Compare Regression Model Building Exercise

Exercise Introduction

This exercise consists of several phases designed to guide you through the full machine learning workflow for several regression model.

Phase 1: Understand the Problem

• Define the business or prediction goal.
• Identify the target variable.
• Identify the input features.
• Determine whether this is a regression or classification problem.

Phase 2: Explore the Data (Exploratory Data Analysis – EDA)

• Load and inspect the dataset.

Phase 3: Prepare the Data

• Select relevant features.
• Encode categorical variables (if any).

Scale/normalize features if needed.

• Split the dataset into training and testing sets (e.g., 80/20 split).

Phase 4: Build the Regression Models (Linear, Ridge, Lasso, Elastic Net Regression) Using the same training and testing data, build:

1. Linear Regression
2. Ridge Regression
3. Lasso Regression
4. Elastic Net Regression
   • Tune hyperparameters (e.g., alpha).
   • Use cross-validation for better model selection.

• Examine model coefficients.
• Understand the intercept and feature importance.

Phase 5: Evaluate the Regression Models (Linear, Ridge, Lasso, Elastic Net Regression)

• Make predictions on the test set.
• Calculate evaluation metrics:
  • MAE (Mean Absolute Error)
  • RMSE (Root Mean Squared Error)
  • R² (R-squared) -Compare training vs. testing performance (check for overfitting).

Phase 6: Compare Model Performance

• Compare MAE, RMSE, and R² across all models.
• Analyze bias vs. variance.
• Identify the best-performing model.

Phase 7: Model Interpretation

• Interpret coefficients.
• Understand how each feature impacts the prediction.
• Discuss regularization effects (Ridge vs. Lasso vs. Elastic Net).

Phase 8: Final Conclusions

• Select the best model.
• Summarize findings.
• Suggest possible improvements (more data, feature engineering, different algorithms).