diff --git a/machine_learning/pipeline.py b/machine_learning/pipeline.py
index bada6df..cc10848 100644
--- a/machine_learning/pipeline.py
+++ b/machine_learning/pipeline.py
@@ -40,14 +40,17 @@ class SensorFeatures:
             self.df_proximity = proximity.get_proximity_data(
                 self.participants_usernames
             )
+            print("Got proximity data from the DB.")
             self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
             self.df_proximity = proximity.recode_proximity(self.df_proximity)
         if "communication" in self.data_types:
             self.df_calls = communication.get_call_data(self.participants_usernames)
             self.df_calls = helper.get_date_from_timestamp(self.df_calls)
+            print("Got calls data from the DB.")
 
             self.df_sms = communication.get_sms_data(self.participants_usernames)
             self.df_sms = helper.get_date_from_timestamp(self.df_sms)
+            print("Got sms data from the DB.")
 
     def get_sensor_data(self, data_type) -> pd.DataFrame:
         if data_type == "proximity":
@@ -65,6 +68,7 @@ class SensorFeatures:
             self.df_features_all = safe_outer_merge_on_index(
                 self.df_features_all, self.df_proximity_counts
             )
+            print("Calculated proximity features.")
 
         if "communication" in self.data_types:
             self.df_calls_sms = communication.calls_sms_features(
@@ -80,6 +84,7 @@ class SensorFeatures:
                 inplace=True,
                 downcast="infer",
             )
+            print("Calculated communication features.")
 
     def get_features(self, data_type, feature_names) -> pd.DataFrame:
         if data_type == "proximity":
@@ -122,7 +127,9 @@ class Labels:
 
     def set_labels(self):
         self.df_esm = esm.get_esm_data(self.participants_usernames)
+        print("Got ESM data from the DB.")
         self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
+        print("ESM data preprocessed.")
         if "PANAS" in self.questionnaires:
             self.df_esm_interest = self.df_esm_preprocessed[
                 (
@@ -135,6 +142,7 @@ class Labels:
                 )
             ]
         self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
+        print("ESM data cleaned.")
 
     def get_labels(self, questionnaire):
         if questionnaire == "PANAS":
@@ -161,6 +169,7 @@ class Labels:
             .rename(columns=QUESTIONNAIRE_IDS_RENAME)
             .set_index(["participant_id"] + self.grouping_variable)
         )
+        print("Labels aggregated.")
 
     def get_aggregated_labels(self):
         return self.df_esm_means
@@ -178,11 +187,13 @@ class ModelValidation:
         self.groups = self.y.index.get_level_values(group_variable)
 
         self.cv_name = cv_name
+        print("ModelValidation initialized.")
 
     def set_cv_method(self):
         if self.cv_name == "loso":
             self.cv = LeaveOneGroupOut()
             self.cv.get_n_splits(X=self.X, y=self.y, groups=self.groups)
+        print("Validation method set.")
 
     def cross_validate(self):
         if self.model is None: