Spam Detection Visualization Using Power BI

Author(s)	Monika Barde, Neha Sahu, Pranay Narnaware, Yashika Deshmukh, Ankeshvar Mawase
Country	India
Abstract	Spam detection is a crucial task in data management, where identifying and filtering unwanted content is essential for enhancing the quality of user experience and system performance. This project presents a visualization approach for spam detection using Power BI, which leverages data analytics to provide an interactive and intuitive platform for understanding and managing spam data. By integrating datasets of email content or messages, Power BI dashboards facilitate real-time monitoring and detection of spam patterns. This study explores the implementation of Naive Bayes for spam detection, leveraging Power BI as a data analysis and visualization tool. Power BI's interactive interface is used to preprocess and visualize the data, allowing the integration of Naive Bayes classification models for effective spam filtering. This combination of Naive Bayes and Power BI offers an efficient, user friendly framework for spam detection, suitable for real-time monitoring and decision-making.
Keywords	Naïve Bayes Classification, Machine Learning, Power BI, Spam Massages, Python
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-18
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.39590
Short DOI	https://doi.org/g882hk

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International Journal For Multidisciplinary Research