# Which ML method for multiclass (non-binary) text classification should I choose (from SparkML)?

I am working on a quite big dataset that will be processed on the cluster, so this is why I am using PySpark for that purpose.

The presentable records of this dataset have a such structure:

+----------+------------+--------------------+--------------------+--------------------+
|         0|  07/29/2013|       Consumer Loan|        Vehicle loan|Managing the loan...|
|         1|  07/29/2013|Bank account or s...|    Checking account|Using a debit or ...|
|         2|  07/29/2013|Bank account or s...|    Checking account|Account opening, ...


After some preprocessing/data cleansing operations I would like to create and then obviously train a model that will classify issues (Issue) into some categories, that are still unknown. I have read some articles about TF-IDF, but not sure if this could be suitable for this case.