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

@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},
      doi = {https://doi.org/10.64388/IREV9I6-1712709}
  }