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Python Machine Learning Cookbook

You're reading from   Python Machine Learning Cookbook Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789808452
Length 642 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Toc

Table of Contents (18) Chapters Close

Preface 1. The Realm of Supervised Learning FREE CHAPTER 2. Constructing a Classifier 3. Predictive Modeling 4. Clustering with Unsupervised Learning 5. Visualizing Data 6. Building Recommendation Engines 7. Analyzing Text Data 8. Speech Recognition 9. Dissecting Time Series and Sequential Data 10. Analyzing Image Content 11. Biometric Face Recognition 12. Reinforcement Learning Techniques 13. Deep Neural Networks 14. Unsupervised Representation Learning 15. Automated Machine Learning and Transfer Learning 16. Unlocking Production Issues 17. Other Books You May Enjoy

Introduction

Predictive modeling is probably one of the most exciting fields in data analytics. It has gained a lot of attention in recent years due to massive amounts of data being available in many different verticals. It is very commonly used in areas concerning data mining to forecast future trends.

Predictive modeling is an analysis technique that is used to predict the future behavior of a system. It is a collection of algorithms that can identify the relationship between independent input variables and the target responses. We create a mathematical model, based on observations, and then use this model to estimate what's going to happen in the future.

In predictive modeling, we need to collect data with known responses to train our model. Once we create this model, we validate it using some metrics, and then use it to predict future values. We can use many different...

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