In this chapter, we will be solving a problem that absolutely interests everyone—predicting stock prices. Getting wealthy by means of smart investment—who isn't interested?! In fact, stock market movements and stock price predictions have been actively researched by a large number of financial, trading, and even technology corporations. A variety of methods have been developed to predict stock prices using machine learning techniques. Herein, we will be focusing on learning several popular regression algorithms, including linear regression, regression tree and regression forest, and support vector regression, as well as neural networks, and utilizing them to tackle this billion (or trillion) dollar problem.
We will cover the following topics in this chapter:
- An introduction to the stock market and stock prices
- What is regression
- Feature engineering
- Acquiring stock data and generating predictive features
- What is linear regression
- Mechanics of linear regression
- Implementations of linear regression (from scratch, and using scikit-learn and TensorFlow)
- What is decision tree regression
- Mechanics of regression tree
- Implementations of regression tree (from scratch and using scikit-learn)
- From regression tree to regression forest
- Implementations of regression forest (using scikit-learn and TensorFlow)
- What is support vector regression
- Mechanics of support vector regression
- Implementations of support vector regression with scikit-learn
- What is a neural network
- Mechanics of neural networks
- Implementations of neural networks (from scratch, and using scikit-learn, TensorFlow, and Keras)
- Regression performance evaluation
- Predicting stock prices with regression algorithms