Best way to narrow down a list and rank based on attributes?

I have a mortgage/credit data set that contains a list of customers (600k rows) and has a 100 columns inclusive of the customer's general info (address, city, zipcode, etc), income, fico scores, number of current mortgages, mortgages in the past, aggregate mortgage amounts, number of bank card trades, etc. The data pertains to customers that are already good candidates to contact for issuing a credit product, however if one is to narrow down the list to 350K:

What would be the best method to rank the list to cut it down?

PS Your insights are much appreciated.

Topic ranking classification machine-learning

Category Data Science

1
answered at

So it a little hard to know how to answer the question. Why do you need to cut the number of records down? If you want to rank them, rank them to what purpose? Ranking implies there are positives and negatives, or at least less positives.

One way you could approach this is to perform PCA. That said, I think you should consider disqualifying some fields, like the address fields.

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