Added tests for explain_model function. gssoc-extd

[NikhilJ2005]

Description: This pull request introduces a series of unit tests for the explain_model and calculate_metrics functions located in the utils.py file of the explainableai directory. These tests use the pytest framework to ensure the functions work correctly for both regression and classification models.

Added Tests:

test_explain_model_regression:

Tests the explain_model function using a LinearRegression model.
Checks if the returned explanation contains keys "feature_importance" and "model_type".
Verifies that the model type matches the expected linear regression model and that the number of feature importances corresponds to the number of input features.
test_explain_model_classification:

Tests the explain_model function using a LogisticRegression model.
Checks if the explanation contains keys "feature_importance" and "model_type".
Confirms the model type matches the logistic regression model and that the number of feature importances matches the input feature count.
test_calculate_metrics_regression:

Tests the calculate_metrics function with a LinearRegression model.
Verifies that the metrics returned include "mse" (Mean Squared Error) and "r2" (R-squared).
test_calculate_metrics_classification:

Tests the calculate_metrics function using a LogisticRegression model.
Ensures the returned metrics contain "accuracy" and "f1_score".

Used Pytest to acheive this.

[ombhojane]

Thank you for your contribution!