Major refactoring to adhere to Scikit-learn API

[msamsami]

• The codebase is refactored into a new structure.
• Both SEFR and LinearBoostClassifier classes are refactored so that they fully adhere to Scikit-learn's conventions and API. Now, they will be standard Scikit-learn estimators that can be used in Scikit-learn pipelines, grid search, etc.
• Unit tests are added (using pytest) to ensure the estimators adhere to Scikit-learn conventions.
• fit_intercept parameter is added to SEFR to make it similar to other linear estimators in Scikit-learn (e.g., LogisticRegression, LinearRegression, etc.).
• random_state parameter is removed from LinearBoostClassifier as it doesn't affect the result, since SEFR doesn't expose random_state. According to Scikit-learn documentation for this parameter in AdaBoostClassifier:

  it is only used when estimator exposes a random_state.

• Docstring is added to both SEFR and LinearBoostClassifier classes.
• uv is used for project and package management.
• ruff and isort are used for formatting and lining.
• A GitHub workflow (.github/workflows/ci.yml) is added for CI on PRs.