There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.

There is a trade-off between the size of your training set and your testing set. If you use most of your data for training, you will have fewer samples to validate your model. Conversely, if you use more samples for testing, you will have fewer samples to train your model. Cross Validation will allow you to reuse your data to use more samples for training and testing.

This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.

In this section, you will understand the relationship between the loss function and the different regularization types.

In this assignment, you will apply regression techniques to analyze a dataset of your choice. Your task is to preprocess the data, build and compare models, extract insights, and suggest next steps.You will focus on presenting key findings and insights—not the code. You may include visuals to support your analysis, but the report should be a clear summary of your process and conclusions.Your final report will be evaluated by an AI graded tool.