Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up.
Basically you're given test data of a bunch of students of their scores in other subjects but math and you are to predict their score in math based off all their other test scores. Say you were passed data of
How would you go about generating that student's score in mathematics or coming up with a prediction engine to generate the math score? I know that's the whole point of this question but can someone give me a hint or a resource to go to so I can have a chance of figuring this out and actually get started? I really want to learn.
I would actually try out regression. Also, don't make the mistake of using the serial number in your machine learning algorithms! The reason why I'm suggesting regression as opposed to 'better' machine learning algorithms is because you said you wanted to learn, and it's important to understand the algorithms (for the long run, and to truly be good at this stuff) that you're using. Regression is the easiest tool in the book that works quite well! Weka is so easy to use that you'll be able to plug and play different machine learning algorithms just for the sake of it. Another pointer that's won me several competitions is to do some feature selection before using regression/machine learning. For example, in your case, it is reasonable to assume that a student who scores high in Physics probably has a better chance of scoring high in Math as opposed to someone who scores high in English (but not necessarily Physics). If you have enough data, the algorithm itself will be able to deduce these positive/negative correlations and train the model accordingly. Sometimes, there isn't enough data, and you have to do some feature selection. Good luck! I'm a regular participant on Kaggle myself, and I think it's great that you're taking the 'hacker' route to learn more. It's the best way to get your hands dirty on real data and engineering problems.