In the era of 5G technology, predicting coverage areas is crucial for optimizing network performance and ensuring reliable connectivity. This study presents a comprehensive analysis of various machine learning algorithms for predicting 5G coverage based on the RF Signal Data. The target column, Band Width, is used to gauge prediction accuracy across different models. Traditional methods such as Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, Support Vector Machine (SVM), XGBoost, LightGBM, AdaBoost, Bayesian Network Classifier, Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM) are evaluated against proposed advanced techniques like Stacking and Voting Classifiers, and Convolutional Neural Networks (CNN). The objective is to identify dominant feature parameters that significantly influence 5G coverage prediction. By implementing a diverse array of models, this research aims to benchmark the performance and accuracy of these algorithms. The comparative analysis highlights the strengths and limitations of each approach, providing valuable insights for network engineers and researchers. The findings suggest that ensemble methods, particularly Stacking and Voting Classifiers, along with CNN, offer superior prediction accuracy and robustness, thereby serving as promising tools for enhancing 5G network planning and deployment.

Keywords: 5G Coverage Prediction, Machine Learning, RF Signal Data, Stacking Classifier, Voting Classifier, Convolutional Neural Network (CNN), Feature Parameters, Prediction Accuracy, Network Optimization, Ensemble Methods.