It is no doubt that the sub-field of Artificial Intelligence, which uses the tenets\nof Machine learning and data mining has progressively gained popularity in\nthe past years to become one of fundamental yet revolutionary technologies. It\nis the basis of systems that can learn and improve using algorithms and big\ndata with minimal programming or none. It is envisaged that mobile computing\nwill empower end-users to seamlessly access and consume digital content\nservices regardless of spatial or temporal orientations. Such are already\nthe features of smart phones that at production are bundled with trending and\nnecessary services. Of the many capabilities that advancement in technology\nhave actualized in smart devices, gaming, video streaming, online library access,\nand m-commerce access services are the commonly among smart device\nowners. Given the near-exponential growth in ownership of smart devices,\nthere is a need to identify and prioritize mobile services, and such was focus of\nthis study. In specific, the study used Decision Tree, a popular machine\nlearning algorithm, to predict the adoption of mobile services among smart\ndevice owners. Besides this purpose, the study identified the core uses of\nsmart phones, and data used in the study was from an open source and was\nretrieved from Pew Research Centre Internet and Technology website. The\ndataset had 140 variables and 2001 themes, from which only the key attributes\nwere selected for analysis. The study established that the level of education\nwas the significant predictor of the mobile phones usage while race of the user\nemerged as the least predictor of smart device usage. The findings indicated\nthat smart mobile phones were mostly used for entertainment, getting locations,\ndirection and for recommendation purposes.
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