Differentiating between a Relaxed State and a Physical Stress Stage is a useful technology for wearable devices. Currently, an accurate physical stress detection system utilizing the Electrodermal Activity Signal is not available. Using the Electrodermal Activity physiological signal, we develop a novel approach to this problem. Assessing physical changes with Electrodermal Activity/physiological signals is a field that has seen significant progress. The Electrodermal Activity signal tries to observe the changes in the electrical output of the eccrine sweat glands. These changes are performed by the sudomotor nerves, part of the nervous system, and by assessing the changes in these nerves (through Electrodermal Activity), it would be possible to develop a technology like that described above. We have used a dataset from a previous experiment, which has 5-minute stages, to provide the data for our experiment. The data for this experiment was taken with a wearable device. The data were pre-processed using a bandpass butterworth filter and a minmax normalizer. After this, feature extraction and selection were performed. For the model training, multiple models were trained for the stratified and non-stratified dataset. Multiple of them had excellent performance, but the Random Forest Classifier especially stood out for the non-stratified dataset, with a macroaverage cross-validated F-1 score of 0.80. After establishing that the best model was the Random Forest Classifier, Shapley values were used to understand and explain the feature importance of the model.