PCA is a major topic in Dimension Reduction. It delivers what it says - dimension reduction.

Imagine you are trying to assess students’ understanding of math by their scores. This is just 1D data. Now imagine evaluating the overall performance of several students in school by looking at scores in all subjects, their athletic performance, and their diet. This is multidimensional data.

When you apply PCA, it analyzes all these factors and identifies patterns in the data. For example, if students who perform well in sports also tend to have a more monitored diet, PCA might group athletic performance and diet based on their actual correlation, not based on any assumptions. The result is that PCA reduces the multidimensional data into fewer dimensions (2D) by capturing the most important relationships, making it easier to analyze the overall performance.