diff --git a/Snakefile b/Snakefile
index 83882ac4..7c5749d7 100644
--- a/Snakefile
+++ b/Snakefile
@@ -551,9 +551,6 @@ if config["PARAMS_FOR_ANALYSIS"]["TARGET"]["COMPUTE"]:
     files_to_compute.extend(expand("data/processed/models/population_model/z_input.csv"))
 
 #files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv", pid=config["PIDS"], cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"]))
-# Put the for loop over STANDARDIZATION providers if all are COMPUTE == True 
-# then merge all that are set to True in z_all_sensors for all and each participant
-# See the logic behind: in each sensor the "data/processed/features/all_participants/all_sensor_features.csv" is listed  
 
 rule all:
     input:
diff --git a/rules/features.smk b/rules/features.smk
index 24f700eb..5331d827 100644
--- a/rules/features.smk
+++ b/rules/features.smk
@@ -1135,7 +1135,7 @@ rule clean_standardized_sensor_features_for_individual_participants:
         script_extension = "{script_extension}",
         sensor_key = "all_cleaning_individual" 
     output:
-        "data/processed/features/{pid}/z_all_sensor_features_cleaned_{provider_key}_{script_extension}.csv" # bo predstavljalo probleme za naprej (kako iskati datoteke + standardizacija itd.)
+        "data/processed/features/{pid}/z_all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
     script:
         "../src/features/entry.{params.script_extension}"
 
diff --git a/rules/models.smk b/rules/models.smk
index 34a7a937..3aade69a 100644
--- a/rules/models.smk
+++ b/rules/models.smk
@@ -30,7 +30,7 @@ rule baseline_features:
 
 rule select_target:
     input:
-        cleaned_sensor_features = "data/processed/features/{pid}/z_all_sensor_features_cleaned_rapids_R.csv"
+        cleaned_sensor_features = "data/processed/features/{pid}/z_all_sensor_features_cleaned_straw_py.csv"
     params:
         target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
     output:
@@ -40,7 +40,7 @@ rule select_target:
 
 rule merge_features_and_targets_for_population_model:
     input:
-        cleaned_sensor_features = "data/processed/features/all_participants/z_all_sensor_features_cleaned_rapids_R.csv",
+        cleaned_sensor_features = "data/processed/features/all_participants/z_all_sensor_features_cleaned_straw_py.csv",
         demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
     params:
         target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
@@ -51,7 +51,7 @@ rule merge_features_and_targets_for_population_model:
 
 # rule select_target:
 #     input:
-#         cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned_rapids_R.csv"
+#         cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned_straw_py.csv"
 #     params:
 #         target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
 #     output:
@@ -61,7 +61,7 @@ rule merge_features_and_targets_for_population_model:
 
 # rule merge_features_and_targets_for_population_model:
 #     input:
-#         cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_rapids_R.csv",
+#         cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_straw_py.csv",
 #         demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
 #     params:
 #         target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
diff --git a/src/models/helper.py b/src/models/helper.py
index 77f3260e..61f9f666 100644
--- a/src/models/helper.py
+++ b/src/models/helper.py
@@ -10,7 +10,7 @@ def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
     if all(~target_variable_index):
         raise ValueError("The requested target (", target_variable_name,
                          ")cannot be found in the dataset.",
-                         "Please check the names of phone_esm_ columns in all_sensor_features_cleaned_rapids_R.csv")
+                         "Please check the names of phone_esm_ columns in z_all_sensor_features_cleaned_straw_py.csv")
     sensor_features_plus_target = df_input.drop(esm_names, axis=1)
     sensor_features_plus_target["target"] = df_input[esm_names[target_variable_index]]
     # We will only keep one column related to phone_esm and that will be our target variable.