Welcome to the Restaurant Recommendation Model repository! This project leverages machine learning to help people get personalized recommendations to visit restaurants based on thier past Behaviour and prefrences. Using real-world data from Yelp and other sources, the model provides top 5 restaurants to vist .
In this project, a system that will be recommending restaurants based on users' profiles such as past reviews, location, and the kind of cuisine will be designed and implemented. By this analysis of user behavior and other historical data, personalized recommendations will be provided to enhance the decision-making process and satisfactions of the customers.
- Business Success Prediction: Provides a success rate (%) prediction for each business based on real-time data.
- Personalized Recommendations: Ranks and recommends the top N businesses in a given industry or location.
- Sentiment Analysis: Incorporates customer reviews to assess customer sentiment and predict business success.
- Real-Time Data Integration: Uses real-time market and competitor data for accurate, up-to-date predictions.
- User-Friendly Dashboard: Simple and intuitive interface for non-technical users.
The project uses the Yelp Open Dataset along with additional sources for financial and geographic data. Key features include:
- Business Data: Name, location, category, rating, review count, operating hours, etc.
- Customer Review Data: Text reviews, ratings, sentiment analysis.
- User Data: Information about reviewers, including review history and credibility.
- Check-In Data: Customer check-ins, which help measure business popularity.
We welcome contributions to improve the platform! Please fork the repository, create a new branch, and submit a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License. See LICENSE for more information.