--- a
+++ b/load.py
@@ -0,0 +1,33 @@
+import plot
+from binary_classification.svm import svm_pipeline
+from binary_classification.cart import cart_pipeline
+from binary_classification.boosted_tree import xgb_pipeline
+from binary_classification.random_forests import rf_pipeline
+from conditional_probability.logistic import lr_pipeline
+from conditional_probability.naive_bayes import nb_pipeline
+from preprocessing import preprocessing, get_train_and_test, standardize_features
+
+import warnings
+warnings.filterwarnings("ignore")
+
+
+def pipeline():
+    """
+    This function acts as a pipeline and calls the needed functions before
+    any actual machine learning occurs.
+    """
+    x_values, y_values = preprocessing()
+    x_train, x_test, y_train, y_test = get_train_and_test(x_values, y_values)
+    x_train, x_test = standardize_features(x_train, x_test)
+
+    print("                AUC              Accuracy")
+    print("SVM:  ", svm_pipeline(x_train, y_train, x_test, y_test))
+    print("CART: ", cart_pipeline(x_train, y_train, x_test, y_test))
+    print("XGB:  ", xgb_pipeline(x_train, y_train, x_test, y_test))
+    print("RF:   ", rf_pipeline(x_train, y_train, x_test, y_test))
+    print("LOG:  ", lr_pipeline(x_train, y_train, x_test, y_test))
+    print("NB:   ", nb_pipeline(x_train, y_train, x_test, y_test))
+    plot.show_data()
+
+
+pipeline()