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
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://osf.io/download/5bb4217e36cd3c001919be81/
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:osf:thesis:378jp
DOI: 10.31219/osf.io/378jp
Access Statistics for this paper
More papers in Thesis Commons from Center for Open Science
Bibliographic data for series maintained by OSF ().