Which type of regression fits better? 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?
 A: *

*You probably need to first normalize your data [A]

*You should never just use just one classifier and end it there.

*Certain choices of classifiers are also done based on the data characteristics. For example, if your data has quite a bit of missing attribute values, decision trees almost always tend to do better than any other model.


The order in which I use the classifiers: 
1] SVMs [B] 
2] Logistic Regression/Multinomial Logistic Regression [C] 
3] Decision Trees [D]
I usually first try SVMs, if I get my results with the standard implementations of SVM then great! If not, then try to use LR/MLR from Weka. The only reason I try SVM first is because Weka is not great at handling large datasets for training. But this might not be the case for you, so you can try LR/MLR first as well if you wish. If neither SVM nor LR give the desired result, then I simply move to Decision Trees. After running all of them, I just pick the one that performed the best.
[A] http://www.quora.com/What-are-the-ways-to-normalize-the-features-for-statistics-or-machine-learning-software 
[B] http://svmlight.joachims.org/svm_multiclass.html or http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 
[C] Weka 
[D] An in-house implementation of Gradient Boosted Decision Tree.
A: 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). 
A: First of all. 9 categories sound like a lot. How big is you sample?
Start by assessing the independent variables. You might want to remove the ones which have a score on 3 standard deviations above them mean. This should reduce you range. Next you want to assess if the independent variables follows a normal distribution. If not, you want to apply some transformation to make them normal distributed. It is not a necessity but it is good common practice.
The way you describe it, logistic regression is the way to go with the methods you are mentioning. It all comes out on the characteristics of your dependent variable Cluster analysis is an explorative/undirected technique so that can not be used for prediction. K-means clustering can however be used for prediction...
