Food Recommendation Engine
wat-to-eat is a sophisticated data engineering pipeline that processes comprehensive Food.com data to build an intelligent recommendation system. The project uses machine learning algorithms to analyze food tags, ingredients, nutritional information, and user preferences to suggest personalized meal recommendations.
Choosing what to eat can be overwhelming with countless options available. Existing recommendation systems often lack personalization and fail to consider dietary preferences, nutritional needs, and taste profiles effectively.
By analyzing Food.com's extensive database and applying machine learning techniques, the system creates personalized food recommendations based on individual taste preferences, dietary restrictions, and nutritional goals.