Feature preprocessing refers to the process of transforming raw data into a clean and usable format for machine learning models, affter Data Preprocessing. This step is crucial for improving model performance and ensuring accurate predictions
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Feature Scaling: Normalizing or standardizing features to ensure they are on a similar scale. Normalization and Scaling: Adjusting the range of features, often using techniques like min-max scaling or z-score normalization, to ensure that all features contribute equally to the model.
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Feature Selection: Identifying and retaining the most relevant features that contribute to the predictive power of the model, often using statistical tests or model-based approaches.
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Dimensionality Reduction: Reducing the number of features while preserving important information, using techniques like Principal Component Analysis (PCA).
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Feature Engineering: Creating new features from existing data to improve model performance, often based on domain knowledge.