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

Use cases of regression

The use cases of regression are huge: it is a very commonly used method in many projects. Still, let's see some examples to get a better idea of the different types of use cases that can benefit from regression models.

Use case 1 – Forecasting

A very common use case for regression algorithms is forecasting. In forecasting, the goal is to predict future values of a variable that is measured over time. Such variables are called time series. Although a number of specific methods exist for time series modeling, regression models are also great contenders for obtaining good performance on future prediction performance.

In some forecasting use cases, real-time responses are very important. An example is stock trading, in which the datapoints of stock prices arrive at a huge velocity and forecasts have to be adapted straight away to use the best possible information for stock trades. Even automated stock trading algorithms exist, and they need...