Metadata-Version: 2.4
Name: mlreserving
Version: 0.3.0
Summary: Model-agnostic Probabilistic Machine Learning Reserving
Home-page: https://github.com/Techtonique/mlreserving
Author: T. Moudiki
Author-email: "T. Moudiki" <thierry.moudiki@gmail.com>
Project-URL: Repository, https://github.com/Techtonique/mlreserving
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: nnetsauce>=0.17.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: joblib>=1.1.0
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# MLReserving

A machine learning-based reserving model for (longitudinal data) insurance claims.

## Installation

```bash
pip install mlreserving
```

## Usage

```python
from mlreserving import MLReserving
import pandas as pd

# Create your triangle data
# Load the dataset
url = "https://raw.githubusercontent.com/Techtonique/datasets/refs/heads/main/tabular/triangle/raa.csv"
data = pd.read_csv(url)

# Initialize and fit the model
model = MLReserving(model=mdl,
                    level=80,  # 80% confidence level
                    random_state=42)
model.fit(data)

# Make predictions
result = model.predict()

# Get IBNR, latest, and ultimate values
ibnr = model.get_ibnr()
latest = model.get_latest()
ultimate = model.get_ultimate()
```

## Features

- Machine learning based reserving model
- Support for prediction intervals
- Flexible model selection
- Handles both continuous and categorical features

## License

BSD Clause Clear License
