Some background - I am a Computer Science student, planning to do a project and I have a data source that I have cleared, filtered and processed but I am really unsure of what exact algorithms I should use for the particular project I am interested in. Also, Anthropometry means - human body measurements.
More background so I used the NHANES Data set to create a table of anthropometric measurements of individuals. Now, with this information, I created an additional field called "Body Fat%" which simply uses the values in the table to compute body fat %. Now this is where it gets tricky.
Using the standard Body Fat % computation, I can classify people as underweight, moderate, obese and extremely obese. So from my very little knowledge of machine learning, I now know that I have what one would call - labels for classes. The question now is, I am trying to build a project that would prompt users to enter their anthropometric measurement in the web/mobile app. Now, these users might or might not enter values for all fields depending on if they have the correct equipment or not. I want to use machine learning/statistical analysis to classify these users up to a certain extent based on this input.
I am fairly new to machine learning so I do not know exactly what algorithm to use or look at but right now I am looking at Logistic Regression, Decision Forest, Decision Jungle, Neural Network, One - v - all, SVM, LDA, ID3. I know that the list does not make a lot of sense but I just need a good starting point.
Additionally, it is worth mentioning, I suppose, that I only have about a total of 10-12 variables with a 10,000 data entries and I would want to classify in a streaming fashion instead of making batch predictions.
Any help with the direction will be appreciated very much.