Regions within proteins can be broadly classified into two types: ordered and disordered. Ordered regions assume a defined three dimensional structure and are identified by their unique sequence of amino acids. Disordered regions, however, can adopt a variety of structures and have amino acid sequences that can vary dramatically between equivalent proteins in different species. The relationship between sequence, disorder, and function in a protein remains poorly understood and as roughly one third of proteins contain significant regions of disorder, this poses a significant barrier to predicting protein function. The goal of this project is to evaluate existing methods of disorder prediction, improving on them as necessary, and to develop models for classifying disordered regions using state-of-the-art machine learning techniques.