Metadata-Version: 2.4
Name: avish_lib
Version: 0.1.0
Summary: Lightweight custom ML algorithms library (avish_lib)
Home-page: https://github.com/yourusername/avish_lib
Author: Avish
Project-URL: Homepage, https://github.com/yourusername/avish_lib
Project-URL: Repository, https://github.com/yourusername/avish_lib
Project-URL: Documentation, https://github.com/yourusername/avish_lib#readme
Keywords: machine learning,regression,pca,numpy,ml
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
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
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# avish_lib

avish_lib is a small collection of custom machine-learning algorithms implemented with minimal dependencies (NumPy).

Install for local development:

```bash
python -m pip install -e .
```

Quick usage:

```python
from avish_lib import LinearRegression, RidgeRegression, KNNRegressor, PCA

# Simple linear fit
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
model = LinearRegression()
model.fit(X, y)
print("coef:", model.coef_, "intercept:", model.intercept_)

# PCA example
p = PCA(n_components=1)
X_reduced = p.fit_transform([[1,2],[3,4],[5,6]])
print(X_reduced)
```

Project layout:

```
your_project_name/
├── src/
│   └── avish_lib/
│       ├── __init__.py
│       └── linear_models.py
├── tests/
├── pyproject.toml
├── README.md
└── LICENSE
```
