In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{de:2016,
author = {de, Montjoye YKJV and Rocher, L and Pentland, AS},
journal = {Journal of Machine Learning Research},
title = {bandicoot: an open-source Python toolbox to analyze mobile phone metadata},
url = {http://hdl.handle.net/10044/1/48286},
volume = {17},
year = {2016}
}
TY - JOUR
AB - bandicoot is an open-source Python toolbox to extract more than 1442 features from standard mobile phone metadata. bandicoot makes it easy for machine learning researchers and practitioners to load mobile phone data, to analyze and visualize them, and to extract robust features which can be used for various classification and clustering tasks. Emphasis is put on ease of use, consistency, and documentation. bandicoot has no dependencies and is distributed under MIT license
AU - de,Montjoye YKJV
AU - Rocher,L
AU - Pentland,AS
PY - 2016///
SN - 1532-4435
TI - bandicoot: an open-source Python toolbox to analyze mobile phone metadata
T2 - Journal of Machine Learning Research
UR - http://hdl.handle.net/10044/1/48286
VL - 17
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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