From the literature survey, we find three broad classes of features used for odor prediction, mass spectra, as they can be used for both mono-molecular and chemical mixture, vibrational spectra not only capture different molecular features but also record vibrational frequency, which plays a role in transferring the vibrational energy, which, according to the vibrational theory of olfaction, plays an important role along with other structural properties of molecules, third is physicochemical features, which are frequently used in cheminformatics studies to encode the features of molecules. Hence, we use these three classes of features for analysis. We assembled a novel dataset with sensory data as the binary presence of odor descriptors used in this study and used three baseline multi-label models-Random Forest (RF) , Binary Relevance (BR) , Classifier Chain (CC) -and a cost-sensitive MLP (CSMLP) for classification . This paper's main contributions are as follows: We compiled a large and novel dataset of having VS, MS and physicochemical features; (2) a systematic comparison of the predictive performance of different features for odor classification; (3) we used random resampling techniques to address data imbalance in the odor datasets; (4) exploratory analysis of multi-modal feature fusion for odor classification; (5) an explainable deep model provides insight into the feature odor relationship.