# Neural Network Base Model
A flexible, from-scratch implementation of neural networks for regression tasks.
## Features
- Customizable network architecture
- Linear and non-linear regression capabilities
- Built-in data preprocessing and scaling
- Model persistence with pickle
- Comprehensive examples included
## Best Practices
- Data Scaling: Always use scaler_fit_transform on training data and scaler_transform on test data
- Architecture: Start with simple architectures and increase complexity as needed
- Learning Rate: Typical values are between 0.001 and 0.1
- Epochs: Monitor loss to determine appropriate training duration
- Validation: Always validate on held-out test data
## Limitation
- Currently supports only linear activations (no non-linearities)
- Basic optimization (no momentum, Adam, etc.)
- No built-in regularization
- CPU-only implementation
## Installation
```bash
pip install neuralnetwork-base
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"description": "# Neural Network Base Model\r\n\r\nA flexible, from-scratch implementation of neural networks for regression tasks.\r\n\r\n## Features\r\n\r\n- Customizable network architecture\r\n- Linear and non-linear regression capabilities\r\n- Built-in data preprocessing and scaling\r\n- Model persistence with pickle\r\n- Comprehensive examples included\r\n\r\n## Best Practices\r\n\r\n- Data Scaling: Always use scaler_fit_transform on training data and scaler_transform on test data\r\n- Architecture: Start with simple architectures and increase complexity as needed\r\n- Learning Rate: Typical values are between 0.001 and 0.1\r\n- Epochs: Monitor loss to determine appropriate training duration\r\n- Validation: Always validate on held-out test data\r\n\r\n## Limitation\r\n- Currently supports only linear activations (no non-linearities)\r\n- Basic optimization (no momentum, Adam, etc.)\r\n- No built-in regularization\r\n- CPU-only implementation\r\n\r\n\r\n## Installation\r\n\r\n```bash\r\npip install neuralnetwork-base\r\n",
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