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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
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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

Building an event predictor

Let's apply all of this knowledge from this chapter to a real-world problem. We will build an SVM to predict the number of people going in and out of a building. The dataset is available at https://archive.ics.uci.edu/ml/datasets/CalIt2+Building+People+Counts. We will use a slightly modified version of this dataset so that it's easier to analyze. The modified data is available in the building_event_binary.txt and the building_event_multiclass.txt files that are already provided to you. In this recipe, we will learn how to build an event predictor.

Getting ready

Let's understand the data format before we start building the model. Each line in building_event_binary.txt consists of...

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