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

Defining regression

In this chapter, you will discover regression. Regression is a supervised machine learning task in which a model is constructed that predicts or estimates a numerical target variable based on numerical or categorical independent variables.

The simplest type of regression model is linear regression. Let's consider a super simple example of how a linear regression could be used for regression.

Imagine that we have a dataset in which we have observations of 10 people. Based on the number of hours they study per week, we have to estimate their average grade (on a 1 to 10 scale). Of course, this is a strongly oversimplified problem.

The data looks as follows:

Code Block 7-1

import pandas as pd
nb_hrs_studies = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
avg_grade = [5.5, 5.8, 6.8, 7.2, 7.4, 7.8, 8.2, 8.8, 9.3, 9.4]
data = pd.DataFrame({'nb_hrs_studies': nb_hrs_studies, 'avg_grade': avg_grade})
data

You will obtain the following...