diff --git a/frontend/pages/clustering.py b/frontend/pages/clustering.py
new file mode 100644
index 0000000..037780d
--- /dev/null
+++ b/frontend/pages/clustering.py
@@ -0,0 +1,48 @@
+import streamlit as st
+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+
+st.header("clustering et Prediction")
+
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("my_form"):
+        header = st.columns([2,1,2])
+        header[0].subheader("Dispersion")
+        header[1].subheader("Number of clusters")
+        header[2].subheader("Data Name")
+
+        row1 = st.columns([2,1,2])
+        cluster_std = row1[0].slider("", 0.2, 3.0, 0.2, 0.2)
+        n_clusters = row1[1].selectbox("", range(1, 10))
+        data_name = row1[2].selectbox("", data.columns)
+
+        st.form_submit_button('launch')
+
+    from sklearn.datasets import make_blobs
+    from sklearn.cluster import KMeans
+    import matplotlib.pyplot as plt
+    import streamlit as st
+    import random
+
+    # Points generator
+    x, _ = make_blobs(n_samples=200, n_features=2, centers=5, cluster_std=cluster_std, shuffle=True, random_state=10)
+
+    x = data[["Unit Price","Unit Cost"]].to_numpy()
+
+    # k-means algorithm 
+    kmeans = KMeans(n_clusters=n_clusters, init='random', n_init=10, max_iter=300, random_state=111)
+    y_kmeans = kmeans.fit_predict(x)
+
+    # Plotting colored clusters 
+    fig, ax = plt.subplots(figsize=(12,8))
+    plt.scatter(x[:, 0], x[:, 1], s=100, c=kmeans.labels_, cmap='Set1')
+    plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=400, marker='*', color='k')
+    st.pyplot(fig)
+
+else:
+    st.error("file not loaded")
+
+# Cached function that returns a mutable object with a random number in the range 0-10
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