When companies work with large datasets, a frequent issue arises: collinearity. This occurs when two variables are highly correlated and essentially reflect the same trend. In the automotive sector, for example, engine displacement and horsepower are closely linked. Using both in a predictive model only adds “noise” and makes results harder to interpret.

The difficulty becomes even greater with multicollinearity, when several variables combine to form cross-relationships. In such cases, it becomes difficult to isolate the individual effect of each variable on the target to predict. The model becomes less interpretable, more unstable, and less effective.

To address this issue, Principal Component Analysis (PCA) offers a robust solution: reducing the number of variables while preserving the essential information.

How principal component analysis works ?

Principal Component Analysis, or PCA, is a dimensionality reduction technique. Its goal is to simplify the information contained in a large number of variables by creating a smaller set of new variables called “principal components.” These components retain the essential variability of the data while eliminating redundancies.

To understand how it works, two key ideas are useful. First, many variables are correlated with each other. In a customer dataset, for example, annual income and spending levels are often related. Working with both separately only adds unnecessary complexity. Second, it is possible to summarize this information into a single axis that captures the common variation. This axis becomes a principal component.

In practice, PCA seeks to identify the directions that best explain the observed differences in the data. The first component retains the largest share of variation, the second captures what remains but in a different direction—thus decorrelating the information—and so on. The result is a small number of new variables that represent most of the original information.

The benefit is twofold: models train faster and become more robust, as they rely on synthetic variables that are “decorrelated” from one another. Instead of handling twenty highly correlated columns, we may end up with just two or three components that already contain 90% of the informative value.

For businesses, PCA provides a pragmatic answer to a growing challenge: exploiting massive datasets without losing efficiency. It reduces the number of variables, speeds up computation, and improves model stability. It is particularly useful when variables are numerous and strongly correlated.

This technique stands as a key preprocessing step in any machine learning project.

Although powerful, PCA should not be seen as a universal solution. It assumes that relevant information is concentrated in linear relationships between variables. However, in some cases, relationships are nonlinear and require other techniques. Furthermore, the new components created are combinations of initial variables: they therefore lose direct interpretability compared to the clear coefficients of a linear regression.

PCA is thus a compromise: it sacrifices some interpretability in exchange for efficiency and robustness.