Epilepsy is a neurological condition that causes recurrent seizures and has a severe impact on an individual's quality of life. The precise diagnosis of epilepsy will assist doctors in providing patients with the relevant treatment. The Discrete Wavelet Transform (DWT) method is used to identify scalp EEG signals from electroencephalogram (EEG) data. The 'db4' mother wavelet of DWT is used for evaluating six sub-bands from EEG inputs. The sub-band signals are used to extract statistical and entropy features. Using the publicly accessible EEG dataset from the Children's Hospital of Boston-MIT, the collected features are subjected to Nave Bayes, Gradient Boosting, and Linear Discriminant Analysis. Conclusions from the analysis show that the Bayes algorithm produces the best results, with accuracy, sensitivity, specificity, and Negative Mean Squared Error of 96.12%, 96.12%, 100%, and 0.0453, respectively.