A Hybrid Predictive Framework For Multi- Employee Retention Using Context-Aware Decision Trees And Job Expectation Alignment Metrics

Predicting the long-term viability of job searchers within firms has become essential in today's changing employment market in order to enhance employee retention and human resource (HR) planning. In order to anticipate job searchers' sustainability based on their likelihood of landing the job description they have in mind within an organization, this research study discusses the design and development of a non-linear prediction system. Finding possible long-term workers early in the hiring process is the main goal in order to give HR departments data-driven insights. To model the intricate, non-linear relationship between candidate attributes (skills, experience, and preferences) and organizational role availability and satisfaction metrics, the methodology makes use of sophisticated machine learning techniques, such as artificial neural networks (ANNs) and ensemble methods like Random Forests and Gradient Boosting. Both synthetic and real-world HR datasets were used to train and verify the model. These datasets included attrition records, job alignment scores, past hiring patterns, and promotion trajectories. Feature engineering, model tuning, data preparation, and performance assessment utilizing measures like accuracy, F1-score, and mean squared error for regression-based outputs were all part of the effort. When it came to predicting the possibility that job searchers will eventually match with the duties and expectations of the business, the system showed a strong predictive power. The outcomes highlight how this method may improve strategic HR analytics. In the end, the system contributes to cost effectiveness and organizational stability by facilitating the early identification of discrepancies between candidate expectations and organizational offerings, supporting well-informed hiring decisions, improving retention forecasting, and coordinating workforce planning with long-term business goals.