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Learn to automate feature selection with lasso regression

BeginnerGuided Project

Learn feature automation with lasso regression using sklearn in Python. Optimize model performance by using regularization techniques and hyperparameter tuning with different Python libraries. Explore why this technique is crucial for feature selection through the creation of insightful data visualizations, while you gain practical experience with lasso regression, a powerful tool for optimizing models and elevating predictive performance.

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 72

Skills You Will Learn

  • Machine Learning, Python, Artificial Intelligence, sklearn, Pandas, Numpy

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • March 13, 2025
About this Guided Project
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

While completing this project, you:
  • 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.

Instructors

Eda Kavlakoglu

Program Director, Technical Content at IBM

Marketing leader with a technical background in data science.

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Lucy Xu

Data Scientist

I am a Data Scientist Intern at IBM. I am also currently in my fourth year at the University of Waterloo studying Statistics with a minor in Computing.

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Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. Follow my projects to learn data science principles, machine learning algorithms, and artificial intelligence agent implementations.

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