Write professional, uniform, and clean Machine-Learning code by adhering to these guidelines.
- Naming: Use clear naming formats. Variables should represent content details.
- Modularity: Design code split cleanly into functions and classes.
- Error Handlers: Always account for boundary checks.
- Split datasets into train/test sets, normalize features to identical scales, prevent overfitting, use matrix operations.