Resume Screening Using Ensemble Models

Recruitment involves processing large volumes of resumes to identify suitable candidates, but traditional screening methods are inefficient, inconsistent, and prone to biases. Manual evaluation often relies on keyword-based filtering, which may overlook qualified candidates and lead to unfair ranking. Machine learning-driven resume screening systems enhance accuracy, efficiency, and fairness in candidate evaluation. The proposed approach employs machine learning models, including ensemble techniques, to improve classification performance and ranking accuracy. Feature extraction and selection methods optimize model performance, while handling class imbalance ensures unbiased candidate classification. Performance evaluation is conducted using metrics such as the Area Under the Curve (AUC) and confusion matrix, demonstrating effectiveness of the approach suggested. Comparative analysis highlights the advantages of machine learning-based approaches in reducing classification errors and improving candidate ranking. The results show that automated resume screening can significantly enhance recruitment efficiency and fairness. This research contributes to developing more reliable, fair, and scalable machine learning-based recruitment systems, with future improvements focusing on optimizing feature selection and refining classification strategies.