rajeshvhankade / travel-recommendation-system

TravelRecommend: ML-based travel planner using K-means & recommendation algorithms. Personalized suggestions, real-time updates, and user feedback for tailored travel experiences.

TravelRecommend

TravelRecommend is a personalized travel recommendation system powered by machine learning algorithms, specifically K-means clustering and recommendation algorithms. It aims to simplify the travel planning process by providing users with tailored recommendations based on their preferences and past behavior.

Features

1. Personalized Recommendations: Utilizes machine learning to analyze user preferences and historical data to generate personalized travel suggestions.

2. Real-time Updates: Provides real-time updates on travel trends, events, and promotions to keep users informed and inspired.

3. Multi-channel Accessibility: Accessible through web browsers, mobile apps, and voice-enabled devices for convenience.

4. User Feedback Integration: Encourages user feedback and engagement to continuously improve recommendations over time.

Installation

To use TravelRecommend, simply clone the repository to your local machine:

git clone https://github.com/RAJESHVHANKADE/Travel-Recommendation-System/tree/main

Usage

Navigate to the project directory.

Run the application using your preferred method (e.g., python app.py).

Input your preferences when prompted.

Receive personalized travel recommendations based on your input.

Contributing

Contributions are welcome! If you'd like to contribute to TravelRecommend, please fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any inquiries or feedback, please contact https://www.linkedin.com/in/rajesh-vhankade-ab7627215/ & [email protected]