In this edition, you’ll have a complete, grounded understanding of how to work with XGBoost and LightGBM in Python, especially within the context of applied, professional machine learning. You’ll see how XGBoost’s regularization-driven, level-wise tree growth differs from LightGBM’s leaf-wise design. And by the time you finish, you’ll not only know how to use these algorithms, you’ll know why they behave the way they do, and when to use one over the other.

Data preparation is one of the most time-consuming steps in any data analytics workflow. Fortunately, Data Wrangler in Microsoft Fabric simplifies this process by offering a graphical, no-code interface for exploring and transforming data directly inside notebooks.

In this article, we will explore how Data Wrangler helps clean and transform data efficiently without writing complex code.