🚀 CancerGuardian is a machine learning-powered web application designed to assist in breast cancer diagnosis using the Breast Cancer Wisconsin (Diagnostic) Dataset. This tool predicts whether a tumor is malignant or benign, helping in early detection and decision-making.

✅ Breast cancer classification using machine learning 📊
✅ Trained on the Breast Cancer Wisconsin (Diagnostic) Dataset 🏥
✅ Standardized input scaling with StandardScaler 🔄
✅ Interactive web interface built with streamlit 🎨
✅ Easy-to-run setup with pre-trained model 🎯

CancerGuardian/
│── assets/
│ └── style.css # CSS for UI styling
│
│── dataset/
│ ├── data.csv # Original dataset
│
│── model/
│ ├── data_cleaned.csv # Processed dataset
│ ├── model.pkl # Trained ML model
│ ├── scaler.pkl # StandardScaler for input normalization
│ ├── train.py # Script for training the model
│
│── app.py # Main application script

This project utilizes the Breast Cancer Wisconsin (Diagnostic) Dataset, available on Kaggle: 🔗 Dataset Link

The dataset contains features extracted from digitized images of breast mass and helps classify tumors into:

• Malignant (cancerous) 🛑
• Benign (non-cancerous) ✅

git clone https://github.com/ArchitJ6/CancerGuardian.git
cd CancerGuardian

pip install -r requirements.txt

streamlit run app.py

1️⃣ User inputs tumor-related features via the web interface 🖥️

2️⃣ Input is standardized using StandardScaler 📏

3️⃣ Pre-trained model.pkl predicts whether the tumor is malignant or benign 🧬

4️⃣ Result is displayed with an intuitive UI 🎨

Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request. 🚀

This project is licensed under the MIT License. 📜

If you found this project helpful, ⭐️ star the repository and share it with others!

Happy coding! 💙