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

R code used in the article "Visualizing Absenteeism At Work"

Project on Absenteeism at Work

This GitHub repository contains a comprehensive analysis of employee absenteeism at work using a real-world dataset. The project leverages Python and popular data analysis libraries to explore, visualize, and draw insights from the data.

Proyecto MLOps - Absenteeism at Work Dataset

The database was created with records of absenteeism at work from July 2007 to July 2010 at a courier company in Brazil.

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This project exposes an analysis of worker absenteeism at work from the ascertained Data-set of employees' records of their given individual and adept information(July 2007 to July 2010).

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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.

Evaluated an HR dataset about absenteeism and health. Provided a data driven approach to determine how to provide a bonus and incentive to healthy employees. Developed a database, SQL queries to answer questions and build a dashboard which meets a wireframe specification.

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HR Absenteeism at Work Data Analysis

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Predict the trend in absenteeism of employees of a given company and what actions should the company undertake to reduce such absenteeism.

Prediction of Absenteeism duration at work.

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Analyzing and building a model using Logistic regression analysis to predict absenteeism of people from a workplace, in order to reorganize the workspace and increase productivity. Visualization is performed using Tableau.

The Prediction of Employee Absenteeism at Work

Analyzing the causes of absenteeism at work. Predicting whether the employee will be absent using logistic regression

Integrated Jupyter Notebook, MySQL Workbench and Tableau

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

Case Study, Data Science Project to address ABSENTEEISM at a company during work time.

Absenteeism can negatively affect the organisation's ability to meet the timelines,but what are the drivers of truancy among the employees? I have a dataset about reasons for employees absenteeism at work and analysis on them

Absenteeism_at_work

Project 16 Absenteeism_at_work

Absenteeism at work

Absenteeism at work

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