Metadata-Version: 2.4
Name: fastbinning
Version: 0.0.1
Requires-Dist: numpy>=1.21.6
License-File: LICENSE
Summary: A high-performance binning library specifically designed for Credit Risk Modeling and Scorecard Development.
Author-email: RektPunk <rektpunk@gmail.com>
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: repository, https://github.com/RektPunk/rektrag

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A high-performance binning library specifically designed for **Credit Risk Modeling** and **Scorecard Development**. 

In financial risk modeling, **Weight of Evidence (WoE)** and **Information Value (IV)** are gold standards for feature engineering. `fastbinning` ensures mathematical rigor with extreme speed.

# Why fastbinning for Credit Scoring?

* **Monotonicity Guaranteed**: In credit scoring, features like 'Utilization Rate' or 'Age' must have a monotonic relationship with default risk to be explainable and compliant.
* **Built for Big Data**: While traditional tools struggle with millions of rows, `fastbinning` handles 10M+ records in milliseconds.
* **Robustness**: Prevents overfitting by enforcing minimum sample constraints (`min_bin_pct`), ensuring each bin is statistically significant.

# Installation
Install using pip:
```bash
pip install fastbinning
```

# Example
Please refer to the [**Examples**](https://github.com/RektPunk/fastbinning/tree/main/examples) provided for further clarification.

