No-Code DB Analysis

Abstract

This paper presents a no-code, AI-assisted dataset analysis system designed to simplify end-to-end data processing for tabular datasets. The system enables users to upload spreadsheet files (CSV, XLSX, XLS) through a web interface and automatically performs dataset profiling, cleaning, statistical analysis, visualization, and report generation. A FastAPI-based backend orchestrates the pipeline, while an external workflow automation tool integrates AI capabilities to recommend data cleaning and visualization strategies. The system combines deterministic data processing techniques with AI-driven planning to produce structured insights and downloadable PDF reports. The proposed solution demonstrates how lightweight web technologies and workflow automation can be integrated to create an accessible, automated data analysis pipeline without requiring programming expertise.

Introduction

The text presents a No-Code Dataset Analysis System designed to simplify and automate the analysis of tabular data without requiring programming skills. Traditional workflows involve manual cleaning, preprocessing, statistical analysis, and visualization, which can be complex and time-consuming. The proposed system allows users to upload datasets via a web interface, after which it performs profiling, AI-assisted cleaning, statistical analysis, visualization, and generates a comprehensive PDF report.

The system architecture consists of four main components: Data Profiling, AI-Assisted Processing, Analysis Engine, and a Web-based Interface. AI-driven workflows determine optimal cleaning and visualization strategies, while the analysis engine computes correlations, categorical impacts, and generates charts along with natural language insights. The frontend provides a user-friendly drag-and-drop interface, and the backend ensures seamless integration, efficiency, and reliability.

The solution is built using Python, FastAPI, HTML, CSS, JavaScript, and key libraries like Pandas, NumPy, Matplotlib, Seaborn, supporting multiple dataset formats (CSV, XLS, XLSX). Functional and non-functional requirements emphasize efficiency, scalability, usability, and fault tolerance, making it suitable for business analytics, data exploration, and decision support applications.

Conclusion

This project presents a robust AI-assisted No-Code Dataset Analysis System designed to automate the complete lifecycle of tabular data processing, from dataset upload to insight generation and report creation. By integrating intelligent workflow automation with traditional data analysis techniques, the system effectively simplifies complex analytical tasks and makes them accessible to users without programming expertise. The system leverages a FastAPI-based backend for efficient processing and orchestration, combined with a lightweight web-based frontend for seamless user interaction. The integration of an AI-driven workflow enables dynamic decision-making for data cleaning, relationship analysis, and visualization planning, allowing the system to adapt to diverse datasets with minimal manual intervention. Additionally, the implementation of structured data profiling, automated preprocessing, statistical analysis, and visualization ensures accurate and meaningful insights. The system demonstrates strong capability in handling real-world datasets with inconsistencies such as missing values, duplicates, and mixed data types. Compared to traditional manual analysis approaches, it significantly reduces processing time while improving usability and efficiency. Furthermore, the automated PDF report generation feature provides a comprehensive and portable summary of analytical results, making the system suitable for applications in business intelligence, data exploration, and decision support. This work highlights the potential of combining AI-assisted planning with deterministic data processing to build scalable and user-friendly analytical platforms. It bridges the gap between complex data science workflows and accessible no-code solutions, enabling wider adoption of data-driven decision-making.

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Copyright

Copyright © 2026 Visweshkishan V, Santhosh Kumar N, Mrs. M. Sasikala. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.