how to deal with high cardinal categorical feature into numeric for predictive machine learning model?
One approach could be to group your categorical levels into smaller buckets using business rules. In your case for the feature area_id you could simply group them based on their geographical location, say all area_ids from a single district (or for that matter any other level of aggregation) will be replaced by a single id. Similarly, for page_entry you could group similar pages based on some attributes like nature of the web page like sports, travel, etc.  In this way you could significantly reduce the number dimensions of your variables.
Hope this helps!
I have two columns of having high cardinal categorical values, one column(area_id) has 21878 unique values and other has(page_entry) 800 unique values. I am building a predictive ML model to predict the hits on a webpage.
column information:
area_id: all the locations that were visited during the session. (has location code number of different areas of a webpage)
page_entry: describes the landing page of the session.
how to change these two columns into numerical apart from one_hot encoding?
thank you.