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What is Data Preprocessing in Machine Learning about?

The document discusses data preprocessing in machine learning, highlighting the importance of cleaning and transforming training data to address issues like missing values and feature scaling. It introduces various tools from the Sklearn library, including transformers for data cleaning, feature extraction, and imputation methods to handle missing values. Key classes such as SimpleImputer and KNNImputer are explained, detailing their strategies for filling in missing data and ensuring uniformity across trai

Author

tanmayg

Language

EN