Emotions are a vital part of humanity. Understanding emotions may be challenging as they mostly depend on circumstances. But in recent years some studies have been done on the human emotional state. The placement of electrodes and identification of significant Electroencephalogram (EEG) features are crucial for efficient emotion recognition. In our proposed work model, the DEAP dataset is used, which includes various physiological signals acquired from 32 individuals of different age groups as they viewed 40 different movie clips. The main aim of this study is to identify human emotion by extracting frequency domain features of the EEG Signal from all the participants. Our proposed method can recognize Valence and Arousal emotions. For the classification of emotions, we used K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Multi-Layer Perceptron (MLP) machine learning classifiers. Among these classifiers, the Random Forests (RF) classifier outperformed in effectively capturing the valence emotions, while K-Nearest Neighbors (KNN) exhibited strength in predicting arousal emotions.