To build a recommendation system that helps users find the best food options using data analytics — combining user preferences, ratings, delivery times, and cost patterns to surface the most relevant restaurants and dishes.
This project analyzes food delivery data and recommends the most suitable dishes or restaurants based on user preferences. It combines exploratory data analysis with machine learning models to deliver personalized, data-backed recommendations.
Users often find it difficult to choose from thousands of food items on delivery platforms. Without personalization, users waste time browsing through irrelevant options. A smart recommendation system addresses this by learning from data and surfacing what truly matches each user's taste.