I am a biologist and I have an algorithm question, I asked on stack exchange but was suggested to come here. Also, I have really tried to explain my problem using simple toy data; note that in real life I have thousands of students/exams; not just the three as in this example.
For example, let's say there are only three students in a class; and from class records, I know their what their #attendance days per year (#), average punctuality (%), class engagement (not good, good, very good) (* = no recorded value)
Over the years:
Student1 = [100, *, not good] Student2 = [50, 60, good] Student3 = [200, 100, very good]
These three students only study three subjects: maths, english and history. I can see that the class exam results for maths, english and history are changing throughout the year.
Student1 = [maths increasing, english decreasing, history decreasing] Student2 = [maths increasing, english stable, *] Student3 = [maths increasing, english increasing, history increasing]
My question: "What student properties are most strongly correlated with changes in exam results"?
My output: A list of students and their properties that are most strongly correlated with changes in exam results, to identify which traits we woul For this, someone suggested that I look at "multi-label classification methods", since each of the data sets have a number of different class labels to be predicted (where each label is an exam score change in a particular subject?) So my questions are:
Do you agree that multi-label classification is the method to address this problem?
Do you know where I should start (remember that I'm a biologist)? I have found this: http://scikit-learn.org/stable/modules/multiclass.html ; but I'm not sure where to start? Would someone have an example of basic code that I would use to do this correlation for this toy data set? Or should I use a different package/software?