<![CDATA[This community service program was conducted to address the low efficiency of conventional machine maintenance systems and the high risk of unexpected breakdowns in vehicle service companies. The program aimed to introduce a predictive maintenance system based on the Random Forest algorithm, enabling partners to shift from reactive maintenance practices toward data-driven decision-making. The applied methods included a needs assessment, theoretical training on machine learning fundamentals for 10 participants, hands-on practice using Python and Google Colab, and guided case studies based on the partner’s historical machine data. Evaluation results indicated a 42% improvement in participants’ technical understanding, while the developed system demonstrated the capability to detect potential failures 2–3 days earlier than traditional methods. The implementation successfully reduced unscheduled downtime and operational costs. Overall, this program enhanced the partner’s capacity to implement predictive maintenance, strengthened technicians’ digital literacy, and supported sustainable digital transformation in the vehicle service sector.]]>
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