I am a newbie in data mining world. I have a general question.
I have a data set which has 10 independent variables and one target variable named as category which has 9 values like: 1, 2, 3, 4, 5, 6, 7, 8, 9.
the 10 independent variables have different kind of range of values. some of them have values between 0 – 5000, some have big range like 5,000,000 – 100,000,000 etc.
Moreover there is no specific relation (linear etc.) existing between target and independent variables.
I am basically trying to predict the target variable category by using all of these independent variables.
Can someone suggest what should be my approach? I am very confused. Should I use regression models, decision trees or cluster analysis?
Best Answer
You can use whatever multi-category supervised classification algorithm you like, for example multinomial logistic regression or trees (but not linear regression or binary logistic regression). Make sure you use training/evaluation/test set or a type of cross validation, though.
(Also, if there is no specific relationship between predictors and target as you say, then your classification will most likely perform poorly).