This article is the companion piece to Episode 82 of the Intelevo YouTube series. Watch the full video walkthrough first, then use this article to review the code, revisit the math, and take notes at your own pace. The video covers the intuition. This post covers the details. So what is Linear Discriminant Analysis, really?

Picture a crowded room full of people from ten different friend groups. Now picture trying to describe that room using only a single photograph. You would capture some of the picture. However, you would lose most of the friendships, the little clusters, and the quiet corners where similar people gathered together. That is exactly what

You collected forty features for your model. Then you added ten more. Now your dataset feels bloated, and half those columns just repeat what the others already say. This is exactly where Principal Component Analysis earns its place in your toolkit. This article is the companion piece to Episode 80 of the Intelevo Machine Learning

Every machine learning course starts the same way. Someone hands you a clean CSV file. The columns already make sense. The categories are already encoded. The scaling is already done. You load it, you split it, you train a model, and it works. Then you try this on your own data, and none of that

Every customer looks the same on a spreadsheet. A name, an income figure, a spending number. Nothing more. But real customers behave in very different ways. Some spend big.However, Some barely spend at all. Some earn a lot but hold back. Others earn little yet spend freely. Customer segmentation with K-Means finds these hidden patterns.

You ran K-Means. You got clean-looking groups. But here’s the real question: are those groups actually good, or do they just look good on a scatterplot? This question trips up almost every beginner in unsupervised learning. Clustering has no labels to check against. So how do you grade your own homework? In this article, you’ll

Have you ever wondered why supermarkets place bread right next to butter? Or why your favorite streaming app keeps suggesting a show the moment you finish another one? Behind both experiences sits one elegant idea: association rule mining. This post breaks the concept down from scratch, walks through a hands-on example, and shows you the

Every dataset hides a few points that don’t fit in. A fraudulent transaction. A failing sensor. A shopper who behaves nothing like the rest. Anomaly detection techniques exist for exactly this reason: they help you spot the point that stands apart, and they do it without a rigid rulebook. Think about how often this shows

Every clustering algorithm you have met so far forces a choice. K-Means picks one centroid for every point. DBSCAN picks one label, or it picks noise. But real data rarely sits in neat, separate piles. A customer often behaves like two segments at once. A gene often expresses two cell states at once. So what

Clustering algorithms usually ask you to make a hard decision before you even see the results. K-Means wants a number of clusters upfront. Hierarchical clustering wants you to trust that your groups look roughly round. DBSCAN clustering skips both demands. It looks at your data, finds where points sit close together, and lets density do