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The ML-GYM repository showcases machine learning projects using **scikit-learn**, covering classification, regression, and clustering. It offers educational resources for beginners and practical examples for experienced users, complete with detailed instructions.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, the challenge is predict the final price of each home.
A repository containing machine learning lab exercises, including regression, neural network modeling, and data augmentation, with Python implementations and relevant datasets.