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With recent advances being made towards artificial intelligence enabled automatic seizure detection, the next frontier is the automatic classification of seizure types. On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification. We used the recently released TUH EEG seizure corpus (V1.4.0 and V1.5.2) and conducted a thorough search space exploration to evaluate the performance of a combination of various pre-processing techniques, machine learning algorithms, and corresponding hyperparameters on this task. 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