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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
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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

Evaluating cars based on their characteristics

In this recipe, let's see how we can apply classification techniques to a real-world problem. We will use a dataset that contains some details about cars, such as number of doors, boot space, maintenance costs, and so on. Our goal is to determine the quality of the car. For the purposes of classification, quality can take four values: unacceptable, acceptable, good, or very good.

Getting ready

You can download the dataset at https://archive.ics.uci.edu/ml/datasets/Car+Evaluation.

You need to treat each value in the dataset as a string. We consider six attributes in the dataset. Here are the attributes along with the possible values they can take:

  • buying: These will be vhigh...
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