What regression is

Regression predicts a number.

Examples:

• house price
• delivery time
• demand forecasting
• temperature

In this phase, you’ll learn:

• simple and multiple linear regression
• polynomial regression (non-linear relationships)
• cost functions like MSE
• gradient descent intuition
• regularization (Ridge and Lasso)
• evaluation metrics like R² and adjusted R²

Phase 3 flow

  flowchart TD
  A[Regression Intuition] --> B[Simple Linear Regression]
  B --> C[Multiple Linear Regression]
  C --> D[Polynomial Regression]
  D --> E["Cost Function (MSE)"]
  E --> F[Gradient Descent]
  F --> G["Regularization (Ridge/Lasso)"]
  G --> H["Metrics (R² / Adjusted R²)"]

Suggested practice dataset

A great first dataset is:

• house prices (Kaggle style)
• medical cost dataset
• advertising dataset (TV/Radio/News → Sales)

Try each model and compare results.