<![CDATA[Cycling requires careful time planning to ensure safety and comfort, especially when consideringweather conditions such as temperature, wind speed, and overall weather status. However, cyclistsoften struggle to determine the optimal time to ride due to the lack of accurate and easily accessiblerecommendations. This study aims to design and implement a mobile application that recommendsthe best cycling time based on real-time weather data. The system applies the Regression CARTDecision Tree method, trained using hourly temperature, wind speed, and weather conditionparameters. Unlike classification approaches, Regression CART Decision Tree produces acontinuous percentage score indicating the suitability level of each hour for cycling. Real-time datais obtained via the OpenWeatherMap API to maintain accuracy. The developed prototype displayshourly weather data along with the recommendation percentage, helping users plan their rides moreeffectively. Model evaluation shows that the Regression CART Decision Tree achieved high accuracywith a low Mean Absolute Error (MAE) and strong correlation between predicted and actualsuitability scores. The results confirm that the model performs consistently in various weatherscenarios. Overall, the system successfully delivers reliable, data-driven recommendations, assistingcyclists in selecting the safest and most comfortable cycling times.]]>
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