I'm building binary classification models on my company's dataset. The problem I'm having is that I haven't been able to increase the accuracy of my models. I have trained, tuned, cross validated models such as logistic regression, knn, neural networks and they all have results with the same accuracy. I feel that I have tried everything. The dataset is about 100 data points with 21 features. I'm aware of the curse of dimensionality so I have tried using only subsets of the features but the accuracy is the same no matter which subsets of features I use.
Is it possible that with this dataset, no further improvements can be done? Should I just tell my boss that this is the maximum accuracy possible given the amount of data we have?