Metadata-Version: 2.1
Name: quickerml
Version: 0.1.0
Summary: Machine learning toolkit to find the best starting model for your project
Home-page: https://quickerml.readthedocs.io/
Author: Rafael Moret
Author-email: rafaelmoretgalan@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: catboost
Requires-Dist: lightgbm
Requires-Dist: xgboost
Requires-Dist: scikit-learn

# quickerml
>Machine learning module intended to be your first shot swiss knife.

If you work on Data Science or Artificial Intelligence projects you probably have had to repeat over and over again the same crucial first steps on your project in order to find what's the best model option for your data.  
There is the idea that gave birth to `quickerml`: Automatize those repetitive initial tests to find the best starting model for your project.

### Installation
```
pip install quickerml
```

### Get started
```
from quickerml import Finder

finder = Finder(
    problem_type='regression', 
    models=[
        LinearSVR(), 
        RandomForestRegressor(), 
        XGBRegressor(), 
        LGBMRegressor()
    ]
)

best = finder.find(X, y)
```

<p align="center">
  <img src="images/terminal_exec.png" alt="Terminal Execution">
</p>


