Metadata-Version: 2.4
Name: mlxtras
Version: 0.0.1
Summary: mlxtras is a machine learning utility library for feature engineering, model validation, reporting, and MLflow logging.
Author-email: Ahmed Alghali <Ahmed@OffSecHQ.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/a7med7x7/flint/
Keywords: machine learning,feature engineering,time series,mlflow,data validation,reporting
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: lightgbm
Requires-Dist: tqdm
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: mlflow
Dynamic: license-file

# mlxtras

mlxtras is a machine learning utility library for feature engineering, model validation, reporting, and MLflow experiment tracking. It provides reusable helpers for pandas, scikit-learn, LightGBM, and time-series workflows.

### How it started
This library began as a personal project to learn Python packaging and create reusable tools for common machine learning workflows. It is designed to help you iterate quickly on baselines, validate models, and standardize data science tasks.

## Features

- Feature selection and model validation utilities
- Time series feature engineering and plotting helpers
- Ensemble and reporting utilities
- MLflow experiment logging helpers
- Data validation and preprocessing tools

## Installation

You can install mlxtras using pip:

```bash
pip install mlxtras
```

## Acknowledgments

- [ChatGPT](chatgpt.com) Handled documentations of our `.md` files most of the times.
- Inspired by work at [AirQo](https://airqo.net)
- Built on top of scikit-learn, pandas, and NumPy
