In this intermediate machine learning course, you learned about some techniques like clustering and logistic regression. In this guided project, you’ll practice what you’ve learned in this course by building a model to predict the stock market.

To do that, we'll be working with data from the S&P500 Index, which is a stock market index. Predicting whether an index will go up or down will help us forecast how the stock market as a whole will perform. Since stocks tend to correlate with how well the economy as a whole is performing, it can also help us make economic forecasts.

(Note: You shouldn't make trades with any models developed in this lesson. Trading stocks has risks, and nothing in this lesson constitutes stock trading advice.)

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 terminal guided projects before or need a refresher, we recommend completing our Working With Data Downloads 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 apply machine learning techniques to predict the stock market.
  • Learn to engineer new features from data.
  • Iterate on machine learning models to improve performance.

Lesson Outline

1. The dataset
2. Reading in the data
3. Generating indicators
4. Splitting up the data
5. Making predictions
6. Improving error
7. Next steps