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turnover-analysis

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The Employee Attrition Control project uses data analysis and predictive modeling to understand and address employee turnover. It provides insights and recommendations to reduce attrition and improve employee satisfaction and retention.

  • Updated Jun 16, 2023
  • Jupyter Notebook

Employee attrition analysis for Salifort Motors using EDA, statistical analysis, and machine learning. Developed Logistic Regression, Decision Tree, and Random Forest models to predict turnover risk, then translated findings into a Power BI analytics report with workforce insights, model eval, and HR retention strategies. For Kaggle notebook check:

  • Updated Jul 27, 2026
  • Jupyter Notebook

Building a complete People Analytics ecosystem from raw data to predictive insights, combining data engineering, analytics and machine learning to drive turnover and retention decisions.

  • Updated Mar 24, 2026
  • TSQL

Data Science Project: Reproducible workforce analytics using employee, job architecture, and listening-survey data to identify structural career ceilings, quantify links to career growth, promotion, and voluntary turnover, assess equity exposure, and prioritize targeted interventions.

  • Updated Jul 23, 2026
  • R

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