The dataset used is Absenteeism at work from UCI Machine learning repository. The aim of this work is to apply classification to predict absenteeism at work.
Absenteeism represents for the company the loss of productivity and quality of work. Predicting it can help companies organize tasks appropriately in order to optimize work and avoid stressful situations for both the company and its employees.
The Analysis is conducted in Python using Colab Notebook, which is a web application that allows you to create an interactive environment that contains live code, visualizations and text.
The dataset contains 740 entries. Each entry has 21 attributes. It was created with records of absenteeism at work from July 2007 to July 2010 at a courier company in Brazil.