Feature Engineering

Feature engineering is the process of selecting and transforming variables (features) in a dataset to improve the performance of machine learning models. It involves identifying the most relevant features for a given problem, creating new features by combining or transforming existing ones, and selecting the best subset of features to use in a model. Feature engineering is a critical step in the machine learning pipeline, as it can significantly impact the accuracy and interpretability of models. Effective feature engineering requires domain knowledge, creativity, and an understanding of the underlying data. It can involve techniques such as one-hot encoding, scaling, normalization, dimensionality reduction, and feature selection.
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