  Conditional Neural Process (QNPy) has shown to be a good tool for modeling
quasar light curves. However, given the complex nature of the source and hence
the data represented by light curves, processing could be time-consuming. In
some cases, accuracy is not good enough for further analysis. In an attempt to
upgrade QNPy, we examine the effect of the prepossessing quasar light curves
via the Self-Organizing Map (SOM) algorithm on modeling a large number of
quasar light curves. After applying SOM on SWIFT/BAT data and modeling curves
from several clusters, results show the Conditional Neural Process performs
better after SOM classification. We conclude that SOM classification of quasar
light curves could be a beneficial prepossessing method for QNPy.
