Enhancing Industrial Efficiency: AI-Powered Data Analysis and Visualization with Tkinter (#1960)

Authors

Hall-Sevilla, Javier Francisco

Carrasco-Bardales, Alberto

Villatoro-Flores, Héctor

Abstract

This research developed a system to enhance efficiency in industrial data processing and analysis, addressing the need for better productivity and strategic decision-making. By employing data analysis and visualization techniques, the project converted CSV production reports into clear, visual formats. Utilizing a Python-based framework, the system integrated libraries such as tkinter, pandas, smtplib, scikit-learn and matplotlib, facilitating data management, graphical representation, and user interaction. The inclusion of email functionality further enabled easy distribution and collaborative analysis of reports, making the function of IoT a more flexible and efficient for the analysis of the machine data. Significantly making the process more efficiently the automation of industrial data analysis, the system streamlined the merging of reports, provided visual data comparisons, and generated detailed analyses through an accessible interface, thus aiding in the interpretation of data even for people who aren’t experts in data analysis, reducing manual analysis time, and supporting evidence-based decision-making.