In today’s world, data is everywhere, and data scientists often face challenges when dealing with large and complex data sets. No need to worry here, because we have a powerful tool called Principal Component Analysis (PCA).

Imagine a room full of many tangled wires. The PCA acts as an effective observer, sorting the loops and revealing the hidden information. Complex data are transformed into more straightforward collections of uncorrelated variables called principal components. These new features are essentially new creations created by combining existing ones, but they are organized by the importance of summarizing the entire dataset.

PCA simplifies complex data by focusing on the important features that explain most of the variation in the data and allows us to find additional reduced variables There are many advantages to this size reduction selection.

Basic Analysis: Simple statistics and diagrams with minimal changes. Using a clean toolbox gives a different feel than using one that is cluttered.

PCA has the potential to improve the performance of machine learning algorithms by improving performance and simplifying interpretation.

Revealing hidden patterns: By analysing overlapping factors, we can reveal hidden relationships between variables that may not be obvious at first. Discovering hidden connections in a giant web of information is like uncovering secrets in a giant puzzle. The application of PCA is broader than just data smoothing. PCA can help create clearer visualisations for complex data and provide an improved understanding of patterns and trends.

Authors: Catalin Bondari & Bohdan Boiprav