Predictive Analytics Dashboard for Telecom Network Faults and Proactive Maintenance

Objective: To develop a data-driven dashboard that predicts telecom network faults and enables proactive maintenance to minimize service disruptions.

In India, telecom operators face frequent network faults due to diverse geography, weather conditions, and aging infrastructure. Such disruptions affect customer satisfaction and regulatory compliance. Proactive fault prediction is critical for maintaining high service quality amid rapid subscriber growth.

The approach involves collecting historical fault data, environmental factors, and maintenance logs from telecom operators. Machine learning models will be used to identify patterns and predict upcoming faults. The project will design and implement a dashboard for operations teams to monitor predictions and schedule preventive actions.

Deliverables include a fully functional dashboard, predictive models, data analysis reports, and user documentation. The analysis will cover model accuracy, operational insights, and recommendations for maintenance scheduling.

Business impact includes reduced downtime, optimized maintenance, improved customer experience, and data-driven decision-making for operations managers. The dashboard informs prioritization of maintenance resources and strategic planning.