I am trying to fit a regression model on a non linear data. The features I have are around 12 and around 800 samples. With the help of PyCaret, i tried to fit the data on to around 22 model, and then selected the best one (Ada Boost) and then tried further to tune it to get better result. However, none of the models gave a positive R2 score, Ada Boost was the least worst performing algorithm. This is the test (red) and predicted test output (green) from the selected algorithm.
After trying all various techniques and still not getting a decent result, can we infer that the features are not enough to account for the variation of the target variable ? In other words the provided features don't explain best the target variable.
It may sound silly but am a beginner in Data Sciences, so please dont mind.