Regression Exercise

What you'll learn

In this hands-on exercise, you'll:

• Master simple and multiple linear regression techniques
• Learn to evaluate regression models using key metrics
• Apply regression to real-world datasets
• Develop skills in feature engineering and model optimization

Total Time: ~90 minutes

Getting Started

:computer: Access the exercise template here: Regression Exercise Template

Exercise Overview

Part One: Simple Linear Regression :alarm_clock: (20 minutes)

:computer: In this section, you will:

1. Understand the fundamentals of simple linear regression
2. Implement linear regression with one independent variable
3. Analyze the relationship between variables
4. Interpret regression coefficients and intercepts

:bulb: Pro tip: Start by visualizing your data with a scatter plot before fitting the regression line. This helps you understand if a linear relationship exists!

Part 2: Multiple Linear Regression :alarm_clock: (25 minutes)

:computer: You will learn to:

1. Build regression models with multiple independent variables
2. Handle multicollinearity issues
3. Compare model performance with additional features
4. Understand feature importance in multiple regression tip Common challenge: If you see very high coefficients or strange results, check for multicollinearity using correlation matrices or VIF (Variance Inflation Factor).

Part 3: Advanced Challenge :alarm_clock: (30 minutes)

:art: This section covers:

1. Apply advanced regression techniques to complex scenarios
2. Perform complex data preprocessing for regression
3. Engineer features for improved model performance
4. Handle real-world regression challenges

:information_source: Note: This section is designed to challenge you! Don't worry if you need extra time. Focus on understanding the concepts rather than rushing through.

Part 4: Regression Model Evaluation :alarm_clock: (15 minutes)

:bar_chart: You will master:

1. Evaluate regression model performance systematically
2. Understand key metrics:
   • R² (R-squared): How well the model explains variance
   • MSE (Mean Squared Error): Average squared prediction errors
   • RMSE (Root Mean Squared Error): Standard deviation of errors
   • MAE (Mean Absolute Error): Average absolute prediction errors
3. Apply cross-validation techniques
4. Compare and select the best model

:bulb: Tip Remember: A high R² doesn't always mean a good model. Check for overfitting using cross-validation!

Exercise Submission

:warning: Warning

:memo: IMPORTANT: Submit Your Work!

Please submit your completed notebook through this link:

Exercise Submission Form

Submission Guidelines

:emoji:️ Follow these steps for submission:

1. Complete all questions in each part (Parts 1-4)
2. Verify your code runs without errors:
   • Run all cells from top to bottom
   • Fix any error messages before submitting
3. Include proper data visualizations:
   • Scatter plots for relationships
   • Residual plots for model diagnostics
   • Feature importance charts where applicable
4. Provide clear interpretations:
   • Explain what your coefficients mean
   • Discuss model performance metrics
   • Include conclusions about your findings
5. Download your completed notebook (File -> Download -> .ipynb)
6. Submit through the form link above

Troubleshooting

Common Issues and Solutions

Problem: "Import error for sklearn"

• Solution: Run !pip install scikit-learn in a new cell

Problem: "ValueError: could not convert string to float"

• Solution: Check for non-numeric values in your features. Use pd.get_dummies() for categorical variables

Problem: "LinAlgError: Singular matrix"

• Solution: You likely have perfect multicollinearity. Remove duplicate or highly correlated features

Problem: "My R² is negative!"

• Solution: This happens when the model performs worse than a horizontal line. Check:
  • Data preprocessing steps
  • Feature selection
  • Whether linear regression is appropriate for your data

:information_source: Need help? If you encounter issues not listed here, review the lesson materials or ask for help in the discussion forum before submission.