Development of a Predictive Model for Forecasting Customer Satisfaction Levels in Enugu Electricity Distribution Company (EEDC) Using Data Mining Techniques

Customer satisfaction is vital for optimizing service delivery within the electricity distribution sector. However, many power distribution firms in Nigeria, such as the Enugu Electricity Distribution Company (EEDC), rely primarily on reactive complaint-handling mechanisms. To enable proactive service management, this study developed a predictive model for forecasting customer satisfaction levels within EEDC using data mining techniques. A comprehensive dataset comprising billing records, outage histories, metering information, complaint logs, payment delays, and customer feedback was analyzed. Three machine learning algorithms, Decision Tree, Logistic Regression, and Random Forest, were implemented and evaluated. System development followed the Object-Oriented Analysis and Design Methodology (OOADM), while the data mining pipeline adhered to the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The predictive system was built using Python, Flask, SQLite, Scikit-learn, and a responsive frontend comprising HTML, CSS, JavaScript, and Bootstrap. The architecture integrates core modules for secure authentication, predictive analytics, dashboard visualization, complaint analysis, historical tracking, and automated recommendation generation. Performance evaluation utilized Accuracy, Precision, Recall, and F1-Score metrics along with confusion matrices. The empirical findings demonstrated that the Random Forest algorithm achieved the highest classification accuracy at 94.6%, outperforming both the Decision Tree and Logistic Regression models. Key drivers of customer dissatisfaction were identified as frequent power outages, estimated billing practices, delayed resolutions, and negative sentiment in feedback. This study concludes that data mining effectively forecasts customer satisfaction, providing power utilities with a practical, intelligent decision-support tool to transition from reactive workflows to proactive, data-driven service delivery.