Take the 2-minute tour ×
Programmers Stack Exchange is a question and answer site for professional programmers interested in conceptual questions about software development. It's 100% free, no registration required.

For a project, I have to classify a list of banking transactions based on their description.

Supose I have 2 categories: health and entertainment. Initially, the transactions will have basic information: date and time, ammount and a description given by the user. For example:

Transaction 1: 09/17/2012 12:23:02 pm - 45.32$ - "medicine payments"
Transaction 2: 09/18/2012 1:56:54 pm - 8.99$ - "movie ticket"
Transaction 3: 09/18/2012 7:46:37 pm - 299.45$ - "dentist appointment"
Transaction 4: 09/19/2012 6:50:17 am - 45.32$ - "videogame shopping"

The idea is to use that description to classify the transaction. 1 and 3 would go to "health" category while 2 and 4 would go to "entertainment".

I want to use the google prediction API to do this. In reality, I have 7 different categories, and for each one, a lot of key words related to that category. I would use some for training and some for testing.

Is this even possible? I mean, to determine the category given a few words? Plus, the number of words is not necesarally the same on every transaction.

Thanks for any help or guidance! Very appreciated

Possible solution: https://developers.google.com/prediction/docs/hello_world?hl=es#theproblem

share|improve this question
    
"medicine payments health" has 160M Google hits, "medicine payments entertainment" only 51M. Crude but effective. –  MSalters Sep 18 '12 at 13:35
add comment

2 Answers

I have 7 different categories, and for each one, a lot of key words related to that category. I would use some for training and some for testing.

Sounds like a simple Bayesian classification should work well. I'm sure there's libraries which implement that for all major programming languages.

share|improve this answer
    
I have some knoledge in machine learning because in college I worked on some projects in which we used neural networks to classify stuff. What I don't remember is working with a variable number of inputs. In the wikipedia entry you posted, they use weight, height and foot size to determine sex. In my case, I have a description, and that could be 1 word, or several words. So, how many neurons on the first layer? .. That's what I can't figure out how to handle –  Alex Sep 17 '12 at 20:02
    
@Alex: Bayesian classification has nothing to do with neural networks. It basically works by first determining from the training input a bunch of data points of the form "input belonging to class N contained the word A in X% of all cases" and from that via Bayes' theorem derives results like "input that consists of the words A, B, and C belongs to class N with a likelihood of X%, to class M with a likelihood of Y%, etc. - the number of words in the input is not fixed. The most well-known application of Bayesian classification is spam filtering. –  Michael Borgwardt Sep 17 '12 at 20:11
add comment

You can use Clustering and Natural Language processing.

Coursera have excellent courses on this subjects:

share|improve this answer
add comment

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.