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Employee Turnover Prediction With Deep Learning - DZone AI

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According to a study from Catalyst, the cost of replacing an employee is around 50% to 75% of the employee's annual salary, on average. Considering a mid-level position with a monthly salary of 20,000 pesos, the total cost of replacing this employee would be around 140,000 pesos. On average, it takes around 50 days to replace an employee, and the costs incurred due to productivity loss will keep adding up. For a big company like everis with over 20,000 employees, considering a turnover rate of 15% and an average salary of $15,000 pesos, the total annual cost of turnover would rise up to at least 270 million pesos.


How to Integrate Driver Safety into Your Culture to Enhance Retention - w3buzz

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Driver turnover has been above 90 percent for more than nine consecutive quarters and shows little sign of slowing down. What's more, is that issues related to management and workplace policies and communication have caused 30 percent of drivers to leave their job. Keeping your drivers safe, recognizing them for doing the right thing, and offering a solid feedback loop is key to increasing driver engagement -- and retention. If the people who drive your products from one place to another do not feel like you have taken extra measures to keep them safe, then they will leave for jobs that do. Where does this leave you?


Incorporating Driver Safety into Your Culture to Increase Retention

#artificialintelligence

Driver turnover has been above 90 percent for more than nine consecutive quarters and shows little sign of slowing down. What's more is that issues related to management and workplace policies and communication have caused 30 percent of drivers to leave their job. Keeping your drivers safe, recognizing them for doing the right thing, and offering a solid feedback loop are key to increasing driver engagement -- and retention. If the people who drive your products from one place to another do not feel like you have taken extra measures to keep them safe, then they will leave for jobs that do. Where does this leave you?


HR Analytics: Using Machine Learning to Predict Employee Turnover

#artificialintelligence

Employee turnover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. First, we'll use the h2o package's new FREE automatic machine learning algorithm, h2o.automl(), to develop a predictive model that is in the same ballpark as commercial products in terms of ML accuracy. Then we'll use the new lime package that enables breakdown of complex, black-box machine learning models into variable importance plots. We can't stress how excited we are to share this post because it's a much needed step towards machine learning in business applications!!! Enjoy.


HR Analytics: Using Machine Learning to Predict Employee Turnover

#artificialintelligence

Employee turnvover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. First, we'll use the h2o package's new FREE automatic machine learning algorithm, h2o.automl(), to develop a predictive model that is in the same ballpark as commercial products in terms of ML accuracy. Then we'll use the new lime package that enables breakdown of complex, black-box machine learning models into variable importance plots.