Bitcoin is a virtual cryptocurrency, that operates in a peer-to-peer network. This thesis presents the first comprehensive study of the Bitcoin ecosystem in GitHub organized around 481 Bitcoin-related projects over eight years (2010-2018). Our work includes manual and data-driven categorization of the projects, defining software health metrics, classification of the projects into three different classes of health, and evaluation of trends in the health of the ecosystem. Four classification algorithms such as Decision tree, Support Vector Machines, K-Nearest Neighbor, and Naive Bayes are leveraged to predict the health of a project. The dataset is a combination of GHTorrent and a dataset collected during this study. The main findings suggest that the Bitcoin ecosystem in GitHub is represented by nine categories by manual categorization and 4 clusters based on the data-driven approach. Moreover, most of the projects are assessed as "Low Risk" and decision tree outperforms with an accuracy of 98%.