There are several considerations to be further investigated. A mean value approach was used to replace missing data points, which theoretically should not affect the data significantly as light curves are continuous. To produce more robust results, a KNN machine learning algorithm or a Random Forest model could be utilized to fill a missing value. While these methods may be computationally expensive, this may be feasible due to the given small dataset. Furthermore, another method may be devised to utilize the outputs of the spectroscopic classifier and the photometric classifier as inputs into another machine learning program to create an even more robust model.