I am fairly new to Datascience and currently working on an assignment that requires me to do a binary classification on a set with about 9 parameters for X. I tried working on it using different methods implemented in scikit (linear regression, neural network, random forests,..). However, my ROC-Curve always ended up looking barely better than random. After looking closer at the scatterplot for all nine parameters against Y, I realized that the datapoints at Y=1 and Y=0 overlapped greatly for all of them, making them somehow 'ambiguous' (see picture). Is there any way I can increase the accuracy of my model by pre-processing the data? Or any other means? I can not change the classification of test and training set.
I really appreciate your help!