Can a neural network regression improve the R² value drastically from other regression techniques? I am doing a multivariate regression analysis with 15 input features, 1 output feature with 1600 samples. I tried SVR, Random forest regressor, KNN, linear, poly and regularized regressions. After trying all, I end up in getting a R² value not more than 0.60. 


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*If I use neural net, is there a possibility to improve the R² value up to 0.90?
(cz, I dunno on what logic does ANN work and I read we can do any thing with ANN)

*Any suggestions to get a better R² value ?

*Is 0.60 is a better R² value? (of course it depends on application and problem type, but I like to know in general)
 A: I know this question is 2 months old and you have probably moved on by now, but for the sake of readers I might suggest that .6 is not a bad R² at all. You say you have 15 features? An R² value of .6 indicates that the correlation of the actual outcome variable values and the predicted variable values is √ .6 = about .77. That's not bad at all. 
I'd also like to add that you don't want to try to artificially push R² too high. First of all, the true ability of your predictors to explain the variance in your outcome variable may only be that much. Hence, doing different types of regression over and over until you get something higher might give you a false positive. Related to this is the problem of over-fitting. Sometimes the model finds an idiosyncrasy in the data that allows for really good predictions, but won't generalize to out-of-sample methods. I suggest doing a split of your data set into a train and test set, or even seeing if you can obtain new observations after building on the new model. Check the R² for the test set (correlation of the predicted test set observations and the true observation values, then square it) and you can get a better idea of how predictive your variables are for the outcome. 
