Python libraries such as pandas, sklearn can be used to evaluate and refine prediction models. With these libraries, you will train and test your model, learn about overfitting and underfitting, and find the best hyperparameter.

Predictive modeling is a statistical technique that uses data, machine learning algorithms, and modeling techniques. Used by many industries and individuals in data science, predictive modeling is a powerful tool to predict future events.

In this guided project, you will learn how to evaluate and refine predictive models by training and testing models, calculating cross-validation scores, and identifying overfitting and underfitting. You will also learn how to apply ridge regression and use grid search to optimize model parameters. By mastering these skills, you will be able to build predictive models that are accurate, reliable, and effective at making predictions on new data. 

Completing this project will provide you with the skills and knowledge required to optimize your predictive models and make data-driven decisions.

After completing this project, you'll be able to:

For this project, you will need:

Your online lab environment has everything you need to get started. Also, note that this platform works best with current versions of modern browsers.