I am relatively new to ML so I apologies in advance if my question shows lack of understating of the field.
A particular study course has a high drop-out rate and we want to reduce it.
We have two data sets:
- Data about the student themselves (e.g. scored of initial aptitude tests, demographic). This dataset includes the feature to predict. E.g.
ID, AGE, RESULT_TEST1, RESULT_TEST2,DROPPED_OUT AAAA, 21, 0.6, 0.4, TRUE BBBB, 20, 0.3, 0.9, FALSE
- Exams taken by the students during the course. This dataset contains data about the type of the exam (e.g. subject, level of complexity) and the results obtained by the student. Some of the exams are required, some are mandatory and some can be taken on voluntary bases. That is, each student can take an arbitrary list of exams. E.g.
ID, EXAM_ID, EXAM_COMPLEXITY, EXAM_SCORE AAAA, XXXXX, 0.8, 0.4 BBBB, YYYYY, 0.2, 0.8
The idea is to use ML to calculate the likelihood of a particular student to drop-out during the course using historical data. The system should be able to predict based on the two datasets, how likely is a student to at drop-out so that we can give him/her more support.
How do I combine the two datasets to train a model? I could create a superset joining the two but what would I then use to predict a result?
E.g. If I train the model on:
ID, AGE, RESULT_TEST1, RESULT_TEST2, EXAM_ID, EXAM_COMPLEXITY, EXAM_SCORE AAAA, 21, 0.6, 0.4,XXXXX, 0.8, 0.4 BBBB, 20, 0.3, 0.9,YYYYY, 0.2, 0.8
How do I predict the likelihood of
DROPPED_OUT passing all the data I have for a new student (multiple samples)?
Any explanation or pointers to documentation would be greatly appreciated.
Thank you in advance.