Epilepsy, a disorder that leads to abnormal activities in the brain is primarily caused by excessive neuronal activity. Patients diagnosed with epilepsy frequently suffer from seizures, the impact of which may vary from abnormal body movements to alterations in the levels of consciousness. An appropriate dosage of medication provided at the right time can help prevent an impending seizure. In this paper, real data obtained from Epilepsy Ecosystem is used for analysis. After preprocessing this data, several signal processing algorithms and mathematical computations are used for feature extraction. Two sets of features are identified viz. lasting features and transitory features. Several combinations of these features along with Machine Learning algorithms such as Extra Trees Classifier and XGBoost are used to train generalized models as well as a patient-specific models, both of which are immune to noise. It is observed that the XGBoost based generalized model which is trained using lasting features gives a relatively better accuracy of 90.41%.