A machine learning-powered restaurant recommendation platform that suggests similar restaurants based on user preferences. Users can refine recommendations using budget, cuisine type, and ratings while exploring restaurant insights through interactive visualizations.
A machine learning-powered restaurant recommendation platform that suggests similar restaurants based on user preferences. Users can refine recommendations using budget, cuisine type, and ratings while exploring restaurant insights through interactive visualizations.
Choosing restaurants can be difficult due to the large number of available options and varying user preferences. Traditional search methods often fail to provide personalized recommendations based on cuisine preferences, ratings, and budget constraints. Users need a system that can intelligently recommend restaurants matching their tastes and requirements.
Developed a content-based restaurant recommendation system using TF-IDF vectorization and cosine similarity. The application analyzes restaurant attributes and user-selected preferences to generate personalized recommendations. Additional filtering options for budget, cuisine, and ratings help users discover restaurants that best match their needs.
1. Restaurant dataset is processed and cleaned. 2. Restaurant descriptions and attributes are transformed using TF-IDF vectorization. 3. Similarity matrices are generated using cosine similarity. 4. User searches for a restaurant. 5. Recommendation engine retrieves similar restaurants. 6. Additional filters apply budget, cuisine, and rating constraints. 7. Results are displayed through FastAPI templates. 8. User authentication is handled using SQLite storage. 9. Insights dashboard displays restaurant trends and dataset analytics.
Challenge 1: Generating meaningful recommendations from restaurant data. Solution: Implemented TF-IDF vectorization and cosine similarity to identify restaurants with similar characteristics. Challenge 2: Improving recommendation relevance. Solution: Added budget, cuisine, and rating filters to provide more personalized results. Challenge 3: Managing user accounts securely. Solution: Integrated SQLite-based authentication and registration system. Challenge 4: Making restaurant trends understandable. Solution: Developed an insights dashboard with visualizations showing rating distributions and popular cuisines. Challenge 5: Handling missing or unmatched restaurant searches. Solution: Added user-friendly error messages and fallback recommendations.
• Personalized restaurant recommendations generated instantly • Content-based recommendation engine using TF-IDF similarity • Cuisine-based recommendation filtering • Budget and rating-based recommendation refinement • User authentication and account management • Interactive restaurant insights dashboard • Popular cuisine trend analysis • Responsive web interface powered by FastAPI • Improved restaurant discovery experience
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