Forecasting/predicting techniques for qualitative data? I have a food alert dataset composed of nominal qualitative variables, such as type of alert, country of origin, action taken, etc. as well as the date on which the alert was recorded.

What techniques are there to predict what type of alert is most likely in a time window when there are only qualitative variables in our dataset?
Or given a variable (for example, country of origin = France) predict the rest of the variables?

I have used machine learning algorithms before but not with datasets composed of nominal qualitative variables.

On the other hand, what are the most appropriate techniques for clustering and classification this kind of datasets?

I know it's a broad question, so I expect broad answers to guide me in a general way


I have about 50000 registered alerts. In the example of the image not all the variables are shown
You can encode the various columns of qualitative data using LabelEncoder(). For example: 

label_encoder = LabelEncoder()
risk = label_encoder.fit_transform(df['RISK_DECISION'])
action = label_encoder.fit_transform(df['ACTION_TAKEN])
etc...