Sprint Challenge: Linear Models

Sprint Challenge Overview

The Sprint Challenge is your opportunity to demonstrate the linear models concepts you've learned throughout this sprint. You'll apply linear regression, ridge regression, and logistic regression to real-world prediction problems.

Challenge Setup

To get started with the Sprint Challenge, follow these steps:

1. Access the Jupyter notebook using the link below.
2. Complete all tasks in the notebook, demonstrating your understanding of the sprint concepts.
3. Submit your completed challenge according to the provided instructions.

Challenge Expectations

The Sprint Challenge is designed to test your mastery of the following key concepts:

• Linear regression: Building and evaluating simple and multiple linear regression models
• Ridge regression: Implementing regularization techniques and feature selection
• Logistic regression: Applying classification models and interpreting results
• Model evaluation: Using appropriate metrics and validation techniques
• Data preprocessing: Handling categorical variables and feature engineering

What to Expect

The Sprint Challenge will involve working with real-world datasets and demonstrating your ability to:

1. Import and explore training data
2. Split data into feature matrices and target vectors
3. Implement train-validation-test splits
4. Establish baseline models and metrics
5. Build and train linear regression models
6. Build and train ridge regression models
7. Calculate and compare model performance metrics
8. Make predictions on test sets
9. Interpret model coefficients and results

Sprint Challenge Resources

Linear Models Documentation

• Scikit-learn Linear Models
• Scikit-learn LinearRegression
• Scikit-learn Ridge Regression
• Scikit-learn LogisticRegression

Model Evaluation and Preprocessing

• Scikit-learn Model Evaluation
• Scikit-learn Preprocessing