Supervised learning
Linear regression
Regression finds the straight line that best predicts y from x. Add noise and watch how confidently the line still captures the trend — measured by R².
Fitted line y = …
Spread of points around the true line.
The line minimizes squared error. More noise lowers R² but the fit stays unbiased.
Unsupervised learning
k-means clustering
With no labels, k-means groups points by closeness. Pick how many clusters to look for, then step through the algorithm as centroids settle into the data.
Iteration 0
Inertia (spread) —
How many groups to find.
Each step reassigns points to the nearest centroid, then moves centroids to their mean — until nothing changes.