In this Guided Project, get hands-on experience with lasso regression, a valuable tool in optimizing models and enhancing predictive performance. Explore the power of lasso regression by learning about its necessity, applications, and significance in the realm of machine learning. Discover why lasso regression is essential for feature selection by producing different data visualizations.

This hands-on project is based on the Apply lasso regression to automate feature selection tutorial. The Guided Project format combines the instructions of the tutorial with the environment to execute these instructions without the need to download, install, and configure tools. Generated with AI

A look at the project ahead

• Gain a solid understanding of regularization concepts in the context of linear regression models.
• Learn to load and manipulate data sets using essential libraries such as NumPy and Pandas.
• Implement lasso regression for linear models using sklearn, and use grid search for hyperparameter tuning.

What you'll need

• No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
• Basic understanding of Python: This project is beginner-friendly, but having a basic understanding of Python will make it easier.
• Basic understanding of statistical concepts: A basic understanding of statistic concepts is beneficial but not required. This tutorial begins with an explanation of lasso regression to guide you throughout the project.