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@article{169660,
        author = {Ruturaj Yadav and Dr. S.M. Patil and Chetan Anil Ajage and Kanhaiya Wagh and Tejas Ahirrao},
        title = {Data Analysis and Data Visualization by automation using GenAI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2018-2022},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169660},
        abstract = {In today’s data-driven world, organizations are increasingly reliant on extracting meaningful insights from vast datasets to guide decision-making. However, traditional data analysis methods are often labor-intensive, prone to human error, and struggle with scalability. This research introduces a novel framework that leverages Generative AI (GenAI) and Large Language Models (LLMs) to automate the data analysis and visualization process from end to end. By streamlining data ingestion, preprocessing, analysis, and visualization, this framework minimizes manual intervention and accelerates insight generation. The automated pipeline employs GenAI for robust data handling and pattern recognition, while LLMs facilitate user interactions and provide natural language summaries of insights. Results demonstrate that this approach enhances efficiency, with a 50% reduction in processing time, and achieves 95% accuracy when compared to manual methods. User studies also indicate high satisfaction with the system’s intuitive, interactive visualizations and real-time data analysis capabilities. This research underscores the potential of AI-driven automation to revolutionize data analytics, offering a scalable, efficient, and user-friendly solution adaptable across industries.},
        keywords = {Generative AI, Large Language Models, data automation, data visualization, data pipeline, machine learning, NLP, data preprocessing, interactive dashboards, real-time insights, predictive analytics, data-driven decision-making, AI-driven analytics, data accuracy, operational efficiency.},
        month = {November},
        }