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README.md

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-license: mit
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+---
+license: mit
+pipeline_tag: tabular-classification
+---
+# Model Card for NZR – Breast Cancer Early Detection AI
+
+This model performs binary classification to detect **malignant** versus **benign** tumors using clinical diagnostic data from the Wisconsin Breast Cancer Diagnostic Dataset. It was built with a Random Forest classifier and designed for early-stage breast cancer screening support.
+
+This modelcard aims to be a base template for open-sourced medical AI, particularly for structured (tabular) data inputs.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Alan Jafari (TekTonic AI)
+- **Funded by [optional]:** Self-funded (independent research)
+- **Shared by:** TekTonic AI
+- **Model type:** Random Forest Classifier (100 estimators)
+- **Language(s) (NLP):** Not applicable (structured data)
+- **License:** MIT
+- **Finetuned from model:** N/A (trained from scratch)
+
+### Model Sources
+
+- **Repository:** [Add GitHub/Kaggle link]
+- **Paper [optional]:** N/A
+- **Demo [optional]:** [Add Streamlit or web demo link if available]
+
+## Uses
+
+### Direct Use
+
+This model can be directly used to classify breast cancer tumors (benign vs malignant) using clinical feature data. It accepts 10 numerical features as input (e.g., radius_mean, texture_mean, etc.) and returns a class prediction.
+
+### Downstream Use
+
+- Integration in medical dashboards
+- Research on breast cancer ML techniques
+- Educational applications in healthcare AI
+
+### Out-of-Scope Use
+
+- Not for use with image data (e.g., mammograms or MRI)
+- Not a substitute for clinical diagnosis
+- Not trained for male breast cancer or pediatric cases
+
+## Bias, Risks, and Limitations
+
+- Trained only on adult female data
+- Demographic diversity not captured
+- Possible overconfidence on limited data
+- No interpretability module (e.g., SHAP not embedded yet)
+
+### Recommendations
+
+- Use under expert medical supervision
+- Complementary to, not a replacement for, radiology or biopsy
+- Retrain with local data for domain adaptation
+
+## How to Get Started with the Model
+
+```python
+import joblib
+import numpy as np
+
+model = joblib.load("nzr_model.pkl")
+
+# Example input: 10 numerical diagnostic features
+x_input = np.array([[14.5, 20.0, 95.0, 660.0, 0.1, 0.15, 0.08, 0.05, 0.18, 0.06]])
+prediction = model.predict(x_input)