The telecommunication companies (TELCO) in Malaysia commonly use the return on investment (ROI) model for techno-economic analysis to strategize their network investment plan in their intended markets. The number of subscribers and average revenue per user (ARPU) are two dominant contributions to a good ROI. Rural areas are lacking in both dominant factors and thus very often fall outside the radar of TELCO’s investment plans. The government agencies, therefore, shoulder the responsibility to provide broadband services in rural areas through the implementation of national broadband initiatives, regulated policies and funding for universal service provision. This thesis outlines a framework of machine learning technique which the TELCOs and government agencies can use to plan for broadband investments in Malaysia, especially for rural areas. The framework is implemented in four stages: data collection, machine learning, machine testing, and machine application. In this framework, a curve-fitting technique will be applied to formulate an empirical model by using prototyping data from the World Bank databank. The empirical model serves as a fitness function for a genetic algorithm (GA) to generate large virtual samples to train, validate and test the support vector machines (SVM). Real-life field data for geographic areas in Malaysia are then provided to the tested SVM to predict which areas have the socioeconomic potential for broadband investment. By using this technique as a policy tool, TELCOs and government agencies will be able to prioritize areas where broadband infrastructure can be implemented using a government-industry partnership approach. Both public and private parties can share the initial cost and collect future revenues appropriately as the socioeconomic correlation coefficient improves
