27VqX/FtxdU46yocc+8j8+fNFbGysUKlUIjQ0VIwfP15s27ZNlv3DkZy4qo9oNBoRFBQknnrqKVFXV9ep735JCCEcP1/knS5evIjY2FhXh0FERERdcOHChQ7vW2uJT4EB0Gq1AJoSGBgY6OJoiIiIyBFGoxGxsbHW7/HOYAGE/zxO1zy/CREREXmOrgwJwKfAiIiISHZYABEREZHs8BKYzCTemITCwvaHCo+K0iP7qPvNVUVERNRTWADJTGFhEe548X/bbbP7+V84KRoiIiLX4CUwIiIikh2eAfIijlzeqqiocFI0RERE7osFkBdx5PLWPxZOdlI0RERE7ouXwIiIiEh2WAARERGR7LAAIiIiItlhAURERESywwKIiIiIZIcFEBEREckOCyAiIiKSHRZAREREJDssgIiIiEh2WAARERGR7LAAIiIiItnhXGDkMo5M3hoVpUf20UwnRURERHLBAohcxpHJW3c//wsnRUNERHLCS2BEREQkOyyAiIiISHZYABEREZHssAAiIiIi2WEBRERERLLDAoiIiIhkhwUQERERyQ4LICIiIpIdFkBEREQkOxwJmshBnLqDiMh7sAAichCn7iAi8h4sgKjTeCaEiIg8HQsg6jSeCSEiIk/Hm6CJiIhIdrpUAA0YMAAlJSV2y8vLyzFgwIBuB0VERETUm7pUAJ0/fx5ms9luuclkwqVLl7odFBEREVFv6tQ9QNu3b7e+//rrr6HT6aw/m81m7NmzB/379++x4Mg1KioqEKbv2+56IiIiT9apAmjWrFkAAEmSkJKSYrNOpVKhf//++NOf/tRjwZFrWCyWdm9y/sfCyU6MhoiIqOd1qgCyWCwAgPj4eHz//fcICwvrlaDI83V0Fqm5DRERkSt06TH4vLy8no6DvExHZ5EA7zyT5EjhxzGSiIhcr8vjAO3Zswd79uyBwWCwnhlq9v777zu0j5UrV+KFF16wWRYZGYmioqZB9oQQeOGFF/Dee++hrKwMY8aMwTvvvIPhw4db25tMJixduhSffvopamtrMXnyZKxduxYxMTFdPTSiLnOk8OMYSURErtelp8BeeOEFTJkyBXv27MHVq1dRVlZm8+qM4cOHo7Cw0PrKzs62rnv99dexevVqvP322/j++++h1+tx5513orKy0trmmWeewbZt27B582Z88803qKqqwowZM1p9So2IiIgI6OIZoHfffRcffPABFixY0P0AlEro9Xq75UIIrFmzBs899xxmz54NANi4cSMiIyOxadMmPP7446ioqMD69evx0Ucf4Y477gAAfPzxx4iNjcXu3bsxderUVj/TZDLBZDJZfzYajd0+DiJ31NG0JbwcR0Ry1aUCqL6+HuPGjeuRAHJzcxEdHQ2NRoMxY8bglVdewYABA5CXl4eioiJMmTLF2laj0WD8+PFIT0/H448/jszMTDQ0NNi0iY6ORkJCAtLT09ssgFatWmV36Y3IG3U0bQkvxxGRXHWpAHr00UexadMmLF++vFsfPmbMGHz44YcYMmQIiouL8fLLL2PcuHE4ceKE9T6gyMhIm20iIyORn58PACgqKoJarUZwcLBdm+btW7Ns2TIsXrzY+rPRaERsbGy3joV6B28qdj1OfktE3qhLBVBdXR3ee+897N69GyNGjIBKpbJZv3r1aof2M336dOv7xMREJCcnY+DAgdi4cSPGjh0LoGnMoWsJIeyWtdRRG41GA41G41CM5FqO3FS85ek7WST1Ik5+S0TeqEsF0PHjx3HDDTcAAHJycmzWdVSctMff3x+JiYnIzc21DrpYVFSEqKgoaxuDwWA9K6TX61FfX4+ysjKbs0AGg6HHLtGR++OTV0RE1FldKoD27dvX03EAaLo5+dSpU7jtttsQHx8PvV6PtLQ03HjjjQCa7j06cOAAXnvtNQBAUlISVCoV0tLScP/99wMACgsLkZOTg9dff71XYiQiIiLP1+VxgHrC0qVLMXPmTPTr1w8GgwEvv/wyjEYjUlJSIEkSnnnmGbzyyisYPHgwBg8ejFdeeQV9+vTBgw8+CADQ6XR45JFHsGTJEoSGhiIkJARLly5FYmKi9akwIiIiopa6VABNnDix3Utde/fudWg/Fy9exNy5c3H16lWEh4dj7NixOHToEOLi4gAAv/3tb1FbW4unnnrKOhDirl27oNVqrft44403oFQqcf/991sHQvzggw/g4+PTlUNzW47ciMqpJeSFfYKIqOu6VAA13//TrKGhAceOHUNOTo7dJKnt2bx5c7vrJUnCypUrsXLlyjbb+Pr64q233sJbb73l8Od6IkduRPXGqSXkytHi5udvprXbhn2CiKh1XSqA3njjjVaXr1y5ElVVVd0KiIhY8BIR9bYuTYXRlvnz5zs8DxgRERGRq/RoAZSRkQFfX9+e3CURERFRj+vSJbDmubmaCSFQWFiII0eOdHt0aCIiIqLe1qUCSKfT2fysUCgwdOhQvPjiizbzchERERG5oy4VQBs2bOjpOIiIiIicplsDIWZmZuLUqVOQJAnDhg2zjthMRERE5M66VAAZDAY88MAD2L9/P4KCgiCEQEVFBSZOnIjNmzcjPDy8p+MkIiIi6jFdegps4cKFMBqNOHHiBEpLS1FWVoacnBwYjUYsWrSop2MkIiIi6lFdOgP01VdfYffu3bj++uuty4YNG4Z33nmHN0ETERGR2+vSGSCLxQKVSmW3XKVSwWKxdDsoIiIiot7UpQJo0qRJePrpp3H58mXrskuXLuE3v/kNJk/m8PxERETk3rp0Ceztt9/Gvffei/79+yM2NhaSJKGgoACJiYn4+OOPezpGIqfoaAJSb5xZvaKiAmH6vh22ISLyNl0qgGJjY5GVlYW0tDScPn0aQggMGzYMd9xxR0/HR+Q0HU1A6o2Tj1osFk66SkSy1KkCaO/evUhNTcWhQ4cQGBiIO++8E3feeSeApv8lDh8+HO+++y5uu+22XgmWqKt4poOIiK7VqQJozZo1eOyxxxAYGGi3TqfT4fHHH8fq1atZAJHb4ZkOIiK6Vqdugv7hhx8wbdq0NtdPmTIFmZmZ3Q6KiIiIqDd1qgAqLi5u9fH3ZkqlEleuXOl2UERERES9qVMFUN++fZGdnd3m+uPHjyMqKqrbQRERERH1pk7dA3TXXXfh+eefx/Tp0+Hr62uzrra2FitWrMCMGTN6NEAib8MbsomIXK9TBdD//M//YOvWrRgyZAhSU1MxdOhQSJKEU6dO4Z133oHZbMZzzz3XW7ESeQXekE1E5HqdKoAiIyORnp6OJ598EsuWLYMQAgAgSRKmTp2KtWvXIjIyslcCJSIiIuopnR4IMS4uDjt27EBZWRnOnj0LIQQGDx6M4ODg3oiPiIiIqMd1aSRoAAgODsbo0aN7MhYiIiIip+jSZKhEREREnowFEBEREckOCyAiIiKSHRZAREREJDssgIiIiEh2WAARERGR7LAAIiIiItlhAURERESywwKIiIiIZIcFEBEREckOCyAiIiKSHRZAREREJDtdngyVHJN4YxIKC4vabRMVpUf20UwnRUREREQsgHpZYWER7njxf9tts/v5XzgpGiIiIgJ4CYyIiIhkiGeAiKjbKioqEKbv224bXuolInfCAoiIus1isfBSLxF5FBZAROQUPEtERO6EBRAROYUjZ4m2PH0niyQicgoWQG7Akf8ZV1RUOCkaItfhpTQichYWQG7AkV/6/1g42UnREBEReT8+Bk9ERESywwKIiIiIZIcFEBEREckO7wEiIo/S0UMDfEqMiBzBAoiIPEpHDw3wKTEicgQLICLyKhxwkYgc4TUF0Nq1a/GHP/wBhYWFGD58ONasWYPbbrvN1WERkZP11FhCiTcmobCwqN02LKRcj39P1FVeUQD9/e9/xzPPPIO1a9filltuwV/+8hdMnz4dJ0+eRL9+/VwdHhG5GUcHH/35m2nttnHm5baOvujl+iVfWFjEwTOpS7yiAFq9ejUeeeQRPProowCANWvW4Ouvv8a6deuwatUqF0dHRO7GmYOPOnKGorq6Gv7+/u226agg88QveWcVdbwsSq3x+AKovr4emZmZ+N3vfmezfMqUKUhPT291G5PJBJPJZP25eZoJo9HY4/FZLBY01Fa320YI4VFt3CkWZ7Zxp1h6qo07xdJTbZwZS3l5OUIiotptYzQace9r/2y3zbb/NxN3vdx+Qbbt/81sNx5HYqmpqUGfPn3cpk1Hufnnf9/rUH47+nsym80Yv2xju232/X5Br3wH9Jaxt96OoqLidtvo9ZE49M1Bt4int2Jp/jsTQnR+Y+HhLl26JACIb7/91mb573//ezFkyJBWt1mxYoUAwBdffPHFF198ecHrwoULna4fPP4MUDNJkmx+FkLYLWu2bNkyLF682PqzxWJBaWkpQkND29xGLoxGI2JjY3HhwgUEBga6Ohy3wJzYYj7sMSe2mA97zIm9nsiJEAKVlZWIjo7u9LYeXwCFhYXBx8cHRUW215ENBgMiIyNb3Uaj0UCj0dgsCwoK6q0QPVJgYCD/kbbAnNhiPuwxJ7aYD3vMib3u5kSn03VpO4+fCkOtViMpKQlpabY3B6alpWHcuHEuioqIiIjcmcefAQKAxYsXY8GCBRg1ahSSk5Px3nvvoaCgAE888YSrQyMiIiI35BUF0Jw5c1BSUoIXX3wRhYWFSEhIwI4dOxAXF+fq0DyORqPBihUr7C4RyhlzYov5sMec2GI+7DEn9lydE0mIrjw7RkREROS5PP4eICIiIqLOYgFEREREssMCiIiIiGSHBRARERHJDgsgL7Ny5UpIkmTz0uv11vVCCKxcuRLR0dHw8/PDhAkTcOLECZt9mEwmLFy4EGFhYfD398c999yDixcv2rQpKyvDggULoNPpoNPpsGDBApSXlzvjEDt08OBBzJw5E9HR0ZAkCZ9//rnNemfmoKCgADNnzoS/vz/CwsKwaNEi1NfX98Zht6ujnDz00EN2/Wbs2LE2bbwpJ6tWrcLo0aOh1WoRERGBWbNm4cyZMzZt5NRPHMmH3PrIunXrMGLECOsgfcnJydi5c6d1vZz6R7OOcuJxfaTTk2eQW1uxYoUYPny4KCwstL4MBoN1/auvviq0Wq3YsmWLyM7OFnPmzBFRUVHCaDRa2zzxxBOib9++Ii0tTWRlZYmJEyeKkSNHisbGRmubadOmiYSEBJGeni7S09NFQkKCmDFjhlOPtS07duwQzz33nNiyZYsAILZt22az3lk5aGxsFAkJCWLixIkiKytLpKWliejoaJGamtrrOWipo5ykpKSIadOm2fSbkpISmzbelJOpU6eKDRs2iJycHHHs2DFx9913i379+omqqiprGzn1E0fyIbc+sn37dvHll1+KM2fOiDNnzohnn31WqFQqkZOTI4SQV/9o1lFOPK2PsADyMitWrBAjR45sdZ3FYhF6vV68+uqr1mV1dXVCp9OJd999VwghRHl5uVCpVGLz5s3WNpcuXRIKhUJ89dVXQgghTp48KQCIQ4cOWdtkZGQIAOL06dO9cFRd1/LL3pk52LFjh1AoFOLSpUvWNp9++qnQaDSioqKiV47XEW0VQPfee2+b23h7TgwGgwAgDhw4IIRgP2mZDyHYR4QQIjg4WPztb3+Tff+4VnNOhPC8PsJLYF4oNzcX0dHRiI+PxwMPPIBz584BAPLy8lBUVIQpU6ZY22o0GowfPx7p6ekAgMzMTDQ0NNi0iY6ORkJCgrVNRkYGdDodxowZY20zduxY6HQ6axt35cwcZGRkICEhwWaSvqlTp8JkMiEzM7NXj7Mr9u/fj4iICAwZMgSPPfYYDAaDdZ2356SiogIAEBISAoD9pGU+msm1j5jNZmzevBnV1dVITk6Wff8A7HPSzJP6iFeMBE3/MWbMGHz44YcYMmQIiouL8fLLL2PcuHE4ceKEdcLYlpPERkZGIj8/HwBQVFQEtVqN4OBguzbN2xcVFSEiIsLusyMiIuwmpXU3zsxBUVGR3ecEBwdDrVa7XZ6mT5+O++67D3FxccjLy8Py5csxadIkZGZmQqPReHVOhBBYvHgxbr31ViQkJACQdz9pLR+APPtIdnY2kpOTUVdXh4CAAGzbtg3Dhg2zfhHLsX+0lRPA8/oICyAvM336dOv7xMREJCcnY+DAgdi4caP1ZjRJkmy2EULYLWupZZvW2juyH3fhrBx4Sp7mzJljfZ+QkIBRo0YhLi4OX375JWbPnt3mdt6Qk9TUVBw/fhzffPON3To59pO28iHHPjJ06FAcO3YM5eXl2LJlC1JSUnDgwAHrejn2j7ZyMmzYMI/rI7wE5uX8/f2RmJiI3Nxc69NgLStkg8Fgrab1ej3q6+tRVlbWbpvi4mK7z7py5YpdVe5unJkDvV5v9zllZWVoaGhw+zxFRUUhLi4Oubm5ALw3JwsXLsT27duxb98+xMTEWJfLtZ+0lY/WyKGPqNVqDBo0CKNGjcKqVaswcuRIvPnmm7LtH0DbOWmNu/cRFkBezmQy4dSpU4iKikJ8fDz0ej3S0tKs6+vr63HgwAGMGzcOAJCUlASVSmXTprCwEDk5OdY2ycnJqKiowOHDh61tvvvuO1RUVFjbuCtn5iA5ORk5OTkoLCy0ttm1axc0Gg2SkpJ69Ti7q6SkBBcuXEBUVBQA78uJEAKpqanYunUr9u7di/j4eJv1cusnHeWjNd7eR1ojhIDJZJJd/2hPc05a4/Z9xOHbpckjLFmyROzfv1+cO3dOHDp0SMyYMUNotVpx/vx5IUTTo5s6nU5s3bpVZGdni7lz57b66GZMTIzYvXu3yMrKEpMmTWr1McURI0aIjIwMkZGRIRITE93mMfjKykpx9OhRcfToUQFArF69Whw9elTk5+cLIZyXg+ZHNSdPniyysrLE7t27RUxMjEseX20vJ5WVlWLJkiUiPT1d5OXliX379onk5GTRt29fr83Jk08+KXQ6ndi/f7/NI7s1NTXWNnLqJx3lQ459ZNmyZeLgwYMiLy9PHD9+XDz77LNCoVCIXbt2CSHk1T+atZcTT+wjLIC8TPNYFCqVSkRHR4vZs2eLEydOWNdbLBaxYsUKodfrhUajEbfffrvIzs622Udtba1ITU0VISEhws/PT8yYMUMUFBTYtCkpKRHz5s0TWq1WaLVaMW/ePFFWVuaMQ+zQvn37BAC7V0pKihDCuTnIz88Xd999t/Dz8xMhISEiNTVV1NXV9ebht6q9nNTU1IgpU6aI8PBwoVKpRL9+/URKSord8XpTTlrLBQCxYcMGaxs59ZOO8iHHPvLwww+LuLg4oVarRXh4uJg8ebK1+BFCXv2jWXs58cQ+IgkhhOPni4iIiIg8H+8BIiIiItlhAURERESywwKIiIiIZIcFEBEREckOCyAiIiKSHRZAREREJDssgIiIiEh2WAARERGR7LAAIiKvNGHCBDzzzDOuDoOI3BQLICJyew899BAkSYIkSVCpVBgwYACWLl2K6urqNrfZunUrXnrpJSdGSUSeROnqAIiIHDFt2jRs2LABDQ0N+Ne//oVHH30U1dXVWLdunU27hoYGqFQqhISEuChSIvIEPANERB5Bo9FAr9cjNjYWDz74IObNm4fPP/8cK1euxA033ID3338fAwYMgEajgRDC7hKYyWTCb3/7W8TGxkKj0WDw4MFYv369df3Jkydx1113ISAgAJGRkViwYAGuXr3qgiMlImdgAUREHsnPzw8NDQ0AgLNnz+Kzzz7Dli1bcOzYsVbb//KXv8TmzZvx5z//GadOncK7776LgIAAAEBhYSHGjx+PG264AUeOHMFXX32F4uJi3H///c46HCJyMl4CIyKPc/jwYWzatAmTJ08GANTX1+Ojjz5CeHh4q+1//PFHfPbZZ0hLS8Mdd9wBABgwYIB1/bp163DTTTfhlVdesS57//33ERsbix9//BFDhgzpxaMhIlfgGSAi8ghffPEFAgIC4Ovri+TkZNx+++146623AABxcXFtFj8AcOzYMfj4+GD8+PGtrs/MzMS+ffsQEBBgfV133XUAgH//+989fzBE5HI8A0REHmHixIlYt24dVCoVoqOjoVKprOv8/f3b3dbPz6/d9RaLBTNnzsRrr71mty4qKqprARORW2MBREQewd/fH4MGDerStomJibBYLDhw4ID1Eti1brrpJmzZsgX9+/eHUslfi0RywEtgROT1+vfvj5SUFDz88MP4/PPPkZeXh/379+Ozzz4DAPzXf/0XSktLMXfuXBw+fBjnzp3Drl278PDDD8NsNrs4eiLqDSyAiEgW1q1bh1/84hd46qmncN111+Gxxx6zDqQYHR2Nb7/9FmazGVOnTkVCQgKefvpp6HQ6KBT8NUnkjSQhhHB1EERERETOxP/aEBERkeywACIiIiLZYQFEREREssMCiIiIiGSHBRARERHJDgsgIiIikh0WQERERCQ7LICIiIhIdlgAERERkeywACIiIiLZYQFEREREsvP/AdyA8Z6Db/xPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data,'Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85705896",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5fb40176",
   "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>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Source_Banglore</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>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>7</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>3</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline    Source  Destination Duration  Total_Stops   Price  journey_day  \\\n",
       "0        3  Banglore            2   2h 50m            0  3897.0           24   \n",
       "1        7   Kolkata            3   7h 25m            2  7662.0            1   \n",
       "\n",
       "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0              3             22               20                  1   \n",
       "1              5              5               50                 13   \n",
       "\n",
       "   Arrival_Time_minute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
       "0                   10               2             50                1   \n",
       "1                   15               7             25                0   \n",
       "\n",
       "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
       "0               0             0               0              0  \n",
       "1               1             0               0              0  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f971252c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['Source','Duration'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8d7394b0",
   "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>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Source_Banglore</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>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>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline  Destination  Total_Stops   Price  journey_day  journey_month  \\\n",
       "0        3            2            0  3897.0           24              3   \n",
       "1        7            3            2  7662.0            1              5   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \\\n",
       "0             22               20                  1                   10   \n",
       "1              5               50                 13                   15   \n",
       "\n",
       "   Duration_hours  Duration_mins  Source_Banglore  Source_Kolkata  \\\n",
       "0               2             50                1               0   \n",
       "1               7             25                0               1   \n",
       "\n",
       "   Source_Delhi  Source_Chennai  Source_Mumbai  \n",
       "0             0               0              0  \n",
       "1             0               0              0  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "49939215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                  int64\n",
       "Destination              int64\n",
       "Total_Stops              int64\n",
       "Price                  float64\n",
       "journey_day              int64\n",
       "journey_month            int64\n",
       "Dep_Time_hour            int64\n",
       "Dep_Time_minute          int64\n",
       "Arrival_Time_hour        int64\n",
       "Arrival_Time_minute      int64\n",
       "Duration_hours           int64\n",
       "Duration_mins            int64\n",
       "Source_Banglore          int64\n",
       "Source_Kolkata           int64\n",
       "Source_Delhi             int64\n",
       "Source_Chennai           int64\n",
       "Source_Mumbai            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcefc15a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "29058203",
   "metadata": {},
   "source": [
    "## Performing Feature Selection !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "22425bea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f9bf6b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop(['Price'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "63c990a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e5d806a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                int64\n",
       "Destination            int64\n",
       "Total_Stops            int64\n",
       "journey_day            int64\n",
       "journey_month          int64\n",
       "Dep_Time_hour          int64\n",
       "Dep_Time_minute        int64\n",
       "Arrival_Time_hour      int64\n",
       "Arrival_Time_minute    int64\n",
       "Duration_hours         int64\n",
       "Duration_mins          int64\n",
       "Source_Banglore        int64\n",
       "Source_Kolkata         int64\n",
       "Source_Delhi           int64\n",
       "Source_Chennai         int64\n",
       "Source_Mumbai          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "32995d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9755106 , 1.00092767, 0.78859004, 0.22741018, 0.63091077,\n",
       "       0.34425231, 0.25698078, 0.40272303, 0.34641153, 0.47925462,\n",
       "       0.34124654, 0.39455947, 0.46172774, 0.51609493, 0.13422836,\n",
       "       0.20914197])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info_regression(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "821813f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "imp=pd.DataFrame(mutual_info_regression(X,y),index=X.columns)\n",
    "imp.columns=['importance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "8c3723e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Destination</th>\n",
       "      <td>1.000775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airline</th>\n",
       "      <td>0.961491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>0.788803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_month</th>\n",
       "      <td>0.625693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source_Delhi</th>\n",
       "      <td>0.515813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_hours</th>\n",
       "      <td>0.462949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <td>0.457759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>0.403356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source_Banglore</th>\n",
       "      <td>0.384063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <td>0.352787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_mins</th>\n",
       "      <td>0.345787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>0.343952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <td>0.257646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_day</th>\n",
       "      <td>0.219118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <td>0.208513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source_Chennai</th>\n",
       "      <td>0.129277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     importance\n",
       "Destination            1.000775\n",
       "Airline                0.961491\n",
       "Total_Stops            0.788803\n",
       "journey_month          0.625693\n",
       "Source_Delhi           0.515813\n",
       "Duration_hours         0.462949\n",
       "Source_Kolkata         0.457759\n",
       "Arrival_Time_hour      0.403356\n",
       "Source_Banglore        0.384063\n",
       "Arrival_Time_minute    0.352787\n",
       "Duration_mins          0.345787\n",
       "Dep_Time_hour          0.343952\n",
       "Dep_Time_minute        0.257646\n",
       "journey_day            0.219118\n",
       "Source_Mumbai          0.208513\n",
       "Source_Chennai         0.129277"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.sort_values(by='importance',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3324378",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "93b5ad75",
   "metadata": {},
   "source": [
    "## Lets build ML Model  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52abb121",
   "metadata": {},
   "source": [
    "#### split dataset into train & test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "43368422",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "29de82e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7b472c",
   "metadata": {},
   "source": [
    "#### what we often do in modelling:\n",
    "    a..Initially ,lets build basic random forest model.\n",
    "    b..then later-on , we will try to improve this model using some parameters..\n",
    "    c..Then we will hyper-tune our model to get optimal value of parameters in order to achieve optimal value of params.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "6320f23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f993a63a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_model=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0a0bc9d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=ml_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "8dee2a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "dc81c1b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16851.76      ,  5226.8       ,  8773.85      , ...,\n",
       "        3490.93      ,  6386.23333333,  6914.61      ])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8f22bba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2671,)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e287d43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2671"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a55a7d50",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "667416b5",
   "metadata": {},
   "source": [
    "### Saving ML model into disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "2517e7b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "### !pip install pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "9eb7faa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "f17bfa1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "file=open(r'C:\\Users\\shivraj\\Desktop\\Notes\\ML\\Project\\P3_Airline-Tickets-Price-Prediction/rf_random.pkl','wb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d668611b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "1ade9047",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=open(r'C:\\Users\\shivraj\\Desktop\\Notes\\ML\\Project\\P3_Airline-Tickets-Price-Prediction/rf_random.pkl','rb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "7f0df3e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest=pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "e3b16596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16851.76      ,  5226.8       ,  8773.85      , ...,\n",
       "        3490.93      ,  6386.23333333,  6914.61      ])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d068e8c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8886524a",
   "metadata": {},
   "source": [
    "## Defining evaluation metric :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1a394304",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mape(y_true,y_pred):\n",
    "    y_true,y_pred=np.array(y_true),np.array(y_pred)\n",
    "    \n",
    "    return np.mean(np.abs((y_true-y_pred)/y_true))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4070ae9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.136333075099241"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape(y_test,forest.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36dc96d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "372afb7a",
   "metadata": {},
   "source": [
    "## Automating ML Pipeline :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "927fbeeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(ml_model):\n",
    "    \n",
    "    model=ml_model.fit(X_train,y_train)\n",
    "    print('Training_score: {}'.format(model.score(X_train,y_train)))\n",
    "    y_prediction=model.predict(X_test)\n",
    "    print('Predictions are : {}'.format(y_prediction))\n",
    "    print('\\n')\n",
    "    \n",
    "    from sklearn import metrics\n",
    "    r2_score=metrics.r2_score(y_test,y_prediction)\n",
    "    print('r2_score: {}'.format(r2_score))\n",
    "    print('MSE : ', metrics.mean_squared_error(y_test,y_prediction))\n",
    "    print('MAE : ', metrics.mean_absolute_error(y_test,y_prediction))\n",
    "    print('RMSE : ', np.sqrt(metrics.mean_squared_error(y_test,y_prediction)))\n",
    "    print('MAPE : ', mape(y_test,y_prediction))\n",
    "    sns.histplot(y_test-y_prediction,kde=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "dd4ff6f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_score: 0.9517644469268397\n",
      "Predictions are : [16886.13  5525.39  8835.83 ...  3563.21  6292.09  6822.41]\n",
      "\n",
      "\n",
      "r2_score: 0.8100316281441529\n",
      "MSE :  3698228.2123831976\n",
      "MAE :  1175.735763613295\n",
      "RMSE :  1923.0777967578945\n",
      "MAPE :  13.183179691452773\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict(RandomForestRegressor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5da9be9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f9e17750",
   "metadata": {},
   "source": [
    "## Hypertuning ml model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "4781ae5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "f2a2ec60",
   "metadata": {},
   "outputs": [],
   "source": [
    "### initialise your estimator\n",
    "reg_rf=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "2a81d83b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1000., 1040., 1080., 1120., 1160., 1200.])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(start=1000,stop=1200,num=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "6f62eb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators=[int(x) for x in np.linspace(start=1000,stop=1200,num=6)]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features=[\"auto\", \"sqrt\"]\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth=[int(x) for x in np.linspace(start=5,stop=30,num=4)]\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split=[5,10,15,100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "8dcd67f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the grid or hyper-parameter space\n",
    "random_grid={\n",
    "    'n_estimators':n_estimators,\n",
    "    'max_features':max_features,\n",
    "    'max_depth':max_depth,\n",
    "    'min_samples_split':min_samples_split\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "6a6e6ccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': [1000, 1040, 1080, 1120, 1160, 1200],\n",
       " 'max_features': ['auto', 'sqrt'],\n",
       " 'max_depth': [5, 13, 21, 30],\n",
       " 'min_samples_split': [5, 10, 15, 100]}"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "9d5c803f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_Random=RandomizedSearchCV(reg_rf,param_distributions=random_grid,cv=3,verbose=2,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "4548bfc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [5, 13, 21, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_split': [5, 10, 15, 100],\n",
       "                                        'n_estimators': [1000, 1040, 1080, 1120,\n",
       "                                                         1160, 1200]},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_Random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "6aebd4a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1040,\n",
       " 'min_samples_split': 10,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 21}"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### to get your best model..\n",
    "rf_Random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "3f67fd9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred2=rf_Random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "022e6400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8301506587252432"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "metrics.r2_score(y_test,pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffafab2a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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