Authors - Anup Vinod Pachghare, Smita Deshmukh, Satish Salunkhe Abstract - Human Resources Management plays a key role in the company’s growth by recruiting high-quality employees and evaluating their performance by using the Machine Learning (ML) technique. Despite these rigorous efforts some employees still resign before their contracts expire, which negatively impacts business. Existing methods have considered various factors influencing employee turnover across different employee groups. This paper proposes an Ensemble Learning approach which integrates AdaBoost, K-Nearest Neighbour (KNN), Random Forest (RF), stacking and Voting to enhance churn prediction accuracy. The ensemble learning mitigates the risk of overfitting by combining predictions from multiple models, making it less sensitive to irrelevant features. This approach efficiently captures diverse patterns in the employee churn data, achieving better accuracy. AdaBoost captures complex patterns, while KNN extracts valuable data from employee churn data. By stacking these methods, their combined strengths lead to enhanced accuracy in predicting churn data. Initially, the data collected from employee churn records and pre-processing phases handle unwanted noise and min-max normalization, which standardizes the feature vector to ensuring uniformity across the dataset. The proposed ensemble model obtained 91.06% accuracy, and 0.8853 of recall on the employee churn dataset compared with conventional techniques like Artificial Neural Networks (ANN).
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