prediction of an algorithm when no model/dataset exists (cold start problem)

[mukeshmk]

prediction of an algorithm when no model/dataset exists or the cold start problem is a huge feature/issue on its own, for now, to prevent this from causing the below run time issue:

Traceback (most recent call last):
File "/app/.heroku/python/lib/python3.7/site-packages/flask/app.py", line 2446, in wsgi_app
response = self.full_dispatch_request()
File "/app/.heroku/python/lib/python3.7/site-packages/flask/app.py", line 1951, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/app/.heroku/python/lib/python3.7/site-packages/flask/app.py", line 1820, in handle_user_exception
reraise(exc_type, exc_value, tb)
File "/app/.heroku/python/lib/python3.7/site-packages/flask/_compat.py", line 39, in reraise
raise value
File "/app/.heroku/python/lib/python3.7/site-packages/flask/app.py", line 1949, in full_dispatch_request
rv = self.dispatch_request()
File "/app/.heroku/python/lib/python3.7/site-packages/flask/app.py", line 1935, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File "/app/src/api.py", line 102, in predict_fmlearn
tt_encoder = fml.get_encoders()[utils.TARGET_TYPE]
KeyError: 'Target Type'

Temporary Fix:
add a fail-safe to check for empty database and return a JSON object indicating no "Model has been Trained!"?

Proper Fix:
TODO: think of a way to solve the cold start problem?