Building Your First Linear Regression Model

Time to put everything together! In this lesson, you'll build a complete linear regression model from start to finish.

The Dataset: House Prices

We'll predict house prices based on features like size, bedrooms, and age.

Step 1: Prepare the Data

Step 2: Train the Model

Step 3: Make Predictions

Step 4: Evaluate the Model

Practice: Build Your Own Model

Complete Workflow Summary

Key Takeaways

• Linear regression follows a clear workflow: prepare → train → predict → evaluate
• Use train/test split to get honest performance estimates
• MAE and RMSE tell you average prediction error
• R² tells you what percentage of variance your model explains
• The learned coefficients tell you how each feature affects the prediction
• In sklearn, it's just: model.fit(X, y) and model.predict(X_new)

Congratulations! You've built your first ML model. Next, we'll tackle classification!