Student Result Analysis System Using Python Libraries
Student Result Analysis System Using Python Libraries
Abstract
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.
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Layman Abstract :
This paper presents a simple Python-based tool that helps teachers and researchers easily extract and analyse student data from PDF files. The tool has a user-friendly interface where users can choose specific academic years and semesters to study. It uses Python libraries to pull out text from PDFs, sort the information correctly, and show it in colourful charts—like comparing GPA by gender. It can also spot top-performing students and save the results in Excel for easy sharing. This tool makes working with student records faster and more useful for academic analysis.
Disclaimer: This scientific-creative video was produced using AI voice-over and royalty-free stock images and clips. All content is sourced exclusively from peer-reviewed journal articles and book chapters, with full citations provided below. The peer-review process for these sources is also detailed here.
Source / Reference: https://doi.org/10.9734/bpi/nhstc/v3/...
Peer Review History: https://peerreviewarchive.com/review-...
#Python #DataAnalysis #Automation #StudentData #PDFtoExcel #EdTech #PythonProjects #DataVisualization #Teachers #educationtechnical #coding
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