Hi everyone,

I'm working on a personal project and would appreciate some advice from people who have experience with machine learning on Databricks.

Imagine having a dataset containing thousands of game compatibility records collected from different emulator versions, graphics backends, hardware configurations, frame rates, and user-reported issues. The goal is to predict whether a game will run correctly on a specific setup and identify the settings most likely to improve performance.

Would this problem be better approached as a classification task or a recommendation system? Which ML algorithms would you start with for this type of structured dataset?

I also wonder whether Databricks AutoML is a good starting point before building custom models. Has anyone here used AutoML for a project with a large number of categorical features and configuration variables?

For context, the dataset is related to retro gaming and PlayStation 2 emulator compatibility, similar to the type of information available on https://ps2biosonline.com/.

I'd appreciate any suggestions on feature engineering, model selection, or best practices for organizing this kind of project in Databricks.