diff --git a/src/P2/preprocessing.py b/src/P2/preprocessing.py
index 6dbf838..c780168 100644
--- a/src/P2/preprocessing.py
+++ b/src/P2/preprocessing.py
@@ -38,6 +38,17 @@ def filter_dataframe(df) -> DataFrame:
     return filtered_df
 
 
+def choose_numerical_values(df):
+    cols = [
+        "TOT_HERIDOS_LEVES",
+        "TOT_HERIDOS_GRAVES",
+        "TOT_VEHICULOS_IMPLICADOS",
+        "TOT_MUERTOS",
+    ]
+    filtered_df = df.filter(items=cols)
+    return filtered_df
+
+
 def normalize_numerical_values(df) -> DataFrame:
     cols = [
         "TOT_HERIDOS_LEVES",
diff --git a/src/P2/processing.py b/src/P2/processing.py
index 02bd9bf..1b91a52 100644
--- a/src/P2/processing.py
+++ b/src/P2/processing.py
@@ -8,7 +8,7 @@ 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, choose_numerical_values
 
 
 def choose_model(
@@ -29,11 +29,12 @@ def choose_model(
 def predict_data(data, model, results, sample) -> DataFrame:
     model = choose_model(model)
     start_time = time.time()
-    prediction = model.fit_predict(data)
+    numerical_data = choose_numerical_values(df=data)
+    prediction = model.fit_predict(numerical_data)
     execution_time = time.time() - start_time
-    calinski = calinski_harabasz_score(X=data, labels=prediction)
+    calinski = calinski_harabasz_score(X=numerical_data, labels=prediction)
     silhouette = silhouette_score(
-        X=data,
+        X=numerical_data,
         labels=prediction,
         metric="euclidean",
         sample_size=sample,
@@ -130,7 +131,8 @@ def populate_results(
 def rename_model(model) -> str:
     short_name = ["kmeans", "birch", "affinity", "meanshift", "dbscan"]
     models = [
-        "KMean(random_state=42)",
+        "KMeans(random_state=42)",
+        "Birch()",
         "AffinityPropagation(random_state=42)",
         "MeanShift()",
         "DBSCAN()",