Understanding Instacart Dataset - before building recommender systems

Описание: You can't build great recommender systems without deeply understanding your data, and the engineering pipeline that transforms it. But most tutorials skip this step, and that's where things fall apart.

In this tutorial, we deconstruct the Instacart Market Basket Analysis dataset and walk through the complete data engineering pipeline that powers our recommendation systems. You'll learn how 6 raw CSV files form a relational ecosystem, how 30+ million product purchases reveal collaborative filtering signals, and how our 7-step preprocessing pipeline transforms messy relational data into model-ready sparse matrices.

We map the raw data landscape (orders, products, and the three-level Department → Aisle → Product hierarchy), then dive deep into the code that forges ML assets. You'll see how load_and_prepare_data() filters 80,000 valid users, selects the top 1,500 products to handle long-tail sparsity, builds the critical user-item interaction matrix, and implements within-user train/test splits for proper evaluation. We'll examine the utility functions that handle sparse matrix operations, generate behavioral user features, and calculate NDCG and other ranking metrics.

This is the complete foundation, data understanding plus production-quality preprocessing code that you need before building ALS and Neural Collaborative Filtering models in the next tutorials. By the end, you'll understand both the data and the engineering decisions behind every transformation.

Link to the dataset: https://www.kaggle.com/datasets/yasse...

Link to code: https://github.com/bnsreenu/Recommend...
(RecSys 3)