thanks for the answer, what do you mean by accuracy ? I’m building a regression model so accuracy doesn’t make sense in my case, if you mean something like R2 score then yes my neural network shows a value of 0.15 but surprisingly random forest gave me a 0.9 score on the training data but 0.3 score on the validation data which is a lot better than the neural net (that confused me more). Now according your suggestion to use linear regression, would that make sense since my data have strong non linearity? I tried it and it wasn’t able to fit the data, it gave a 0.005 score and a high loss, I also tried SVR but it didn’t work well, the only machine learning approach that worked well(well not good but better than the others until now) is random forest regressor but as I said it overfits the data and that’s also bad, I also tried cross validation but that didn’t work, it alway overfits no matter what I do. Now coming to your suggestion whether something is wrong with the data, how can I know that? or what does it exactly mean? I think a neural network should be able to map any non linearity, maybe not a perfect fit but at least a good fit not like in my case