What does this research mean for the field?

An integrated system combining machine learning for attrition prediction and generative AI for natural language explanations improves data management, prediction accuracy, and interpretability for HR decision-making. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to develop a system that predicts employee attrition and performance using advanced machine learning techniques.

April 23, 2026International Journal Of Informative and Futuristic Research0 citationsOpen Access

Employee Performance and Attrition Prediction Using Machine Learning and Generative AI

PBPudu Bhargava SRS. Manjunath Reddy

Key Points

The study aims to develop a system that predicts employee attrition and performance using advanced machine learning techniques.
Utilized Django and SQLite for system development and data storage.
Employed Python libraries like Scikit-learn for predictive analytics and Openpyxl for data export.
Implemented a classification model to assess attrition risk based on employee performance metrics.
The system enhances accuracy in predicting attrition risk based on performance and attendance data.
It provides clear explanations of predictions using generative AI, making outcomes more understandable for HR.
Improvements in data management lead to better workforce analysis and decision-making processes.

Abstract

Employee attrition is a major challenge for organizations, leading to productivity loss, higher recruitment costs, and disruption in operations. This paper presents an Employee Performance and Attrition Prediction System that combines HR management with machine learning and generative AI. The system is built using Django with SQLite as the database and uses Python libraries such as Scikit-learn for prediction, Openpyxl for data export, and Google Gemini API for generating explanations. It stores structured employee data including performance, attendance, and evaluations for analysis. A classification model predicts attrition risk based on factors like performance rating, projects completed, and salary. To improve interpretability, a generative AI module explains predictions in simple HR-friendly language.Overall, the system improves data management, enhances prediction accuracy, and provides an easy-to-understand, scalable solution for workforce analysis and decision-making.

问 AI

Bookmark

View Full Paper