Update app.py

app.py

@@ -53,7 +53,7 @@ if st.session_state.toggle:
     
     # Slider for Advanced in the sidebar
     st.sidebar.header('K-Means Parameters')
-    n_clusters_advanced = st.sidebar.slider('Number of Clusters (K)', 1, 10, n_clusters_advanced)
+    n_clusters_advanced = st.sidebar.slider('Number of Clusters (K)', 1, 8, n_clusters_advanced)
 
 
 st.markdown("""
@@ -68,7 +68,7 @@ st.markdown("""
 with tab1:
     st.write("""
     ### What is Clustering?
-    #### Clustering with K-Means is a machine learning concept like tidying a messy room by grouping similar items, but for data instead of physical objects.
+    ##### Clustering with K-Means is a machine learning concept like tidying a messy room by grouping similar items, but for data instead of physical objects.
     """)
     
     # K-Means Algorithm
@@ -117,7 +117,7 @@ with tab1:
 
     st.write("""
     ### Visualizing Groups
-    #### Here are the groups from our tidying method. Each color has a number at its center, representing its group.
+    ##### Here are the groups from our tidying method. Each color has a number at its center, representing its group.
     """)
     #st.pyplot(fig)
     st.plotly_chart(fig)
@@ -128,7 +128,7 @@ with tab1:
     # Closing Note
     st.write("""
     ### Wrap Up    
-    #### Just as sorting toys in a room, we group flowers by features; adjust the data to pick a flower and set how many boxes (groups) you want to use.
+    ##### Just as sorting toys in a room, we group flowers by features; adjust the data to pick a flower and set how many boxes (groups) you want to use.
     """)
 
 with tab2: