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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

Estimating traffic

An interesting application of SVMs is to predict traffic, based on related data. In the previous recipe, we used an SVM as a classifier. In this recipe, we will use an SVM as a regressor to estimate the traffic.

Getting ready

We will use the dataset available at https://archive.ics.uci.edu/ml/datasets/Dodgers+Loop+Sensor. This is a dataset that counts the number of cars passing by during baseball games at the Los Angeles Dodgers home stadium. We will use a slightly modified form of that dataset so that it's easier to analyze. You can use the traffic_data.txt file, already provided to you. Each line in this file contains comma-separated strings formatted in the following manner:

  • Day
  • Time
  • The opponent...
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