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Scala Machine Learning Projects

You're reading from Scala Machine Learning Projects

Product type Book

Published in Jan 2018

Publisher Packt

ISBN-13 9781788479042

Pages 470 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Table of Contents (17) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Analyzing Insurance Severity Claims

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

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Index

Summary

In this chapter, we have seen how to develop an ML project to predict whether a customer is likely to cancel their subscription or not, and then used it to develop a real-life predictive model. We have developed predictive models using LR, SVMs, DTs, and Random Forest. We have also analyzed what types of customer data are typically used to do preliminary analysis of the data. Finally, we have seen how to choose which model to use for a production-ready environment.

In the next chapter, we will see how to develop a real-life project that collects historical and live Bitcoin data and predicts the price for an upcoming week, month, and so on. In addition to this, we will see how to generate a simple signal for online cryptocurrency trading.

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