Update pages/3_EDA_and_Feature_Engineering.py

pages/3_EDA_and_Feature_Engineering.py

@@ -621,7 +621,7 @@ Customers who exited (especially "Inactive - Exited") tend to have a lower age r
     st.write("### Grouped Data by Geography and Exiting Category")
     
     # Group the data by Geography and Exiting Category
-    grouped_data = dfc.groupby(['geography', 'exiting_category']).agg({
+    grouped_data = df.groupby(['geography', 'exiting_category']).agg({
         'creditscore': 'mean',
         'balance': 'mean',
         'estimatedsalary': 'mean',
@@ -660,7 +660,7 @@ Customers who exited (especially "Inactive - Exited") tend to have a lower age r
     st.write("### Average Balance by Geography and Exiting Category")
     
     # Create the pivot table
-    pivot_table = dfc.pivot_table(
+    pivot_table = df.pivot_table(
         index='geography', columns='exiting_category',
         values='balance', aggfunc='mean'
     )