Back to Catalog

Predict house prices with regression algorithms and sklearn

BeginnerGuided Project

Learn various regression algorithms using Python and scikit-learn, including multiple linear regression, random forest, and decision trees. Visualize your results with Matplotlib and perform a comparative study of different regression models, highlighting their importance in predicting house prices. Use Pandas and scikit-learn to understand and implement these regression techniques and produce insightful visualizations to enhance your analysis.

4.5 (59 Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 550

Skills You Will Learn

  • Pandas, sklearn, Python, Machine Learning

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • January 2, 2026
About this Guided Project
In this project, learn how to develop a regression model to predict house prices based on various features such as the year it was built, its size, and the number of rooms. By using a comprehensive data set, you'll explore and preprocess the data, and train different regression models such as linear, and multiple linear, as well as decision trees and random forest trees to make price predictions and compare each of the models.

This hands-on project is based on the Learn regression algorithms using Python and scikit-learn 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. 

A look at the project ahead

By completing this project, you are able to:
  • Implement regression models: Use Python and scikit-learn to develop various regression models.
  • Master data preparation: Acquire skills in cleaning and preparing data for regression analysis.
  • Evaluate model performance: Learn to use metrics like MSE and R-squared to assess model accuracy.
  • Apply regression to real estate: Demonstrate how regression predicts real estate prices, which aids in investment decisions.

What you'll need

  • No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
  • Basic understanding of Python: Some basic understanding of Python is beneficial.
  • Some understanding of statistical concepts: It's helpful to have some understanding of regression concepts, particularly linear, multiple, and polynomial regression as well as random forest and decision trees.

Instructors

Kang Wang

Data Scientist

I was a Data Scientist in the IBM. I also hold a PhD from the University of Waterloo.

Read more

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.

Read more

Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

Wojciech "Victor" Fulmyk is a Data Scientist and AI Engineer on IBM’s Skills Network team, where he focuses on helping learners build expertise in data science, artificial intelligence, and machine learning. He is also a Kaggle competition expert, currently ranked in the top 3% globally among competition participants. An economist by training, he applies his knowledge of statistics and econometrics to bring a distinctive perspective to AI and ML—one that considers both technical depth and broader socioeconomic implications.

Read more

Ricky Shi

Data Scientist at IBM

Ricky Shi is a Data Scientist at IBM, specializing in deep learning, computer vision, and Large Language Models. He applies advanced machine learning and generative AI techniques to solve complex challenges across various sectors. As an enthusiastic mentor, Ricky is committed to helping colleagues and peers master technical intricacies and drive innovation.

Read more