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MACHINE LEARNING PROJECT ON EMPLOYEE ATTRITION PREDICTION WITH PYTHON

G. MANOJ KUMAR, ITHI SURYA VENKATA SAI PRAKASH,

1Assistant Professor,
2Student,
2MCA Final Semester,
1Master of Computer Applications,

1Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

 

 

Employee attrition is a critical challenge for organizations, leading to increased recruitment costs, loss of talent, and reduced productivity. This project aims to predict employee attrition using machine learning techniques, providing organizations with insights to mitigate this issue. By analyzing various employee features, we identify key factors contributing to attrition and build a predictive model using data preprocessing, visualization, and machine learning algorithms such as Random Forest. The results demonstrate the model's effectiveness in predicting potential attrition cases and highlight significant features influencing employee turnover. Furthermore, this project emphasizes the importance of data-driven decision-making in human resource management. By leveraging advanced analytics and machine learning, organizations can move from reactive to proactive strategies in managing their workforce. This predictive approach enables HR departments to implement targeted interventions, enhance employee engagement, and ultimately reduce turnover rates. The insights gained from the model can also guide policy-making and strategic planning, ensuring a more stable and motivated workforce.

Index Terms -Employee Attrition, Machine Learning, Random Forest, Predictive Analytics, HR Analytics, Data Preprocessing, Employee Retention, Feature Engineering, Python, Scikit-learn

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