Machinelearningmodelmaker

This is a well-structured machine learning framework specifically designed for financial time series analysis and prediction. The project demonstrates solid engineering practices with a comprehensive ...

A machinelearningmodelmaker built with Python. This project provides Provides machinelearningmodelmaker functionality.

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🚀 Features

✅ Support for Linear Regression, Random Forest, and Neural Networks ✅ Advanced data preprocessing and feature engineering capabilities ✅ Model interpretability and feature importance analysis ✅ Model performance evaluation and validation

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📋 Table of Contents

• Installation
• Configuration
• Usage
• Examples
• API Reference
• Testing
• Contributing
• License

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🛠️ Installation

Prerequisites

• Python 3.8+
• [Other dependencies]

Setup

# Clone the repository
git clone https://github.com/[USERNAME]/machinelearningmodelmaker.git
cd machinelearningmodelmaker

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

API Keys Setup (if required)

⚠️ Important: This project requires API keys for certain features.

1. Copy the configuration template:

cp config.example.json config.json
# OR
cp .env.example .env

2. Get API keys from the following services:

3. Add your keys to the configuration file.

Never commit real API keys to version control.

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⚙️ Configuration

The application can be configured through:

• Environment variables (recommended for production)
• Configuration files (for development)
• Command line arguments (for one-off runs)

Environment Variables

export API_KEY=your_api_key_here
export DATABASE_URL=sqlite:///data/app.db
export LOG_LEVEL=INFO

Configuration File

{
  "api_key": "your_api_key_here",
  "database_url": "sqlite:///data/app.db",
  "log_level": "INFO"
}

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🚀 Usage

Basic Usage

# Run the main application
python main.py

# Run with specific configuration
python main.py --config config.json

# Show help
python main.py --help

Advanced Usage

# Run with custom settings
python main.py --api-key YOUR_KEY --database-url YOUR_DB_URL

# Run in development mode
python main.py --debug --log-level DEBUG

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📖 Examples

Example 1: Basic Setup

from machinelearningmodelmaker import MachinelearningmodelmakerClient

# Initialize the client
client = MachinelearningmodelmakerClient(api_key="your_key")
result = client.[MAIN_METHOD]()
print(result)

Example 2: Advanced Configuration

import machinelearningmodelmaker as pkg

# Configure with custom settings
config = {
    "api_key": "your_key",
    "timeout": 30,
    "retries": 3
}

client = pkg.create_client(config)
data = client.fetch_data()

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📚 API Reference

Core Classes

MachinelearningmodelmakerClient

Main client class for interacting with [SERVICE/API].

Parameters:

• api_key (str): Your API key
• timeout (int): Request timeout in seconds (default: 30)
• retries (int): Number of retry attempts (default: 3)

Methods:

• connect(): Establish connection
• fetch_data(query): Fetch data with query
• disconnect(): Close connection

Utility Functions

helper_function(param)

Helper function description.

Parameters:

• param (str): Parameter description

Returns:

• result: Return value description

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🧪 Testing

Run the test suite:

# Run all tests
pytest

# Run with coverage
pytest --cov=src

# Run specific test file
pytest tests/test_specific.py

# Run tests in verbose mode
pytest -v

Test Structure

tests/
├── unit/              # Unit tests
├── integration/       # Integration tests
├── fixtures/          # Test data and fixtures
└── conftest.py        # Test configuration

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Fork and clone
git clone https://github.com/[USERNAME]/machinelearningmodelmaker.git
cd machinelearningmodelmaker

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest

# Run linting
flake8 src/
black src/

Pull Request Process

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass
6. Update documentation if needed
7. Submit a pull request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• [Library/Framework 1] for [purpose]
• [Library/Framework 2] for [purpose]
• Community contributors

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [contact email]

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machinelearningmodelmaker - Advanced machinelearningmodelmaker solution