Problem Statement: Employee absenteeism is a major problem faced by every employer, which eventually leads to backlogs, piling of work, delays in deploying projects, and can significantly affect company finances. The aim of this project is to identify issues that lead to employee absence and provide solutions to reduce absenteeism in the company.The task is to build a regression model that will predict the number of absent hours for an employee based on various factors.

Dataset to be used for employees

Experiment with random feature selection by randomly choosing $5$ features for both the Linear Regression and Decision Tree Regression models. For each iteration, report the selected features and evaluate the model performance using MAE and R$-$squared $($R$^2).$ Discuss how feature selection impacts model performance and what this suggests about the dataset.