Our hackathon project, "CommunityConnect AI," aims to leverage machine learning to enhance Umass Boston's contribution to the local Boston community. The primary focus is on creating an intelligent system that recommends the most suitable restaurants in Boston based on user preferences.
The AI system actively learns and adapts to user preferences for restaurants in Boston. As users input their preferences, the AI refines its understanding, ensuring personalized and accurate recommendations. Preference-Based Recommendations:
The AI analyzes the unique characteristics of various restaurants in Boston. It tailors its recommendations to match individual user preferences, providing a curated list of options.
The more a user interacts with the system, the higher the accuracy of personalized recommendations. Machine learning algorithms continuously evolve, ensuring that the system stays up-to-date with the user's evolving tastes. How It Works:
Users provide their preferences, such as cuisine type, ambiance, or specific dietary requirements. The AI system gathers and analyzes this input to understand individual tastes. Learning Phase:
The AI leverages machine learning algorithms to identify patterns and correlations in restaurant characteristics. As users input more preferences, the system refines its model, enhancing the accuracy of future recommendations. Personalized Recommendations:
When users seek restaurant recommendations, the AI suggests options tailored to their unique preferences. The system considers a wide range of factors, ensuring a comprehensive and personalized dining experience.
- Create an app.py to launch on a website like Stream
- Create a machine that gets and learns the dataset of Boston restaurants.
- Bring the machine into app.py so that the expected output can be calculated for the user's input.