Markov chain modelling?

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I am working on a personal loan dataset. For each loan, we recorded its credit status monthly after it was drawn by the borrower. Let's say there were 6 status coded by A-F. My project is to use Markov chain model to train the data and estimate the transition matrix as shown below. Then we can predict the future movement of any single loan in probability.

Meanwhile, the dataset contained substantial features for a single loan, like loan amount, borrower age, income, dwelling region, bank account profile, last 90 days bank statement data, some other credit bureau data etc.

I just get this project from the very beginning and all thoughts are rough but not accurate. I need brain storm. Thanks heaps.

How can I use the data properly to predict the next state given the current state? Any assumption should be seriously considered in this project and any advice or experiance for me? I will do it in Python and/or R.

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LUSAQX

Posted 2016-11-15T20:50:09.410

Reputation: 713

Cross-posted: https://math.stackexchange.com/q/2023257/14578. Please do not post the same question on multiple sites. Each community should have an honest shot at answering without anybody's time being wasted.

– D.W. – 2017-06-28T04:19:03.100

Agree, though I think I'd close the other copy as it's reasonably on topic here. – Sean Owen – 2017-06-30T15:57:21.610

No answers