📌 Overview
PredictoCare is a machine learning-based breast cancer detection system that achieves 97% accuracy. It was developed as a machine learning exercise using the Breast Cancer Wisconsin (Diagnostic) Dataset .

⚠️ Disclaimer: This dataset may not be fully reliable. This project was developed for educational purposes only in the field of machine learning and is not intended for professional medical use.

🔗 Live Demo: Try the application on Streamlit Community Cloud

✨ High Accuracy – Achieves 97% accuracy with optimized preprocessing and model tuning.
📊 Data Processing – Cleans and preprocesses medical datasets to enhance model reliability.
🤖 Machine Learning Model – Implements [mention algorithm(s) used, e.g., Random Forest, SVM, Neural Networks].
🔍 Model Explainability – Uses SHAP/LIME for transparent predictions.
💻 Interactive UI – Hosted on Streamlit for real-time analysis.
🎥 Smooth Animations – Integrated animations for an engaging experience.

• Source: Breast Cancer Wisconsin (Diagnostic) Data Set
• Features: [Mention key features, e.g., mean radius, texture, perimeter, area, smoothness]

• Programming Language: Python
• Libraries: Scikit-Learn, TensorFlow/PyTorch, Pandas, NumPy, Matplotlib, Seaborn, Streamlit
• Visualization: Plotly, SHAP, LIME
• Deployment: Streamlit Cloud

Here's the updated Model Performance section in your README.md file with your metrics formatted neatly:

PredictoCare demonstrates high accuracy (97%), effectively distinguishing between benign and malignant cases. Below are the detailed evaluation metrics:

This keeps your README professional, ATS-friendly, and well-structured. Let me know if you want any more tweaks! 🔥

1. Clone the repository:

   git clone https://github.com/yourusername/PredictoCare.git

2. Navigate to the project directory:

   cd PredictoCare

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run app.py