# 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.

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For any questions or suggestions, please open an issue in the repository or contact the maintainers.