The previous lesson built models by hand. This one shows the library you reach for once the interview moves past “implement it from scratch” to “now build something that works.” scikit-learn is the standard machine learning toolkit in Python, and its real value is not the long list of models. It is one consistent interface that every model shares, plus the plumbing (splitting, cross-validation, tuning, preprocessing) that lets you validate a model honestly.
This lesson covers that workflow end to end: the estimator and transformer API, the train/test split, the Pipeline that chains preprocessing to a model, cross-validation with cross_val_score, hyperparameter tuning with GridSearchCV, and ColumnTransformer for mixed numeric and categorical data. It closes on the single most important idea in applied machine learning, and the one interviewers probe hardest: data leakage, and why the Pipeline is what prevents it. We demonstrate leakage with code that turns pure noise into a fake 72 percent accuracy.
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