Prediction of absenteeism at work, with machine learning models for classification.

From given dataset of a firm, this model is trained to predict what are the chances whether a employee at firm will be present or absent at a given day.

Employee absenteeism prediction ML model with Streamlit app"

Predicting Employee Absenteeism using Logistic Regression

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Machine Learning model that predicts, an employee will take a leave or not from the work based on the general information of the employees.

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A data analysis and machine learning project to predict absenteeism frequency in future

Dealing with chronic absenteeism in workplace using machine learning

Absenteeism is the behavior that ones are absent during working time. We use Logistic Regression here to predict whether ones have excessive absenteeism or not, and use Tableau to visualize the result.

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Project uses logistic regression to predict workers absenteeism

Prediction of Absenteeism duration at work.

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A complete logistic regression pipeline in Python to identify employees at risk of excessive absenteeism. This project includes data preprocessing, model training, and evaluation, with predicted output visualized in Power BI. The final deliverable is a self-contained, integration-ready module for predicting new employee records.

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 Prediction of Employee Absenteeism at Work

Dashboard for absenteeism hour prediction using streamlit.

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Integrated Jupyter Notebook, MySQL Workbench and Tableau

Python+SQL+Tableau

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Skills used are Python (NumPy, Pandas, Matplotlib, Seaborn, SKLEARN and Keras)

This project focuses on analyzing and predicting employee absenteeism during working days using various analytical tools such as SQL, Python, and Tableau. The aim is to understand the underlying reasons behind absenteeism and identify patterns based on factors such as education level, family situation, commuting distance, and medical reasons.

I have used Python, MySQL and Tableau combination to do Logistic Regression and visualize analysis and predictions

Loading the ‘absenteeismmodule’: In this section we will load the ‘absenteeismmodule’ and use its methods to obtain predictions

Absenteeism is defined as absence to work as expected, represents for the company the loss of productivity and quality of work. The purpose of this project is to apply an artificial neural network to prediction of absenteeism at work. The database used in the experiment has 21 attributes and 741 records from documents that prove that they are absent from work and were collected from July 2007 to July 2010. AROMA model was used for prediction of Absenteeism time in hours for 2011.

Absenteeism Prediction