AI-Powered Data Visualization Platform: An Intelligent Web Application for Automated Dataset Analysis

• URL: http://arxiv.org/abs/2511.08363v2
• Date: Sat, 15 Nov 2025 15:56:25 GMT
• Title: AI-Powered Data Visualization Platform: An Intelligent Web Application for Automated Dataset Analysis
• Authors: Srihari R, Pallavi M, Tejaswini S, Vaishnavi R C,
• Abstract summary: The system establishes the process of AI-based analysis and visualization from the context of data-driven environments.<n>Key contributions include automatic and intelligent data cleaning, with imputation for missing values, and detection of outliers.<n>The initial analysis was performed in real-time on datasets as large as 100000 rows, while the cloud-based demand platform scales to meet requests.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: An AI-powered data visualization platform that automates the entire data analysis process, from uploading a dataset to generating an interactive visualization. Advanced machine learning algorithms are employed to clean and preprocess the data, analyse its features, and automatically select appropriate visualizations. The system establishes the process of automating AI-based analysis and visualization from the context of data-driven environments, and eliminates the challenge of time-consuming manual data analysis. The combination of a Python Flask backend to access the dataset, paired with a React frontend, provides a robust platform that automatically interacts with Firebase Cloud Storage for numerous data processing and data analysis solutions and real-time sources. Key contributions include automatic and intelligent data cleaning, with imputation for missing values, and detection of outliers, via analysis of the data set. AI solutions to intelligently select features, using four different algorithms, and intelligent title generation and visualization are determined by the attributes of the dataset. These contributions were evaluated using two separate datasets to assess the platform's performance. In the process evaluation, the initial analysis was performed in real-time on datasets as large as 100000 rows, while the cloud-based demand platform scales to meet requests from multiple users and processes them simultaneously. In conclusion, the cloud-based data visualization application allowed for a significant reduction of manual inputs to the data analysis process while maintaining a high quality, impactful visual outputs, and user experiences

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