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Scala Machine Learning Projects

You're reading from   Scala Machine Learning Projects Build real-world machine learning and deep learning projects with Scala

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788479042
Length 470 pages
Edition 1st Edition
Languages
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Author (1):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Analyzing Insurance Severity Claims FREE CHAPTER 2. Analyzing and Predicting Telecommunication Churn 3. High Frequency Bitcoin Price Prediction from Historical and Live Data 4. Population-Scale Clustering and Ethnicity Prediction 5. Topic Modeling - A Better Insight into Large-Scale Texts 6. Developing Model-based Movie Recommendation Engines 7. Options Trading Using Q-learning and Scala Play Framework 8. Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks 9. Fraud Analytics Using Autoencoders and Anomaly Detection 10. Human Activity Recognition using Recurrent Neural Networks 11. Image Classification using Convolutional Neural Networks 12. Other Books You May Enjoy

Spark-based movie recommendation systems

The implementation in Spark MLlib supports model-based collaborative filtering. In the model-based collaborative filtering technique, users and products are described by a small set of factors, also called LFs. In this section, we will see two complete examples of how it works toward recommending movies for new users.

Item-based collaborative filtering for movie similarity

Firstly, we read the ratings from a file. For this project, we can use the MovieLens 100k rating dataset from http://www.grouplens.org/node/73. The training set ratings are in a file called ua.base, while the movie item data is in u.item. On the other hand, ua.test contains the test set to evaluate our model. Since...

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