This repository is created to provide suggestions for solving mathematical problems using Python, as well as suggestions for implementing statistical models and Python tools that can help, based on ...

Learn three methods to perform PCA on categorical or mixed data types in Python: one-hot encoding, factor analysis, and mixed data PCA. Compare their advantages and disadvantages.

The essence of PCA is to find the directions of maximum variance in high dimensional data, and project it into a smaller dimensional space while still retaining most of the information. The mpg/mtcars ...

Learn how to perform PCA on big data using Spark, Python, and other tools. Find out how to prepare your data, choose the number of components, and interpret the results.

Since 2002 many works have been published applying Principal Component Analysis (PCA) in the study of network traffic. These investigations have revealed some issues inherent to the temporal and ...