In the world of machine learning, data can sometimes have many features, making it complex and difficult to visualize or analyze.

In the world of machine learning, data can sometimes have many features, making it complex and difficult to visualize or analyze. Dimensionality reduction techniques come to the rescue! These techniques aim to reduce the number of features in your data while preserving the most important information. Imagine a high-dimensional wardrobe with clothes scattered everywhere. Dimensionality reduction techniques help you fold and organize those clothes into a smaller closet, making it easier to browse and find what you’re looking for. Here, we’ll explore two popular dimensionality reduction techniques: Principal Component Analysis (PCA) and t-SNE.