  This report is based on the modified NIST challenge, Too Close For Too Long,
provided by the SFI Centre for Machine Learning (ML-Labs). The modified
challenge excludes the time calculation (too long) aspect. By handcrafting
features from phone instrumental data we develop two machine learning models, a
GBM and an MLP, to estimate distance between two phones. Our method is able to
outperform the leading NIST challenge result by the Hong Kong University of
Science and Technology (HKUST) by a significant margin.
