Best Hotel Booking Cancellation Prediction Using Machine Learning

Hotel Booking Cancellation Prediction

Predicting hotel booking cancellations is one of the most impactful applications of data-driven decision-making in the hospitality industry. Hotels often face challenges due to last-minute cancellations, which affect revenue, resource management, and customer satisfaction. This project, Hotel Booking Cancellation Prediction Using Machine Learning, aims to address this issue by building a predictive model that can identify whether a booking is likely to be canceled.

As a student exploring this project, I learned how machine learning models can analyze historical booking data, detect patterns, and make predictions that support better decision-making. The project also gave me practical experience with model training, validation, and deploying predictions through a Flask-based web application.

Project Overview

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Available Features

Based on the files and implementation, here are the actual features provided in this project:

• Data Preprocessing and Validation: Raw booking data is cleaned, validated, and transformed for use in model training and prediction.
• Exploratory Data Analysis (EDA): Jupyter Notebook (booking_eda.ipynb) is included to analyze booking trends and cancellation patterns.
• Model Training: Machine learning algorithms are trained on the dataset (trainingModel.py).
• Prediction Module: The system predicts whether a new booking is likely to be canceled (predictFromModel.py).
• Flask Integration: A Flask-based interface (main.py) is available to interact with the model.
• Schema Validation: JSON schema files ensure that both training and prediction datasets follow the correct structure.

Installation Guide (VS Code)

Follow these steps to set up the project in Visual Studio Code:

1. Clone or extract the project files into a folder. cd Hotel-Booking-Cancellation-Prediction
2. Open the folder in VS Code.
   • Launch VS Code.
   • Click File > Open Folder and select the extracted project folder.
3. Create and activate a virtual environment. python -m venv venv source venv/bin/activate # On macOS/Linux venv\Scripts\activate # On Windows
4. Install project dependencies using the requirements.txt file. pip install -r requirements.txt
5. Run the Flask application. python main.py
6. Access the application in your browser at: http://127.0.0.1:5000/

Usage

The project supports different roles through its workflow:

• Hotel Admins: Upload new booking data for training and validation. They can also use the interface to run cancellation predictions on fresh bookings.
• Data Scientists/Students: Explore the dataset, perform EDA, and retrain models using trainingModel.py.
• End Users (Hotel Managers): Use the Flask web app to input booking details and receive instant predictions on whether the booking may get canceled.

This workflow ensures the project is useful both for technical users (students, developers) and non-technical users (hotel management).

Contributing

Contributions are welcome to enhance the project. If you are interested in improving the model accuracy, adding new features, or refining the Flask interface, you can contribute by:

1. Forking the project.
2. Creating a new branch for your feature.
3. Testing changes locally before submission.
4. Submitting a pull request with detailed explanations of the updates.

License

This project is licensed under the MIT License. You are free to use, modify, and distribute it for educational and research purposes, provided proper credit is given to the developer.

Final Thoughts

From a student’s perspective, working on the Hotel Booking Cancellation Prediction project is both educational and practical. It covers the entire lifecycle of a machine learning project — from preprocessing and training to deployment and prediction. The project is an excellent way to understand real-world applications of machine learning in the hospitality industry.

In real life, hotels can use such a system to reduce revenue loss by preparing for likely cancellations, offering incentives to retain customers, or reallocating rooms more efficiently. For students, this project builds hands-on skills in Python, Flask, and machine learning, while also highlighting the importance of data validation and model deployment.

Overall, this project is a great stepping stone toward mastering applied machine learning and solving industry-specific problems.

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