The proposed research work is done on the multimodal dataset WESAD to predict if the subject is undergoing stress or not. The WESAD dataset was visualized and cleaned to be used for machine learning and deep learning algorithms. The dataset was used for multi-classification (transient, stress, baseline, amusement, meditation) and binary classification (stress or no stress). Stratified K Fold Cross Validation was used for generalization of the model keeping k as 5. Sampling was used to make the highly imbalanced dataset to balanced dataset. Ensemble machine learning methods such as AdaBoost, ExtraaTrees and XGBoost classifiers were used as predictive models. Of the three machine learning models, ExtraTrees classifier gave the best performance with an accuracy of 99.99% for binary classification and 99.49% for multi-classification. While, in deep learning MLP classifiers were used and gave performance of 98.64% for binary classification and 97.00% for multi-classification. Various parameters were changed and tweaked in order to achieve best performance.