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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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Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Required Software Tools FREE CHAPTER
2. A Machine Learning Refresher 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

Using Keras for movie recommendations

In this section, we will utilize Keras as a deep learning framework in order to build our models. Keras can easily be installed by using either pip (pip install keras) or conda (conda install -c conda-forge keras). In order to build the neural networks, we must first understand our data. The MovieLens dataset consists of almost 100,000 samples and 4 different variables:

  • userId: A numeric index corresponding to a specific user
  • movieId: A numeric index corresponding to a specific movie
  • rating: A value between 0 and 5
  • timestamp: The specific time when the user rated the movie

A sample from the dataset is depicted in the following table. As is evident, the dataset is sorted by the userId column. This can potentially create overfitting problems in our models. Thus, we will shuffle the data before any split happens. Furthermore, we will not utilize...

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