https://github.com/spafic/hotelreservationpredictionsystem
Integrate machine learning into a Flask app to predict hotel reservation status.
https://github.com/spafic/hotelreservationpredictionsystem
Last synced: about 2 months ago
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Integrate machine learning into a Flask app to predict hotel reservation status.
- Host: GitHub
- URL: https://github.com/spafic/hotelreservationpredictionsystem
- Owner: Spafic
- License: mit
- Created: 2025-02-20T10:25:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-20T13:32:56.000Z (over 1 year ago)
- Last Synced: 2025-03-02T11:33:44.180Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 25.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Hotel Reservation Prediction System 🏨
A sophisticated Flask web application leveraging machine learning to predict hotel reservation outcomes with high accuracy.
Why did the robot apply for a job at the hotel? Because it wanted to work with reservations!
## 🌐 Live Demo
Experience it live: [Hotel Reservation Predictor](https://hotelreservationpredictionsystem-production.up.railway.app/)
## 📋 Overview
This intelligent system seamlessly integrates advanced machine learning algorithms with a Flask web application to predict hotel reservation outcomes (successful/cancelled) based on multiple parameters and historical patterns.
## ✨ Key Features
- **Smart Predictions**: Advanced ML models for accurate booking predictions
- **Interactive UI**: Clean, responsive web interface
- **Real-time Results**: Instant prediction feedback
- **Cloud Deployment**: Hosted on Railway for high availability
- **Secure Processing**: Safe handling of user inputs
- **Data Visualization**: Clear presentation of results
## 🚀 Getting Started
1. Clone the repository
2. Install dependencies: `pip install -r requirements.txt`
3. Run locally: `python app.py`
## 🧠 Machine Learning Models
The system utilizes a combination of five robust machine learning models:
* **Random Forest**: Ensemble learning with multiple decision trees
* **K-Nearest Neighbors (KNN)**: Pattern recognition based on proximity
* **Logistic Regression**: Linear decision boundaries with probabilistic output
* **XGBoost**: Advanced gradient boosting implementation
* **Support Vector Machine (SVM)**: Optimal hyperplane separation
## 📈 Performance Metrics
Our model demonstrates robust performance across key metrics:
### Classification Report for Random Forest
```
precision recall f1-score support
Cancelled 0.85 0.83 0.84 3565
Not Cancelled 0.92 0.93 0.92 7310
accuracy 0.90 10875
macro avg 0.88 0.88 0.88 10875
weighted avg 0.89 0.90 0.90 10875
```
### Key Statistics for Random Forest
- Overall Accuracy: 90%
- Precision: 89%
- Recall: 90%
- F1-Score: 90%
The model shows particularly strong performance in predicting Not Cancelled reservations, with precision and recall both exceeding 92%.
## 🤝 How to Contribute
We welcome contributions to enhance the Hotel Reservation Prediction System. To contribute, follow these steps:
1. Fork the repository
2. Create a new branch: `git checkout -b feature-branch`
3. Make your changes and commit them: `git commit -m 'Add new feature'`
4. Push to the branch: `git push origin feature-branch`
5. Submit a pull request
Please ensure your code adheres to our coding standards and includes appropriate tests.
## 📜 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.