Competition - 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

import pandas as pd
hotels = pd.read_csv("data/hotel_bookings.csv")
hotels

The Challenge

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

Note:

To ensure the best user experience, we currently discourage using Folium and Bokeh in Workspace notebooks.

Judging Criteria

CATEGORY	WEIGHTING	DETAILS
Recommendations	35%	Clarity of recommendations - how clear and well presented the recommendation is. Quality of recommendations - are appropriate analytical techniques used & are the conclusions valid? Number of relevant insights found for the target audience.
Storytelling	35%	How well the data and insights are connected to the recommendation. How the narrative and whole report connects together. Balancing making the report in-depth enough but also concise.
Visualizations	20%	Appropriateness of visualization used. Clarity of insight from visualization.
Votes	10%	Up voting - most upvoted entries get the most points.

Checklist before publishing

Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
Remove redundant cells like the judging criteria, so the workbook is focused on your work.
Check that all the cells run without error.

Time is ticking. Good luck!

In order to produce recommendations for the hotel on what factors affect whether customers cancel their booking, we will have to investigte the data on multiple levels. The initial phase would be to compile the relevant columns, that would protrude to having an affect on cancelations, and investigate the associated descriptive statistics.

import pandas as pd
hotels = pd.read_csv("data/hotel_bookings.csv")
hotels.shape
hotels.columns

#Calculate the % of Hotel reservations that have been canceled
booking_count = hotels["booking_status"].value_counts()
print(booking_count)

pct_canceled = booking_count["Canceled"] / \
              (booking_count["Not_Canceled"] + booking_count["Canceled"])

pct_canceled = round(pct_canceled, 4)*100
print(pct_canceled)

#Groupby Year to determine which years have seen the highest cancelations
year_canceled = hotels.groupby("arrival_year")["booking_status"].value_counts(normalize=True)
print(round(year_canceled*100, 2))

#binary booking status
# create a dictionary to map categories to binary values
booking_status_map = {'Canceled': 0, 'Not_Canceled': 1}

# apply the mapping to the booking_status column
hotels['booking_status_binary'] = hotels['booking_status'].map(booking_status_map)
print(hotels)

#Correlation Matric 
numeric_cols = hotels.select_dtypes(include=['int', 'float']).columns

correlation_matrix = hotels[numeric_cols].corr()

print(correlation_matrix)

import seaborn as sns

#Make concise Correlation table for visualisation

c_drop = ["no_of_adults", "no_of_children","no_of_weekend_nights", "no_of_week_nights",                       "required_car_parking_space","arrival_month","arrival_date",                                         "no_of_previous_cancellations","no_of_previous_bookings_not_canceled"]

c_correlation_matrix = correlation_matrix.drop(c_drop, axis=0)
c_correlation_matrix = c_correlation_matrix.drop(c_drop, axis=1)

ax = sns.heatmap(c_correlation_matrix, annot=True, cmap='coolwarm', linewidth=1, robust=True)
ax.set_xticklabels(ax.get_xticklabels(), rotation=45)

Chi-Square test of independence is most commonly used to test association between two categorical variables. The output gives us p-value, degrees of freedom and expected values. Code for checking correlation between TWO categorical variables is easily available