A comprehensive analysis of hotel booking patterns using data science and machine learning techniques, implemented in Jupyter Notebook.
This project analyzes hotel booking data to uncover patterns and predict booking cancellations using machine learning. The entire analysis and implementation can be found in Hotel_booking_Project.ipynb.
- Booking cancellation patterns
- Peak booking seasons
- Customer demographics
- Length of stay analysis
- Deposit type impact
- Market segment analysis
The machine learning model considers various factors including:
- Lead time
- Previous cancellations
- Room type
- Deposit type
- Customer type
- Seasonality
Python - Core implementation Pandas - Data manipulation & analysis Scikit-learn - Machine learning implementation Matplotlib/Seaborn - Data visualization Jupyter Notebook - Development environment
- Clone the repository
git clone https://github.com/Clyde0513/Hotel-Booking-Prediction.git
cd Hotel-Booking-Prediction
- Install dependencies
pip install scikit-learn1.4.1.post1, numpy1.26.4, scipy1.12.0, pandas2.2.1, matplotlib3.8.3, jupyterlab4.1.5
- Launch Jupyter Notebook or Install Jupyter Notebook into your editor or vscode
jupyter notebook
- Cancellation rate patterns
- Seasonal booking trends
- Customer booking behaviors
- Impact of pricing on cancellations
- Market segment analysis
- Used a RandomForest model that fits a decision tree classifiers on various subsamples
- Improve prediction accuracy by 89% on a 69000 samples in the dataset
- Handles non-linearly data well through RandomForest model
Contributions are welcome! Please:
- Fork the repository
- Create a feature branch
- Commit your changes
- Push to the branch
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
⭐ Star this repo if you find it useful!