In this course, we explored the fundamentals of machine learning using the k-nearest neighbors algorithm. To make learning smoother and more efficient, we learned about different topics in isolation. 

In guided projects, we go one step further and combine some of the skills you learned to build a real project to put in your portfolio to help you land a job in data!. Moreover, we'll also make use of what we learned in the Machine Learning Fundamentals course.

In this guided project, you'll practice the machine learning workflow you've learned so far to predict a car's market price using its attributes. The dataset we will be working with contains information on various cars. For each car we have information about the technical aspects of the vehicle such as the motor's displacement, the weight of the car, the miles per gallon, how fast the car accelerates, and more

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 clean data in preparation for maching learning.
  • Practice iterating on k-nearest neighbors models.

Lesson Outline

1. Introduction to the data set
2. Data Cleaning
3. Univariate Model
4. Multivariate Model
5. Hyperparameter Tuning
6. Next Steps