I hope this isn't too basic of a question, I'm banking on the Data Science site description being true where it says "...and those interested in learning more about the field". I'm not looking for programming help, just validation that machine learning could help me with a problem.
I'm trying to find all customer phone numbers in our databases. One database has a field with free-form comments from our customer service center. Here is an obfuscated snippet:
multiple #'s 123-456-7890 and 2345678901...current account #2233445566
As you can see, this record contains two phone numbers and also a 10 digit account number. One of the phone numbers has dashes while the 2nd doesn't. Looking for parenthesis helps, but only finds a small set. There are also other 10 digit numbers that could look like a phone number but in fact aren't.
If I run a query to return all records with a 10 digit number formatted with dashes, I get thousands of records. If I check for any 10 digit number, I get tens of thousands. So manually scanning these records to validate accurate matches is not practical.
I'm wondering if I could build a machine learning model that I could train to accurately find phone numbers in this mess. When I say "accurately", I don't mean 100%, just better than standard SQL queries. If I can, I would use this going forward to parse new data that is created in this database.
It seems to me this problem could be a good candidate for machine learning. But I'm new to machine learning, and the research I've done so far talks about different scenarios that don't seem quite the same.