Breast Cancer is the second most important cause of death among women. As per the clinical expert, breast cancer is one of prominent cancers after lung cancer. However, early detection of this type of cancer in its initial stage helps to save lifes and increases lifespan. The survival chance of a patient can increase if there is a classifier that helps with a quick prediction of breast cancer. Therefore, a smart framework is required that can effectively detect and predict with high accuracy early stage of breast cancer. In this article, six machine learning classification algorithms, namely Logistic Regression (LR), K-Nearest Neighbours (kNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) are implemented in order to evaluate the performance and the prediction power of the model. The main target of this work is to compare these algorithm performances using the Wisconsin Breast Cancer (original) dataset. The number of performance metrics such as accuracy, precision, recall, f-1 score, and specificity are taken into consideration Our analysis of the results shows that the Support Vector Machine achieved the highest accuracy of 97.07% with the least error rate and Naive Bayes gives the lowest accuracy of 96%. All these experiments were carried out using SciKit.