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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 2. Analyzing and Predicting Telecommunication Churn FREE CHAPTER 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

Deploying the trained LDA model

For this mini deployment, let's use a real-life dataset: PubMed. A sample dataset containing PubMed terms can be downloaded from: https://nlp.stanford.edu/software/tmt/tmt-0.4/examples/pubmed-oa-subset.csv. This link actually contains a dataset in CSV format but has a strange name, 4UK1UkTX.csv.

To be more specific, the dataset contains some abstracts of some biological articles, their publication year, and the serial number. A glimpse is given in the following figure:

Figure 6: A snapshot of the sample dataset

In the following  code, we have already saved the trained LDA model for future use as follows:

params.ldaModel.save(spark.sparkContext, "model/LDATrainedModel")

The trained model will be saved to the previously mentioned location. The directory will include data and metadata about the model and the training...

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