An Automated Smart Waste Management Systems using DT

Every household in ancient Athens engaged in the routine activity of collecting rubbish and disposing of it in dumps. The most crucial aspect was self-waste management. Every day, people ought to have gone to every street to remove the trash from the community. The drivers of waste collection trucks in the regular waste management system proceed along an established route without checking the level of the containers. Because this technology is unable to detect the levels in the containers, half-filled containers may be emptied. Additionally, the system's collection routes result in time waste, increased fuel consumption, and unnecessary resource use when trucks collect the empty bins. Therefore, this approach is introduced to address these problems. In this system machine learning technique (Decision Tree) is used for Smart Waste Management System. So, by using this system, dust bin can predicted in each and every area by taking last data as input values (such as weight of dustbin and time taken to fill the dustbin) and trained with machine learning technique. Hence, by using this system dustbin can be predicated accurately.