Faults incurred by Base Transceiver Stations pose challenges to telecommunication organisations. Mostly the faults are due to BTS failures. BTS power system failures can have a significant impact on organizational performance in the telecommunications industry. These failures can cause disruptions in mobile network coverage, leading to dropped calls, slow data speeds, and difficulty connecting to the network. ECONET Zimbabwe has been experiencing unprecedented BTS power system failures for the past five years. Team Data Science Process was the pillar of the study methodology. The XGBoost algorithm was employed to develop a predictive model for the maintenance of Base Transceiver Station power failure. By using Machine Learning techniques to predict power system failures, ECONET Zimbabwe can take proactive measures to prevent disruptions in service, resulting in improved resource utilization and revenue. The number of dropped calls decreases and data speeds increase. The XG Boost algorithm reached 97% accuracy
