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app.py

@@ -405,6 +405,16 @@ elif page == "KMeans Clustering & Recommendations":
     plt.tight_layout()
     st.pyplot(plt.gcf())
 
+    # New Input: Select a cluster and display top 10 data points
+    st.subheader("Explore a Cluster")
+    selected_cluster = st.selectbox("Select a Cluster", options=sorted(df_original['cluster'].unique()))
+
+    # Filter data based on selected cluster
+    cluster_data = df_original[df_original['cluster'] == selected_cluster]
+
+    st.write(f"Top 10 items in Cluster {selected_cluster}:")
+    st.write(cluster_data.head(10))
+
     # Dynamic input for the new data point
     st.subheader("Input New Data Point for Recommendations")
 
@@ -441,6 +451,7 @@ elif page == "KMeans Clustering & Recommendations":
     
     st.subheader("Top 5 Similar Items to the Input")
     st.write(f"The new data point belongs to cluster: {new_cluster}")
+    
     # Get all data points in the same cluster
     cluster_data = df_original[df_original['cluster'] == new_cluster]
 
@@ -462,10 +473,10 @@ elif page == "KMeans Clustering & Recommendations":
     recommendations = df_kiva_loans_cleaned.loc[recommended_indices]
 
     # Display the original rows as the top 5 recommendations
-
     st.write(recommendations)
 
 
+
 # Page 8: Hierarchical Clustering & Dendrogram
 elif page == "Hierarchical Clustering & Dendrogram":
     st.subheader("Hierarchical Clustering & Dendrogram")