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Machine Learning With Python
Data Preparation, Regression, Classification, Model Evaluation, Pipelines and Avoiding the Mistakes That Fool Beginners
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A model that scores 97 percent in a notebook can still fail the first week it meets real data. This book shows you how to build classical machine learning models in Python that hold up. You prepare messy tables with pandas, split data into training, validation and test sets, and learn exactly how data leakage inflates scores. You fit and judge regression and classification models, measure rare-class problems with precision, recall and PR-AUC, and use cross-validation to see how far a score can be trusted. You wrap everything in pipelines, tune settings without fooling yourself, and finish with a complete customer churn project. Every example uses data that ships with scikit-learn or fixed-seed synthetic data or small hand-made tables, and the printed results come from real runs.
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