The problem of epilepsy has grown exponentially and is now considered as one of the most prevailing neurological disorders affecting around 50 million people around the globe. Epilepsy is identified by analyzing the interictal activity present in the EEG signal. Visual analysis of EEG is a tedious process and subject to human error. This work proposes a robust method to ease the burden of intractable seizures by automatic recognition of ictal epileptiform activity in the EEG of epileptic patients. The classification between EEG having an epileptic seizure and non-seizure is done using various machine learning algorithms. The classifiers used are weighted KNN, Boosted Trees, Bagged Trees, subspace discrimination, Subspace KNN and RUS boosted tree. Based on the accuracy of the classifier we will select one method and we will export the model as function to use for validation purpose. These are the methods used to classify epileptic seizure EEG signals.