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BDS24_Weekly_Assignments/app.py

@@ -380,14 +380,14 @@ elif page == "KMeans Clustering & Recommendations":
     # Visualize the clustering results at different iterations
     max_iters = [1, 2, 5, 6, 8, 10]  # Different iterations you want to visualize
 
-    # Increase the figure size for better visibility
-    plt.figure(figsize=(15, 55))  # Adjusted the figsize to make plots larger
+    
+    plt.figure(figsize=(15, 55))  
     for i, max_iter in enumerate(max_iters, start=1):
         kmeans = KMeans(n_clusters=optimal_clusters, random_state=42, max_iter=max_iter)
         df_original['cluster'] = kmeans.fit_predict(df_pca)
         
         # Plotting the clusters
-        plt.subplot(6, 1, i)  # Changed the layout to 3 rows x 2 columns for larger plots
+        plt.subplot(6, 1, i)  
         sns.scatterplot(x=df_pca[:, 0], y=df_pca[:, 1], hue=df_original['cluster'], palette='viridis', s=100)
         
         # Plotting the centroids
@@ -405,7 +405,7 @@ elif page == "KMeans Clustering & Recommendations":
     plt.tight_layout()
     st.pyplot(plt.gcf())
 
-    # New Input: Select a cluster and display top 10 data points
+    # 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()))