Book Image

Machine Learning for Streaming Data with Python

By : Joos Korstanje
Book Image

Machine Learning for Streaming Data with Python

By: Joos Korstanje

Overview of this book

Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.
Table of Contents (17 chapters)
1
Part 1: Introduction and Core Concepts of Streaming Data
5
Part 2: Exploring Use Cases for Data Streaming
11
Part 3: Advanced Concepts and Best Practices around Streaming Data
15
Chapter 12: Conclusion and Best Practices

Overview of regression algorithms in River

There is a large number of online regression models available in the River online machine learning package.

A selection of relevant ones are as follows:

  • LinearRegression
  • HoeffdingAdaptiveTreeRegressor
  • SGTRegressor
  • SRPRegressor

Regression algorithm 1 – LinearRegression

Linear regression is one of the most basic regression models. A simple linear regression is a regression model that fits a straight line through the datapoints. The following graph illustrates this:

Figure 7.3 – A linear model in a scatter plot

This orange line is a result of the following formula:

Here, y represents avg_grades and x represents nb_hrs_studies. When fitting the model, the a and b coefficients are estimates. The b coefficient in this formula is called the intercept. It indicates the value of y when x equals 0. The a coefficient represents the slope of the line. For...