Predicting Employee Turnover
Reading:
Harbert, T. (2020). The people puzzle. HRMagazine, 65(4), 39-45.
Ramamurthy, K. N., Singh, M., Yu, Y., Aspis, J., Iames, M., Peran, M., & Held, Q. S. (2015). A talent management tool using propensity to leave analytics. IEEE International Conference on Data Science and Advanced Analytics, 1-10.
Assignment:
1. In your own words, summarize the steps you need to take to develop a model that predicts turnover. Don’t directly summarize the Ramamurthy et al. (2015) article; rather, based on what you learned from the article, describe the steps you would take to create your own model for this purpose.
2. Given the issues raised by Harbert, how useful could such a model have been for 2020? How useful do you think such a model could be for 2024?
Reading Assignment 7
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Reading Assignment 7
A model to predict employee turnover would include several crucial steps. Such a model would have been useful in 2020. However, it still poses great promise in predicting turnover in 2024.
The first step is defining the problem, which is predicting turnover. The second step is collecting data on employees. Such data would include past turnover information, employees’ roles, performance scores, demographics, and any other additional information that is relevant (Ramamurthy et al., 2015). The data would be collected from surveys or records from the Human Resources desk. The third step is cleaning and preparing the data to get rid of any errors and ensure data consistency. The next step would be identifying specific and relevant aspects that may be affecting turnover. After this, the data would be split into three main classes. These include training, validation, and setting tests as a way of evaluating how the model will perform.
The next step would be to make a selection of the most appropriate machine learning model. Models to choose from would include a decision tree or logistic regression. After that, the chosen model would be trained, which involves changing parameters to fit available data. The model would then go through the evaluation process before tuning all parameters to elevate its performance capacity. Finally, the model would be tested as an assessment of how well it generalizes data before being deployed into the system to make turnover predictions.
Such a model could have been useful in 2020 in various ways. First, it could have helped organizations to predict and successfully manage turnover amid the crisis. Secondly, the model could have enforced stability in the workforce by allowing the organizations to manage and retain talent during the crisis (Harbert, 2020). Thirdly, with the ...