Breast Cancer Classification using Neural Networks

This project uses a neural network built with TensorFlow/Keras to classify tumors in the Breast Cancer Wisconsin dataset as malignant or benign. The model is trained on features extracted from cell nuclei in digitized images of breast masses.

📌 Dataset

• Source: Breast Cancer Wisconsin (Diagnostic) Data Set
• Features: 30 numeric features describing characteristics of the cell nuclei.
• Target: Binary classification (0 = Malignant, 1 = Benign)

🧠 Model Architecture

• Input layer: 30 features
• Hidden layers:
  • Dense(64, ReLU) + Dropout
  • Dense(32, ReLU)
• Output layer: Dense(1, Sigmoid)
• Loss function: Binary Crossentropy
• Optimizer: Adam

📊 Evaluation Metrics

• Accuracy on test data
• Confusion Matrix
• Classification Report (Precision, Recall, F1-score)
• ROC Curve and AUC Score

✅ Getting Started

# Clone the repository
git clone https://github.com/your-username/breast-cancer-nn.git
cd breast-cancer-nn

# Install dependencies
pip install -r requirements.txt

# Run the notebook
jupyter notebook Breast_Cancer_Classification.ipynb