A comprehensive, visually-driven beginner's guide to linear regression featuring 100+ original visualizations and 33 animations. Covers the full pipeline: building a simple linear model using the least squares analytical solution, evaluating model quality through visual diagnostics (scatter plots, residual plots, Q-Q plots) and metrics (R², RMSE, MAE, MAPE, SMAPE), statistical significance testing via the F-test, prediction intervals, multiple linear regression, feature engineering and preprocessing, outlier handling methods (RANSAC, LOF, Cook's distance), gradient descent, L1/L2 regularization, and cross-validation. Uses apartment price prediction as a running example throughout.
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Table of contentsWho is this article forWhat this post coversA brief literary reviewA good model starts with dataWhy do we need a model?How to build a simple modelHow to measure model qualityImagine that there are only 45 apartments in the world…Improving model qualityNumerical methodsRegularizationOverfittingHyperparameters tuningLinear regression is a whole worldConclusionSort: