Metadata-Version: 2.4
Name: novatium
Version: 1.1.5
Summary: Novatium — Cosmic-Grade Machine Learning with NovaAutoRegressor and NovaAutoClassifier for Tabular Data
Author: Praneeth Kumar Palepu
Author-email: praneeth.palepu@gmail.com
License: Apache-2.0
Project-URL: Homepage, https://github.com/<you>/novatium
Project-URL: Issues, https://github.com/<you>/novatium/issues
Keywords: machine-learning,regression,classification,fusion,ai
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24
Requires-Dist: scikit-learn>=1.2
Dynamic: license-file

# Novatium
**Cosmic-Grade Machine Learning** — supercharged Base+Delta models for regression and classification.

```
`pip install novatium`

## Quickstart

### NovaRegressor
```python
from novatium import NovaAutoRegressor
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor

X, y = load_diabetes(return_X_y=True)
model = NovaAutoRegressor()
model.fit(X, y)
pred = model.predict(X[:5])
print(pred)
```

### NovaClassifier
```python
from novatium import NovaAutoClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingRegressor

X, y = load_breast_cancer(return_X_y=True)
clf = NovaAutoClassifier()
clf.fit(X, y)
proba = clf.predict_proba(X[:5])
print(proba)
```

## Why Novatium?
- Drop-in **Base+Delta** pattern to uplift strong baselines.
- **Sklearn-compatible**: works with pipelines, grid search, etc.
- **Domain-agnostic**: tabular now; CV/NLP-ready APIs.

## License
Apache-2.0 — see `LICENSE`.
