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Hands-On Ensemble Learning with Python

You're reading from   Hands-On Ensemble Learning with Python Build highly optimized ensemble machine learning models using scikit-learn and Keras

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781789612851
Length 298 pages
Edition 1st Edition
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Authors (2):
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Konstantinos G. Margaritis Konstantinos G. Margaritis
Author Profile Icon Konstantinos G. Margaritis
Konstantinos G. Margaritis
George Kyriakides George Kyriakides
Author Profile Icon George Kyriakides
George Kyriakides
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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Required Software Tools
2. A Machine Learning Refresher FREE CHAPTER 3. Getting Started with Ensemble Learning 4. Section 2: Non-Generative Methods
5. Voting 6. Stacking 7. Section 3: Generative Methods
8. Bagging 9. Boosting 10. Random Forests 11. Section 4: Clustering
12. Clustering 13. Section 5: Real World Applications
14. Classifying Fraudulent Transactions 15. Predicting Bitcoin Prices 16. Evaluating Sentiment on Twitter 17. Recommending Movies with Keras 18. Clustering World Happiness 19. Another Book You May Enjoy

Predicting Bitcoin Prices

Bitcoin and other cryptocurrencies have attracted the attention of many parties over the years, mainly due to their explosion in price levels, as well as the business opportunities that blockchain technologies offer. In this chapter, we will attempt to predict the next day's Bitcoin (BTC) price using historical data. There are many sources that offer cryptocurrency's historical price data. We will use Yahoo finance data, available at https://finance.yahoo.com/quote/BTC-USD/history/. In this chapter, we will focus on predicting future prices and leveraging that knowledge to invest in bitcoin.

The following topics will be covered in this chapter:

  • Time series data
  • Voting
  • Stacking
  • Bagging
  • Boosting
  • Random forests
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