Breast Cancer Classification using Deep Learning

Overview

This project demonstrates Breast Cancer Classification using a deep learning model built with TensorFlow and Keras. The model is trained on the Breast Cancer Wisconsin Dataset and predicts whether a tumor is Benign (1) or Malignant (0).

Features

• Loads the dataset from sklearn.datasets.
• Performs data preprocessing (handling missing values, standardization).
• Builds a Neural Network for classification.
• Trains the model using Adam optimizer & sparse categorical crossentropy loss.
• Evaluates the model using accuracy & loss visualization plots.
• Implements a predictive system to classify new data.

Installation

Clone the repository:

git clone https://github.com/yourusername/Breast_Cancer_Classification.git
cd Breast_Cancer_Classification

Install dependencies:

pip install -r requirements.txt

Usage

Run the Jupyter Notebook:

jupyter notebook Breast_Cancer_Classification.ipynb

Dataset

The dataset used is the Breast Cancer Wisconsin Dataset, available in the sklearn.datasets module.

Model Architecture

• Input Layer: Takes standardized feature data.
• Hidden Layers: Fully connected layers with ReLU activation.
• Output Layer: A softmax layer for binary classification.

Results

Accuracy & Loss Plots:

Example Prediction:

Features	Prediction
[15.3, 20.4, ...]	Malignant (0)
[12.1, 18.5, ...]	Benign (1)

Next Steps

• Apply hyperparameter tuning to improve accuracy.
• Deploy the model as a Flask or FastAPI web app.
• Convert the notebook into a Python script for automation.

Contributing

Feel free to open an issue or submit a pull request if you find any improvements!

License

This project is licensed under the MIT License.