Leveraging Machine Learning for Call Traffic Analysis and Network Optimization

Analyzing call traffic data is crucial for improving network performance and managing congestion in communication systems. Features like call duration, signal strength, and tower distance play a key role in determining network performance. Machine learning (ML) techniques have been applied to predict and optimize network traffic in cellular networks. The data were first pre-processed by handling missing values, encoding categorical variables, and extracting time-based features. Exploratory analysis provided insights into feature distributions, correlations, and the presence of outliers. Anomaly detection was performed using Isolation Forest to identify unusual patterns, such as short calls or weak signals. These anomalies were visualized through scatter plots and analyzed for their impact on network quality. Dimensionality reduction using principal component analysis (PCA) helped highlight the data structure and improve visualization from the analysis of the clusters. Predictive modeling with Gradient Boosting Regressor was used to estimate call durations based on signal and environmental factors. Additionally, Gradient Boosting Classifier was used to classify congestion levels into low, medium, or high categories, providing useful insights for traffic management. Feature importance analysis identified the key drivers of network performance, and hyperparameter tuning further improved model accuracy. The results demonstrate effective anomaly detection, congestion control, and call traffic optimization for communication networks.