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Employee Turnover Prediction With Deep Learning - DZone AI

#artificialintelligence

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 use data science to grow and manage your team

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Today's unemployment rate in the U.S. stands at 3.9 per cent. Talent managers, recruiters, and human resource professionals understand that the realities of the current labour market require using every available tool to fill the seats on the bus. One tool these professional career placement experts didn't have in the past is data science. Data science can be applied to the hiring process to help human resources work smarter -- and be more successful in finding those elusive candidates. Here is how HR and data science can team up to grow your business.


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.


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.


Analysing data to retain staff

#artificialintelligence

The real value in analysing data is to create worthwhile change in an organisation. Analysing data may not be for everyone, but the use of analytics in talent retention is being touted as one of the next big trends for companies. According to Deloitte, the use of people analytics by businesses is gathering speed, with half of 7000 global companies surveyed saying they now use data to predict and improve workforce performance. However, more than 60 per cent still described themselves as "weak" in using big data in recruitment. Auckland University of Technology professor Jarrod Haar says using data to understand why staff leave should be something all companies consider.