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+# Patient Risk Profiling using Machine Learning
+
+## Overview
+This repository contains a Jupyter Notebook that implements three different machine learning models to create patient risk profiles using healthcare and clinical datasets. This is only a sample model. The models included are:
+
+1. **Logistic Regression** - A simple baseline model for binary classification.
+2. **Random Forest** - An ensemble-based model for improved performance.
+3. **XGBoost** - A gradient boosting model optimized for structured data.
+
+## Dataset
+The script expects a healthcare dataset in CSV format. The dataset should include a `Risk` column as the target variable (0: Low Risk, 1: High Risk) and a `PatientID` column, which will be dropped during processing. All other numerical features will be used for training the models.
+
+## Prerequisites
+Ensure you have the following dependencies installed before running the notebook:
+
+```bash
+pip install pandas numpy scikit-learn xgboost
+```
+
+## Usage
+1. Clone the repository:
+
+```bash
+git clone https://github.com/rkumar1010/patient-risk-profiling.git
+cd patient-risk-profiling
+```
+
+2. Place your dataset in the project directory and update the `healthcare_data.csv` filename in the notebook if necessary.
+
+3. Run the Jupyter Notebook:
+
+```bash
+jupyter notebook patient_risk_models.ipynb
+```
+
+4. The script will:
+   - Load and preprocess the dataset.
+   - Train and evaluate three different machine learning models.
+   - Print performance metrics including accuracy and classification reports.
+
+## Model Performance
+The notebook compares model performance based on accuracy and classification metrics. The best-performing model can be selected for further deployment.
+
+## Contributing
+Feel free to fork this repository and submit pull requests for improvements, additional models, or dataset enhancements.
+
+## License
+This project is licensed under the MIT License.
+
+---
+
+For any questions or suggestions, please open an issue in the repository or contact the maintainers.
+