diff --git a/exploration/ex_ml_pipeline.py b/exploration/ex_ml_pipeline.py
index 681cd78..88b09a8 100644
--- a/exploration/ex_ml_pipeline.py
+++ b/exploration/ex_ml_pipeline.py
@@ -218,14 +218,9 @@ with open("../machine_learning/config/minimal_labels.yaml", "r") as file:
 # %%
 labels = machine_learning.labels.Labels(**labels_params)
 labels.participants_usernames = ptcp_2
+labels.set_participants_label("nokia_0000003")
 labels.questionnaires
 
-# %%
-all_features = sensor_features.get_features("all", "all")
-
-# %%
-all_features.isna().any().any()
-
 # %%
 labels.set_labels()
 
diff --git a/machine_learning/features_sensor.py b/machine_learning/features_sensor.py
index f25e37e..50850f4 100644
--- a/machine_learning/features_sensor.py
+++ b/machine_learning/features_sensor.py
@@ -125,7 +125,7 @@ class SensorFeatures:
             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:
+    def get_sensor_data(self, data_type: str) -> pd.DataFrame:
         if data_type == "proximity":
             return self.df_proximity
         elif data_type == "communication":
diff --git a/machine_learning/labels.py b/machine_learning/labels.py
index 9c4b968..f685e6b 100644
--- a/machine_learning/labels.py
+++ b/machine_learning/labels.py
@@ -1,11 +1,21 @@
 import datetime
+import warnings
+from pathlib import Path
 from typing import Collection
 
 import pandas as pd
+from pyprojroot import here
 
 import participants.query_db
 from features import esm
 from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
+from machine_learning.helper import to_csv_with_settings
+
+WARNING_PARTICIPANTS_LABEL = (
+    "Before aggregating labels, please set participants label using self.set_participants_label() "
+    "to be used as a filename prefix when exporting data. "
+    "The filename will be of the form: %participants_label_%grouping_variable_%data_type.csv"
+)
 
 
 class Labels:
@@ -14,12 +24,13 @@ class Labels:
         grouping_variable: str,
         labels: dict,
         participants_usernames: Collection = None,
-    ):
+    ) -> None:
         self.grouping_variable_name = grouping_variable
         self.grouping_variable = [grouping_variable]
 
         self.questionnaires = labels.keys()
 
+        self.participants_label: str = ""
         if participants_usernames is None:
             participants_usernames = participants.query_db.get_usernames(
                 collection_start=datetime.date.fromisoformat("2020-08-01")
@@ -32,9 +43,13 @@ class Labels:
         self.df_esm_clean = pd.DataFrame()
 
         self.df_esm_means = pd.DataFrame()
+
+        self.folder: Path = Path()
+        self.filename_prefix = ""
+        self.construct_export_path()
         print("Labels initialized.")
 
-    def set_labels(self):
+    def set_labels(self) -> None:
         print("Querying database ...")
         self.df_esm = esm.get_esm_data(self.participants_usernames)
         print("Got ESM data from the DB.")
@@ -54,13 +69,13 @@ class Labels:
         self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
         print("ESM data cleaned.")
 
-    def get_labels(self, questionnaire):
+    def get_labels(self, questionnaire: str) -> pd.DataFrame:
         if questionnaire == "PANAS":
             return self.df_esm_clean
         else:
             raise KeyError("This questionnaire has not been implemented as a label.")
 
-    def aggregate_labels(self):
+    def aggregate_labels(self) -> None:
         print("Aggregating labels ...")
         self.df_esm_means = (
             self.df_esm_clean.groupby(
@@ -81,6 +96,24 @@ class Labels:
             .set_index(["participant_id"] + self.grouping_variable)
         )
         print("Labels aggregated.")
+        to_csv_with_settings(
+            self.df_esm_means,
+            self.folder,
+            self.filename_prefix,
+            data_type="_".join(self.questionnaires),
+        )
 
-    def get_aggregated_labels(self):
+    def get_aggregated_labels(self) -> pd.DataFrame:
         return self.df_esm_means
+
+    def construct_export_path(self) -> None:
+        if not self.participants_label:
+            warnings.warn(WARNING_PARTICIPANTS_LABEL, UserWarning)
+        self.folder = here("machine_learning/intermediate_results/labels", warn=True)
+        self.filename_prefix = (
+            self.participants_label + "_" + self.grouping_variable_name
+        )
+
+    def set_participants_label(self, label: str) -> None:
+        self.participants_label = label
+        self.construct_export_path()