Develop a Supervised Learning Model Builder Component

[antunsz]

The objective of this issue is to design and implement a Supervised Learning Model Builder as part of our package's architecture. This component will play a crucial role in facilitating the streamlined development of supervised learning models by providing a robust framework for model training and deployment.

Requirements:

Abstract Interface: The first step involves creating an abstract interface for the Model Builder within the interfaces directory. This interface should define the essential methods and properties that any Model Builder implementation must adhere to, ensuring a consistent and predictable behavior across different implementations.

Concrete Implementation: Following the interface definition, we need to develop a concrete implementation of the Model Builder in the builders directory. This implementation will serve as the backbone of our supervised learning model creation process. It should be designed to accept input in the form of a pandas DataFrame along with the name of the target column. The output will be a fully trained model ready for predictions.

Functionality and Features:

The implementation must efficiently handle the given input data and target column to train a model using supervised learning techniques.
Ensure the implementation is flexible enough to support various supervised learning algorithms and model types.
Incorporate best practices for data preprocessing, model training, and validation to ensure the generation of high-quality models.
Provide comprehensive documentation and examples on how to use the Model Builder, including any necessary configurations and customizations.
Testing and Validation: Develop thorough unit tests and integration tests to validate the functionality of the Model Builder. Ensure the tests cover various scenarios and edge cases to guarantee the robustness of the implementation.

Documentation:
Comprehensive documentation is essential for this component. It should include an overview of the Model Builder, detailed instructions for usage, a description of the interface and implementation, and examples demonstrating the training of different types of supervised learning models.