Predictive Analytics for Employee Retention: Insights from Machine Learning Models 

This project is designed to help our HR team identify which employees are most likely to leave the company. We used a data-driven approach with tools in Python to analyze employee information and uncover patterns linked to turnover. By understanding these patterns, HR can take proactive steps to improve retention and reduce the costs associated with hiring new employees. 

Key Insights:

• Main Factors Affecting Retention: We found that employees with lower job satisfaction, poor work-life balance, and less positive relationships at work are more likely to leave. These factors stood out as consistent drivers of turnover.

• Predictive Power: Our model can accurately predict about 80% of potential employee departures, giving HR a valuable tool to address retention before it becomes an issue.

• Opportunities for Improvement: Enhancing job satisfaction and work-life balance can make a significant difference. By focusing on these areas, we can improve overall employee retention.

Predictive Tool for HR: This tool highlights employees at risk of leaving, helping HR focus their retention efforts where it matters most.

Visual Dashboard: We created an easy-to-use dashboard that displays which factors most influence turnover, so HR can quickly understand and act on these insights.

Targeted Recommendations: To reduce turnover, we recommend initiatives focused on job satisfaction and work-life balance, as these factors play a crucial role in employee decisions to stay or leave.

Click here to visit the GitHub repository for more details: GitHub Repository