Breast Cancer Prediction using Machine Learning Overview This project implements a machine learning model to predict breast cancer diagnosis (benign or malignant) using the Wisconsin Breast Cancer dataset. The model uses Fine Needle Aspiration (FNA) data to assist in early detection and diagnosis of breast cancer. Features
Binary classification of breast cancer (benign/malignant) Uses Logistic Regression algorithm Built with scikit-learn machine learning library Includes data visualization and analysis Performance metrics evaluation
Dependencies
Python 3.x pandas numpy matplotlib scikit-learn
Dataset The project uses the Wisconsin Breast Cancer dataset from scikit-learn, which includes features computed from digitized images of FNA of breast mass. Features include:
Radius Texture Perimeter Area Smoothness And other cellular characteristics
Model
Algorithm: Logistic Regression Data split: Training (80%) and Testing (20%) Evaluation metrics: Accuracy Score
Future Improvements
Implement additional machine learning algorithms for comparison Add cross-validation Include feature importance analysis Create a web interface for predictions
Contributing Contributions, issues, and feature requests are welcome. Feel free to check issues page if you want to contribute.
Contact Email - [waqaskhan600.wk@gmail.com] Project Link: [https://github.com/Waqaskhan600/breast-cancer-prediction]