The goal

Regression learns a function:

• inputs (features) XX
• output (target) yy (continuous number)

So the model can predict ŷŷ for new inputs.

  flowchart LR
  X[Features] --> M[Regression Model] --> Y[Predicted target ŷ]

A toy example

Predict house price from size:

• X = size_sqftX = size_sqft
• y = pricey = price

Regression tries to find a “best-fit” relationship.

Common regression use-cases

• forecasting (demand, sales)
• risk modeling (credit risk)
• resource planning
• personalization (expected spend)

Assumptions (important)

Different regression models make different assumptions.

Linear regression assumes (roughly):

• a linear relationship between features and target
• errors are random (noise)

These assumptions are often “wrong” but still useful.

Baselines matter

Before complex models, always try:

• predicting the mean
• simple linear regression

If your fancy model doesn’t beat a baseline, something’s off.

Mini-checkpoint

Given a dataset, write down:

• your target column (y)
• 5 features (X)
• what a “good enough” error means for the business

🧪 Try It Yourself

Exercise 1 – Train-Test Split

Exercise 2 – Fit a Linear Model

Exercise 3 – Evaluate with MSE