diff --git a/0_2_Dataset_construction.py b/0_2_Dataset_construction.py
index 917dee9..1c410f5 100644
--- a/0_2_Dataset_construction.py
+++ b/0_2_Dataset_construction.py
@@ -66,6 +66,10 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path):
     df_customerplus_clean_0 = display_databases(directory_path, file_name = "customerplus_cleaned")
     df_campaigns_information = display_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])
     df_products_purchased_reduced = display_databases(directory_path, file_name = "products_purchased_reduced", datetime_col = ['purchase_date'])
+
+    # if directory_path == "101":
+    #     df_products_purchased_reduced_1 = display_databases(directory_path, file_name = "products_purchased_reduced_1", datetime_col = ['purchase_date'])
+    #     df_products_purchased_reduced = pd.concat([df_products_purchased_reduced, df_products_purchased_reduced_1])
     
     # Filtre de cohérence pour la mise en pratique de notre méthode
     max_date =  pd.to_datetime(max_date, utc = True, format = 'ISO8601') 
@@ -131,7 +135,7 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path):
 
 ## Exportation
 
-companies = {'musee' : ['1', '2', '3', '4', '101'],
+companies = {'musee' : ['1', '2', '3', '4'], # , '101'
             'sport': ['5', '6', '7', '8', '9'],
             'musique' : ['10', '11', '12', '13', '14']}
 
@@ -142,12 +146,31 @@ 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_train = 0.7)
+start_date = "2021-05-01"
+end_of_features = "2022-11-01"
+final_date = "2023-11-01"
+
+anonymous_customer = {'1' : 1_1, '2' : 2_12184, '3' : 3_1, '4' : 4_2, '101' : 101_1,
+                      '5' : 5_191835, '6' : 6_591412, '7' : 7_49632, '8' : 8_1942, '9' : 9_19683}
 
 for company in list_of_comp:
-    dataset_test = dataset_construction(min_date = start_date, end_features_date = end_of_features,
+    dataset = dataset_construction(min_date = start_date, end_features_date = end_of_features,
                                         max_date = final_date, directory_path = company)    
+    
+    # On retire le client anonyme
+    dataset = dataset[dataset['customer_id'] != anonymous_customer[company]]
 
+    #train test set
+    np.random.seed(42)
+    
+    # Dataset Test
+    split_ratio = 0.7
+    split_index = int(len(dataset) * split_ratio)
+    dataset = dataset.sample(frac=1).reset_index(drop=True)
+    dataset_train = dataset.iloc[:split_index]
+    dataset_test = dataset.iloc[split_index:]
+    
     # Exportation
     FILE_KEY_OUT_S3 = "dataset_test" + company +  ".csv"
     FILE_PATH_OUT_S3 = BUCKET_OUT + "/Test_set/" + FILE_KEY_OUT_S3
@@ -157,12 +180,11 @@ for company in list_of_comp:
     
     print("Exportation dataset test : SUCCESS")
 
-# Dataset train
-    dataset_train = dataset_construction(min_date = start_date, end_features_date = end_of_features,
-                                        max_date = final_date, directory_path = company)
+    # Dataset train
+
     # Export
     FILE_KEY_OUT_S3 = "dataset_train" + company + ".csv" 
-    FILE_PATH_OUT_S3 = BUCKET_OUT + "/Train_test/" + FILE_KEY_OUT_S3
+    FILE_PATH_OUT_S3 = BUCKET_OUT + "/Train_set/" + FILE_KEY_OUT_S3
     
     with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
         dataset_train.to_csv(file_out, index = False)