G. Divya, Vakiti Kulashekar Reddy, Dusa Maniteja, Thaduka Nuthan Kumar 03 04 The rapid growth of ecommerce and online delivery platforms has increased the importance of efficient lastmile courier services Companies such as Amazon and Flipkart depend on courier performance to ensure timely delivery and maintain customer satisfaction The surge in online orders generates large volumes of structured delivery data and unstructured customer feedback containing valuable insights into courier efficiency delivery accuracy service reliability and overall experience Traditional evaluation methods including manual review and basic statistical summaries like average ratings fail to effectively analyze large datasets or interpret complex textual feedback To address this challenge this study proposes a machine learning MLbased analytical framework for sentimentdriven performance benchmarking of lastmile couriers The framework integrates data preprocessing text feature extraction and multiple classification models to analyze courierrelated data A graphical user interface GUI allows users and administrators to interact with the system while Redis serves as a lightweight inmemory database for secure authentication and credential management The study implements several ML algorithms including Gaussian Naive Bayes GNB Random Forest RF and KNearest Neighbors KNN as baseline classifiers Additionally a hybrid model Leaf2VecKNN is proposed to enhance classification performance In this approach RF converts courier features into leafnode representations which are encoded and classified using KNN similarity learning Experimental results show that the proposed Leaf2VecKNN model achieves the highest accuracy of 9121 outperforming baseline models The framework provides an efficient solution for automated courier feedback classification and supports datadriven decisionmaking to improve delivery service quality Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026