diff --git a/src/P2/preprocessing.py b/src/P2/preprocessing.py
index e7ec4e1..c68f8eb 100644
--- a/src/P2/preprocessing.py
+++ b/src/P2/preprocessing.py
@@ -22,30 +22,17 @@ def process_na(df, action):
 
 def filter_dataframe(df):
     relevant_columns = [
-        "HORA",
-        "DIASEMANA",
-        "COMUNIDAD_AUTONOMA",
-        "ISLA",
         "TOT_HERIDOS_LEVES",
         "TOT_HERIDOS_GRAVES",
         "TOT_VEHICULOS_IMPLICADOS",
         "TOT_MUERTOS",
-        "TIPO_VIA",
-        "LUMINOSIDAD",
-        "FACTORES_ATMOSFERICOS",
     ]
     filtered_df = df.filter(items=relevant_columns)
     return filtered_df
 
 
 def normalize_numerical_values(df):
-    cols = [
-        "TOT_HERIDOS_LEVES",
-        "TOT_HERIDOS_GRAVES",
-        "TOT_VEHICULOS_IMPLICADOS",
-        "TOT_MUERTOS",
-    ]
-    filtered_df = df.filter(items=cols)
+    filtered_df = filter_dataframe(df=df)
     normalized_data = normalize(X=filtered_df)
     normalized_df = DataFrame(data=normalized_data, columns=filtered_df.columns)
     df.update(normalized_df)
@@ -55,6 +42,5 @@ def normalize_numerical_values(df):
 def parse_data(source, action):
     df = read_csv(filepath_or_buffer=source, na_values="?")
     processed_df = process_na(df=df, action=action)
-    filtered_df = filter_dataframe(df=processed_df)
-    normalized_df = normalize_numerical_values(df=filtered_df)
+    normalized_df = normalize_numerical_values(df=processed_df)
     return normalized_df
diff --git a/src/P2/processing.py b/src/P2/processing.py
index 2364651..39db65e 100644
--- a/src/P2/processing.py
+++ b/src/P2/processing.py
@@ -7,7 +7,7 @@ from seaborn import heatmap, set_style, set_theme, pairplot
 from sklearn.metrics import silhouette_score, calinski_harabasz_score
 from sklearn.cluster import KMeans, Birch, AffinityPropagation, MeanShift, DBSCAN
 
-from preprocessing import parse_data
+from preprocessing import parse_data, filter_dataframe
 
 
 def choose_model(model):
@@ -160,9 +160,10 @@ def main():
     data = parse_data(source="data/accidentes_2013.csv", action=str(argv[1]))
     individual_result, complete_results = create_result_dataframes()
     case_data = construct_case(df=data, choice=case)
+    filtered_data = filter_dataframe(df=case_data)
     for model in models:
         model_results = predict_data(
-            data=case_data,
+            data=filtered_data,
             model=model,
             results=individual_result,
             sample=sample,