  As much as data science is playing a pivotal role everywhere, healthcare also
finds it prominent application. Breast Cancer is the top rated type of cancer
amongst women; which took away 627,000 lives alone. This high mortality rate
due to breast cancer does need attention, for early detection so that
prevention can be done in time. As a potential contributor to state-of-art
technology development, data mining finds a multi-fold application in
predicting Brest cancer. This work focuses on different classification
techniques implementation for data mining in predicting malignant and benign
breast cancer. Breast Cancer Wisconsin data set from the UCI repository has
been used as experimental dataset while attribute clump thickness being used as
an evaluation class. The performances of these twelve algorithms: Ada Boost M
1, Decision Table, J Rip, Lazy IBK, Logistics Regression, Multiclass
Classifier, Multilayer Perceptron, Naive Bayes, Random forest and Random Tree
are analyzed on this data set. Keywords- Data Mining, Classification
Techniques, UCI repository, Breast Cancer, Classification Algorithms
