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Predicting Features that Drive Retention using Heterogenous Supervised Models Ensembles

Stefan Niculae

No 378jp, Thesis Commons from Center for Open Science

Abstract: Making a decision on what to invest development time in is dif- ficult. Today’s market is so competitive that you cannot afford to focus on negligible product features. Based on reported customer behavior, I propose a ranking of the most important features with the help of statistics and machine learning. Following this advice leads to making informed decisions leading to good use of devel- opers’ time.

Date: 2016-06-01
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Persistent link: https://EconPapers.repec.org/RePEc:osf:thesis:378jp

DOI: 10.31219/osf.io/378jp

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