• Kunika Sirpor
• Pritam Ahire
• Srushti Waghmare
• Vishakha Sable
• Anuja Kore

Modern data analysis faces significant challenges due to the increasing volume, velocity, and variety of data. Traditional techniques, designed for smaller, static datasets, struggle with the complexities of non-IID data, big data, and dynamic data streams. Non-IID data, characterized by dependencies, heterogeneity, and non-stationarity, violates the assumptions of many classical methods, leading to biased results and inaccurate predictions. The sheer volume and velocity of big data necessitate distributed computing frameworks and real-time analysis techniques. Dynamic data streams require online methods that can adapt to evolving data distributions and ensure continuous model calibration. This paper proposes an integrated data analysis framework that combines techniques from various fields to address these challenges. The framework aims to provide a holistic and adaptable approach, enabling more accurate, reliable, and actionable insights across a wide range of applications.

Big Data Analytics, Clustering Algorithms, Outlier Detection, Non-IID Data, Machine Learning, Hadoop, Approximate Computing, Visualization

"Data-Driven Marketing: Leveraging Big Data Clustering, Regression, Outlier Detection, Partitioning, and Visualization for Application Advancement.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.i713-i718, April-2025, Available :http://www.jetir.org/papers/JETIR2504896.pdf

"Data-Driven Marketing: Leveraging Big Data Clustering, Regression, Outlier Detection, Partitioning, and Visualization for Application Advancement.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppi713-i718, April-2025, Available at : http://www.jetir.org/papers/JETIR2504896.pdf