Data Pilot Dashboard

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Gitesh Kund
Tejas Mokale
Praneet Mane
Abhijeet Rangari
Madhura Naralkar

Abstract

The Data Pilot Dashboard is a web-based platform designed to enhance  data processing, analysis, and visualization by automating tasks such as data cleaning, visualization, and machine learning (ML) integration. Developed using Python, Streamlit, Plotly, and MongoDB, it provides a scalable and user-friendly interface catering to both technical and non-technical users. The system ensures data integrity through automated pre-processing, while real-time visualization features improve data interpretation. Predictive analytics and anomaly detection help identify trends and deviations, enabling informed decision-making. By incorporating ML algorithms, the dashboard facilitates data-driven forecasting; increasing accuracy and operational efficiency. Automating data analysis significantly reduces manual effort, enhances accessibility, and provides secure, real-time updates. The platform improves efficiency across multiple domains, including business intelligence, healthcare, education, and smart cities, addressing the growing need for automated analytics tools that support dynamic data exploration. Its scalable architecture ensures adaptability across various industries, making it a valuable asset for organizations seeking to leverage data-driven strategies. Future enhancements may include advanced ML capabilities, real-time collaboration, and cross-platform support, further strengthening its functionality. By demonstrating the impact of automation in data analytics, the Data Pilot Dashboard fosters better decision-making and efficiency, positioning itself as a powerful tool for professionals and organizations aiming to optimize their data-driven workflows.

Article Details

How to Cite
Kund, G., Mokale, T., Mane, P., Rangari, A., & Naralkar, M. (2025). Data Pilot Dashboard. International Journal on Advanced Computer Theory and Engineering, 14(1), 264–268. Retrieved from https://journals.mriindia.com/index.php/ijacte/article/view/478
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Articles

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