diff --git a/src/P2/processing.py b/src/P2/processing.py
index 989fa3f..40e039b 100644
--- a/src/P2/processing.py
+++ b/src/P2/processing.py
@@ -3,18 +3,18 @@ from sys import argv
 
 from matplotlib.pyplot import *
 from pandas import DataFrame
-from seaborn import heatmap, set_style, set_theme, pairplot
+from seaborn import clustermap, set_style, set_theme, pairplot
 from sklearn.metrics import silhouette_score, calinski_harabasz_score
 from sklearn.cluster import KMeans, Birch, SpectralClustering, MeanShift, DBSCAN
 
 from preprocessing import parse_data, filter_dataframe
 
 
-def choose_model(model):
+def choose_model(model, cluster_number):
     if model == "kmeans":
-        return KMeans(random_state=42)
+        return KMeans(n_clusters=cluster_number, random_state=42)
     elif model == "birch":
-        return Birch()
+        return Birch(n_clusters=cluster_number)
     elif model == "spectral":
         return SpectralClustering()
     elif model == "meanshift":
@@ -23,9 +23,9 @@ def choose_model(model):
         return DBSCAN()
 
 
-def predict_data(data, model, results, sample):
+def predict_data(data, model, cluster_number, results):
     model_name = model
-    model = choose_model(model)
+    model = choose_model(model=model, cluster_number=cluster_number)
     start_time = time.time()
     prediction = model.fit_predict(data)
     execution_time = time.time() - start_time
@@ -35,7 +35,6 @@ def predict_data(data, model, results, sample):
         X=data,
         labels=prediction,
         metric="euclidean",
-        sample_size=sample,
         random_state=42,
     )
     populated_results = populate_results(
@@ -52,10 +51,13 @@ def predict_data(data, model, results, sample):
 
 def plot_heatmap(results):
     fig = figure(figsize=(20, 10))
-    heatmap(
-        data=results,
-        cmap="Blues",
-        square=True,
+    results.reset_index()
+    matrix = results["prediction"]
+    print(matrix)
+    clustermap(
+        data=matrix,
+        cmap="mako",
+        metric="euclidean",
         annot=True,
     )
     fig_title = "Heatmap"
@@ -66,10 +68,10 @@ def plot_heatmap(results):
 
 def plot_scatter_plot(results):
     fig = figure(figsize=(20, 10))
-    original_data = results.drop("prediction")
+    matrix = results.filter(items=["input", "prediction"])
     pairplot(
         data=results,
-        vars=original_data,
+        vars=matrix,
         hue="prediction",
         palette="Paired",
         diag_kind="hist",
@@ -138,12 +140,14 @@ def construct_case(df, choice):
 
 
 def usage():
-    print("Usage: " + argv[0] + "<preprocessing action> <case> <sample size>")
+    print("Usage: " + argv[0] + "<preprocessing action> <case> <number of clusters>")
     print("preprocessing actions:")
     print("fill: fills the na values with the mean")
     print("drop: drops the na values")
     print("cases: choice of case study")
-    print("sample size: size of the sample when computing the Silhouette Coefficient")
+    print(
+        "number of clusters: number of clusters for the algorithms that use a fixed number"
+    )
     exit()
 
 
@@ -151,7 +155,7 @@ def main():
     models = ["kmeans", "birch", "spectral", "meanshift", "dbscan"]
     if len(argv) != 4:
         usage()
-    case, sample = argv[2], int(argv[3])
+    case, cluster_number = argv[2], int(argv[3])
     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)
@@ -161,13 +165,13 @@ def main():
             data=filtered_data,
             model=model,
             results=individual_result,
-            sample=sample,
+            cluster_number=cluster_number,
         )
         complete_results = complete_results.append(
             individual_result.append(model_results)
         )
     complete_results.set_index("model")
-    print(complete_results)
+    show_results(results=complete_results)
 
 
 if __name__ == "__main__":