New Horizons of Science, Technology and Culture Vol. 3

In the domain of educational data analysis, the extraction and interpretation of student records from PDF documents present significant challenges. This paper introduces a Python-based PDF Analysis Tool designed to streamline the extraction, analysis, and visualisation of academic data embedded within PDF files. Featuring a user-friendly graphical user interface (GUI), the tool enables users to select specific academic years and semesters for targeted analysis. By leveraging the PyPDF2 library, the tool efficiently extracts text from PDF files, while dynamically configured regular expressions ensure accurate data parsing across diverse academic formats. The analysed data is visualised using the Matplotlib library, producing bar charts for gender GPA distributions. Additionally, the tool highlights top-performing students based on GPA and supports exporting analysed data to Excel files for further exploration and collaboration. This paper discusses the tool's architecture, implementation details, and the significant role of Python libraries such as PyPDF2, OpenPyxl, Tkinter, and Matplotlib in enhancing the tool’s functionality. The PDF Analysis Tool exemplifies a robust, adaptable solution for academic data analysis, providing educators and researchers with actionable insights through streamlined data extraction and comprehensive visualisations.