What is PCA?
Principal Component Analysis is a technique used for dimensionality reduction in machine learning. It is useful for converting larger datasets into smaller datasets with maintaining all the patterns. It helps reduce the features in the data while preserving the maximum amount of information.
PCA and SVM Pipeline in Python
Principal Component Analysis (PCA) and Support Vector Machines (SVM) are powerful techniques used in machine learning for dimensionality reduction and classification, respectively. Combining them into a pipeline can enhance the performance of the overall system, especially when dealing with high-dimensional data. The aim of the article is demonstrate how we can utilize PCA and SVM in single pipeline in Python.