In the last post, you predicted house prices using one feature. One number in, one number out. Real problems don't work like that. House prices depend on size, location, number of rooms, age of the building, crime rate in the area, school ratings, and a dozen other things. Using just one feature leaves most of the useful information on the table. Multiple regression is the same idea as linear regression, just with more inputs. Sounds simple. But more features brings new problems you need to know about. What You'll Learn Here How multiple regression extends single-feature regression What multicollinearity is and why it messes up your model How to check for it and what to do about it Feature selection: picking what actually matters Regularization basics: Ridge and Lasso Full working code on a real dataset From One Feature to Many Single feature linear regression: y = w * x + b Enter fullscreen mode Exit fullscreen mode Multiple regression with 3 features: y = w1 * x1 + w2 * x2 + w3 * x3 + b Enter fullscreen…