A predictive analytics system for the apparel industry that forecasts daily absenteeism using LSTM/XGBoost and classifies employee risk levels to optimize workforce planning. Built with Python and Streamlit.

The Absenteeism Prediction System forecasts daily employee absenteeism counts and predicts individual absenteeism risk for a given day in an apparel company. Using historical attendance, work patterns, and operational factors, it supports proactive workforce planning, shift adjustments, and productivity optimization.

A machine learning project that predicts employee absenteeism using logistic regression with 85% accuracy. The system includes data preprocessing, feature engineering, and a production-ready deployment module.

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Employee absenteeism prediction system built using Python, Logistic Regression, MySQL, and Tableau for data-driven workforce analytics.

The Crew Absenteeism Prediction System is a data-driven platform solution that helps alleviate the operational challenges arising from such unplanned workforce absences. It will essentially serve several aims of great importance in improving workforce productivity and organizational effectiveness.