In the recent years, Breast cancer has been a worrying concern as the number of women getting affected by it has increased by multi-folds. Breast Cancer is acommon type of cancer for women around the world. It occurs due touncontrolled growth of breast cells. Early detection of it can greatly improveprognosis and survival chances by promoting clinical treatment to patients as early as possible.
This project deals with predicting whether a patient hasbenign or malignant type of tumor. It presents a comparison of seven differentmachine learning algorithms which includes Logistic Regression, KNeighborsClassifier, SVC Linear, SVC RBF, GaussianNB, Decision Tree Classifier, Random Forest Classifier on the Wisconsin Diagnostic Breast Cancer dataset, by measuring their classification test accuracy, and their sensitivity and specificity values thereby figuring out the best algorithm.