A persistent problem of machine learning (ML) predictions is potential discrimination towards individuals from specific population groups, e.g., based on gender, religion, etc. Numerous algorithms have been proposed to tackle biased predictions leading to such discrimination, particularly for classification problems. These algorithms typically aim to reduce bias as defined by specific metrics. Given the large variety of algorithms and metrics, selecting a method suited for a particular application is tedious and challenging. FairCR is an extensible system that allows the evaluation of fair classification algorithms in a systematic and unified way. It further recommends which fair classification algorithms to use based on several application preferences. We showcase FairCR's functionality on a large set of readily implemented algorithms and metrics over multiple datasets, demonstrating how it can support the comparative evaluation of algorithms and help select specific fair classification alaorithms for given application preferences.