Hotel-Room-Booking-Cancellation-Prediction-Modeling

This project explores the use of Machine Learning Techniques to identify key factors influencing a high number of cancellations of Hotel Room Bookings and to build a Machine Learning model that can predict which booking is going to be canceled, in advance and help in formulating profitable policies for cancellations and refunds.

Data Description

The data contains the different attributes of customers’ booking details. The detailed data dictionary is given below.

Data Dictionary

• Booking_ID: the unique identifier of each booking
• no_of_adults: Number of adults
• no_of_children: Number of Children
• no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
• no_of_week_nights: Number of weeknights (Monday to Friday) the guest stayed or booked to stay at the hotel
• type_of_meal_plan: Type of meal plan booked by the customer:
• Not Selected – No meal plan selected
• Meal Plan 1 – Breakfast
• Meal Plan 2 – Half board (breakfast and one other meal)
• Meal Plan 3 – Full board (breakfast, lunch, and dinner)
• required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)
• room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels Group
• lead_time: Number of days between the date of booking and the arrival date
• arrival_year: Year of arrival date
• arrival_month: Month of arrival date
• arrival_date: Date of the month
• market_segment_type: Market segment designation.
• repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes)
• no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking
• no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking
• avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros)
• no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc)
• booking_status: Flag indicating if the booking was canceled or not.

Methodology

Define the problem and perform an Exploratory Data Analysis

• Problem definition
• Univariate analysis
• Bivariate analysis
• Use appropriate visualizations to identify the patterns and insights
• Key meaningful observations on individual variables and the relationship between variables

Data Pre-processing

Prepare the data for modelling:

• Missing value Treatment (if needed)
• Outlier Detection(treat, if needed)
• Feature Engineering (if needed)
• Data split

Model building - Logistic Regression

• Build the Logistic Regression model using statsmodels library and comment on the model statistics
• Check assumptions of Logistic Regression
• Provide interpretations based on coefficients obtained from the logistic regression model
• Comment on model performance

Model Performance evaluation and improvement - Logistic Regression

• Try and improve the model performance by changing the classification threshold
• Comment on model performance after changing the threshold

Model building - Decision Tree

• Build the model and comment on the model performance.

Model Performance evaluation and improvement - Decision Tree

• Try and improve the model performance by pruning
• Comment on model performance
• Find the decision rules and check feature importance

Actionable Insights & Recommendations

• Compare decision tree and Logistic regression models
• Conclude with the key takeaways for the business