Predicting House Sale Prices

In this linear regression for machine learning course, we started by building intuition for model-based learning, explored how the linear regression model worked, understood how the two different approaches to model fitting worked, and some techniques for cleaning, transforming, and selecting features. In this guided project, you can practice what you learned in this course by exploring ways to improve the models we build.

While practicing what you learned in this course to improve the models we build, we’ll be working with a dataset that describes characteristics of houses sold between 2006 and 2010 in the city of Ames (located in the American state of Iowa).

Working on guided projects will give you hands-on experience with real-world examples, so we encourage you to not only complete them, but to take the time to really understand the concepts.

These projects are meant to be challenging to better prepare you for the real world, so don’t be discouraged if you have to refer back to previous lessons. If you haven’t worked with Jupyter Notebook before or need a refresher, we recommend completing our Jupyter Notebook Guided Project before continuing.

As with all guided projects, we encourage you to experiment and extend your project, taking it in unique directions to make it a more compelling addition to your portfolio!


  • Learn to construct your own machine learning model from scratch.
  • Learn to build a machine learning pipeline.

Lesson Outline

  1. Introduction
  2. Feature Engineering
  3. Feature Selection
  4. Train And Test
  5. Next Steps

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