Breast Cancer Prediction

Project Overview

This project leverages Machine Learning to predict breast cancer using medical imaging and patient data. Built with Streamlit, it provides an intuitive interface for healthcare professionals to input data and receive predictions with over 95% accuracy. The model uses advanced classification algorithms to identify malignant cases early, improving patient outcomes.

Technologies Used

• Streamlit: For building the interactive web application.
• Scikit-learn: For implementing ML classification models.
• Python: Core programming language for data processing and model training.
• Pandas & NumPy: For data manipulation and preprocessing.

Key Features

• Real-time predictions with high accuracy.
• User-friendly interface for non-technical users.
• Data visualization for model performance and patient data.
• Scalable deployment using cloud infrastructure.

Impact

The application has been tested with real-world datasets, achieving a 95%+ accuracy rate. It supports early detection, reducing diagnostic delays and aiding medical professionals in decision-making.