{"repo":"dyneth02/Breast-Cancer-Prediction-Machine-Learning-App","free":true,"listed":false,"github":"https://github.com/dyneth02/Breast-Cancer-Prediction-Machine-Learning-App","clone":"git clone https://github.com/dyneth02/Breast-Cancer-Prediction-Machine-Learning-App.git","description":"A comprehensive machine learning application that predicts breast cancer malignancy using cytology measurements. Features an interactive Streamlit web interface with real-time visualizations including radar charts for cell nuclei analysis. Implements logistic regression with data preprocessing pipelines for accurate benign/malignant classification.","language":"Python","stars":11,"topics":["ai-in-healthcare","breast-cancer","classification-model","data-preprocessing","data-science","data-visualization","healthcare-technology","interactive-dashboard","logistic-regression","machine-learning"],"license":"MIT","category":"machine-learning","readme_excerpt":"🩺 Breast Cancer Diagnosis ML Web Application 📋 Project Overview An end-to-end machine learning application for breast cancer diagnosis that predicts whether a breast mass is benign or malignant based on cytology lab measurements. The project includes both model training and an interactive web interface. 🚀 Features 1. Machine Learning Pipeline - Data preprocessing and cleaning from the Wisconsin Breast Cancer Dataset - Feature scaling using StandardScaler - Logistic Regression classification model - Model evaluation with accuracy metrics and classification reports - Serialized model and scaler for production use 2. Interactive Web Application (Streamlit) - Real-time interactive sliders for 30+ cell nuclei measurements - Dynamic radar chart visualization comparing: - Mean values - Standard error values - Worst-case values - Instant prediction results with probability scores - Responsive two-column layout design 3. Key Functionalities - Data Cleaning : Automatic handling of missing values and column mapping - Feature Scaling : Min-max scaling for visualization and model input - Model Prediction : Real-time inference with probability outputs - Visual Analytics : Plotly-based radar charts for multi-dimensional data visualization - User-Friendly Interface : Intuitive sidebar controls and clear result displays 📁 Project Structure 🔧 Installation & Setup Prerequisites - Python 3.8+ - pip package manager Installation Steps 1. Clone the repository: bash pip install -r requirements.","default_branch":null,"files":null,"tree":[],"storefront":"/r/dyneth02","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/dyneth02/Breast-Cancer-Prediction-Machine-Learning-App/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}