Update pages/3_EDA_and_Feature_Engineering.py

pages/3_EDA_and_Feature_Engineering.py

@@ -258,14 +258,23 @@ if 'cleaned_data' in st.session_state:
 - The bar chart clearly shows the difference in customer numbers across countries.
 - The pie chart visually represents the percentage distribution of customers.
     ''')
-    # countplot for Gender distribution
-    plt.figure(figsize=(8, 6))
-    sns.countplot(data=df, x='exiting_category', color='#A52A2A')
-    plt.title('Distribution Of Exiting Category')
-    plt.xlabel('Exiting_Category')
-    plt.ylabel('Count')
+    st.write("### Distribution of Exiting Category")
+
+    # Create the figure
+    fig, ax = plt.subplots(figsize=(8, 6))
+    
+    # Create the countplot
+    sns.countplot(data=df, x='exiting_category', color='#A52A2A', ax=ax)
+    
+    # Add title and labels
+    ax.set_title('Distribution Of Exiting Category')
+    ax.set_xlabel('Exiting Category')
+    ax.set_ylabel('Count')
+    
     plt.tight_layout()
-    plt.show()
+    
+    # Display the plot in Streamlit
+    st.pyplot(fig)
     st.markdown('''
     **Insights Distribution Of Exiting Category**
 1. **Category Breakdown**: