diff --git a/frontend/pages/clustering:_dbscan.py b/frontend/pages/clustering:_dbscan.py
new file mode 100644
index 0000000..da51aa9
--- /dev/null
+++ b/frontend/pages/clustering:_dbscan.py
@@ -0,0 +1,29 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+from sklearn.cluster import DBSCAN
+
+st.header("Clustering: dbscan")
+
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("my_form"):
+        data_name = st.multiselect("Data Name", data.select_dtypes(include="number").columns, max_selections=2)
+        eps = st.slider("eps", min_value=0.0, max_value=1.0, value=0.5, step=0.01)
+        min_samples = st.number_input("min_samples", step=1, min_value=1, value=5)
+        st.form_submit_button("launch")
+
+    if len(data_name) == 2:
+        x = data[data_name].to_numpy()
+
+        dbscan = DBSCAN(eps=eps, min_samples=min_samples)
+        y_dbscan = dbscan.fit_predict(x)
+
+
+        fig, ax = plt.subplots(figsize=(12,8))
+        plt.scatter(x[:, 0], x[:, 1], c=y_dbscan, s=50, cmap="viridis")
+        st.pyplot(fig)
+
+else:
+    st.error("file not loaded")
\ No newline at end of file
diff --git a/frontend/pages/clustering:_kmeans.py b/frontend/pages/clustering:_kmeans.py
new file mode 100644
index 0000000..69d9920
--- /dev/null
+++ b/frontend/pages/clustering:_kmeans.py
@@ -0,0 +1,36 @@
+import streamlit as st
+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+
+st.header("Clustering: kmeans")
+
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("my_form"):
+        row1 = st.columns([1,1,1])
+        n_clusters = row1[0].selectbox("Number of clusters", range(1,data.shape[0]))
+        data_name = row1[1].multiselect("Data Name",data.select_dtypes(include="number").columns, max_selections=2)
+        n_init = row1[2].number_input("n_init",step=1,min_value=1)
+
+        row2 = st.columns([1,1])
+        max_iter = row1[0].number_input("max_iter",step=1,min_value=1)
+
+
+        st.form_submit_button("launch")
+
+    if len(data_name) == 2:
+        x = data[data_name].to_numpy()
+
+        kmeans = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, max_iter=max_iter, random_state=111)
+        y_kmeans = kmeans.fit_predict(x)
+
+        fig, ax = plt.subplots(figsize=(12,8))
+        plt.scatter(x[:, 0], x[:, 1], c=y_kmeans, s=50, cmap="viridis")
+        centers = kmeans.cluster_centers_
+        plt.scatter(centers[:, 0], centers[:, 1], c="black", s=200, marker="X")
+        st.pyplot(fig)
+
+else:
+    st.error("file not loaded")