Staff turnover in large Peruvian manufacturing industries has been increasing in recent years. While job rotation is a natural effect in organizations, it generates higher training costs for new staff and impacts work performance and climate when unwanted. Given this problem arises the need to identify the possible causes of rotation of operational personnel and predict these events through data analysis at an early stage to avoid and reduce its impact on the company. This article of quantitative approach and exploratory scope-explanatory aims to identify the propensity of rotation of the operation of a company manufacturing cleaning and disinfection through a model of forecast by collecting data using Machine Learning and encourage proposals that enable solutions to be found to the factors influencing staff turnover. MS Excel and Orange software were used for data analysis, where the da...