Identifying factors that predict hotel cancellation

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

The Challenge

Use your skills to produce recommendations for the hotel on what factors affect whether customers cancel their booking.

Imports

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

sns.set_style('whitegrid')

import missingno as msno
from datetime import datetime

from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, GridSearchCV
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline

from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.feature_selection import SelectPercentile, mutual_info_regression

# models
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier

hotels = pd.read_csv("data/hotel_bookings.csv")
print(hotels.shape)
hotels.head()

Hidden output

Preprocessing

df = hotels.copy()

# drop unique identifier column
df = df.drop('Booking_ID', axis = 1)

Null values

1 hidden cell

msno.matrix(df);

nulldf = df.isnull().sum().sort_values(ascending = False).reset_index()
nulldf.columns = ['feature', 'null_count']
nulldf = nulldf[nulldf['null_count'] > 0]

remove = df[nulldf['feature'].values].sum(axis = 1).sort_values().reset_index()
remove.columns = ['index', 'value']
remove = remove[remove['value'] == 0]
remove_indices = remove['index'].values

df = df[~df.index.isin(remove_indices)]

Create Date Column

‌
‌
‌