The rapid growth of urban populations in developing cities has intensified the need for sustainable and efficient transport solutions. However, despite the increasing adoption of micro-mobility services in the Philippines, behavioural and contextual determinants remain under investigated, creating a gap in understanding the factors that drive user adoption. This study explores a micro mobility marketplace by utilizing machine learning to estimate the likelihood of user adoption from behavioural and contextual conditions. Based on a structured survey distributed across the National Capital Region (NCR), collected survey data from 500 respondents were analyzed using machine learning algorithms. Results show that K-Nearest Neighbours outperformed the rest of the models, though XGBoost and Support Vector Machines also offered good results. Trust, attitude, and price value were the significantly high-ranking factors present in all the models and were the most important for decision-making. The study illustrates how the combination of behavioural understanding and machine learning can enhance user-centric services and encourages sustainable mobility service provision. These findings are relevant to service providers and policy makers seeking to upgrade urban transport infrastructure in fast-growing cities, particularly in the Philippines.

This study aims to identify the most critical attributes influencing retention and dropout risk in the Alternative Learning System (ALS) by analyzing various demographic, socio-economic, academic, and behavioral factors. Using Gradient Boosting Decision Trees (GBDT) for predictive modeling, the research explores feature importance scores to rank and prioritize the key attributes. The researcher used Knowledge Discovery in Databases as analytics methodology. Using principal component analysis, it was identified that regular attendance, availability, financial support, parental cohabitation (living together), and internet access positively influence retention. Furthermore, attending public schools, having a widowed parent, and possibly other features like distance to school are linked to increased dropout risk. The results provide insights into the main factors affecting student success, enabling more focused and data-driven interventions. The findings can help ALS administrators and educators develop personalized support plans for at-risk students and allocate resources more effectively.