The Ultimate Guide to Unsupervised Learning: Improve Your Data Analysis with Clustering and PCA

Discover how unsupervised learning with clustering and PCA can unlock hidden patterns in data, improve analysis, and drive smarter business decisions.

Machine learning is generally split into two main types: supervised and unsupervised learning. In previous parts of this series, we looked at supervised learning, where the model learns from data that already has labels attached—each piece of data comes with a known outcome.

Now, we're shifting to unsupervised learning. Here it’s the Wild West, there are no labels or known outcomes. Instead of trying to predict something specific, unsupervised learning looks for patterns and structures hidden in the data.

Think about walking into a Blockbuster (for those young readers, blockbuster was basically amazing. It was a place to rent movies before Redbox and Netflix came along) with thousands of movies, but no categories or labels.

Your job is to organize them into meaningful groups. Without knowing the specific genres or classifications, you'd probably sort the movies based on similarities—like themes, acting style, or cover design.

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That’s what unsupervised learning is all about—finding order in what seems like chaotic data.

Two popular techniques in unsupervised learning are clustering and dimensionality reduction. Clustering groups similar data points together, while dimensionality reduction simplifies complex data by cutting down on the number of features, but keeping the important parts.

These methods are used in many areas, from marketing to biology, helping to reveal valuable insights hidden in large amounts of data.

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