Breast Cancer is the most customarily recognized cancer amongst ladies and fundamental purpose for the growing mortality rate amongst ladies. As the analysis of this sickness manually takes long hours and the lesser availability of systems, there's a want to broaden the automated analysis gadget for early detection of most cancers. Data mining techniques contribute a lot to the development of such a system. To classify benign and malignant tumors, we have used classification techniques of machine learning within which the machine is learned from past knowledge and might predict the class of recent input. There are many Classification techniques used to predict breast cancer, several of which can be Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB), k Nearest Neighbors (k-NN), and random forest. This paper is a comparative study on implementing a model using a Support Vector Machine (SVM). These techniques are coded in python and executed in a jupyter notebook, the Scientific Python Development Environment. Our experiments have proven that support vector machine(SVM) is exceptional for predictive evaluation with an accuracy of 96.4%.