diff --git a/models/hyperparam_tuning.py b/models/hyperparam_tuning.py
index e3e623850411efc6ac0ca1950b3caa0e932908ea..311a7187f8126fb71c868ff104dc102c23e2c157 100644
--- a/models/hyperparam_tuning.py
+++ b/models/hyperparam_tuning.py
@@ -152,9 +152,7 @@ if __name__ == "__main__":
     # Store each df as a sheet in an excel file
     sheets_dict = {}
     for i, group in enumerate(['pre', 'post']):
-        print(group, end = ' ')
         for j, method in enumerate(['', '', 'over_', 'under_']):
-            print(method, end = ' ')
             # Get dataset based on group and method
             X = data_dic['X_train_' + method + group]
             y = data_dic['y_train_' + method + group]
@@ -163,7 +161,7 @@ if __name__ == "__main__":
             # Save results: params and best score for each of the mdodels of this method and group
             hyperparam_df = pd.DataFrame(index=list(models.keys()), columns=['Parameters','Score'])
             for model_name, model in models.items():
-                print(model_name + "\n\n")
+                print(f"{group}-{method}-{model_name} \n\n")
                 # Find optimal hyperparams for curr model
                 params = hyperparameters[model_name]
                 search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=1, scoring='precision')
diff --git a/models/shap.py b/models/shap.py
new file mode 100644
index 0000000000000000000000000000000000000000..afca58d504b45cfe4f6b3f5a90bd8f062d1aed00
--- /dev/null
+++ b/models/shap.py
@@ -0,0 +1,10 @@
+# Libraries
+# --------------------------------------------------------------------------------------------------------
+import shap
+import numpy as np
+# --------------------------------------------------------------------------------------------------------
+
+# Load test data
+X_test_pre = np.load('../gen_train_data/data/output/pre/X_test_pre.npy', allow_pickle=True)
+
+print(list(X_test_pre.columns.values))
\ No newline at end of file