Build a Plugin-Based Architecture for ML Algorithm Extensions

Description:
Refactor the repository to follow a plugin-based architecture where models, preprocessors, and evaluators can be plugged in dynamically. This makes the project scalable and flexible for advanced experimentation.

Expected Tasks:

• Create a plugin_registry.py to manage discovery and registration of components.
• Define base classes/interfaces like BaseModel, BasePreprocessor, and BaseEvaluator.
• Add a decorator or registration function to auto-register components into a central registry.
• Refactor at least three models (e.g., KNN, Perceptron, Naive Bayes) to follow the plugin system.
• Provide a dynamic way to load and instantiate registered models by name or type.
• Update training scripts to use models through the registry instead of hardcoded imports.
• Write a guide in docs/PLUGIN_GUIDE.md explaining how to create and register a new plugin.
• Add error handling and graceful failure if a plugin is missing or incompatible.

Stretch Tasks:

• Allow loading plugins from external .py files in a plugins/ folder.
• Add YAML or JSON-based pipeline configuration for training workflows.
• Create a CLI command to list all available registered plugins.