diff --git a/0_2_Dataset_construction.py b/0_2_Dataset_construction.py
index 1c410f5..ae96532 100644
--- a/0_2_Dataset_construction.py
+++ b/0_2_Dataset_construction.py
@@ -42,7 +42,7 @@ def compute_time_intersection(datecover):
     return sorted(formated_dates)
 
 
-def df_coverage_modelization(sport, coverage_train = 0.7):
+def df_coverage_modelization(sport, coverage_features = 0.7):
     """
     This function returns start_date, end_of_features and final dates
     that help to construct train and test datasets
@@ -81,7 +81,7 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path):
     df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')
     
     #Filtre de la base df_products_purchased_reduced
-    df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= end_features_date) & (df_products_purchased_reduced['purchase_date'] >= min_date)]
+    df_products_purchased_features = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= end_features_date) & (df_products_purchased_reduced['purchase_date'] >= min_date)]
 
     print("Data filtering : SUCCESS")
     
@@ -91,7 +91,7 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path):
     df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) 
 
     # KPI sur le comportement d'achat
-    df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)
+    df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_features)
 
     # KPI sur les données socio-démographiques
     df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0)
@@ -146,7 +146,7 @@ BUCKET_OUT = f'projet-bdc2324-team1/Generalization/{type_of_comp}'
 
 # Create test dataset and train dataset for sport companies
 
-# start_date, end_of_features, final_date = df_coverage_modelization(list_of_comp, coverage_train = 0.7)
+#start_date, end_of_features, final_date = df_coverage_modelization(list_of_comp, coverage_features = 0.7)
 start_date = "2021-05-01"
 end_of_features = "2022-11-01"
 final_date = "2023-11-01"