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Most common diseases and the leading cause of death to most women across the globe is Breast Cancer (BC). Although many individuals who suffer breast cancer have no family history but women who have blood relatives suffering from the same disease are at higher risk. Besides, a high risk of developing breast cancer includes aging, genes, thick breast tissues, obesity, and radiation exposure. Malignant and benign are two different types of tumors and to distinguish between these two, physicians need a reliable diagnostic procedure. The mammography method is used to detect breast cancer but radiologists exhibit significant variation in interpretation. Fine Needle Aspiration Cytology (FNAC) is commonly adopted in the diagnosis of breast cancer. Moreover, early diagnosis is vital to treatment with a better chance of success. Classification and data mining attributes are an efficient and effective way of categorizing results. Using machine learning models that will play a vital role in early prediction. In this paper, we present a prediction of breast cancer with different machine learning algorithms compare their prediction accuracy, area under the receiver operating characteristic curve (AUC) and performance parameters. For Simulation purposes, we are using the Wisconsin Dataset of Breast Cancer (WDBC). After analysis, the Support Vector Machine (SVM) model has achieved 96.25% accuracy with AUC of 99.4. Further, these algorithms can be modified with their mathematical models to increase the prediction of breast cancer.