Yogesh Shrestha_NP03A190299.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Set\n",
    "# Data set is a collection of data numbers or values that is corelated to a particular subject. \n",
    "# A collection of documents or files can also be included in a data set.\n",
    "# for example, example scoresos each students in a particular class is a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Preprocessing\n",
    "#Data preprocessing is a data mining approach that entails converting raw data into a format that can be understood. \n",
    "#Real-world data is frequently inadequate, inconsistent, and/or missing in specific behaviors or patterns, as well as including numerous mistakes. \n",
    "#Preprocessing data is a tried and true way of overcoming such problems.\n",
    "#Data preprocessing is divided into 4 stages\n",
    "#1.Data cleaning\n",
    "#2.Data integration\n",
    "#3.Data reduction\n",
    "#4.Data calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary Library\n",
    "import pandas as pd #to work with the array\n",
    "import numpy as np #data science tool for data wrangling and analysis\n",
    "import matplotlib.pyplot as plt # to visualize data\n",
    "import seaborn as sns # to make statical graphics\n",
    "import warnings #to ignire unwanted warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10678</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>9/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>19:55</td>\n",
       "      <td>22:25</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10679</th>\n",
       "      <td>Air India</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>20:45</td>\n",
       "      <td>23:20</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10680</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>08:20</td>\n",
       "      <td>11:20</td>\n",
       "      <td>3h</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10681</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>11:30</td>\n",
       "      <td>14:10</td>\n",
       "      <td>2h 40m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10682</th>\n",
       "      <td>Air India</td>\n",
       "      <td>9/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → GOI → BOM → COK</td>\n",
       "      <td>10:55</td>\n",
       "      <td>19:15</td>\n",
       "      <td>8h 20m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>11753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10683 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Airline Date_of_Journey    Source Destination  \\\n",
       "0           IndiGo      24/03/2019  Banglore   New Delhi   \n",
       "1        Air India       1/05/2019   Kolkata    Banglore   \n",
       "2      Jet Airways       9/06/2019     Delhi      Cochin   \n",
       "3           IndiGo      12/05/2019   Kolkata    Banglore   \n",
       "4           IndiGo      01/03/2019  Banglore   New Delhi   \n",
       "...            ...             ...       ...         ...   \n",
       "10678     Air Asia       9/04/2019   Kolkata    Banglore   \n",
       "10679    Air India      27/04/2019   Kolkata    Banglore   \n",
       "10680  Jet Airways      27/04/2019  Banglore       Delhi   \n",
       "10681      Vistara      01/03/2019  Banglore   New Delhi   \n",
       "10682    Air India       9/05/2019     Delhi      Cochin   \n",
       "\n",
       "                       Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
       "0                  BLR → DEL    22:20  01:10 22 Mar   2h 50m    non-stop   \n",
       "1      CCU → IXR → BBI → BLR    05:50         13:15   7h 25m     2 stops   \n",
       "2      DEL → LKO → BOM → COK    09:25  04:25 10 Jun      19h     2 stops   \n",
       "3            CCU → NAG → BLR    18:05         23:30   5h 25m      1 stop   \n",
       "4            BLR → NAG → DEL    16:50         21:35   4h 45m      1 stop   \n",
       "...                      ...      ...           ...      ...         ...   \n",
       "10678              CCU → BLR    19:55         22:25   2h 30m    non-stop   \n",
       "10679              CCU → BLR    20:45         23:20   2h 35m    non-stop   \n",
       "10680              BLR → DEL    08:20         11:20       3h    non-stop   \n",
       "10681              BLR → DEL    11:30         14:10   2h 40m    non-stop   \n",
       "10682  DEL → GOI → BOM → COK    10:55         19:15   8h 20m     2 stops   \n",
       "\n",
       "      Additional_Info  Price  \n",
       "0             No info   3897  \n",
       "1             No info   7662  \n",
       "2             No info  13882  \n",
       "3             No info   6218  \n",
       "4             No info  13302  \n",
       "...               ...    ...  \n",
       "10678         No info   4107  \n",
       "10679         No info   4145  \n",
       "10680         No info   7229  \n",
       "10681         No info  12648  \n",
       "10682         No info  11753  \n",
       "\n",
       "[10683 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Dataset\n",
    "data_frame = pd.read_excel('airlines.xlsx') #loading data or reading xlxs file\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame. #to find shape of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            False\n",
       "Date_of_Journey    False\n",
       "Source             False\n",
       "Destination        False\n",
       "Route               True\n",
       "Dep_Time           False\n",
       "Arrival_Time       False\n",
       "Duration           False\n",
       "Total_Stops         True\n",
       "Additional_Info    False\n",
       "Price              False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.isnull().any() # To find column missing value is located"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.isna().sum() #to know number if missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame.dropna(inplace = True) # to drop nan values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10682 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10682 non-null  object\n",
      " 1   Date_of_Journey  10682 non-null  object\n",
      " 2   Source           10682 non-null  object\n",
      " 3   Destination      10682 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10682 non-null  object\n",
      " 6   Arrival_Time     10682 non-null  object\n",
      " 7   Duration         10682 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10682 non-null  object\n",
      " 10  Price            10682 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 1001.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data_frame.info() #to know type of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_to_datetime(df,col): #to change columns object to date time\n",
    "    df[col] = pd.to_datetime(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date_of_Journey\n",
      "Dep_Time\n",
      "Arrival_Time\n"
     ]
    }
   ],
   "source": [
    "for i in['Date_of_Journey','Dep_Time','Arrival_Time']: #using loop to print certain data\n",
    "    print(i)\n",
    "    change_to_datetime(data_frame,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.dtypes #to find data typyes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting object data type into date time formati\n",
    "data_frame['journey_day']= data_frame['Date_of_Journey'].dt.day  \n",
    "data_frame['journey_month']= data_frame['Date_of_Journey'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2021-07-06 22:20:00</td>\n",
       "      <td>2021-03-22 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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2021-07-06 05:50:00</td>\n",
       "      <td>2021-07-06 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>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2021-07-06 09:25:00</td>\n",
       "      <td>2021-06-10 04:25:00</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>2021-07-06 18:05:00</td>\n",
       "      <td>2021-07-06 23:30:00</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>2021-07-06 16:50:00</td>\n",
       "      <td>2021-07-06 21:35:00</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </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      2019-01-05   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways      2019-09-06     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      2019-12-05   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      2019-01-03  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "             Dep_Time        Arrival_Time Duration Total_Stops  \\\n",
       "0 2021-07-06 22:20:00 2021-03-22 01:10:00   2h 50m    non-stop   \n",
       "1 2021-07-06 05:50:00 2021-07-06 13:15:00   7h 25m     2 stops   \n",
       "2 2021-07-06 09:25:00 2021-06-10 04:25:00      19h     2 stops   \n",
       "3 2021-07-06 18:05:00 2021-07-06 23:30:00   5h 25m      1 stop   \n",
       "4 2021-07-06 16:50:00 2021-07-06 21:35:00   4h 45m      1 stop   \n",
       "\n",
       "  Additional_Info  Price  journey_day  journey_month  \n",
       "0         No info   3897           24              3  \n",
       "1         No info   7662            5              1  \n",
       "2         No info  13882            6              9  \n",
       "3         No info   6218            5             12  \n",
       "4         No info  13302            3              1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame.drop('Date_of_Journey', axis = 1, inplace = True) #droping unneccessary data colmun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
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       "  </thead>\n",
       "  <tbody>\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>2021-07-06 22:20:00</td>\n",
       "      <td>2021-03-22 01:10:00</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
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       "      <td>24</td>\n",
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       "      <td>Air India</td>\n",
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       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
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       "      <td>2021-07-06 13:15:00</td>\n",
       "      <td>7h 25m</td>\n",
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       "      <td>7662</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "     Airline    Source Destination                  Route            Dep_Time  \\\n",
       "0     IndiGo  Banglore   New Delhi              BLR → DEL 2021-07-06 22:20:00   \n",
       "1  Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR 2021-07-06 05:50:00   \n",
       "\n",
       "         Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0 2021-03-22 01:10:00   2h 50m    non-stop         No info   3897   \n",
       "1 2021-07-06 13:15:00   7h 25m     2 stops         No info   7662   \n",
       "\n",
       "   journey_day  journey_month  \n",
       "0           24              3  \n",
       "1            5              1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "data_frame.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extra Hour, Minute from Dep_time and Arrival_time and drop Dep_time and Arrival_time\n",
    "def extract_hour(df, col):\n",
    "    df[col+'_hour'] = df[col].dt.hour\n",
    "def extract_minute(df,col):\n",
    "    df[col+'_minute'] = df[col].dt.minute\n",
    "def drop_column(df, col):\n",
    "    df.drop(col,axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_hour(data_frame,'Dep_Time')\n",
    "extract_minute(data_frame,'Dep_Time')\n",
    "\n",
    "extract_hour(data_frame,'Arrival_Time')\n",
    "extract_minute(data_frame,'Arrival_Time')\n",
    "\n",
    "drop_column(data_frame,'Dep_Time')\n",
    "drop_column(data_frame,'Arrival_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <td>non-stop</td>\n",
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       "      <td>3897</td>\n",
       "      <td>24</td>\n",
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       "      <td>22</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
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       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
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       "      <td>IndiGo</td>\n",
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       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
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       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            5              1   \n",
       "2     2 stops         No info  13882            6              9   \n",
       "3      1 stop         No info   6218            5             12   \n",
       "4      1 stop         No info  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \n",
       "0             22               20                  1                   10  \n",
       "1              5               50                 13                   15  \n",
       "2              9               25                  4                   25  \n",
       "3             18                5                 23                   30  \n",
       "4             16               50                 21                   35  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       " '3h 5m',\n",
       " '1h 30m',\n",
       " '7h 5m',\n",
       " '6h 10m',\n",
       " '8h',\n",
       " '12h 5m',\n",
       " '12h 55m',\n",
       " '9h 35m',\n",
       " '15h 10m',\n",
       " '11h 40m',\n",
       " '2h 55m',\n",
       " '4h 30m',\n",
       " '2h 40m',\n",
       " '2h 30m',\n",
       " '24h 10m',\n",
       " '28h 20m',\n",
       " '1h 25m',\n",
       " '10h 10m',\n",
       " '11h 30m',\n",
       " '2h 50m',\n",
       " '13h 45m',\n",
       " '6h 25m',\n",
       " '12h 55m',\n",
       " '4h 25m',\n",
       " '13h 30m',\n",
       " '24h 5m',\n",
       " '16h 5m',\n",
       " '8h 20m',\n",
       " '3h 40m',\n",
       " '9h 30m',\n",
       " '28h 15m',\n",
       " '13h 20m',\n",
       " '17h 50m',\n",
       " '20h 20m',\n",
       " '28h 5m',\n",
       " '15h 25m',\n",
       " '12h 5m',\n",
       " '10h 20m',\n",
       " '12h 25m',\n",
       " '7h 35m',\n",
       " '2h 50m',\n",
       " '4h 35m',\n",
       " '11h 15m',\n",
       " '2h 35m',\n",
       " '26h',\n",
       " '2h 25m',\n",
       " '2h 35m',\n",
       " '14h 15m',\n",
       " '2h 35m',\n",
       " '22h 5m',\n",
       " '6h 35m',\n",
       " '8h 30m',\n",
       " '18h 45m',\n",
       " '35h 15m',\n",
       " '2h 15m',\n",
       " '2h 30m',\n",
       " '2h 20m',\n",
       " '2h 30m',\n",
       " '7h 35m',\n",
       " '35h 35m',\n",
       " '26h 30m',\n",
       " '11h 30m',\n",
       " '26h 40m',\n",
       " '3h',\n",
       " '1h 30m',\n",
       " '2h 55m',\n",
       " '14h 15m',\n",
       " '16h 55m',\n",
       " '12h 20m',\n",
       " '29h 35m',\n",
       " '20h 20m',\n",
       " '8h 25m',\n",
       " '13h 50m',\n",
       " '10h',\n",
       " '19h 25m',\n",
       " '28h',\n",
       " '2h 50m',\n",
       " '17h 20m',\n",
       " '2h 45m',\n",
       " '2h 55m',\n",
       " '13h 25m',\n",
       " '12h 55m',\n",
       " '9h 45m',\n",
       " '21h 25m',\n",
       " '15h 40m',\n",
       " '2h 20m',\n",
       " '14h 25m',\n",
       " '2h 50m',\n",
       " '6h 35m',\n",
       " '22h 10m',\n",
       " '14h 25m',\n",
       " '2h 30m',\n",
       " '23h 35m',\n",
       " '13h 5m',\n",
       " '1h 30m',\n",
       " '5h 5m',\n",
       " '1h 20m',\n",
       " '22h 55m',\n",
       " '1h 30m',\n",
       " '2h 35m',\n",
       " '37h 20m',\n",
       " '2h 50m',\n",
       " '2h 50m',\n",
       " '36h 10m',\n",
       " '9h 15m',\n",
       " '1h 25m',\n",
       " '5h 5m',\n",
       " '2h 15m',\n",
       " '15h 10m',\n",
       " '25h 55m',\n",
       " '5h 40m',\n",
       " '7h 30m',\n",
       " '17h 45m',\n",
       " '2h 20m',\n",
       " '8h 15m',\n",
       " '3h 40m',\n",
       " '5h 55m',\n",
       " '2h 30m',\n",
       " '17h 30m',\n",
       " '13h 50m',\n",
       " '2h 15m',\n",
       " '21h 35m',\n",
       " '9h 30m',\n",
       " '8h 40m',\n",
       " '10h 25m',\n",
       " '35h 5m',\n",
       " '14h 15m',\n",
       " '3h',\n",
       " '25h 50m',\n",
       " '2h 30m',\n",
       " '8h 40m',\n",
       " '10h',\n",
       " '11h 30m',\n",
       " '7h 30m',\n",
       " ...]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration = list(data_frame['Duration']) #list od data inside duration\n",
    "duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2h', '25m']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration[5].split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(duration[5].split(' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(duration)): #using for loop \n",
    "    if len(duration[i].split(' '))==2:\n",
    "        pass\n",
    "\n",
    "    else:\n",
    "        if 'h' in duration[i]:\n",
    "            duration[i]= duration[i] + ' 0m'\n",
    "        else:\n",
    "            duration[i] = '0h '+duration[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_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>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h 0m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   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",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
       "0    non-stop         No info   3897           24              3   \n",
       "1     2 stops         No info   7662            5              1   \n",
       "2     2 stops         No info  13882            6              9   \n",
       "3      1 stop         No info   6218            5             12   \n",
       "4      1 stop         No info  13302            3              1   \n",
       "\n",
       "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \n",
       "0             22               20                  1                   10  \n",
       "1              5               50                 13                   15  \n",
       "2              9               25                  4                   25  \n",
       "3             18                5                 23                   30  \n",
       "4             16               50                 21                   35  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['Duration'] = duration #initiallizing data \n",
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2h', '25m']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separating hour and minute from duration column\n",
    "duration[5].split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'25'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration[5].split(' ')[1][:-1] #slicling method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hour(x):\n",
    "    return x.split(' ')[0][:-1]\n",
    "def minute(x):\n",
    "    return x.split(' ')[1][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame['Duration_Hour'] =data_frame['Duration'].apply(hour)\n",
    "data_frame['Duration_Minute'] =data_frame['Duration'].apply(minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_Hour</th>\n",
       "      <th>Duration_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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Airline    Source Destination      Route Duration Total_Stops  \\\n",
       "0  IndiGo  Banglore   New Delhi  BLR → DEL   2h 50m    non-stop   \n",
       "\n",
       "  Additional_Info  Price  journey_day  journey_month  Dep_Time_hour  \\\n",
       "0         No info   3897           24              3             22   \n",
       "\n",
       "   Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute Duration_Hour  \\\n",
       "0               20                  1                   10             2   \n",
       "\n",
       "  Duration_Minute  \n",
       "0              50  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>journey_day</th>\n",
       "      <th>journey_month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <th>Duration_Hour</th>\n",
       "      <th>Duration_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>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Airline    Source Destination      Route Total_Stops Additional_Info  Price  \\\n",
       "0  IndiGo  Banglore   New Delhi  BLR → DEL    non-stop         No info   3897   \n",
       "\n",
       "   journey_day  journey_month  Dep_Time_hour  Dep_Time_minute  \\\n",
       "0           24              3             22               20   \n",
       "\n",
       "   Arrival_Time_hour  Arrival_Time_minute Duration_Hour Duration_Minute  \n",
       "0                  1                   10             2              50  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.drop('Duration', axis = 1,inplace = True)\n",
    "data_frame.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                object\n",
       "Source                 object\n",
       "Destination            object\n",
       "Route                  object\n",
       "Total_Stops            object\n",
       "Additional_Info        object\n",
       "Price                   int64\n",
       "journey_day             int64\n",
       "journey_month           int64\n",
       "Dep_Time_hour           int64\n",
       "Dep_Time_minute         int64\n",
       "Arrival_Time_hour       int64\n",
       "Arrival_Time_minute     int64\n",
       "Duration_Hour          object\n",
       "Duration_Minute        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting object types to int type\n",
    "data_frame['Duration_Hour'] = data_frame['Duration_Hour'].astype(int) \n",
    "data_frame['Duration_Minute'] = data_frame['Duration_Minute'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                object\n",
       "Source                 object\n",
       "Destination            object\n",
       "Route                  object\n",
       "Total_Stops            object\n",
       "Additional_Info        object\n",
       "Price                   int64\n",
       "journey_day             int64\n",
       "journey_month           int64\n",
       "Dep_Time_hour           int64\n",
       "Dep_Time_minute         int64\n",
       "Arrival_Time_hour       int64\n",
       "Arrival_Time_minute     int64\n",
       "Duration_Hour           int32\n",
       "Duration_Minute         int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_column = [col for col in data_frame.columns if data_frame[col].dtype=='O'] # to Fetch Object Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Airline', 'Source', 'Destination', 'Route', 'Total_Stops', 'Additional_Info']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "integer_column = [col for col in data_frame.columns if data_frame[col].dtype!='O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Price',\n",
       " 'journey_day',\n",
       " 'journey_month',\n",
       " 'Dep_Time_hour',\n",
       " 'Dep_Time_minute',\n",
       " 'Arrival_Time_hour',\n",
       " 'Arrival_Time_minute',\n",
       " 'Duration_Hour',\n",
       " 'Duration_Minute']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integer_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  \n",
       "0         No info  \n",
       "1         No info  \n",
       "2         No info  \n",
       "3         No info  \n",
       "4         No info  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data = data_frame[categorical_column]\n",
    "categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1751\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              818\n",
       "Vistara                               479\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data['Airline'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performing One hot encoding on Airline Column with dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Trujet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Air India  GoAir  IndiGo  Jet Airways  Jet Airways Business  \\\n",
       "0          0      0       1            0                     0   \n",
       "1          1      0       0            0                     0   \n",
       "2          0      0       0            1                     0   \n",
       "3          0      0       1            0                     0   \n",
       "4          0      0       1            0                     0   \n",
       "\n",
       "   Multiple carriers  Multiple carriers Premium economy  SpiceJet  Trujet  \\\n",
       "0                  0                                  0         0       0   \n",
       "1                  0                                  0         0       0   \n",
       "2                  0                                  0         0       0   \n",
       "3                  0                                  0         0       0   \n",
       "4                  0                                  0         0       0   \n",
       "\n",
       "   Vistara  Vistara Premium economy  \n",
       "0        0                        0  \n",
       "1        0                        0  \n",
       "2        0                        0  \n",
       "3        0                        0  \n",
       "4        0                        0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Airline = pd.get_dummies(categorical_data['Airline'], drop_first = True)\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Airline', ylabel='Price'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,5)) #ploting relation od data into graph\n",
    "sns.boxplot(x='Airline',y='Price',data = data_frame.sort_values('Price',                                                             ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Performing One Hot encoding on Source Column with dummy varriable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi       4536\n",
       "Kolkata     2871\n",
       "Banglore    2197\n",
       "Mumbai       697\n",
       "Chennai      381\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data['Source'].value_counts() #to find multiple sum of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Chennai  Delhi  Kolkata  Mumbai\n",
       "0        0      0        0       0\n",
       "1        0      0        1       0\n",
       "2        0      1        0       0\n",
       "3        0      0        1       0\n",
       "4        0      0        0       0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Source = pd.get_dummies(categorical_data['Source'], drop_first = True)\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Performing One hot encoding on destination column with dummy variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       4536\n",
       "Banglore     2871\n",
       "Delhi        1265\n",
       "New Delhi     932\n",
       "Hyderabad     697\n",
       "Kolkata       381\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data['Destination'].value_counts() #to count values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cochin  Delhi  Hyderabad  Kolkata  New Delhi\n",
       "0       0      0          0        0          1\n",
       "1       0      0          0        0          0\n",
       "2       1      0          0        0          0\n",
       "3       0      0          0        0          0\n",
       "4       0      0          0        0          1"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Destination = pd.get_dummies(categorical_data['Destination'], drop_first = True)\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling Route Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_data['Route_01'] = categorical_data['Route'].str.split('→').str[0]\n",
    "\n",
    "categorical_data['Route_02'] = categorical_data['Route'].str.split('→').str[1]\n",
    "categorical_data['Route_03'] = categorical_data['Route'].str.split('→').str[2]\n",
    "categorical_data['Route_04'] = categorical_data['Route'].str.split('→').str[3]\n",
    "categorical_data['Route_05'] = categorical_data['Route'].str.split('→').str[4]\n",
    "categorical_data['Route_06'] = categorical_data['Route'].str.split('→').str[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_data.drop('Route', axis =1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Route_01</th>\n",
       "      <th>Route_02</th>\n",
       "      <th>Route_03</th>\n",
       "      <th>Route_04</th>\n",
       "      <th>Route_05</th>\n",
       "      <th>Route_06</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>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>BLR</td>\n",
       "      <td>DEL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>CCU</td>\n",
       "      <td>IXR</td>\n",
       "      <td>BBI</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Airline    Source Destination Total_Stops Additional_Info Route_01  \\\n",
       "0     IndiGo  Banglore   New Delhi    non-stop         No info     BLR    \n",
       "1  Air India   Kolkata    Banglore     2 stops         No info     CCU    \n",
       "\n",
       "  Route_02 Route_03 Route_04 Route_05 Route_06  \n",
       "0      DEL      NaN      NaN      NaN      NaN  \n",
       "1     IXR      BBI       BLR      NaN      NaN  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                0\n",
       "Source                 0\n",
       "Destination            0\n",
       "Total_Stops            0\n",
       "Additional_Info        0\n",
       "Route_01               0\n",
       "Route_02               0\n",
       "Route_03            3491\n",
       "Route_04            9116\n",
       "Route_05           10636\n",
       "Route_06           10681\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['Route_03','Route_04','Route_05']:\n",
    "    categorical_data[i].fillna('None',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                0\n",
       "Source                 0\n",
       "Destination            0\n",
       "Total_Stops            0\n",
       "Additional_Info        0\n",
       "Route_01               0\n",
       "Route_02               0\n",
       "Route_03               0\n",
       "Route_04               0\n",
       "Route_05               0\n",
       "Route_06           10681\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Route_01</th>\n",
       "      <th>Route_02</th>\n",
       "      <th>Route_03</th>\n",
       "      <th>Route_04</th>\n",
       "      <th>Route_05</th>\n",
       "      <th>Route_06</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>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>BLR</td>\n",
       "      <td>DEL</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>CCU</td>\n",
       "      <td>IXR</td>\n",
       "      <td>BBI</td>\n",
       "      <td>BLR</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>DEL</td>\n",
       "      <td>LKO</td>\n",
       "      <td>BOM</td>\n",
       "      <td>COK</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>CCU</td>\n",
       "      <td>NAG</td>\n",
       "      <td>BLR</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>BLR</td>\n",
       "      <td>NAG</td>\n",
       "      <td>DEL</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Total_Stops Additional_Info Route_01  \\\n",
       "0       IndiGo  Banglore   New Delhi    non-stop         No info     BLR    \n",
       "1    Air India   Kolkata    Banglore     2 stops         No info     CCU    \n",
       "2  Jet Airways     Delhi      Cochin     2 stops         No info     DEL    \n",
       "3       IndiGo   Kolkata    Banglore      1 stop         No info     CCU    \n",
       "4       IndiGo  Banglore   New Delhi      1 stop         No info     BLR    \n",
       "\n",
       "  Route_02 Route_03 Route_04 Route_05 Route_06  \n",
       "0      DEL     None     None     None      NaN  \n",
       "1     IXR      BBI       BLR     None      NaN  \n",
       "2     LKO      BOM       COK     None      NaN  \n",
       "3     NAG       BLR     None     None      NaN  \n",
       "4     NAG       DEL     None     None      NaN  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airline has total 12 categories\n",
      "Source has total 5 categories\n",
      "Destination has total 6 categories\n",
      "Total_Stops has total 5 categories\n",
      "Additional_Info has total 10 categories\n",
      "Route_01 has total 5 categories\n",
      "Route_02 has total 45 categories\n",
      "Route_03 has total 30 categories\n",
      "Route_04 has total 14 categories\n",
      "Route_05 has total 6 categories\n",
      "Route_06 has total 1 categories\n"
     ]
    }
   ],
   "source": [
    "for i in categorical_data.columns:\n",
    "    print('{} has total {} categories'.format(i, len(categorical_data[i].value_counts())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoder for different routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Source', 'Destination', 'Total_Stops', 'Additional_Info',\n",
       "       'Route_01', 'Route_02', 'Route_03', 'Route_04', 'Route_05', 'Route_06'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['Route_01','Route_02','Route_03','Route_04','Route_05','Route_06']:\n",
    "    categorical_data[i] = encoder.fit_transform(categorical_data[i])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_stops ={'non-stop':0,\n",
    "            '2 stops':2,\n",
    "             '1 stop':1,\n",
    "             '3 stops':3,\n",
    "             '4 stops':4\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_data['Total_Stops']= categorical_data['Total_Stops'].map(dict_stops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Route_01</th>\n",
       "      <th>Route_02</th>\n",
       "      <th>Route_03</th>\n",
       "      <th>Route_04</th>\n",
       "      <th>Route_05</th>\n",
       "      <th>Route_06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>No info</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>No info</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>No info</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops Additional_Info  Route_01  \\\n",
       "0       IndiGo  Banglore   New Delhi            0         No info         0   \n",
       "1    Air India   Kolkata    Banglore            2         No info         2   \n",
       "2  Jet Airways     Delhi      Cochin            2         No info         3   \n",
       "3       IndiGo   Kolkata    Banglore            1         No info         2   \n",
       "4       IndiGo  Banglore   New Delhi            1         No info         0   \n",
       "\n",
       "   Route_02  Route_03  Route_04  Route_05  Route_06  \n",
       "0        13        29        13         5         1  \n",
       "1        25         1         3         5         1  \n",
       "2        32         4         5         5         1  \n",
       "3        34         3        13         5         1  \n",
       "4        34         8        13         5         1  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Route_01</th>\n",
       "      <th>Route_02</th>\n",
       "      <th>Route_03</th>\n",
       "      <th>Route_04</th>\n",
       "      <th>Route_05</th>\n",
       "      <th>Route_06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Airline    Source Destination  Total_Stops  Route_01  Route_02  Route_03  \\\n",
       "0     IndiGo  Banglore   New Delhi            0         0        13        29   \n",
       "1  Air India   Kolkata    Banglore            2         2        25         1   \n",
       "\n",
       "   Route_04  Route_05  Route_06  \n",
       "0        13         5         1  \n",
       "1         3         5         1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.drop('Additional_Info' , axis = 1, inplace = True)\n",
    "\n",
    "categorical_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Route_01</th>\n",
       "      <th>Route_02</th>\n",
       "      <th>Route_03</th>\n",
       "      <th>Route_04</th>\n",
       "      <th>Route_05</th>\n",
       "      <th>Route_06</th>\n",
       "      <th>...</th>\n",
       "      <th>New Delhi</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_Hour</th>\n",
       "      <th>Duration_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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination  Total_Stops  Route_01  Route_02  \\\n",
       "0       IndiGo  Banglore   New Delhi            0         0        13   \n",
       "1    Air India   Kolkata    Banglore            2         2        25   \n",
       "2  Jet Airways     Delhi      Cochin            2         3        32   \n",
       "3       IndiGo   Kolkata    Banglore            1         2        34   \n",
       "4       IndiGo  Banglore   New Delhi            1         0        34   \n",
       "\n",
       "   Route_03  Route_04  Route_05  Route_06  ...  New Delhi  Price  journey_day  \\\n",
       "0        29        13         5         1  ...          1   3897           24   \n",
       "1         1         3         5         1  ...          0   7662            5   \n",
       "2         4         5         5         1  ...          0  13882            6   \n",
       "3         3        13         5         1  ...          0   6218            5   \n",
       "4         8        13         5         1  ...          1  13302            3   \n",
       "\n",
       "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0              3             22               20                  1   \n",
       "1              1              5               50                 13   \n",
       "2              9              9               25                  4   \n",
       "3             12             18                5                 23   \n",
       "4              1             16               50                 21   \n",
       "\n",
       "   Arrival_Time_minute  Duration_Hour  Duration_Minute  \n",
       "0                   10              2               50  \n",
       "1                   15              7               25  \n",
       "2                   25             19                0  \n",
       "3                   30              5               25  \n",
       "4                   35              4               45  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame = pd.concat([categorical_data, Airline, Source, Destination, data_frame[integer_column]], axis=1) #concatination method\n",
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Route_01</th>\n",
       "      <th>Route_02</th>\n",
       "      <th>Route_03</th>\n",
       "      <th>Route_04</th>\n",
       "      <th>Route_05</th>\n",
       "      <th>Route_06</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>...</th>\n",
       "      <th>New Delhi</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_Hour</th>\n",
       "      <th>Duration_Minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Route_01  Route_02  Route_03  Route_04  Route_05  Route_06  \\\n",
       "0            0         0        13        29        13         5         1   \n",
       "1            2         2        25         1         3         5         1   \n",
       "\n",
       "   Air India  GoAir  IndiGo  ...  New Delhi  Price  journey_day  \\\n",
       "0          0      0       1  ...          1   3897           24   \n",
       "1          1      0       0  ...          0   7662            5   \n",
       "\n",
       "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
       "0              3             22               20                  1   \n",
       "1              1              5               50                 13   \n",
       "\n",
       "   Arrival_Time_minute  Duration_Hour  Duration_Minute  \n",
       "0                   10              2               50  \n",
       "1                   15              7               25  \n",
       "\n",
       "[2 rows x 36 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.drop(['Airline','Source','Destination'], axis = 1, inplace= True)\n",
    "\n",
    "data_frame.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To see all the column in my dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',36) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Route_01</th>\n",
       "      <th>Route_02</th>\n",
       "      <th>Route_03</th>\n",
       "      <th>Route_04</th>\n",
       "      <th>Route_05</th>\n",
       "      <th>Route_06</th>\n",
       "      <th>Air India</th>\n",
       "      <th>GoAir</th>\n",
       "      <th>IndiGo</th>\n",
       "      <th>Jet Airways</th>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <th>Multiple carriers</th>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <th>SpiceJet</th>\n",
       "      <th>Trujet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>New Delhi</th>\n",
       "      <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_Hour</th>\n",
       "      <th>Duration_Minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7662</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13882</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Stops  Route_01  Route_02  Route_03  Route_04  Route_05  Route_06  \\\n",
       "0            0         0        13        29        13         5         1   \n",
       "1            2         2        25         1         3         5         1   \n",
       "2            2         3        32         4         5         5         1   \n",
       "3            1         2        34         3        13         5         1   \n",
       "4            1         0        34         8        13         5         1   \n",
       "\n",
       "   Air India  GoAir  IndiGo  Jet Airways  Jet Airways Business  \\\n",
       "0          0      0       1            0                     0   \n",
       "1          1      0       0            0                     0   \n",
       "2          0      0       0            1                     0   \n",
       "3          0      0       1            0                     0   \n",
       "4          0      0       1            0                     0   \n",
       "\n",
       "   Multiple carriers  Multiple carriers Premium economy  SpiceJet  Trujet  \\\n",
       "0                  0                                  0         0       0   \n",
       "1                  0                                  0         0       0   \n",
       "2                  0                                  0         0       0   \n",
       "3                  0                                  0         0       0   \n",
       "4                  0                                  0         0       0   \n",
       "\n",
       "   Vistara  Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  \\\n",
       "0        0                        0        0      0        0       0       0   \n",
       "1        0                        0        0      0        1       0       0   \n",
       "2        0                        0        0      1        0       0       1   \n",
       "3        0                        0        0      0        1       0       0   \n",
       "4        0                        0        0      0        0       0       0   \n",
       "\n",
       "   Delhi  Hyderabad  Kolkata  New Delhi  Price  journey_day  journey_month  \\\n",
       "0      0          0        0          1   3897           24              3   \n",
       "1      0          0        0          0   7662            5              1   \n",
       "2      0          0        0          0  13882            6              9   \n",
       "3      0          0        0          0   6218            5             12   \n",
       "4      0          0        0          1  13302            3              1   \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",
       "2              9               25                  4                   25   \n",
       "3             18                5                 23                   30   \n",
       "4             16               50                 21                   35   \n",
       "\n",
       "   Duration_Hour  Duration_Minute  \n",
       "0              2               50  \n",
       "1              7               25  \n",
       "2             19                0  \n",
       "3              5               25  \n",
       "4              4               45  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlier Detection and Outlier imputation if available in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(df,col):\n",
    "    fig, (ax1,ax2) = plt.subplots(2,1)\n",
    "    sns.distplot(df[col],ax=ax1)\n",
    "    sns.boxplot(df[col],ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data_frame,'Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame['Price']= np.where(data_frame['Price']>=40000, data_frame['Price'].median(),\n",
    "                          data_frame['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data_frame,'Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3897.0\n",
       "1    7662.0\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Seperating independent and dependent variable\n",
    "X_data = data_frame.drop(['Price'],axis=1)\n",
    "X_data.head(2)\n",
    "y_data = data_frame['Price']\n",
    "y_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying feature selection technique by importing mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.18128992, 2.02948404, 2.79252836, 2.33104837, 1.45194091,\n",
       "       0.67901625, 1.79604696, 0.75735184, 0.11170964, 0.66852197,\n",
       "       0.92595193, 0.01801459, 0.55254776, 0.0095116 , 0.31592977,\n",
       "       0.00598202, 0.21259658, 0.        , 0.18474919, 1.53462756,\n",
       "       0.89279625, 0.29329623, 1.58422581, 0.40427894, 0.2969579 ,\n",
       "       0.17146382, 0.37675755, 1.08264084, 0.85602141, 1.43635903,\n",
       "       1.21008409, 1.82818876, 1.55819757, 1.77132379, 1.10022746])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_feature = mutual_info_classif(X_data, y_data)\n",
    "my_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>2.181290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_01</th>\n",
       "      <td>2.029484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_02</th>\n",
       "      <td>2.792528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_03</th>\n",
       "      <td>2.331048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_04</th>\n",
       "      <td>1.451941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_05</th>\n",
       "      <td>0.679016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_06</th>\n",
       "      <td>1.796047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air India</th>\n",
       "      <td>0.757352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoAir</th>\n",
       "      <td>0.111710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndiGo</th>\n",
       "      <td>0.668522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways</th>\n",
       "      <td>0.925952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <td>0.018015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers</th>\n",
       "      <td>0.552548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <td>0.009512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpiceJet</th>\n",
       "      <td>0.315930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trujet</th>\n",
       "      <td>0.005982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara</th>\n",
       "      <td>0.212597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>0.184749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1.534628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.892796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.293296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cochin</th>\n",
       "      <td>1.584226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.404279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>0.296958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.171464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi</th>\n",
       "      <td>0.376758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_day</th>\n",
       "      <td>1.082641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_month</th>\n",
       "      <td>0.856021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>1.436359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <td>1.210084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>1.828189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <td>1.558198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_Hour</th>\n",
       "      <td>1.771324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_Minute</th>\n",
       "      <td>1.100227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          0\n",
       "Total_Stops                        2.181290\n",
       "Route_01                           2.029484\n",
       "Route_02                           2.792528\n",
       "Route_03                           2.331048\n",
       "Route_04                           1.451941\n",
       "Route_05                           0.679016\n",
       "Route_06                           1.796047\n",
       "Air India                          0.757352\n",
       "GoAir                              0.111710\n",
       "IndiGo                             0.668522\n",
       "Jet Airways                        0.925952\n",
       "Jet Airways Business               0.018015\n",
       "Multiple carriers                  0.552548\n",
       "Multiple carriers Premium economy  0.009512\n",
       "SpiceJet                           0.315930\n",
       "Trujet                             0.005982\n",
       "Vistara                            0.212597\n",
       "Vistara Premium economy            0.000000\n",
       "Chennai                            0.184749\n",
       "Delhi                              1.534628\n",
       "Kolkata                            0.892796\n",
       "Mumbai                             0.293296\n",
       "Cochin                             1.584226\n",
       "Delhi                              0.404279\n",
       "Hyderabad                          0.296958\n",
       "Kolkata                            0.171464\n",
       "New Delhi                          0.376758\n",
       "journey_day                        1.082641\n",
       "journey_month                      0.856021\n",
       "Dep_Time_hour                      1.436359\n",
       "Dep_Time_minute                    1.210084\n",
       "Arrival_Time_hour                  1.828189\n",
       "Arrival_Time_minute                1.558198\n",
       "Duration_Hour                      1.771324\n",
       "Duration_Minute                    1.100227"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = pd.DataFrame(my_feature,index = X_data.columns)\n",
    "imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.16789995e+00, 2.08101597e+00, 2.76595591e+00, 2.33190552e+00,\n",
       "       1.53286404e+00, 7.36917159e-01, 1.80200721e+00, 7.97171703e-01,\n",
       "       9.35059888e-02, 6.81714391e-01, 9.22458584e-01, 2.07675916e-03,\n",
       "       5.48883739e-01, 0.00000000e+00, 3.12312268e-01, 5.14669628e-03,\n",
       "       2.36719409e-01, 1.38124084e-02, 1.69378737e-01, 1.53001249e+00,\n",
       "       8.90985746e-01, 2.93021895e-01, 1.54760853e+00, 3.93441861e-01,\n",
       "       2.86819251e-01, 1.78578467e-01, 3.78838269e-01, 1.08850759e+00,\n",
       "       8.94363479e-01, 1.43583311e+00, 1.23002468e+00, 1.87341321e+00,\n",
       "       1.53435614e+00, 1.78945114e+00, 1.08512372e+00])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_feature = mutual_info_classif(X_data, y_data)\n",
    "my_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>Important Feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Route_02</th>\n",
       "      <td>2.792528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_03</th>\n",
       "      <td>2.331048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>2.181290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_01</th>\n",
       "      <td>2.029484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>1.828189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_06</th>\n",
       "      <td>1.796047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_Hour</th>\n",
       "      <td>1.771324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cochin</th>\n",
       "      <td>1.584226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_minute</th>\n",
       "      <td>1.558198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1.534628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_04</th>\n",
       "      <td>1.451941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>1.436359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_minute</th>\n",
       "      <td>1.210084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_Minute</th>\n",
       "      <td>1.100227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_day</th>\n",
       "      <td>1.082641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways</th>\n",
       "      <td>0.925952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.892796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>journey_month</th>\n",
       "      <td>0.856021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air India</th>\n",
       "      <td>0.757352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route_05</th>\n",
       "      <td>0.679016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndiGo</th>\n",
       "      <td>0.668522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers</th>\n",
       "      <td>0.552548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.404279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi</th>\n",
       "      <td>0.376758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpiceJet</th>\n",
       "      <td>0.315930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>0.296958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.293296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara</th>\n",
       "      <td>0.212597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>0.184749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkata</th>\n",
       "      <td>0.171464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoAir</th>\n",
       "      <td>0.111710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Airways Business</th>\n",
       "      <td>0.018015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple carriers Premium economy</th>\n",
       "      <td>0.009512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trujet</th>\n",
       "      <td>0.005982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vistara Premium economy</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Important Feature\n",
       "Route_02                                    2.792528\n",
       "Route_03                                    2.331048\n",
       "Total_Stops                                 2.181290\n",
       "Route_01                                    2.029484\n",
       "Arrival_Time_hour                           1.828189\n",
       "Route_06                                    1.796047\n",
       "Duration_Hour                               1.771324\n",
       "Cochin                                      1.584226\n",
       "Arrival_Time_minute                         1.558198\n",
       "Delhi                                       1.534628\n",
       "Route_04                                    1.451941\n",
       "Dep_Time_hour                               1.436359\n",
       "Dep_Time_minute                             1.210084\n",
       "Duration_Minute                             1.100227\n",
       "journey_day                                 1.082641\n",
       "Jet Airways                                 0.925952\n",
       "Kolkata                                     0.892796\n",
       "journey_month                               0.856021\n",
       "Air India                                   0.757352\n",
       "Route_05                                    0.679016\n",
       "IndiGo                                      0.668522\n",
       "Multiple carriers                           0.552548\n",
       "Delhi                                       0.404279\n",
       "New Delhi                                   0.376758\n",
       "SpiceJet                                    0.315930\n",
       "Hyderabad                                   0.296958\n",
       "Mumbai                                      0.293296\n",
       "Vistara                                     0.212597\n",
       "Chennai                                     0.184749\n",
       "Kolkata                                     0.171464\n",
       "GoAir                                       0.111710\n",
       "Jet Airways Business                        0.018015\n",
       "Multiple carriers Premium economy           0.009512\n",
       "Trujet                                      0.005982\n",
       "Vistara Premium economy                     0.000000"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.columns= ['Important Feature']\n",
    "imp.sort_values(by = 'Important Feature', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Data wrangling, also known as data munging, is the act of converting and mapping data through one \"raw\" data type into another with the goal of making it more suitable and profitable for downstream applications such as data analysis. The objective of data wrangling is to ensure that the data is of high quality and usable. When it comes to data analysis, data analysts usually spend the bulk of their time wrangling data rather than really analyzing it.\n",
    "\n",
    "One of the most important jobs in data science and analysis is data wrangling, which involves procedures such as: \n",
    "Data Sorting: To rearranging values in ascending or descending order. \n",
    "Data Filtration: To generate a subset of the data that is available. \n",
    "Data Reduction: To remove or replace Unwanted values in data reduction.\n",
    "Data Access: To load data files\n",
    "Data Processing: To conduct aggregation, statistical, and other operations on particular values.\n",
    "\n",
    "With more raw data comes more data that isn't intrinsically valuable, which means more effort is needed cleaning and organizing the data before it can be examined, which is where data wrangling comes into effect.The outcome of data wrangling can give useful metadata statistics for gaining more insights into the data; nevertheless, it is critical to ensure that information is accurate, since inconsistent metadata might present obstacles. Data wrangling enables analysts to swiftly evaluate more complicated data, provide more reliable data, and make better judgments as a consequence. Because of the results it has provided, many firms have shifted to data wrangling.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The practice of correcting or eliminating inaccurate, corrupted, poorly structured, duplicated, or missing data from a dataset is known as data cleaning.\n",
    "\n",
    "There are several chances for data to be repeated or incorrectly labeled when integrating different data sources. Even if the data is right, results and algorithms are untrustworthy if the data is wrong. Because the procedures will differ from dataset to dataset, there is no one-size-fits-all approach to prescribing the exact stages in the data cleaning process. However, it's critical to create a pattern for the data cleaning procedure so you can be sure you're doing it correctly every time.\n",
    "\n",
    "We can clean data many ways:\n",
    "1) Removeing duplicate or irrelevant observations: Erase any undesired observations, such as duplicates or unnecessary observations, from your dataset. Repeated observations are most likely to occur during the data gathering process. Duplicate data can be created when you merge data sets from various sources, scraped data, or get data from clients or several departments.\n",
    "\n",
    "2) Fixing structural errors: When you analyze or transmit data and detect odd naming system, typos, or improper punctuation, you have structural problems. Incorrectly labeled sections or classes might result from these discrepancies. For example, the terms \"N/A\" and \"Not Applicable\" may occur in the same section, but they should be treated as such.\n",
    "\n",
    "3) Filtering unwanted outliers: There may frequently be one-off observations that do not fit inside the dataset you are examining at first look. If you have a good cause to eliminate an outliers, such as incorrect data input, doing so will make the data you're dealing with perform better. The presence of an outliers, on the other hand, might occasionally validate a hypothesis you're working on.\n",
    "\n",
    "4) Handleing missing data: You can delete observations with incomplete data, but this will result in the loss of data, so be aware of this before using it.\n",
    "\n",
    "Benefits of data cleaning\n",
    "Cleaning the data will boost overall productivity and help you to make decisions based on the best quality information available. The following are some of the advantages:\n",
    "1) Issues are eliminated when many data sources are used.\n",
    "2) Clients will be satisfied and staff will be less upset if there are fewer mistakes.\n",
    "3) Capability to lay out the various functions and what your data is supposed to do.\n",
    "4) Data cleansing will allow for more efficient corporate processes and faster decision-making."
   ]
  },
  {
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   "metadata": {},
   "source": [
    "# Data Integration"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The practice of integrating data from many sources to build a separate, unified picture is known as data integration.\n",
    "The centralised data offering gives consumers self-service access to their data in a uniform manner. It provides a comprehensive overview of key performance indicators (KPIs), user experience, market prospects, and so on.\n",
    "\n",
    "Data integration is important in today's company since it allows you to:\n",
    "1) Enhance cooperation and make omnichannel user experience easier.\n",
    "2) Save time by eliminating the need for manually data collection and analysis.\n",
    "3) Reduce rework and mistakes to increase productivity.\n",
    "4) Increase the value of your data insights and business intelligence.\n",
    "\n",
    "We talk about merging data from many sources in data integration, and you'd be asking what people understand by data. Modern businesses, including small businesses, have used a variety of digital technologies to aid them in their daily operations. Product development tools, as well as logistical and transactional processing tools, are examples of these. \n",
    "For example, a small business with 10 employees may not believe they require data integration, but consider their IT stack: Email tool (e.g. Mailchimp), Web host (e.g. Squarespace), Social media tool (e.g. Hootsuite) and others. \n",
    "Data integration is a complicated process, and there is no one-size-fits-all solution that will work for every circumstance or organization, particularly since many of the IT technologies needed to carry out connectivity are in continual flux.\n",
    "\n",
    "The three types of data integration methods are as follows:\n",
    "1) Manual: Manually data integration is time-consuming, requiring hours of duplicate, CSV uploads, spreadsheets, irritation, and time spent, among other things.\n",
    "\n",
    "2. Fully automated\n",
    "Integrating libraries with APIs are used to synchronize automatically with a single click.\n",
    "\n",
    "3. Manufactured\n",
    "To build data-flows and allow integration, a technical team creates and maintains custom APIs and webhooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Reduction"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Data reduction is a storage capacity reduction technique that reduces data to its basic form to save up space on a storage device. There are numerous techniques to decrease data, but the concept is simple: fit as much info as possible into storage space to increase capacity.\n",
    "Terabytes of data can be stored in a database or database server. As a result, data analysis and mining on such massive datasets may take a long time.Data reduction methods can be used to provide a smaller-volume representation of the data collection that still contains important information.\n",
    "\n",
    "Data reduction strategy:\n",
    "1) Data Cube Aggregation: In the creation of a data cube, aggregation procedures are done to the data.\n",
    "\n",
    "2) Reduction of two dimensions: In the reduction of dimensionality The data set size is reduced by detecting and removing unnecessary characteristics.\n",
    "\n",
    "3) Compression of Data: To decrease the amount of the data collection, encoding methods are utilized.\n",
    "\n",
    "Benefits of data reduction\n",
    "The fundamental advantage of data compression is obvious: the more data you can store into a terabyte of disk space, the less capacity you'll need to buy. Data reduction can be used to:\n",
    "1) Conserve energy.\n",
    "\n",
    "2) Physical storage expenses should be reduced.\n",
    "\n",
    "3) Reduce the size of your data center\n",
    "\n",
    "Data reduction improves the efficiency of a storage device and has a direct influence on the amount of money you spend on capacity.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "The process of converting data from one format to another is known as data transformation. Gathering original data and turning it into clean, useable data is the most frequent data transformation procedure. \n",
    "Data transformation helps organizations make better data-driven choices by increasing the efficiency of business and analytical operations. An analysis will decide the data structure throughout the data transformation process. \n",
    "Before conducting the transformation, they'll do data mapping and retrieve the features from its actual source. Finally, they'll save the modified data in a database using the proper technology.\n",
    "\n",
    "The Process of Data Transformation\n",
    "ETL stands for Extract, Load, and Transform, and it refers to the complete data transformation process. Analysts can transform data to the required format using the ETL procedure. The following are the steps in the data transformation process:\n",
    "1) Data discovery\n",
    "2) Data Mapping\n",
    "3) Data Extraction\n",
    "4) Code Generation and Execution\n",
    "5) Review \n",
    "6) sending\n",
    "\n",
    "Benefits of data transformation \n",
    "Companies may benefit from data transformation in a variety of different ways. Here are a few of the most significant advantages of data transformation:\n",
    "1) Better Organization: Humans and computers may both benefit from transformed data.\n",
    "\n",
    "2) Improved Data Quality: Poor data comes with a slew of dangers and expenses. Data transformation can aid in the elimination of quality concerns like missing values and other discrepancies in your organization's data.\n",
    "\n",
    "3) Performance Faster Queries: Because altered data is saved and standardized at an origin, it may be retrieved quickly and simply.\n",
    "\n",
    "4) Better Data Management: Data is continuously being generated by organizations from a growing number of sources. It might be more difficult to manage and interpret metadata if there are discrepancies in it. Data transformation improves the organization and understanding of your metadata.\n",
    "\n",
    "5) More Use Of Out Data: While organizations acquire data on a regular basis, much of it remains unexamined. By standardizing and providing data more accessible, modification makes it simpler to get the most out of it."
   ]
  }
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