A STUDY ON THE USE OF ARTIFICIAL INTELLIGENCE IN INDUSTRY

The introduction of Artificial Intelligence (AI) is revolutionizing the way businesses operate. With the ability to analyze large volumes of data in real-time, AI makes it possible to optimize processes, improve operational efficiency, reduce costs, and develop more innovative products and services. Project management is an area that benefits significantly from the application of AI, being used at various stages of a project's lifecycle, from planning to execution and monitoring. Based on this information, the present work aims to create and send production monitoring reports, including analysis of data on cost, production time and other characteristics of the product considered. Among the machine learning techniques, the Random Forest Regressor method was used, an ensemble learning technique that employs multiple decision trees to improve the accuracy of the model and reduce overfitting. Initially, a set of fictitious data related to upholstery production was used, containing information on quality, cost, production time, style, material, feedback, complaints, durability and color. After the exploratory analysis of the data, the described methods were applied, generating scatter plots with the Seaborn library to visualize the relationships between the variables. The final prediction of the Random Forest Regressor model is the average of the predictions of all decision trees