This project implements a Breast Cancer Detection system using an Artificial Neural Network (ANN) built with Python and TensorFlow. The system can predict whether a tumor is benign or malignant based on patient features, and it also supports batch predictions via CSV files.

• Overview
• Features
• Dataset
• Technologies Used

Breast cancer is one of the most common cancers affecting women worldwide. Early detection can significantly improve survival rates. This project uses machine learning to classify tumors as benign or malignant based on patient data using an ANN model.

• Predict tumor type for a single patient.
• Upload CSV files for batch predictions.
• Feature scaling for better model performance.
• Interactive visualization of dataset features.
• High model accuracy with precision and recall metrics.

• The model uses the Breast Cancer Wisconsin (Diagnostic) dataset from sklearn.datasets.
• Features include measurements like mean radius, mean texture, mean perimeter, mean area, and more.
• Target labels: 0 = Malignant, 1 = Benign.

• Python 3.x
• TensorFlow / Keras
• scikit-learn
• Pandas & NumPy
• Matplotlib & Seaborn
• Joblib
• Streamlit (optional for web app interface)