diff --git a/0_2_Dataset_construction.py b/0_2_Dataset_construction.py
index f88952b..a27b08b 100644
--- a/0_2_Dataset_construction.py
+++ b/0_2_Dataset_construction.py
@@ -6,6 +6,7 @@ import os
 import s3fs
 import re
 import warnings
+from datetime import date, timedelta, datetime
 
 # Create filesystem object
 S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
@@ -18,6 +19,47 @@ exec(open('0_KPI_functions.py').read())
 # Ignore warning
 warnings.filterwarnings('ignore')
 
+
+def display_covering_time(df, company, datecover):
+    """
+    This function draws the time coverage of each company
+    """
+    min_date = df['purchase_date'].min().strftime("%Y-%m-%d")
+    max_date = df['purchase_date'].max().strftime("%Y-%m-%d")
+    datecover[company] = [datetime.strptime(min_date, "%Y-%m-%d") + timedelta(days=x) for x in range((datetime.strptime(max_date, "%Y-%m-%d") - datetime.strptime(min_date, "%Y-%m-%d")).days)]
+    print(f'Couverture Company {company} : {min_date} - {max_date}')
+    return datecover
+
+
+def compute_time_intersection(datecover):
+    """
+    This function returns the time coverage for all companies
+    """
+    timestamps_sets = [set(timestamps) for timestamps in datecover.values()]
+    intersection = set.intersection(*timestamps_sets)
+    intersection_list = list(intersection)
+    formated_dates = [dt.strftime("%Y-%m-%d") for dt in intersection_list]
+    return sorted(formated_dates)
+
+
+def df_coverage_modelization(sport, coverage_train = 0.7):
+    """
+    This function returns start_date, end_of_features and final dates
+    that help to construct train and test datasets
+    """
+    datecover = {}
+    for company in sport:
+        df_products_purchased_reduced = display_databases(company, file_name = "products_purchased_reduced",
+                                                          datetime_col = ['purchase_date'])
+        datecover = display_covering_time(df_products_purchased_reduced, company, datecover)
+    #print(datecover.keys())
+    dt_coverage = compute_time_intersection(datecover)
+    start_date = dt_coverage[0]
+    end_of_features = dt_coverage[int(0.7 * len(dt_coverage))]
+    final_date = dt_coverage[-1]
+    return start_date, end_of_features, final_date
+    
+
 def dataset_construction(min_date, end_features_date, max_date, directory_path):
     
     # Import customerplus
@@ -97,32 +139,43 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path):
 
 ## Exportation
 
+companies = {'musee' : ['1', '2', '3', '4', '101'],
+            'sport': ['5', '6', '7', '8', '9'],
+            'musique' : ['10', '11', '12', '13', '14']}
+
+type_of_comp = input('Choisissez le type de compagnie : sport ? musique ? musee ?')
+list_of_comp = companies[type_of_comp] 
 # Dossier d'exportation
-BUCKET_OUT = "projet-bdc2324-team1/2_Output/Logistique Regression databases - First approach"
+BUCKET_OUT = f'projet-bdc2324-team1/Generalization/{type_of_comp}'
 
-# Dataset test
-dataset_test = dataset_construction(min_date = "2021-08-01", end_features_date = "2023-08-01", max_date = "2023-11-01", directory_path = "1")
+# Create test dataset and train dataset for sport companies
 
-# Exportation
-FILE_KEY_OUT_S3 = "dataset_test.csv"
-FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
+start_date, end_of_features, final_date = df_coverage_modelization(list_of_comp, coverage_train = 0.7)
 
-with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
-    dataset_test.to_csv(file_out, index = False)
+for company in list_of_comp:
+    dataset_test = dataset_construction(min_date = start_date, end_features_date = end_of_features,
+                                        max_date = final_date, directory_path = company)    
 
-print("Exportation dataset test : SUCCESS")
+    # Exportation
+    FILE_KEY_OUT_S3 = "dataset_test" + company +  ".csv"
+    FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
+    
+    with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
+        dataset_test.to_csv(file_out, index = False)
+    
+    print("Exportation dataset test : SUCCESS")
 
 # Dataset train
-dataset_train = dataset_construction(min_date = "2021-05-01", end_features_date = "2023-05-01", max_date = "2023-08-01", directory_path = "1")
-
-# Export
-FILE_KEY_OUT_S3 = "dataset_train.csv"
-FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
-
-with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
-    dataset_train.to_csv(file_out, index = False)
+    dataset_train = dataset_construction(min_date = start_date, end_features_date = end_of_features,
+                                        max_date = final_date, directory_path = company)
+    # Export
+    FILE_KEY_OUT_S3 = "dataset_train" + company + ".csv" 
+    FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
     
-print("Exportation dataset train : SUCCESS")
+    with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
+        dataset_train.to_csv(file_out, index = False)
+        
+    print("Exportation dataset train : SUCCESS")
 
 
 print("FIN DE LA GENERATION DES DATASETS : SUCCESS")