Metadata-Version: 2.1
Name: lazylearn
Version: 0.0.2
Summary: lazy-learn is a high-level Python interface for automated machine learning (AutoML) for the lazy data scientist. While there are many AutoML libraries available each typically solves a niche area of the overall ML pipeline without providing a covering and approachable end-to-end system. lazy-learn aims at providing the most approachable and fastest access to building baseline models.
Project-URL: Homepage, https://github.com/frederikhoengaard/lazy-learn
Author-email: "Frederik P. Høngaard" <mail@frederikhoengaard.com>
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown


<img width="500" src="doc/logo/transparent_small.png">

**lazy-learn** is a high-level Python interface for automated machine learning (AutoML). While there are many AutoML libraries available each typically solves a niche area of the overall ML pipeline without providing a covering and approachable end-to-end system.

The aim of lazy-learn is exactly that. Given a dataset, easy-learn will analyse types and distributions of attributes, preprocess, feature-engineer and ultimately train models to be used for further evaluation or inference. 

## Usage

Using lazy-learn revolves around the `LazyLearner` class. You can think of it as a kind of project, and it is the wrapper for any experiment within lazy-learn.

## Installation

### Dependencies

lazy-learn requires:

- pandas
- scikit-learn

### User Installation 
```
pip install lazy-learn
```

## Help and Support
### Documentation

### Citation