Using machine learning and deep learning models, the study attempts to fill two gaps in the prediction of odorant perception by humans. The first is to compare the predictive performance of three frequently used molecular features (VS, MS, and fingerprint features), and the second is to address the inherent challenge of data imbalance in all the datasets used to predict human perceptions. To this end, we assembled a novel dataset of 2606 molecules and used four different kinds of models to evaluate the performance; results established superior performance of fingerprint features, then VS, and lastly, MS. We used random resampling techniques to address the data imbalance.