An Approach for Sub Selecting Variables that have Higher Influence on the Outcome in Developing Predictive Model using Staff Turnover
Authors
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Research Scholars, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur ,IN
Mohan Sangli -
Professor and Head, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur ,INRajeshwar S. Kadadevaramath
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Research Scholars, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur ,INJerin Joseph
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Engineer, Intel India Pvt. Ltd., Bangalore, Karnataka ,INAkarsha Kadadevaramath
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Professor and Head, Engg. Tech, Industrial and Manufacturing EnggDept, The University of Texas ,USImmanuel Edinbarough
DOI:
https://doi.org/10.18311/jmmf/2023/34502Keywords:
Predictive model, Sensitive parameter, DimensionalityAbstract
Predictive models are built by learning the combined effects of several independent variables that directly or indirectly influence the outcome. H. Response or dependent variable. In practice, data collection has data on a large number of independent variables that are outcome-sensitive and may or may not be related to the outcome. Some independent variables have a large impact on the results, while others may have little or no impact on the results. The presence of some independent variables that are irrelevant to the outcome can affect the performance of the predictive model. In this context, it is desirable and essential to identify the independent variables that most influence the forecast model to keep it lean and efficient. In this work, we used a dataset containing employee turnover rates and explored how to identify a subset of outcome-sensitive variables, thus eliminating variables that hinder the development of effective predictive models. By partially selectively influencing the independent variables, we developed lean and efficient predictive models that enabled us to act on an actionable subset of the variables to reduce staff turnover, thereby improving corporate save effort and cost.
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