Metadata-Version: 2.4
Name: breezeml
Version: 0.1.1
Summary: Beginner-friendly wrapper around scikit-learn: train, evaluate, explain, and compare ML models in one line.
Author-email: Akash Anipakalu Giridhar <your.email@example.com>
License: MIT
Project-URL: Homepage, https://github.com/your-username/breezeml
Project-URL: Repository, https://github.com/your-username/breezeml
Project-URL: Documentation, https://github.com/your-username/breezeml#readme
Keywords: machine-learning,scikit-learn,beginners,data-science,education
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
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: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3
Requires-Dist: numpy>=1.21
Requires-Dist: scikit-learn>=1.1
Requires-Dist: joblib>=1.1
Requires-Dist: matplotlib>=3.5
Dynamic: license-file

# BreezeML v0.1.0

If you can load a CSV, you can train a model.

Quickstart:
```python
from breezeml import datasets, fit, predict, creator
print(creator())
df = datasets.iris()
model = fit(df, "species")
print(predict(model, df.drop(columns=["species"]))[:5])
```
