AUTOMATED RESULT ANALYSIS USING PYTHON AND STREAMLIT

Abstract

Educational institutions worldwide continuously generate large volumes of student performance data, which necessitate efficient processing, detailed analysis, and meaningful interpretation . Traditionally, result analysis has involved manual handling of data, spreadsheet computations, and basic statistical methods. These conventional techniques are time-consuming, error-prone, and lack the interactive and dynamic visualization features needed for modern educational environments With advancements in data science, programming languages, and web-based frameworks, there is a growing opportunity to develop sophisticated automated result analysis systems that can transform educational data processing . This research introduces an innovative automated system designed to streamline the complete process of academic result analysis. The system employs technologies such as the Python Imaging Library (PIL) for converting PDF files into images, and Tesseract OCR for accurate text extraction and localization using bounding boxes. The extracted text is structured and appended into a CSV file, serving as the primary dataset for further analysis. The backend utilizes Python’s Pandas library, offering powerful capabilities for data manipulation, statistical analysis, and transformation. The frontend is built using Streamlit, an emerging Python framework that allows for the rapid development of interactive and user-friendly web applications. Educational staff can easily upload CSV files containing student result data, which the system then automatically processes to produce statistical summaries, detailed performance insights, and interactive visualizations. This automated approach eliminates human errors in calculation, greatly reduces processing time, and enhances data comprehension through dynamic charts and dashboards. As a result, educational stakeholders—administrators, faculty, and students—can gain a deeper understanding of academic performance trends and make well-informed decisions. The system offers substantial benefits in accuracy, efficiency, and usability, providing a centralized and effective tool for academic performance evaluation and strategic planning. Future enhancements may include integrating machine learning algorithms for predictive analytics and risk assessment, as well as deploying the system on cloud platforms for improved accessibility and scalability.