- In the realm of telecommunication, the deployment of 5G networks is pivotal for realizing the full potential of highspeed connectivity and the Internet of Things (IoT). This paper explores the application of Machine Learning (ML) techniques
in analyzing and predicting the success of 5G network deployments. Through a comprehensive dataset encompassing deployment
statuses, types, and operator profiles, we applied ML algorithms to interpret current trends and project future deployment
outcomes. Our comparative analysis between Logistics Regression and Random Forest models two prominent ML
approacheshighlights their respective predictive performances. Logistics Regression demonstrated exceptional proficiency, with
an accuracy of 99.85%, Precision of 99.85%, recall at an almost perfect 99.99%, and an F1 Score of 99.92%. Meanwhile, the
Random Forest model, upon adjustment, showed respectable results with an accuracy of 89.00%, precision of 87.00%, recall of
91.00%, and an F1 score matching its accuracy. These findings suggest that Logistics Regression may offer a more reliable
predictive model for 5G deployment in various contexts, although Random Forest models have their merits in handling complex
interactions. The study's outcomes provide valuable insights for telecom stakeholders aiming to optimize 5G network rollout
strategies and reinforce the decision-making processes with robust data-driven support.
Keywords - 5G, Predicting, Machine Learning, Analysis, Logistics Regression, Random Forest.