Design and Testing of a Big Data-Base ML System

Introduction: Overview of how Big Data and Machine Learning together improve large-scale network analysis.

Problem Definition: Identification of challenges in handling and analyzing vast network data efficiently.

Objectives: Establishing goals for designing a scalable and accurate machine learning framework.

System Architecture: Description of the framework integrating Big Data tools with machine learning algorithms.

Data Collection and Processing: Methods for acquiring, cleaning, and organizing large network datasets.

Model Development: Building predictive models to detect patterns, anomalies, and performance issues.

Experimental Testing: Validation of the system using real-world datasets to ensure scalability and accuracy.

Results and Discussion: Analysis of outcomes demonstrating performance improvements and data insights.

Conclusion: Summary of findings emphasizing the system’s efficiency, reliability, and future enhancement potential.