Metadata-Version: 2.1
Name: TezzAutoML
Version: 0.1.5
Summary: Just another AutoML library, but better and faster.
Home-page: https://github.com/Japkeerat/TezzAutoML
Author: Japkeerat Singh
Author-email: japkeerat21@gmail.com
License: CC0-1.0
Keywords: automl machine-learning ml data-science
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas ==2.1.2
Requires-Dist: optuna ==3.4.0
Requires-Dist: xgboost ==2.0.1
Requires-Dist: scikit-learn ==1.3.2
Requires-Dist: mlflow ==2.8.0
Requires-Dist: lightgbm ==3.3.5

# TezzAutoML

Just focus on the data, TezzAutoML will do the rest.

You'll need to do all the preprocessing with the data the way you want.

## Installation

## Usage

```python
from tezzautoml.automl import AutoML

automl = AutoML(data=df, target='target', task='classification', n_trials=100, fast_mode=False)
```

When Fast Mode is False, it will use KFold for Regression tasks and StratifiedKFold for Classification
tasks.

When Fast Mode is True, it will use train_test_split for both the tasks.

NOTE: Will be writing complete documentation when 0.2 version is ready. Please wait for the version as this version is still in development with multiple release daily.

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
