Dimensionality Reduction: Unraveling Data Complexity

More data isn't always better; in fact, it can literally curse your AI model.

It's called the "Curse of Dimensionality." Add too many features, and your model's performance can

suddenly crash because the data becomes too sparse and everything starts to look equally far away.

So, how do we fix it? With dimensionality reduction. Techniques like

PCA, t-SNE, and the super-fast UMAP are like magic spells.

They take thousands of features—like every movie a Netflix user has rated—and compress them into

just two or three dimensions. This lets us visualize massive datasets, revealing the hidden

shapes and clusters within the chaos.