I have a dataset with 1175 examples and 21 features which are in the range of [-1, +1], and two class labels 1 and 0. As I read in the most of the resources, it is good to have data in the range of [-1, +1] or [0, 1]. So I thought I don't need any preprocessing. But when I run SVM and decision tree classifiers from scikit-learn, I got 100% accuracy using cross-validation with 10 folds. However the classification accuracy seems to decrease as I perform more iterations.
I am collecting these datas from a Kinect device which gives me angles and positions of the certain joints of the human body. Because of hardware faults I am sure that there are noise on data. So getting 100% is almost impossible.
My dataset is avaiable here