A Comparative Analysis of Algorithms for Effective Waste Classification

Waste classification plays a crucial role in effective waste management practices, aiding in the identification and sorting of different types of waste materials. With the increasing volume and complexity of waste generated globally, there is a growing need for automated waste classification systems. This research paper presents a comparative analysis of various algorithms used for waste classification, highlighting their strengths, limitations, and performance metrics. The study focuses on popular algorithms: Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes Classifier, Decision Tree, K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN). Each algorithm's suitability for waste classification is assessed based on their accuracy. To evaluate the performance of these algorithms, a comprehensive dataset of waste images is employed.