Book Image

Python Machine Learning By Example

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example

By: Yuxi (Hayden) Liu

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.
Table of Contents (9 chapters)

Stock Price Prediction with Regression Algorithms

In this chapter, we will be solving a problem that absolutely interests everyone—predicting stock price. Gaining wealth by smart investment, who doesn't! In fact, stock market movements and stock price prediction has been actively researched by a large number of financial and trading, and even technology, corporations. A variety of methods have been developed to predict stock price using machine learning techniques. We herein will be focusing on learning several popular regression algorithms including linear regression, regression tree and regression forest, as well as support vector regression, and utilizing them to tackle this billion (or trillion) dollar problem.

We will cover the following topics in this chapter:

  • Introduction to the stock market and stock price
  • What is regression?
  • Feature engineering
  • Acquiring stock data and generating predictive...