Sprint Challenge: Linear Models

In this sprint challenge, you will demonstrate your understanding of linear regression, ridge regression, and logistic regression. You'll apply what you've learned to solve real-world prediction problems.

This sprint challenge consists of several key tasks:

1. Import training data
2. (stretch goal): Create scatter plot
3. Split data into feature matrix and target vector
4. Split data into training and validation sets
5. Establish baseline mean absolute error
6. Build and train linear regression model
7. Build and train ridge regression model
8. Calculate mean absolute error for training and validation sets
9. Calculate for validation set
10. Make predictions based on test set
11. (stretch goal): Get below 18,000 for test set MAE
12. (stretch goal): Plot model coefficients

Your submission will be evaluated based on the following:

• Technical Proficiency (50%): Correct implementation of linear models, appropriate feature engineering, proper model evaluation.
• Data Analysis (20%): Quality of exploratory data analysis, insights derived from the data.
• Model Interpretation (20%): Accurate interpretation of model coefficients, clear explanation of model predictions.
• Code Quality (10%): Well-documented, readable code, efficient implementation.

Click the link below to access the sprint challenge repository. Follow the instructions in the README file to complete the challenge.