Review on the Impact of Waste in Nigerian Manufacturing Firms and Waste Management Practices Using Machine Learning

Abstract

Abstract: The study examined the impact of waste in Nigeria manufacturing firm and waste management practices using machine learning. This paper presents ways of interaction of man with his environment in terms of waste management in Nigeria with the aim of promoting sustainable development. It shows the policies, the agents making these policies less likely to happen and the damage it can cause for the next generation.This article presents how data has been collected and analyzed through survey and series of progressive steps and present some guidelines when practicing waste management. It presents how a region can discover and implement some efficient technique for efficient waste management and comes up with enormous constructive results.In order to automate and optimize garbage generation, collection, transportation, treatment, and disposal, this study examines a variety of machine learning techniques. In order to provide accurate and efficient forecasts for trash creation, segregation, and collection, the system combines several machine learning techniques, such as decision trees (DT), random forests (RF), k-nearest neighbors (KNN), support vector machines (SVM), and clustering algorithms