semvec/pypi-packages huggingface dataset:
decision-tree-dmazilkin 0.1.3
This package contains the Decition Tree algrotihm implementation.
License: MIT
Requires Python: >=3.12

# Decision Tree algorithm from scratch
This repository contains Decision Tree implementation from scratch for classification problem.

## About
This Decision Tree implementation is based on **Leaf-wise algorithm**. 

- Supports **bins** hyperparameter for speeding up the algorithm.
- Supports **Feature Importance** calculation, which can help to understand the importance of features.
- Supports classification heuristics:
  - **Entropy** and **Information Gain**,
  - **Gini Impurity** and **Gini Gain**,
- Supports regression heuristics:
  - **MSE** and **MSE Gain**.

## Dependencies
To install all required dependencies, execute the following command:
```console
poetry install
```

## Usage
To start main script, execute the following command:
```console
poetry run python main.py [OPTIONS]
```

### Available options
- **-e, --example** (required) - type of example to run. Available examples: classification.
- **-c, --config** (required) - path to configuration file.

## Tests
Test cases are placed in *tests/* folder. To run tests use pytest module with the following command:
```console
poetry run pytest tests/
```
stash-mcp-server 0.1.5
Stash MCP Server for issue analysis and task management
License: MIT
Requires Python: >=3.12
Keywords: issue-analysis, mcp, stash, task-management

# Stash MCP Server

A Model Context Protocol (MCP) server for Stash issue analysis and task management.

## Features

- Issue analysis and similarity search
- Document similarity search
- Code file similarity search
- Project management integration
- Authentication via MCP tokens

## Installation

```bash
pip install stash-mcp-server
```

## Usage

1. Set up your Stash API credentials
2. Configure the MCP server with your Stash instance URL
3. Use with any MCP-compatible client
datagent 0.0.2
A placeholder package for future data agent project
License: MIT
Requires Python: >=3.8
Keywords: data, agent, placeholder, future-project

# DataAgent

A placeholder package for a future data agent project.

## Description

This is a dummy PyPI package created as a placeholder for a future data agent project. The actual functionality will be implemented in future versions.

## Installation

### From PyPI (when published)
```bash
pip install datagent
```

### From source
```bash
git clone https://github.com/yourusername/datagent.git
cd datagent
pip install -e .
```

## Development Installation

For development, install with development dependencies:

```bash
pip install -e .[dev]
```

## Usage

Once installed, you can use the command-line interface:

```bash
datagent --help
```

Or import the package in Python:

```python
import datagent

# Placeholder functionality
print("DataAgent package loaded successfully!")
```

## Project Structure

```
datagent/
├── setup.py              # Package setup and metadata
├── README.md             # This file
├── requirements.txt      # Runtime dependencies
├── requirements-dev.txt  # Development dependencies
├── pyproject.toml       # Modern Python project configuration
├── .gitignore           # Git ignore patterns
├── LICENSE              # MIT License
├── datagent/            # Main package directory
│   ├── __init__.py      # Package initialization
│   ├── core.py          # Core functionality (placeholder)
│   └── cli.py           # Command-line interface
└── tests/               # Test directory
    ├── __init__.py
    └── test_core.py     # Unit tests
```

## Development

### Running Tests
```bash
pytest
```

### Code Formatting
```bash
black datagent/ tests/
```

### Linting
```bash
flake8 datagent/ tests/
```

### Type Checking
```bash
mypy datagent/
```

## Contributing

This is currently a placeholder project. Contributions will be welcome once the actual project scope is defined.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Version History

- **0.0.2**: Initial placeholder release

## Roadmap

- [ ] Define project scope and requirements
- [ ] Implement core data processing functionality
- [ ] Add data source connectors
- [ ] Implement agent-based data analysis
- [ ] Add web interface
- [ ] Documentation and examples