CME-learn: An interactive playground to benchmark CME databases for the time of arrival (ToA) prediction for using machine learning methods.

Coronal mass ejections are one of the most significant drivers of space weather. The ToA predictions along with the Arrival speed of the CMEs are one of the crucial pieces of information for preparing for the possible geomagnetic storms. Geomagnetic storms can have adverse effects on several key components of modern society e.g. communications and electrical grids. The development of many machine learning methods provides us with the opportunity to use these tools in space weather applications. There have been several studies using machine learning methods for ToA predictions. In this study, we present an interactive dashboard to apply several machine learning methods (regression models) to test on the several CME databases used in the community. We also use this opportunity to benchmark various CME databases for TOA and CME arrival speed predictions. We also welcome the community to use this interactive dashboard as a tool to learn about machine learning.