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I have a dataset of 1400 data points. My fields are Description and Category. I have 1200 data points as the training dataset and 200 for testing purpose. My goal is to analyze the Description column and predict the Category column using the keywords in the Description column.

The total number of categories possible are 3 and all data points fall under these 3 categories only.

I am looking for a package or algorithm which can help me

  1. learn from the training dataset
  2. predict the category for testing dataset.

I am new to text mining and would be really grateful if you guys can suggest any packages or algorithms that can help me. I am a beginner in R but for my project I have to somehow test the training dataset.

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closed as off-topic by gung, Nick Cox, Andy, John, Xi'an Jul 18 '15 at 8:46

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Naive Bayes would be good start for obtaining a classification model. There is a text mining package in R called tm that you could use. However, I don't know if it has a routine for Naive Bayes. Python's NLTK has Naive Bayes, if you didn't mind using Python.

Or you could probably just implement it yourself in R, since you only need to calculate conditional probabilities.

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CRAN maintains a nice set of Task Views. The one you want is here. No doubt there is something in there that will help you find what you need. And there are lots of questions on this topic over on Stack Overflow. Finally, check the 'related' questions on the right of this page.

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