DATA SHEET.csv

Breast cancer is one of the most prevalent cancers among women worldwide, and early detection is crucial for reducing mortality rates and improving treatment outcomes. Mammography has been the gold standard for breast cancer screening, offering non-invasive imaging to identify suspicious abnormalities. However, mammography has limitations, such as variability in interpretation, false positives, false negatives, and challenges in distinguishing between benign and malignant lesions.Machine learning has the potential to revolutionize breast cancer detection by enhancing the capabilities of mammography. Its ability to improve accuracy, efficiency, and consistency in diagnosis makes it an indispensable tool for early detection efforts.This study focuses on developing a machine learning-based predictive model for the early detection and classification of breast cancer, utilizing the Wisconsin Breast Cancer Diagnostic dataset. Special emphasis is placed on the potential of ML algorithms, particularly the Support Vector Classifier with a Radial Basis Function (SVC-RBF), to enhance diagnostic accuracy and efficiency.Machine learning has the potential to revolutionize breast cancer detection by enhancing the capabilities of mammography. Its ability to improve accuracy, efficiency, and consistency in diagnosis makes it an indispensable tool for early detection efforts.