Summary

In this chapter, we worked on the last project in this book, predicting stock (specifically stock index) prices using machine learning regression techniques. We started with a short introduction to the stock market and factors that influence trading prices. To tackle this billion-dollar problem, we investigated machine learning regression, which estimates a continuous target variable, as opposed to discrete output in classification. We followed this with an in-depth discussion of three popular regression algorithms, linear regression, regression trees and regression forests, and SVR. 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 whole chapter to solve our stock price prediction problem.

In the next chapter, we will continue...