AI-Driven Recruitment Intelligence System Using SQL, Power BI and Machine Learning

2026-07-06T05:06:23+00:00

Recruitment teams often handle candidate screening, hiring source review, and reporting through manual steps. This slows down decision-making and hides useful patterns in candidate data. This study presents an AI-driven recruitment intelligence system using SQL, Power BI, and machine learning. The study developed a structured academic prototype that stores candidate records in MySQL, analyzes recruitment patterns with SQL queries, visualizes hiring indicators in Power BI, and predicts candidate hiring status through a supervised Decision Tree model. The system classifies outcomes such as Hired, Rejected, and Screened. It used attributes such as application source, experience, role, location, skill score, and application date to support analysis and prediction. The dashboard presents KPI cards, slicers, hiring trends, source-wise analysis, and prediction-based visuals. The study demonstrates how a small recruitment dataset can become a decision support tool for HR teams. The model accuracy remained high because the dataset was controlled and limited in size. The study works as an academic prototype, yet it reflects a real HR analytics problem. The framework can be extended into a full-stack recruitment application by adding live data entry, recruiter login, candidate fit scoring, resume screening, and larger datasets.

International Journal of Artificial Intelligence in Mechanical Engineering

AI-Driven Recruitment Intelligence System Using SQL, Power BI and Machine Learning