diff --git a/frontend/pages/clustering.py b/frontend/pages/clustering.py
index b3bf971..2c2fb8e 100644
--- a/frontend/pages/clustering.py
+++ b/frontend/pages/clustering.py
@@ -1,15 +1,19 @@
 import streamlit as st
 import matplotlib.pyplot as plt
 from clusters import DBSCANCluster, KMeansCluster, CLUSTERING_STRATEGIES
+from sklearn.decomposition import PCA
+from sklearn.metrics import silhouette_score
+import numpy as np
 
 st.header("Clustering")
 
 if "data" in st.session_state:
     data = st.session_state.data
 
-    general_row = st.columns([1, 1])
+    general_row = st.columns([1, 1, 1])
     clustering = general_row[0].selectbox("Clustering method", CLUSTERING_STRATEGIES)
-    data_name = general_row[1].multiselect("Data Name",data.select_dtypes(include="number").columns, max_selections=3)
+    data_name = general_row[1].multiselect("Columns", data.select_dtypes(include="number").columns)
+    n_components = general_row[2].number_input("Reduce dimensions to (PCA)", min_value=1, max_value=3, value=2)
 
     with st.form("cluster_form"):
         if isinstance(clustering, KMeansCluster):
@@ -18,20 +22,50 @@ if "data" in st.session_state:
             clustering.n_init = row1[1].number_input("n_init", min_value=1, value=clustering.n_init)
             clustering.max_iter = row1[2].number_input("max_iter", min_value=1, value=clustering.max_iter)
         elif isinstance(clustering, DBSCANCluster):
-            clustering.eps = st.slider("eps", min_value=0.0001, max_value=1.0, step=0.1, value=clustering.eps)
-            clustering.min_samples = st.number_input("min_samples", min_value=1, value=clustering.min_samples)
+            row1 = st.columns([1, 1])
+            clustering.eps = row1[0].slider("eps", min_value=0.0001, max_value=1.0, step=0.05, value=clustering.eps)
+            clustering.min_samples = row1[1].number_input("min_samples", min_value=1, value=clustering.min_samples)
 
         st.form_submit_button("Launch")
 
-    if len(data_name) >= 2 and len(data_name) <=3:
+    if len(data_name) > 0:
         x = data[data_name].to_numpy()
+        n_components = min(n_components, len(data_name))
+        if len(data_name) > n_components:
+            pca = PCA(n_components)
+            x = pca.fit_transform(x)
+            if n_components == 2:
+                (fig, ax) = plt.subplots(figsize=(8, 8))
+                for i in range(0, pca.components_.shape[1]):
+                    ax.arrow(
+                        0,
+                        0,
+                        pca.components_[0, i],
+                        pca.components_[1, i],
+                        head_width=0.1,
+                        head_length=0.1
+                    )
 
-        result = clustering.run(x)
+                    plt.text(
+                        pca.components_[0, i] + 0.05,
+                        pca.components_[1, i] + 0.05,
+                        data_name[i]
+                    )
+                circle = plt.Circle((0, 0), radius=1, edgecolor='b', facecolor='None')
+                ax.add_patch(circle)
+                plt.axis("equal")
+                ax.set_title("PCA result - Correlation circle")
+                st.pyplot(fig)
 
+        result = clustering.run(x)
+        st.write("## Cluster stats")
         st.table(result.statistics)
 
+        st.write("## Graphical representation")
         fig = plt.figure()
-        if len(data_name) == 2:
+        if n_components == 1:
+            plt.scatter(x, np.zeros_like(x))
+        elif n_components == 2:
             ax = fig.add_subplot(projection='rectilinear')
             plt.scatter(x[:, 0], x[:, 1], c=result.labels, s=50, cmap="viridis")
             if result.centers is not None:
@@ -43,6 +77,10 @@ if "data" in st.session_state:
             if result.centers is not None:
                 ax.scatter(result.centers[:, 0], result.centers[:, 1], result.centers[:, 2], c="black", s=200, marker="X")
         st.pyplot(fig)
+        if not (result.labels == 0).all():
+            st.write("Silhouette score:", silhouette_score(x, result.labels))
+    else:
+        st.error("Select at least one column")
 
 else:
     st.error("file not loaded")