Help choosing analytics algorithm

Hi,

I’m new to machine learning, and am trying to apply it to some data I have, but I don’t know which method or algorithm to select for the problem I’m trying to solve.

I have 3 datasets:

1.

Set of transactions. Each record consists of a unique ID for the transaction a timestamp of when the transaction was processed, the time of how long it took to process the transaction, and the size of the transaction. The size of the transaction is the primary feature that influences the time of the transaction.

1.

Set of transactions that are outliers for their size. For example, the expected time for a transaction of size 1 is 100-150ms; this dataset will have all transactions with times that exceed the expected times for their size (like all transactions of size 1 with times > 150ms).

1.

Set of input data for each transaction. Each transaction contains lots of data (features?) that gets processed, like codes, costs, type, etc.

What I’m trying to do; for the transactions that are outlers, determine which features of the input data may be correlated to the high processing time; Do all the outlier transactions with size 1 have a data feature in common that may be causing their unusual processing time?

Is this a good candidate for applying a machine learning method/algorithm?

If so, what’s the best way to proceed?

Thanks :slight_smile: