Andrews-Osei/breast-cancer-prediction-system_New

AI-powered breast cancer prediction app - Group 3 Thrive Africa Project # 🎗️ Breast Cancer Prediction System ### 🚀 **LAUNCH LIVE APP** 🚀 *Click above to try the application - no installation required!* --- An AI-powered web application for breast cancer prediction using machine learning. This project was developed by **Group 3** as part of the **Thrive Africa** learning program. ## 🌐 Live Application **🚀 Try the app now:** andrews-breast-cancer-predi… > Access our AI-powered breast cancer prediction tool directly in your browser - no installation needed! --- !\Python !\Streamlit --- \## 👥 Team Members (Group 3) \*\*👨‍💼 Team Leader:\*\* Osei Andrews \*\*Team Members:\*\* \- Osei Andrews \- Obeng Godfred \- Solace Kumi \- Addai Kingsford Boateng \- Azeko Emmanuel \- Portia Bentum \- Abigail Aboagyewaa Osei \- Prince Louis Appiah --- \## 🎯 Project Overview This application uses machine learning to predict whether a breast mass is \*\*benign\*\* or \*\*malignant\*\* based on 30 features extracted from digitized images of fine needle aspirate (FNA) of breast masses. \### 📊 Model Performance | Metric | Score | |--------|-------| | \*\*Accuracy\*\* | 97.4% | | \*\*Precision\*\* | 100% | | \*\*Recall\*\* | 92.9% | | \*\*F1 Score\*\* | 96.3% | --- \## 📚 Dataset Information The application uses the \*\*Breast Cancer Wisconsin (Diagnostic) Dataset\*\* from the UCI Machine Learning Repository. \*\*Features (30 total):\*\* \- 🔵 \*\*Mean measurements\*\* (10 features) \- 🟢 \*\*Standard error measurements\*\* (10 features) \- 🔴 \*\*Worst/largest measurements\*\* (10 features) Each category includes: \- Radius \- Texture \- Perimeter \- Area \- Smoothness \- Compactness \- Concavity \- Concave points \- Symmetry \- Fractal dimension \*\*📖 Citation:\*\* Wolberg, W., Street, W., \& Mangasarian, O. (1995). Breast Cancer Wisconsin (Diagnostic) Dataset. UCI Machine Learning Repository. --- \## 🚀 Installation \& Setup \### Prerequisi …