"Smart Bin Insights: Data Set for Unveiling Waste Management Trends"
This paper introduces the "Smart Bin Waste Prediction" system, which forecasts trash bin fill levels. By harnessing sensor data and historical trends, the system employs advanced machine learning techniques to enhance waste collection schedules. A comprehensive comparison of regression and classification algorithms is undertaken to attain optimal outcome
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Classification algorithms demonstrated a significant performance advantage over regression algorithms in the context of the "Smart Bin Waste Prediction" system. The ability of categorization algorithms to efficiently condense the data is blamed for the findings' notable discrepancy. The classification strategy expedited the prediction process and shown improved accuracy by using a variety of strategies, such as balancing unbalanced data. This result emphasises the need of carefully selecting algorithms and pre-processing methods to provide the best possible predicted results in waste management applications.