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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

Historical and live-price data collection

As stated earlier, we will utilize both historical as well live data. We will be using the Bitcoin historical price data from Kaggle. For the real-time data, Cryptocompare API will be used.

Historical data collection

For training the ML algorithm, there is a Bitcoin Historical Price Data dataset available to the public on Kaggle (version 10). The dataset can be downloaded from https://www.kaggle.com/mczielinski/bitcoin-historical-data/. It has 1 minute OHLC data for BTC-USD pairs from several exchanges.

At the beginning of the project, for most of them, data was available from January 1, 2012 to May 31, 2017; but for the Bitstamp exchange, it's available until October 20, 2017 (as well as for Coinbase, but that dataset became available later):

Figure 4: The Bitcoin historical dataset on Kaggle

Note that you need to be a registered user and be logged in in order to download the file. The file that we are using is bitstampUSD_1-min_data_2012-01-01_to_2017...

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