DataScribe: An Automated EDA and Narrative Reporting Framework for Accessible Data Analysis
Abstract
Exploratory Data Analysis (EDA) remains time-intensive and inaccessible to non-technical users despite its critical role in data science workflows. DataScribe addresses this gap through an automated pipeline that generates visualizations, statistical summaries, and human-readable narrative explanations from uploaded CSV/Excel datasets. The system produces multi-format reports (PDF, HTML, Excel, R-code) in under 12 seconds. Testing on the Titanic dataset (891 rows, 12 columns) demonstrated 83% reduction in analysis time compared to manual approaches, with 87% of non-technical users successfully interpreting results without statistical training. Deployed at https://datascribe.onrender.com/, the system bridges the accessibility gap in data analysis through automated narrative generation and reproducible code export.
Keywords
Automated EDA, Data Visualization, Narrative Reporting, Python-R Integration, Data Storytelling
Citations
IRE Journals:
Anushree, Sakun Choudhary, Mansi Lakhmani, Roopali Gupta "DataScribe: An Automated EDA and Narrative Reporting Framework for Accessible Data Analysis" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 892-896 https://doi.org/10.64388/IREV9I6-1712709
IEEE:
Anushree, Sakun Choudhary, Mansi Lakhmani, Roopali Gupta
"DataScribe: An Automated EDA and Narrative Reporting Framework for Accessible Data Analysis" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712709
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