This blog presents a relatively simple machine learning approach, using R, to harnessing workforce data to understand a company’s employee turnover, and predict future employee turnover before it happens so that actions can be taken now, before it’s too late.
GOALS:
- Create model to accurately predict employees who leave
- Identify key factors related to employee churn
The full code can be found on Github here, along with extra code for analyses that supported this blog and might be useful to others.
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Published by Brian Richmond
Brian Richmond (PhD, Anthropological Sciences) is an award-winning data scientist with expertise in machine learning, statistics, experimental design, and data visualization. As a senior data scientist at Aura Health, he built the recommendation AI that delivers a personalized experience to millions of people. At WeWork, Brian Richmond was a Founder of the People Analytics team and awarded a '2018 Excellence Award' for his major contributions after only 8 months. As a thought leader in people analytics, he participates in the Bay Area People Analytics Meetup, building on his experience co-organizing the New York HR Analytics Meetup, a professional group with over 1200 members. He built and managed research teams as a professor and Chair of Anthropology at the George Washington University and as a research scientist at the Smithsonian Institution and American Museum of Natural History, among others. His research and expertise have been featured on NOVA, National Geographic, the History Channel, NPR, BBC, CNN, Washington Post, and media outlets around the world. Awards and recognition for his research include a prestigious 2014 Humboldt Award for Research Excellence. His full list of over 100 academic publications can be found on GoogleScholar, ResearchGate, and AcademiaEdu, and his television appearances can be found on IMDb. Brian and his family live in the Bay Area, CA, where they can be found often enjoying the local cuisine and natural beauty of the area.
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