Machine Learning Based Cellular Traffic Prediction

The rapid growth of mobile users and data-intensive applications has significantly increased cellular network traffic, leading to challenges in maintaining Quality of Service (QoS). This project presents the design and implementation of a Machine Learning-Based Cellular Traffic Prediction system using data reduction techniques to efficiently analyze and forecast network traffic patterns. The system utilizes advanced machine learning algorithms combined with clustering-based data reduction methods to improve prediction accuracy and reduce computational complexity. The proposed framework incorporates modules such as data pre-processing, clustering-based data reduction, model training, and adaptive model selection. Large-scale multi-dimensional network datasets are processed using techniques like Principal Component Analysis (PCA) and density-based clustering algorithms such as DBSCAN and Kernel Density Clustering. These methods help in identifying similar traffic patterns and reducing redundant data. Further, multiple machine learning models including Linear Regression, Support Vector Regression (SVR), Decision Tree, and Light Gradient Boosting Machine (LGBM) are trained and evaluated. The optimal model is selected based on accuracy and computational efficiency. The system demonstrates high prediction accuracy and reduced training time, making it suitable for real-time traffic analysis.