Breast cancer is the most frequently occurring cancer disease in women. It is reported almost 14% of cancers in Indian women are breast cancer. It becomes very crucial to predict breast cancer earlier to minimize the deaths. This research article helps to predict breast cancer earlier and reduce the immature deaths of women in India. In this paper, the authors have used the Logistic Regression method to classify the disease. The authors simulate the results using logistic regression with 10-fold cross-validations and with a different train-test split of the dataset. The 10-fold cross validations display its potential with almost 94% performance in the research paper. With all features and 90-10 , 80-20,50-50, 66-34 splits, and 10-fold cross-validations the authors achieve 96% accuracy. we have used different accuracy measures like accuracy, sensitivity, specificity, and kappa statistics to get the novelty of the model. In this study, the authors use the Wisconsin (Diagnostic) Data Set for Breast Cancer, Created by Dr. William H. Wolberg, General Surgery Dept., University of Wisconsin, Clinical Science Centre, Madison, WI 53792 wolberg@eagle.surgery.wisc.edu available at the UCI ML Repository website.