The most serious sort of cancer that affects women most frequently in modern times is breast cancer (BC). If it is not detected in the beginning stages, the death rate is significant. Breast cancer affects around 12% of woman, and the percentage is continually rising. The invention of a ML-based breast cancer classification system that can identify the disease from a patient's digital scan is artificial intelligence's greatest contribution to healthcare. Breast cancer is found using the mammography method however, radiologists' interpretations vary greatly. Fine needle aspiration cytology is commonly used in the diagnosis of breast cancer (FNAC). Uniform cell size, if the tumour has a consistent cell shape and other factors are taken into account, the prediction will determine if the tumour is benign or malignant. We have gathered both the characteristics of breast cancer cells and cells from healthy individuals. We were able to differentiate between malignant and benign employing a supervised machine learning classifier system to identify tumours. However, by taking the right medications, needless therapy can be avoided if patients are correctly recognised early on employing ML approaches. Though computer vision, ML technologies have demonstrated a high level of accuracy in healthcare applications, Physical examinations shouldn't solely be conducted using these systems. These are meant to support doctors, not replace them. Machine learning has a distinct advantage in that it can find relevant breast cancer features in large datasets. In predictive modelling and pattern recognition, the technique is extensively used. Assessing each classifier's effectiveness in terms of accuracy, precision, and recall is the project's main objective. Examining the effectiveness, accuracy, and early identification of breast cancer using different machine learning classifiers is the goal.

Keywords : Breast Cancer (BC), Mammography, Fine Needle Aspiration Cytology(FNAC).