International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com T. Chalapathi Rao 2 3 (May - June) 2020 06 May-June enCancer DataClassification AlgorithmsMachine LearningData AnalysisPredictive ModelingMedical Diagnosis Accurate classification of cancer data plays a vital role in early diagnosis effective treatment planning and patient survival prediction This paper presents a comparative analysis of various machine learning classification algorithms applied to cancer datasets The study evaluates algorithms such as Decision Tree Support Vector Machine SVM Random Forest KNearest Neighbor KNN and Nave Bayes to determine their performance in identifying cancer types or malignancy levels Data preprocessing techniques including normalization and feature selection are applied to improve model accuracy and reduce noise The models are trained and tested using benchmark cancer datasets such as the Wisconsin Breast Cancer Dataset WBCD to ensure reliability Performance metrics such as accuracy precision recall and F1score are used to assess the effectiveness of each algorithm Experimental results reveal that ensemblebased and kernelbased models achieve higher predictive performance compared to simple classifiers This study demonstrates the importance of selecting an appropriate classification algorithm for cancer data analysis and supports the integration of machine learning in medical diagnosis systems Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2020 All Rights Reserved