feat(Core): added the first classification model & the feature necessary (#4)

* feat(Core): added the first classification model & the feature necessary

* feat(Models): added basic transformers model

utils/evaluate.py

@@ -1,9 +1,13 @@
 from math import sqrt
-from sklearn.metrics import mean_squared_error, mean_absolute_error
-
-def print_metrics(y_true, y_pred):
+from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score
+from sklearn.metrics import confusion_matrix
+def print_regression_metrics(y_true, y_pred):
     rmse = sqrt(mean_squared_error(y_true, y_pred))
     print("RMSE: %.2f" % rmse)
 
     mae = mean_absolute_error(y_true, y_pred)
-    print("MAE: %.2f" % mae)
+    print("MAE: %.2f" % mae)
+
+def print_classification_metrics(y_true, y_pred):
+    print("Accuracy: %.2f" % accuracy_score(y_true, y_pred))
+    print("Confusion Matrix: \n", confusion_matrix(y_true, y_pred))