Prediction-of-Absenteeism-At-Work-using-Python-MySQL-Tableau

• The aim of this project is to understand the flow of data in a real life business model.
• To understand this, I have integrated Jupyter notebook, MySQL workbench and Tableau.
• The figure below shows the responsibilities of Data Scientists/ML Engineers and Business Intelligence/Data Analysts.

Table of Contents

• Problem Definition
• Technologies and Tools
• Important Libraries
• Code Example
• Result

Problem Definition

1. Absenteeism is the term given when an employee is habitually and frequently absent from work. This excludes paid leave and occasions where an employer has granted an employee time off.
2. According to Forbes, Absenteeism costs U.S. companies billions of dollars each year in lost productivity, wages, poor quality of goods/services and excess management time.
3. In addition, the employees who do show up to work are often burdened with extra duties and responsibilities to fill in for absent employees, which can lead to feelings of frustration and a decline in morale.
4. It is important for a company to understand the causes of absenteeism and make policies inorder to reduce these causes.

• In this project, I will build a machine learning model to predict the absenteeism. The goal is to predict whether or not an employee presenting certain characteristics can be expected to be missing on a certain workday.
• Having such information in advance can help Managers in decision making by reorganizing the work process in such a way that will allow an organization to avoid lack of productivity and increase the quality of work.

The data for the analysis is taken from UC Irvine Machine Learning Repository. Also the .csv file is further simplified which can be found here.

Technologies and Tools

• Python (Jupyter Notebook)
• MySQL WorkBench
• Tableau

Important Libraries

• Library used to connect Python and MySQL