This project focuses on developing a personalized food recommendation system using machine learning techniques. By analyzing user preferences and dietary restrictions, the system suggests tailored meal options to promote healthier eating habits.
Data Aggregation: Compiled datasets with nutritional information and user dietary preferences.
Data Preprocessing: Standardized data, addressing inconsistencies and missing values.
Model Implementation: Applied collaborative and content-based filtering algorithms for personalized recommendations.
Performance Evaluation: Measured model accuracy using precision, recall, and F1-score metrics.
User Interface Design: Developed an intuitive interface for users to input preferences and receive tailored meal suggestions.