<ul><li>Data preprocessing is essential for machine learning as it involves cleaning, organizing, and preparing raw data for model training.</li><li>Preprocessing is crucial as it ensures that data is accurate and reliable for model building, preventing issues like missing values, inconsistencies, unseen categories, and outliers.</li><li>Key steps in data preprocessing for tabular data include data cleaning (handling missing values, correcting inconsistencies), data transformation (encoding categorical data, feature scaling), feature engineering, outlier detection, and handling imbalanced data.</li><li>By following these preprocessing steps, data scientists can create well-prepped data that forms the foundation for effective machine learning models.</li></ul>

Data Preprocessing – The Unsung Hero of Machine Learning (Part 2)

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