I am quite new to data analysis and Machine Learning, that's why I am asking for help with a problem I am facing.
It's an outliers detection problem. I have a quite big amount of data that I need to create a model (meaning that all these data have a standard behaviour, so there are no outliers among them). After this, I would need to check another set of data - clearly of the same type of the first set- and I need to check this data for possible outliers with respect to the model identified from the first set.
The data features are categorical and numerical, but I should be able to solve this problem using an OneHotEncoder process.
My idea was to use a Machine Learning (or Neural network) unsupervised method to create clusters for the first set, and check if any data of the 2nd set is an outlier for the clusters. Would that make sense?
Any idea of how I can deal with this problem?