Breast Cancer Detection using Machine Learning This project aims to develop a machine learning model for detecting breast cancer based on various features extracted from breast tissue samples. The model is trained and evaluated using the Wisconsin Breast Cancer Dataset, which is a widely-used dataset in the field of breast cancer research. Dataset The Wisconsin Breast Cancer Dataset is obtained from the UCI Machine Learning Repository. It contains 699 instances with 10 features describing characteristics of the cell nuclei present in the digitized image of a fine needle aspirate (FNA) of a breast mass. The features include radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. The target variable indicates whether the tumor is benign or malignant. Requirements To run this project, you'll need the following Python libraries:
NumPy Pandas Scikit-learn Matplotlib SciPy