submit your model's prediction on Unknown dataset, and compete with peers.
This is your AT3 task for 32130
You need to build classifiers using the techniques covered in the lectures to predict the class attribute.
At the very minimum, you need to produce a classifier for each method we have covered.
However, if you explore the problem very thoroughly (as you should do in Industry), preprocessing the data, looking at different methods, choosing their best parameters settings, and identifying the best classifier in a principled and explainable way, then you should be able to get a better mark.
If you show 'expert' use either KNIME or Python (i.e. exploring multiple classifiers, with different settings, choosing the best in a principled way, and being able to explain why you built the model the way you did), this will attract a better mark.
Submissions are evaluated on area under the ROC curve between the predicted probability and the observed target.
For each index in the test set, you must predict a class for the SpType-ELS variable. The file should contain a header and have the following format:
id, Diabetes_binary
26681,1
58585,1
54339,0
17273,0
etc.