Summary

In this chapter, we worked on the last project of the 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 the billion dollar problem, we investigated machine learning regression, which estimates a continuous target variable, as opposed to a discreet output in classification. It followed with in-depth discussion of three popular regression algorithms, linear regression, regression tree and regression forest, as well as support vector regression. It covered the definition, mechanics, and implementation from scratch and with existing modules, along with applications in examples. We also learned how to evaluate the performance of a regression model. Finally, we applied what we have learned in this chapter in solving our stock price prediction problem...