Emotions are a result of internal and external factors which are unique to every individual and they influence human decisions to a considerable extent. Emotion detection contributes for a large domain of research. Identifying and predicting emotions based on data can, in fact, sabotage many mishaps in an early stage. In this project, we have taken into consideration four physiological signals – body temperature, heart rate, skin resistance and pulse wave. These signals are obtained from a skin temperature sensor, a heart rate sensor, a skin response sensor and a custom designed pulse wave sensor. These signals are processed using Arduino Uno microcontroller. The microcontroller transmits the data to a computer via USB. The data is used for analyses and classification using machine learning algorithms to find out which algorithm provides the highest accuracy. The four basic emotions taken into account in this project are normal (relaxed), happy, sad and angry. The data has been collected from 22 healthy individuals, including both male and female, with ages ranging from 20 to 22 years. The performance of the dataset for different machine-learning algorithms are checked through Weka and TensorFlow. Among all the algorithms applied to the dataset, Random Forest Tree proved to provide the highest accuracy of 82.55% for the entire dataset using Weka. We were able to achieve an accuracy of 98.75% for individual dataset through fully connected 10 hidden layered neural network using TensorFlow.