Most of the researchers focused on detecting stress involved in a person, which causes in a person several emotional problems like anxiety, grief, low self esteem and other mental health problems. Rece nt studies have shown that stress can also affect the aspects of your life, including your thinking ability and physical health. To reduce riskiness from being stress and affected with its adverse effects. Stress Lysis is a publicly available dataset for w earable stress and affect detection. This multimodal dataset features physiological and motion data, recorded from both a wrist and a chest worn device, of many subjects during a lab study. The following sensor modalities are included: blood volume pulse, body temperature, electrodermal activity, heart rate, humidity and step count. Moreover, the dataset bridges the gap between previous lab studies on stress and emotions, by containing three different affective states (neutral = 0, stress = 1, amusement = 2). Stress detector classifies a stressed individual from a normal one by acquiring his/her physiological signals through appropriate sensors such as Electrocardiogram (ECG), Galvanic Skin Response (GSR) etc. These signals are pre processed to extract the d esired features which depicts the stress level in working individuals. XGBoost, Logistic Regression, KNN and Adaboost are investigated to classify these extracted feature set. The result indicates feature vector with best features having a strong influence in stress identification. An attempt is made to determine the best feature set that results in maximum classification accuracy

: Stress lysis, Multi modal dataset, XGBoost.