  Software developed on public platform is a source of data that can be used to
make predictions about those projects. While the individual developing activity
may be random and hard to predict, the developing behavior on project level can
be predicted with good accuracy when large groups of developers work together
on software projects.
  To demonstrate this, we use 64,181 months of data from 1,159 GitHub projects
to make various predictions about the recent status of those projects (as of
April 2020). We find that traditional estimation algorithms make many mistakes.
Algorithms like $k$-nearest neighbors (KNN), support vector regression (SVR),
random forest (RFT), linear regression (LNR), and regression trees (CART) have
high error rates. But that error rate can be greatly reduced using
hyperparameter optimization.
  To the best of our knowledge, this is the largest study yet conducted, using
recent data for predicting multiple health indicators of open-source projects.
