  Solar energetic particle (SEP) events are one of the most crucial aspects of
space weather. Their prediction depends on various factors including the source
solar eruptions such as flares and coronal mass ejections (CMEs). The
Geostationary Solar Energetic Particle (GSEP) Events catalog was developed as
an extensive data set towards this effort for solar cycles 22, 23 and 24. In
the present work, we review and extend the GSEP data set by; (1) adding "weak"
SEP events that have proton enhancements from 0.5 to 10 pfu in the E>10 MeV
channel, and (2) improving the associated solar source eruptions information.
We analyze and discuss spatio-temporal properties such as flare magnitudes,
locations, rise times, and speed and width of CMEs. We check for the
correlation of these parameters with peak proton fluxes and event fluences. Our
study also focuses on understanding feature importance towards the optimal
performance of machine learning (ML) models for SEP event forecasting. We
implement random forest (RF), extreme gradient boosting (XGBoost), logistic
regression (LR) and support vector machines (SVM) classifiers in a binary
classification schema. Based on the evaluation of our best models, we find both
the flare and CME parameters are requisites to predict the occurrence of an SEP
event. This work is a foundation for our further efforts on SEP event
forecasting using robust ML methods.
