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by Vinodh Mohan, Rich Hagarty | Published October 30, 2018
AnalyticsArtificial intelligenceData scienceMachine learningPython
This code pattern demonstrates how data scientists can leverage remote Spark clusters and compute environments to train and deploy a spam filter model. The model is built using natural language processing and machine learning algorithms and is used to classify whether a given text message is spam or not.
This code pattern is a demonstration of how data scientists can leverage remote Spark clusters and compute environments from Hortonworks Data Platform (HDP) to train and deploy a spam filter model using Watson Studio Local
A spam filter is a classification model built using natural language processing and machine learning algorithms. The model is trained on an SMS spam collection dataset to classify whether a given text message is spam, or ham (not spam).
This code pattern provides multiple examples to tackle this problem, utilizing both local (Watson Studio Local) and remote (HDP cluster) resources.
After completing this code pattern, you’ll understand how to:
Get the detailed instructions in the README file. These steps will show you how to:
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