Machine learning case study in Spark
In this section, we will look into how to implement text classification using the Spark ML and Naive Bayes algorithms. The classification of text is one of NLP's most common cases of use. Text classification can be used to detect email spam, identify retail product hierarchy, and analyze feelings. This process is typically a problem of classification in which we try to identify a specific subject from a natural language source with a large volume of data. We can discuss several topics within each of the data groups and it is therefore important to classify the article or textual information in logical groups. The techniques of text classification help us to do this. These techniques require a lot of computing power if the data volume is large and a distributed computing framework for text classification is recommended. For example, if we want to classify legal documents in a knowledge repository on the internet, text classification techniques can be used...
