Prediction systems Datathon Kaufland Solution – Team Total Kaputt! – Why da faQ the machine broke down? Posted 29.09.201812.10.2018 milena-piryankova, stephen, nike, bogomil-filipov, vasilmarchev, martinmarinov, sergeyvi4ev, michael-opitsch, penchodobrev, sist Mentors: junior, pepe What we tried to do to solve the Kaufland case for the Global Datathon 2018. This article just contains our exploratory data analysis in the form of many plots and some explanations. There isn’t any modeling stage described here. 5votes Global_Datathon_2018_team_Kaputt Share this
0votes Positive Aspects ————————— – good analysis – laid out ideas Negative Aspects ————————- – no modeling – plot interpretations and explanations missing – no finding/definition of proxy variable for downtimes Log in to Reply
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Positive Aspects
—————————
– good analysis
– laid out ideas
Negative Aspects
————————-
– no modeling
– plot interpretations and explanations missing
– no finding/definition of proxy variable for downtimes