Scikit Learn

This is your go-to for classical machine learning tasks when you need something interpretable and battle-tested. Covers the full workflow: train/test splits, preprocessing with StandardScaler and OneHotEncoder, classification with RandomForest or GradientBoosting, clustering with KMeans or DBSCAN, and proper evaluation with cross-validation. The Pipeline and ColumnTransformer examples are especially useful since they prevent data leakage and make it easy to handle mixed numeric and categorical features in one workflow. Skip this if you're doing deep learning, but for tabular data, customer segmentation, or any project where you need to explain your model to stakeholders, scikit-learn remains the standard for good reason.