Machine Learning-Based Cellular Traffic Prediction Using Data Reduction Techniques - Batchu Bharat,; Saipriya Vissapragada, (Utilitas Mathematica, 2025)
For modern networks to maximise Quality of Service (QoS), precise cellular traffic prediction is crucial, particularly given the increasing need for real-time applications. In order to increase predictive performance, this study introduces an upgraded Adaptive Machine Learning-based Cellular Traffic Prediction (AML-CTP) framework that incorporates cutting-edge machine learning algorithms such as XGBoost, CatBoost, and Voting Regression with parameter adjustment. The suggested system makes use of density-based clustering techniques like DBSCAN to concentrate on high-similarity data clusters, PCA for dimensionality reduction, and robust preprocessing approaches like Min-Max Scaling. These techniques minimise computing complexity while guaranteeing effective model training. The Flask framework is used in the system's implementation to improve accessibility and real-time deployment, enabling smooth user interaction for data uploading and prediction generation. With the greatest R2 score of 98%, experimental findings illustrate how successful XGBoost is at adapting to changing traffic patterns, allocating resources optimally, and improving overall quality of service (QoS) in cellular networks.