Principal Component Analysis As The Final Step In Feature Selection.

This article provides an in-depth examination of the role of Principal Component Analysis (PCA) in the feature engineering pipeline of machine learning. While PCA is widely recognized as a powerful dimensionality reduction tool, it is often misapplied or prematurely executed during data preprocessing. This paper argues that PCA should be positioned as the final step in feature selection, after filtering irrelevant features, normalizing data, and encoding categorical variables. The study compares PCA with traditional feature selection techniques; filter, wrapper, and embedded methods; highlighting the conceptual and functional differences. Using the Iris dataset as a case study, it demonstrates the consequences of applying PCA too early and the benefits of leveraging it at the final stage for optimal model performance and clarity. This work is valuable for data scientists, analysts, and ML engineers who seek to streamline preprocessing workflows, reduce multicollinearity, and enhance generalization in predictive modeling; all while preserving interpretability as much as possible before applying PCA.