It has become common for people to experience stress, mainly because of its eclectic nature - physical, psychological, emotional, social, etc. Unmonitored stress may prove harmful to one's health resulting in even chronic diseases. Since stress is very subjective, stress management is not straightforward. Many attempts have been made to detect and quantify stress. However, an accurate assessment can be made from physiological measurements only. In this study, we have demonstrated how electrodermal activity (EDA), which represents the sympathetic response to stress, could be used for accurate classification of stress by developing a machine learning based classification model. 30 participants were subjected to Trier Social Stress Test (TSST), and EDA and accelerometer data were recorded using a wrist-worn device. Datasets containing stress and non-stress periods were segmented and manually tagged for model training, based on recorded stress protocol timeline. A kNN-classifier model was trained on datasets from 15 participants and tested on datasets from the remaining 15 participants, and the results were verified with salivary cortisol levels recorded before and after TSST. The proposed kNN classifier has sensitivity and specificity of 94% and 93% respectively. Motion corruptions due to hand movements were detected using the accelerometer data and were classified as 'motion affected'. The classifier was able to classify - the baseline regions of all participants as non-stress, 93% of the TSST regions as stress and 63% of the post-stress regions as non-stress. © 2018 IEEE.