In the highly competitive hospitality industry, efficient management of hotel bookings is essential for maximizing revenue. This project utilizes machine learning to address the challenge of predicting booking cancellations, optimizing occupancy rates, and boosting profitability.

Hotel cancellations can result in revenue loss and operational disruptions. Accurate forecasting of cancellations is vital for efficient room allocation, staff scheduling, and pricing strategies.

• Cancellations Prediction: Develop a robust ML model for accurate cancellations forecasting.
• Optimize Occupancy: Minimize overbookings and last-minute cancellations for higher revenue and guest satisfaction.
• Dynamic Pricing: Suggest pricing adjustments based on demand fluctuations.
• Enhanced Guest Experience: Reduce guest disappointments and improve satisfaction.

1. Data Collection: Gather historical booking data, including reservation details and cancellations.

2. Data Preprocessing: Clean and transform data for analysis.

3. Feature Engineering: Create relevant features to enhance predictive performance.

4. Model Selection: Identify the best-performing ML algorithm.

5. Training and Testing: Evaluate model accuracy and generalization capability.

6. Deployment: Integrate the model into hotel management systems for real-time predictions.

7. Continuous Improvement: Monitor and update the model to adapt to changing booking patterns.

• Detailed documentation and code samples can be found in the /docs directory.

• Ruturaj Ranpise

This project is licensed under the MIT License.