Identifying the causes of employee turnover

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Reference no: EM133381037

Project Proposal

Current Landscape -

One organizational issue that can be solved through data mining is identifying factors that contribute to employee turnover. High employee turnover can be a major challenge for organizations across different industries. Not only does it disrupt workflow, but it can also be costly for companies to replace and train new hires. Identifying the causes of employee turnover is crucial for organizations looking to reduce their turnover rates and improve employee retention.

The problem to solve -

Data mining techniques can be applied to this problem by analyzing data related to employee turnover and potential contributing factors. This can include data on employee demographics, job titles, salaries, benefits, job satisfaction, workload, management style, and company culture. By analyzing this data, organizations can identify patterns and relationships that contribute to employee turnover.

For example, data mining algorithms can be used to predict which employees are likely to leave the company based on specific factors. These algorithms can classify employees based on different features such as their job tenure, salary, and job satisfaction levels. This can help organizations take proactive measures to address potential concerns and retain employees.

Potential functions or concepts that will be used from the course -

Classification algorithms can be used to predict which employees are likely to leave based on certain factors, while clustering algorithms can group employees into categories based on similarities in their turnover rates and potential contributing factors. Association rule mining can also be used to identify relationships between different factors and employee turnover.

Clustering algorithms can be used to group employees based on similarities in their turnover rates and potential contributing factors. This can help organizations identify common factors that may be driving employee turnover and develop strategies to address these issues.

Reference no: EM133381037

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