This article demonstrates how basic statistics and techniques like PCA can be used to analyze tabular datasets, highlighting the importance of data preprocessing, statistical tests, and handling multicollinearity.
This article explains the PCA algorithm and its implementation in Python. It covers key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues. The tutorial aims to provide a solid understanding of the algorithm's inner workings and its application for dealing with high-dimensional data and the curse of dimensionality.
