🧬 Breast Cancer Prediction using Machine Learning

📌 Overview

This project applies machine learning techniques to predict whether a breast tumor is **malignant** or **benign** using the Wisconsin Breast Cancer Diagnostic dataset. It leverages various clinical features extracted from digitized images of fine needle aspirates (FNAs) to assist in early cancer detection and decision-making support.

🚀 Features

• Exploratory Data Analysis

• Data Preprocessing: Handling missing values, feature scaling

• Model Selection: Evaluation of multiple ML classifiers (Logistic Regression, KNN, SVM, Random Forest, XGBoost, etc.)

• Performance Metrics: Accuracy, precision, recall, F1-score, ROC-AUC curve

• Model Saving: Export of trained model and scaler for deployment

• Deployment: Streamlit app for interactive prediction

🛠️ Installation

Clone the repository

git clone https://github.com/auspicie/Breast-Cancer_Prediction-ML.git
cd Breast-Cancer_Prediction-ML

**Install dependencies**

pip install -r requirements.txt

💻 Usage

Run the Streamlit app: streamlit run Cancer_app.py

📷 Streamlit App Preview

📊 Dataset

-Name: Wisconsin Breast Cancer Diagnostic Dataset

• Source:Kaggle
• Size:* 569 samples × 30 features + 1 target
• Target Variable: diagnosis (M = Malignant, B = Benign)

🤝 Contributing

Contributions are welcome! Feel free to: Open an issue Submit a pull request

📄 License

This project is licensed under the MIT License.

📌 Notes

• Ensure that the heart\_disease\_model.pkl and scaler.pkl are in the same directory as the app.

Author: Samsudeen Bankole

Built with ❤️ using Streamlit and Scikit-learn.