EDA/Exploratory_Data_Analysis_Flight_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5fe9a30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c633e2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data\n",
    "df_train = pd.read_excel(\"/home/darshan/Desktop/Codes/IMP_Jupyter_Notebooks/data/Flight Prediction/Data_Train.xlsx\")\n",
    "df_test = pd.read_excel(\"/home/darshan/Desktop/Codes/IMP_Jupyter_Notebooks/data/Flight Prediction/Test_set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "eea2c50c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10683, 11)\n",
      "(2671, 10)\n"
     ]
    }
   ],
   "source": [
    "print(df_train.shape)\n",
    "print(df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "61117799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1d3bf654",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = df_train.append(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "17d9e193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13354, 11)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f19e5e4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>3897.0</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>7662.0</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>13882.0</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>6218.0</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>13302.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info    Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897.0  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662.0  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882.0  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218.0  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302.0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4465eab2",
   "metadata": {},
   "source": [
    "### feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "64214a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       24\n",
       "1        1\n",
       "2        9\n",
       "3       12\n",
       "4       01\n",
       "        ..\n",
       "2666     6\n",
       "2667    27\n",
       "2668     6\n",
       "2669     6\n",
       "2670    15\n",
       "Name: Date_of_Journey, Length: 13354, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Date_of_Journey'].str.split(\"/\").str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dd2c9d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df[\"day\"] = final_df['Date_of_Journey'].str.split(\"/\").str[0].astype(int)\n",
    "final_df[\"month\"] = final_df['Date_of_Journey'].str.split(\"/\").str[1].astype(int)\n",
    "final_df[\"year\"] = final_df['Date_of_Journey'].str.split(\"/\").str[2].astype(int)\n",
    "\n",
    "final_df.drop([\"Date_of_Journey\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "76eb3a11",
   "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",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>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>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>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>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>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>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    22:20   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL    16:50   \n",
       "\n",
       "   Arrival_Time Duration Total_Stops Additional_Info    Price  day  month  \\\n",
       "0  01:10 22 Mar   2h 50m    non-stop         No info   3897.0   24      3   \n",
       "1         13:15   7h 25m     2 stops         No info   7662.0    1      5   \n",
       "2  04:25 10 Jun      19h     2 stops         No info  13882.0    9      6   \n",
       "3         23:30   5h 25m      1 stop         No info   6218.0   12      5   \n",
       "4         21:35   4h 45m      1 stop         No info  13302.0    1      3   \n",
       "\n",
       "   year  \n",
       "0  2019  \n",
       "1  2019  \n",
       "2  2019  \n",
       "3  2019  \n",
       "4  2019  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "94879aa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13354 entries, 0 to 2670\n",
      "Data columns (total 13 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Airline          13354 non-null  object \n",
      " 1   Source           13354 non-null  object \n",
      " 2   Destination      13354 non-null  object \n",
      " 3   Route            13353 non-null  object \n",
      " 4   Dep_Time         13354 non-null  object \n",
      " 5   Arrival_Time     13354 non-null  object \n",
      " 6   Duration         13354 non-null  object \n",
      " 7   Total_Stops      13353 non-null  object \n",
      " 8   Additional_Info  13354 non-null  object \n",
      " 9   Price            10683 non-null  float64\n",
      " 10  day              13354 non-null  int64  \n",
      " 11  month            13354 non-null  int64  \n",
      " 12  year             13354 non-null  int64  \n",
      "dtypes: float64(1), int64(3), object(9)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b8bcc4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_df[\"Arrival_Time\"].str.split(\" \")[0]\n",
    "final_df[\"Arrival_Time\"] = final_df[\"Arrival_Time\"].apply(lambda x:x.split(\" \")[0])\n",
    "final_df[\"Dep_Time\"] = final_df[\"Dep_Time\"].apply(lambda x:x.split(\" \")[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6915c209",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df[\"Arrival_hour\"] = final_df[\"Arrival_Time\"].apply(lambda x:x.split(\":\")[0]).astype(int)\n",
    "final_df[\"Arrival_minute\"] = final_df[\"Arrival_Time\"].apply(lambda x:x.split(\":\")[1]).astype(int)\n",
    "\n",
    "final_df[\"Dep_hour\"] = final_df[\"Dep_Time\"].apply(lambda x:x.split(\":\")[0]).astype(int)\n",
    "final_df[\"Dep_minute\"] = final_df[\"Dep_Time\"].apply(lambda x:x.split(\":\")[1]).astype(int)\n",
    "\n",
    "final_df.drop([\"Arrival_Time\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f3a7ca70",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop([\"Dep_Time\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "974a7fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_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>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info    Price  day  month  year  Arrival_hour  \\\n",
       "0    non-stop         No info   3897.0   24      3  2019             1   \n",
       "1     2 stops         No info   7662.0    1      5  2019            13   \n",
       "2     2 stops         No info  13882.0    9      6  2019             4   \n",
       "3      1 stop         No info   6218.0   12      5  2019            23   \n",
       "4      1 stop         No info  13302.0    1      3  2019            21   \n",
       "\n",
       "   Arrival_minute  Dep_hour  Dep_minute  \n",
       "0              10        22          20  \n",
       "1              15         5          50  \n",
       "2              25         9          25  \n",
       "3              30        18           5  \n",
       "4              35        16          50  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e4526e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline               0\n",
       "Source                0\n",
       "Destination           0\n",
       "Route                 1\n",
       "Duration              0\n",
       "Total_Stops           1\n",
       "Additional_Info       0\n",
       "Price              2671\n",
       "day                   0\n",
       "month                 0\n",
       "year                  0\n",
       "Arrival_hour          0\n",
       "Arrival_minute        0\n",
       "Dep_hour              0\n",
       "Dep_minute            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "985b3e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', nan, '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[\"Total_Stops\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e7565657",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df[\"Total_Stops\"] = final_df[\"Total_Stops\"].map({'non-stop':0,'1 stop':1,'2 stops':2,'3 stops':3, '4 stops':4,'nan':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "51b98433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
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       "    </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>0.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
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       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "   Total_Stops Additional_Info    Price  day  month  year  Arrival_hour  \\\n",
       "0          0.0         No info   3897.0   24      3  2019             1   \n",
       "1          2.0         No info   7662.0    1      5  2019            13   \n",
       "2          2.0         No info  13882.0    9      6  2019             4   \n",
       "3          1.0         No info   6218.0   12      5  2019            23   \n",
       "4          1.0         No info  13302.0    1      3  2019            21   \n",
       "\n",
       "   Arrival_minute  Dep_hour  Dep_minute  \n",
       "0              10        22          20  \n",
       "1              15         5          50  \n",
       "2              25         9          25  \n",
       "3              30        18           5  \n",
       "4              35        16          50  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "72bd30db",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop(['Route'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "748d3c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Dep_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>2h 50m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
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       "      <td>Air India</td>\n",
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       "      <td>7h 25m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>19h</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination Duration  Total_Stops Additional_Info  \\\n",
       "0       IndiGo  Banglore   New Delhi   2h 50m          0.0         No info   \n",
       "1    Air India   Kolkata    Banglore   7h 25m          2.0         No info   \n",
       "2  Jet Airways     Delhi      Cochin      19h          2.0         No info   \n",
       "\n",
       "     Price  day  month  year  Arrival_hour  Arrival_minute  Dep_hour  \\\n",
       "0   3897.0   24      3  2019             1              10        22   \n",
       "1   7662.0    1      5  2019            13              15         5   \n",
       "2  13882.0    9      6  2019             4              25         9   \n",
       "\n",
       "   Dep_minute  \n",
       "0          20  \n",
       "1          50  \n",
       "2          25  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e94470e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No info', 'In-flight meal not included',\n",
       "       'No check-in baggage included', '1 Short layover', 'No Info',\n",
       "       '1 Long layover', 'Change airports', 'Business class',\n",
       "       'Red-eye flight', '2 Long layover'], dtype=object)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[\"Additional_Info\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "78a16a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Duration_hour']=final_df['Duration'].str.split(\" \").str[0].str.split('h').str[0]\n",
    "# Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f3825a84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Airline, Source, Destination, Duration, Total_Stops, Additional_Info, Price, day, month, year, Arrival_hour, Arrival_minute, Dep_hour, Dep_minute, Duration_hour]\n",
       "Index: []"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "final_df[final_df[\"Duration_hour\"] == \"5m\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "001e6f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop(final_df[final_df[\"Duration_hour\"] == \"5m\"].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "019171ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Duration_hour'] = final_df['Duration_hour'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "2506b5f4",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Airline, Source, Destination, Duration, Total_Stops, Additional_Info, Price, day, month, year, Arrival_hour, Arrival_minute, Dep_hour, Dep_minute, Duration_hour]\n",
       "Index: []"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df[\"Duration_hour\"] == \"5m\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "8e4420f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Duration_minute']=final_df['Duration'].str.split(\" \").str[1].str.split('m').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "519ccfbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "duration_min = final_df['Duration_minute'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "1e8ecb9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               variable value\n",
      "200265  Duration_minute    50\n",
      "200266  Duration_minute    25\n",
      "200267  Duration_minute   NaN\n",
      "200268  Duration_minute    25\n",
      "200269  Duration_minute    45\n",
      "...                 ...   ...\n",
      "213611  Duration_minute    55\n",
      "213612  Duration_minute    35\n",
      "213613  Duration_minute    35\n",
      "213614  Duration_minute    15\n",
      "213615  Duration_minute    20\n",
      "\n",
      "[13351 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='variable', ylabel='count'>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.melt(final_df)\n",
    "print(df[df[\"variable\"] == \"Duration_minute\"])\n",
    "sns.countplot(data=df[df[\"variable\"] == \"Duration_minute\"],x='variable', hue='value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "ab76c542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline               0\n",
       "Source                0\n",
       "Destination           0\n",
       "Duration              0\n",
       "Total_Stops           0\n",
       "Additional_Info       0\n",
       "Price              2670\n",
       "day                   0\n",
       "month                 0\n",
       "year                  0\n",
       "Arrival_hour          0\n",
       "Arrival_minute        0\n",
       "Dep_hour              0\n",
       "Dep_minute            0\n",
       "Duration_hour         0\n",
       "Duration_minute       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "a40589a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df[\"Total_Stops\"] = final_df[\"Total_Stops\"].fillna(2)\n",
    "final_df[\"Duration_minute\"] = final_df[\"Duration_minute\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "d0cd66c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline               0\n",
       "Source                0\n",
       "Destination           0\n",
       "Duration              0\n",
       "Total_Stops           0\n",
       "Additional_Info       0\n",
       "Price              2670\n",
       "day                   0\n",
       "month                 0\n",
       "year                  0\n",
       "Arrival_hour          0\n",
       "Arrival_minute        0\n",
       "Dep_hour              0\n",
       "Dep_minute            0\n",
       "Duration_hour         0\n",
       "Duration_minute       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "302137d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop([\"Duration\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "a742e16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IndiGo', 'Air India', 'Jet Airways', 'SpiceJet',\n",
       "       'Multiple carriers', 'GoAir', 'Vistara', 'Air Asia',\n",
       "       'Vistara Premium economy', 'Jet Airways Business',\n",
       "       'Multiple carriers Premium economy', 'Trujet'], dtype=object)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[\"Airline\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "3a170c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "\n",
    "final_df[\"Airline\"]= labelencoder.fit_transform(final_df[\"Airline\"])\n",
    "final_df[\"Source\"]= labelencoder.fit_transform(final_df[\"Source\"])\n",
    "final_df[\"Destination\"]= labelencoder.fit_transform(final_df[\"Destination\"])\n",
    "final_df[\"Additional_Info\"]= labelencoder.fit_transform(final_df[\"Additional_Info\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "58e5080b",
   "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>Price</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_minute</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_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>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3897.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>7662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>13882.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "      <td>6218.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "      <td>13302.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline  Source  Destination  Total_Stops  Additional_Info    Price  day  \\\n",
       "0        3       0            5          0.0                8   3897.0   24   \n",
       "1        1       3            0          2.0                8   7662.0    1   \n",
       "2        4       2            1          2.0                8  13882.0    9   \n",
       "3        3       3            0          1.0                8   6218.0   12   \n",
       "4        3       0            5          1.0                8  13302.0    1   \n",
       "\n",
       "   month  year  Arrival_hour  Arrival_minute  Dep_hour  Dep_minute  \\\n",
       "0      3  2019             1              10        22          20   \n",
       "1      5  2019            13              15         5          50   \n",
       "2      6  2019             4              25         9          25   \n",
       "3      5  2019            23              30        18           5   \n",
       "4      3  2019            21              35        16          50   \n",
       "\n",
       "   Duration_hour Duration_minute  \n",
       "0              2              50  \n",
       "1              7              25  \n",
       "2             19              30  \n",
       "3              5              25  \n",
       "4              4              45  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc406140",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can use OneHotEncoding"
   ]
  }
 ],
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