Machine Learning based Employee Attrition Prediction and Layoff Prediction System | Python Project
Machine Learning based Employee Attrition Prediction and Layoff Prediction System | Python Final Year IEEE Project 2024 - 2025.
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🔗Email: [email protected],
🌐Website: https://www.jpinfotech.org
📌Our Proposed Project Title: Machine Learning based Employee Attrition Prediction and Layoff Prediction System
💡Implementation: Python.
🔬Algorithm / Model Used: Random Forest Classifier, Bagging Classifier & Gradient Boosting Regressor, Random Forest Regressor.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/
📌IEEE Base Paper Title: Analyzing Employee Retention Factors using Machine Learning.
📌IEEE Base paper Abstract:
This research proposal aims to employ machine learning techniques to analyze employee retention factors in Software Companies, recognizing its crucial role in organizational success and the potential costs of high turnover rates. Through Watson Analytics' advanced analytics capabilities, the study seeks to identify key factors contributing to employee attrition and retention, culminating in the development of a predictive model using machine learning algorithms. The outcomes are expected to include actionable recommendations to improve retention strategies, insights into the relative importance of different factors, and the creation of a data-driven, proactive talent management approach for Software Companies. By empowering organizations to retain top talent and fostering a positive work environment, this research envisions a transformative impact on long-term success and performance.
📌REFERENCE:
Moshiur Rahman; Md Rashedul Islam; Partho Bala; Abdus Sattar, “Analyzing Employee Retention Factors using Machine Learning”, IEEE CONFERENCE, 2024.
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