DataScribe: An Automated EDA and Narrative Reporting Framework for Accessible Data Analysis

• Author(s): Anushree; Sakun Choudhary; Mansi Lakhmani; Roopali Gupta
• Paper ID: 1712709
• Page: 892-896
• Published Date: 10-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 6 December-2025
• DOI: https://doi.org/10.64388/IREV9I6-1712709

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, vol. 9, no. 6, Dec. 2025, doi: https://doi.org/10.64388/IREV9I6-1712709

APA:
Anushree, Sakun Choudhary, Mansi Lakhmani, Roopali Gupta (2025). DataScribe: An Automated EDA and Narrative Reporting Framework for Accessible Data Analysis. Iconic Research And Engineering Journals, 9(6). doi: https://doi.org/10.64388/IREV9I6-1712709

MLA:
Anushree, Sakun Choudhary, Mansi Lakhmani, Roopali Gupta "DataScribe: An Automated EDA and Narrative Reporting Framework for Accessible Data Analysis" Iconic Research And Engineering Journals, vol. 9, no. 6, Dec. 2025. Crossref, https://doi.org/10.64388/IREV9I6-1712709

BibTeX

@article{1712709,
author = {Anushree, Sakun Choudhary, Mansi Lakhmani, Roopali Gupta},
title = {DataScribe: An Automated EDA and Narrative Reporting Framework for Accessible Data Analysis},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {892-896},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712709.pdf},
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},
month = {December}
}