Amritsar Eats — Food Recommendation Model
Built a food recommendation model using data-driven personalization techniques tailored to local cuisine preferences in Amritsar. The project required preparing and transforming user and item data into signals used for recommendation outputs. This constitutes AI training-style modeling work rather than manual data labeling. • Engineered features and applied data-driven techniques for personalized suggestions. • Iterated on model logic to improve relevance of recommendations. • Framed the recommendation task using structured input-output data. • Produced recommendation outputs for real-world use within the Amritsar context.