diff --git a/utils_ml.py b/utils_ml.py
index 767f7db..e8598d3 100644
--- a/utils_ml.py
+++ b/utils_ml.py
@@ -28,7 +28,7 @@ import warnings
 
 
 def load_train_test(type_of_activity):
-    BUCKET = f"projet-bdc2324-team1/Generalization/{type_of_activity}"
+    BUCKET = f"projet-bdc2324-team1/Generalization_v2/{type_of_activity}"
     File_path_train = BUCKET + "/Train_set.csv"
     File_path_test = BUCKET + "/Test_set.csv"
     
@@ -83,9 +83,7 @@ def compute_recall_companies(dataset_test, y_pred, type_of_activity, model):
 
 
 def features_target_split(dataset_train, dataset_test):
-    features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 
-            'time_between_purchase', 'nb_tickets_internet',  'is_email_true', 'opt_in', #'is_partner',
-            'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened', 'country_fr']
+    features_l = [']
     X_train = dataset_train[features_l]
     y_train = dataset_train[['y_has_purchased']]
 
@@ -94,30 +92,29 @@ def features_target_split(dataset_train, dataset_test):
     return X_train, X_test, y_train, y_test
 
 
-def preprocess(type_of_model):
+def preprocess(type_of_model,  type_of_activity):
+
+    numeric_features = ['nb_campaigns', 'taux_ouverture_mail', 'prop_purchases_internet', 'nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',
+                           'purchases_10_2021','purchases_10_2022', 'purchases_11_2021', 'purchases_12_2021','purchases_1_2022', 'purchases_2_2022', 'purchases_3_2022',
+                            'purchases_4_2022', 'purchases_5_2021', 'purchases_5_2022', 'purchases_6_2021', 'purchases_6_2022', 'purchases_7_2021', 'purchases_7_2022', 'purchases_8_2021',
+                            'purchases_8_2022','purchases_9_2021', 'purchases_9_2022', 'purchase_date_min', 'purchase_date_max', 'nb_targets']
+
+    binary_features = ['gender_female', 'gender_male', 'country_fr', 'achat_internet', 'categorie_age_0_10', 'categorie_age_10_20', 'categorie_age_20_30','categorie_age_30_40',
+                           'categorie_age_40_50', 'categorie_age_50_60', 'categorie_age_60_70', 'categorie_age_70_80', 'categorie_age_plus_80','categorie_age_inconnue',
+                           'country_fr', 'is_profession_known', 'is_zipcode_known', 'opt_in']
+    
     if type_of_model=='premium':
-        numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 
-                    'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet',
-                    'nb_campaigns', 'nb_campaigns_opened']
+        if type_of_activity=='musique':
+            binary_features.extend(['target_optin', 'target_newsletter'])
+        elif type_of_activity=='sport':
+            binary_features.extend(['target_jeune', 'target_entreprise', 'target_abonne'])
+        else:
+             binary_features.extend([ 'target_scolaire', 'target_entreprise', 'target_famille', 'target_newsletter'])
+       
 
-        binary_features = ['gender_female', 'gender_male', 'gender_other', 'country_fr']
-        categorical_features = ['opt_in']
-
-    else: 
-        numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 
-                    'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet',
-                    'nb_campaigns', 'nb_campaigns_opened']
-
-        binary_features = ['gender_female', 'gender_male', 'gender_other', 'country_fr']
-        categorical_features = ['opt_in']
-        
     numeric_transformer = Pipeline(steps=[
         ("scaler", StandardScaler()) 
     ])
-    categorical_features = ['opt_in']  
-    categorical_transformer = Pipeline(steps=[
-        ("onehot", OneHotEncoder(handle_unknown='ignore', sparse_output=False))
-    ])
 
     binary_transformer = Pipeline(steps=[
         ("imputer", SimpleImputer(strategy="most_frequent")),  
@@ -125,7 +122,6 @@ def preprocess(type_of_model):
     preproc = ColumnTransformer(
         transformers=[
             ("num", numeric_transformer, numeric_features),
-            ("cat", categorical_transformer, categorical_features),
             ("bin", binary_transformer, binary_features)
         ]
     )