Predicting Hotel Cancellations
🏨 Background
You are supporting a hotel with a project aimed to increase revenue from their room bookings. They believe that they can use data science to help them reduce the number of cancellations. This is where you come in!
They have asked you to use any appropriate methodology to identify what contributes to whether a booking will be fulfilled or cancelled. They intend to use the results of your work to reduce the chance someone cancels their booking.
The Data
They have provided you with their bookings data in a file called hotel_bookings.csv, which contains the following:
| Column | Description |
|---|---|
Booking_ID | Unique identifier of the booking. |
no_of_adults | The number of adults. |
no_of_children | The number of children. |
no_of_weekend_nights | Number of weekend nights (Saturday or Sunday). |
no_of_week_nights | Number of week nights (Monday to Friday). |
type_of_meal_plan | Type of meal plan included in the booking. |
required_car_parking_space | Whether a car parking space is required. |
room_type_reserved | The type of room reserved. |
lead_time | Number of days before the arrival date the booking was made. |
arrival_year | Year of arrival. |
arrival_month | Month of arrival. |
arrival_date | Date of the month for arrival. |
market_segment_type | How the booking was made. |
repeated_guest | Whether the guest has previously stayed at the hotel. |
no_of_previous_cancellations | Number of previous cancellations. |
no_of_previous_bookings_not_canceled | Number of previous bookings that were canceled. |
avg_price_per_room | Average price per day of the booking. |
no_of_special_requests | Count of special requests made as part of the booking. |
booking_status | Whether the booking was cancelled or not. |
Source (data has been modified): https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset
import pandas as pd
hotels = pd.read_csv("data/hotel_bookings.csv")
hotelsimport numpy as np
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
import matplotlib.pyplot as plt
import seaborn as sns
# Training XGBoost on the Training set
from xgboost import XGBClassifierremove duplicates and null values
hotels.drop_duplicates(inplace=True)
hotels.dropna(inplace=True)remove column attached to personal effects
hotels.drop('Booking_ID',axis=1,inplace=True)change categorical feature by one-hotencoding them
dumps=pd.get_dummies(hotels[['type_of_meal_plan','room_type_reserved','market_segment_type','booking_status']],drop_first=True)
hotels.drop(['type_of_meal_plan','room_type_reserved','market_segment_type','booking_status'],axis=1,inplace=True)
hotels = hotels.join(dumps,how='right')
hotelssplit data into train and test sets
Xtr,Xte,ytr,yte = train_test_split(hotels.iloc[:,:-1],hotels.iloc[:,-1], random_state=0)sanity check of the right selection of variables
len(ytr)==len(yte)