Predictive Analytics for Employee Attrition: A Comparative Study of Machine Learning Algorithms

Employee attrition is a significant and widespread challenge that organizations face globally. This issue significantly affects productivity, team dynamics, and overall profitability. The main objective of this research project is to tackle the significant issue of employee attrition. This undertaking involves conducting a thorough analysis using a dataset from a fictional organization specifically designed to mimic real-world HR situations closely. The research employs predictive analytics and machine learning techniques to identify possible cases of attrition. It also provides managerial recommendations to improve employee retention strategies. This study evaluates the efficacy of different machine learning algorithms, specifically Random Forest, K-Nearest Neighbours (K-NN), and Naive Bayes, in forecasting employee attrition. Moreover, the methodology being proposed integrates the utilisation of an ensemble technique. This approach integrates the favourable attributes of multiple models, leading to enhanced predictive accuracy. Based on the research outcomes, the Naive Bayes model exhibits a higher level of effectiveness than alternative models, particularly when employing meticulously chosen features. The model as mentioned earlier demonstrates a significant level of precision in its ability to forecast attrition. The study suggests that it would be beneficial to investigate advanced prediction models, employee sentiment analysis based on natural language processing, and personalised staff engagement initiatives. To monitor attrition patterns over time, it is necessary to conduct longitudinal attrition studies. This tool assists companies in developing effective retention strategies that can adapt to evolving circumstances. Implementing these measures can effectively reduce employee turnover and contribute to developing a stable and thriving team.