Breast cancer is the most common cancer affecting women worldwide and the second leading cause of cancer-related deaths. Early diagnosis is critical in improving survival rates and reducing the impact of the disease. Typically, the diagnosis process involves detecting abnormalities, such as lumps or calcium specks, through self-examinations or imaging techniques like x-rays. Once abnormalities are found, doctors perform additional tests to confirm whether the tumor is cancerous and assess the extent of its spread.

This project focuses on analyzing breast cancer data to identify malignant tumors based on specific tumor characteristics. Using features such as the size, shape, and texture of the tumor, we aim to build a classification model that can distinguish between malignant (cancerous) and benign (non-cancerous) tumors.

The dataset used for this analysis, breast_cancer_data.rda, provides key insights into tumor properties and serves as the foundation for developing machine learning models to enhance diagnostic accuracy. Through this analysis, we hope to contribute to the ongoing efforts in leveraging data science to improve early detection and diagnosis of breast cancer.