diff --git a/src/pages/evaluate.py b/src/pages/evaluate.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/pages/prediction.py b/src/pages/prediction.py
index 6a87c01..e2a95ab 100644
--- a/src/pages/prediction.py
+++ b/src/pages/prediction.py
@@ -6,10 +6,13 @@ sys.path.append('./back/')
 import clustering_csv as cc
 import prediction as p
 
-def display_prediction_results(df, targetCol):
-    df_cols.remove(col)
-    original_col = df[col]
-    predicted_col = p.getColumnsForPredictionAndPredict(df, df_cols, "Route Type", "Linear Regression")    
+def handle_column_multiselect(df, method_name):
+    selected_columns = st.multiselect(f"Select the columns you want for {method_name}:", df.columns.tolist(), placeholder="Select dataset columns")
+    return selected_columns
+    
+def display_prediction_results(df, targetCol, sourceColumns, method):
+    original_col = df[targetCol]
+    predicted_col = p.getColumnsForPredictionAndPredict(df, sourceColumns, targetCol, method)    
     
     new_df = pd.DataFrame()
     new_df['Original'] = original_col
@@ -19,22 +22,50 @@ def display_prediction_results(df, targetCol):
 
 if 'df' in st.session_state:
     df = st.session_state.df
-    df_cols = df.columns.tolist()
 
     st.write("# 🔮 Prediction")
 
-    if st.button("K-means"):
-        st.pyplot(cc.launch_cluster_knn(df, ["Route Type", "Traffic Control"]))
+    tab1, tab2 = st.tabs(["Clustering", "Predictions"])
+
+    with tab1:
+        st.header("Clustering")
+        selected_columns = handle_column_multiselect(df, "clustering")
+            
+        
+        tab_names = ["K-means", "DBSCAN"] 
+        tab11, tab12 = st.tabs(tab_names)
+
+        with tab11:
+            if st.button(f"Start {tab_names[0]}"):
+                st.pyplot(cc.launch_cluster_knn(df, selected_columns))
+
+        with tab12:
+            if st.button(f"Start {tab_names[1]}"):
+                st.pyplot(cc.launch_cluster_dbscan(df, selected_columns))
+
+    with tab2:
+        st.header("Predictions")
+        target_column = st.selectbox(
+                            "Target column:",
+                            df.columns.tolist(),
+                            index=None,
+                            placeholder="Select target column"
+                        )
 
-    if st.button("DBSCAN"):
-        st.pyplot(cc.launch_cluster_dbscan(df, ["Route Type", "Traffic Control"]))
+        if target_column != None:
+            selected_columns_p = handle_column_multiselect(df, "predictions")
+        
+        tab_names = ["Linear Regression", "Random Forest"] 
+        tab21, tab22 = st.tabs(tab_names)
 
-    if st.button("Linear Regression"):
-        col = "Route Type"
-        display_prediction_results(df, col)
+        with tab21:
+            if st.button(f"Start {tab_names[0]}"):
+                st.write(target_column)
+                st.write(selected_columns_p)
+                display_prediction_results(df, target_column, selected_columns_p, tab_names[0])
 
-    if st.button("Random Forest"):
-        col = "Route Type"
-        display_prediction_results(df, col)
+        with tab22:
+            if st.button(f"Start {tab_names[1]}"):
+                display_prediction_results(df, target_column, selected_columns_p, tab_names[1])
 else:
     st.write("Please clean your dataset.")