Summary

In this chapter, we worked on the project of predicting stock (specifically stock index) prices using machine learning regression techniques. Regression estimates a continuous target variable, as opposed to discrete output in classification

We started with a short introduction to the stock market and the factors that influence trading prices. We followed this with an in-depth discussion of three popular regression algorithms, linear regression, regression trees, and regression forests. We covered their definitions, mechanics, and implementations from scratch with several popular frameworks, including scikit-learn and TensorFlow, along with applications on toy datasets. You also learned the metrics used to evaluate a regression model. Finally, we applied what was covered in this chapter to solve our stock price prediction problem.

In the next chapter, we will continue working on the stock price prediction project, but with powerful neural networks. We will see whether...