Abstract
Breast cancer continues to pose a significant challenge in the field of healthcare, serving as the primary cause of cancer-related deaths in women on a global scale. The present study aims to investigate the intricate relationship between breast cancer, statistical analysis, and feature engineering. By conducting an extensive analysis of a comprehensive dataset and employing sophisticated statistical methodologies, this research endeavor aims to unveil concealed insights that can enrich the medical community's existing knowledge base. Through the implementation of rigorous feature selection and extraction methodologies, the overarching aim is to augment the comprehension of breast cancer. Moreover, the study showcases the successful incorporation of univariate and bivariate analysis in order to enhance the accuracy of diagnostic procedures. The convergence of these disciplines exhibits considerable promise in the realm of breast cancer detection and prediction, facilitating cooperative endeavours aimed at addressing this widespread malignancy.
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