Documentation Best Practices

README Template for ML Projects

# Project Title

Brief description (1-2 sentences)

## Problem Statement
What problem does this solve? Why does it matter?

## Dataset
- Source: Kaggle, UCI ML Repository, etc.
- Size: 10K samples, 50 features
- Target variable: Classification/Regression

## Architecture
- Model: XGBoost, LSTM, Transformer, etc.
- Framework: PyTorch, TensorFlow, scikit-learn
- Deployment: FastAPI + Docker

## Results
| Metric | Score |
|--------|-------|
| Accuracy | 92.5% |
| Precision | 90.1% |
| Recall | 94.3% |
| F1 Score | 92.2% |

## Setup
```bash
pip install -r requirements.txt
python train.py
python app.py

Demo

[Live Demo Link] or [Screenshot/GIF]

Future Improvements

• Add real-time monitoring
• Implement A/B testing
• Scale to handle 1M+ requests/day

## Code Comments Best Practices

**Good Comments:**
```python
def preprocess_text(text: str) -> List[str]:
    """
    Clean and tokenize input text for NLP model.
    
    Args:
        text: Raw input string
        
    Returns:
        List of cleaned tokens
    """
    # Remove URLs and mentions
    text = re.sub(r'http\S+|@\S+', '', text)
    
    # Tokenize and lowercase
    tokens = text.lower().split()
    
    return tokens

Bad Comments:

# This function processes text
def process(t):
    t = t.lower()  # make lowercase
    return t.split()  # split into words

:::