Machine Learning Algorithm for Work from Home Analysis during Epidemic (2022)

Employee productivity is critical to the company's success in achieving its objectives. Predicting future employee productivity is critical for organizations that work from home to flourish. This article used machine learning to estimate work from home employee productivity in a corporation. The machine learning approach is based on a cross-industry data mining standard (CRIPS-DM). We have analyzed some variables which are important for productivity prediction. The random forest method is used in the machine learning process. The random forest method is employed to analyze the prediction model. This will help organizations to improve work from home by using analyzed results. The results reveal that the random forest method properly identified instances in 91.66 percent of the cases.