ML assisted enhanced recommendation system for reliable volunteering environment

作     者：Prabakaran N Meenakshi K J. Kumarnath Rajasekaran P 

作者机构：School of Computer Science and Engineering Vellore Institute of Technology Vellore 632014 India Department of Mathematics Presidency University Bengaluru 560064 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul 624005 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu 603203 India 

出 版 物：《Procedia Computer Science》 

年 卷 期：2024年第235卷

页      面：22-31页

主　　题：Decision Tree ID3 C4.5 CART Volunteers NGO 

摘      要：The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created.