diff --git a/src/preprocessing.py b/src/preprocessing.py
index 6849acf..32709a4 100644
--- a/src/preprocessing.py
+++ b/src/preprocessing.py
@@ -1,7 +1,7 @@
 from pandas import DataFrame, read_csv
 from sklearn.preprocessing import LabelEncoder
 from sklearn.model_selection import KFold
-from imblearn.combine import SMOTETomek
+from imblearn.combine import SMOTEENN
 
 
 def construct_dataframes(train, test):
@@ -20,10 +20,9 @@ def rename_columns(df_list) -> DataFrame:
 
 
 def drop_null_values(df_list):
+    drop_columns = ["tipo_marchas", "descuento", "ciudad"]
     for df in df_list:
-        df.drop(columns="tipo_marchas", inplace=True)
-        df.drop(columns="descuento", inplace=True)
-        df.drop(columns="ciudad", inplace=True)
+        df.drop(columns=drop_columns, inplace=True)
         df.dropna(inplace=True)
     return df_list
 
@@ -63,9 +62,9 @@ def split_data_target(df, dataset):
 
 
 def balance_training_data(df):
-    smote_tomek = SMOTETomek(random_state=42)
+    smote_enn = SMOTEENN(random_state=42)
     data, target = split_data_target(df=df, dataset="data")
-    balanced_data, balanced_target = smote_tomek.fit_resample(data, target)
+    balanced_data, balanced_target = smote_enn.fit_resample(data, target)
     balanced_data_df = DataFrame(
         balanced_data, columns=df.columns.difference(["precio_cat"])
     )