semvec/pypi-packages huggingface dataset:
scribo 1.0.0
A simple, efficient and content-first static site generator (SSG).
Requires Python: >=3.8
Keywords: documentation, jinja, markdown, scribo, static-site-generator

<div align="center">
    <a href="#"><img width="400" src="https://raw.githubusercontent.com/sujaudd1n/scribo/main/docs/assets/static/scribo.png" alt="Scribo logo"></a>
</div>

<p align="center">
A simple, efficient and content-first static site generator (SSG). 
</p>


<div align="center">

![License](https://img.shields.io/badge/License-MIT-green)
[![PyPI - Version](https://img.shields.io/pypi/v/scribo.svg)](https://pypi.org/project/scribo)
    
</div>

Write docs, blogs or anything you like. Read the [docs](https://scribo-docs.netlify.app).

## Features

- Extended markdown syntax support
- File-system based routing
- Customization
- Automatic table of contents generation
- Syntax highlighting of codeblocks
- Metadata


## Installation

```console
pip install scribo
```

## Quick Start

### Initialize

```bash
scribo --init sitename
```

Edit `index.md`.

### Build

```console
scribo --build sitename
```

`dist` dir contains the build of the project.


## Contributing

Please read [CONTRIBUTING.md](./CONTRIBUTING.md).

## License

`scribo` is distributed under the terms of the [MIT](./LICENSE) license.
Neuronn 0.1.0
A custum Python ML package
Requires Python: >=3.6


# Py-Neuronn

Neuronn is a collection of machine learning models implemented from scratch. This library provides simple and easy-to-use implementations of various machine learning algorithms, including linear regression, multiple regression, logistic regression, k-nearest neighbors (KNN), decision trees,random forests,XGB classifier and SVM.

## Installation

You can install Neuronn using pip:

```bash
pip install Neuronn
```

## Usage

Here an examples of how to use the models provided by Neuron:

### Linear Regression

```python
from Neuronn import LinearRegression
import numpy as np

# Example data
X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])

# Create and train the model
model = LinearRegression(learning_rate=0.01, epochs=1000)
model.fit(X, y)

# Make predictions
predictions = model.predict(X)
print("Predictions:", predictions)

```


## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Contact

For any questions or feedback, please contact [Karthikeyan](mailto:karthikkrishna0907@gmail.com).