jphall663/GWU_data_mining

Covers core data mining and machine learning techniques including regression, decision trees, neural networks, clustering, association rules, and text mining, with emphasis on feature engineering, ensemble methods, model validation, and interpretability. Content is structured as practical workshops where students apply techniques to real Kaggle competitions (Advanced Regression and Digit Recognizer), supported by curated code examples under MIT/Apache 2.0 licenses. Includes supplementary resources on AutoML, data visualization, and interview preparation to bridge academic foundations with competitive data science practices.

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