Update pages/3_EDA_and_Feature_Engineering.py

pages/3_EDA_and_Feature_Engineering.py

@@ -129,26 +129,28 @@ if df is not None:
     st.pyplot(fig)
     st.markdown('''**Insights:**
     **Right-skewed distribution:** Most clients have low balances, while a few have very high balances.
+    
     **Segmentation potential:** Clients can be segmented based on balance levels (low, medium, high).
+    
     **Marketing and product implications:** Tailor marketing and products to each segment.
+    
     **Risk management:** High-balance clients may pose higher risk.
     ''')
     
-    # Product Price Binning
-    # Plot the pie chart for the 'gender' columnplt.figure(figsize=(8, 8))
-    df['Gender'].value_counts().plot(kind='pie', autopct='%1.1f%%', startangle=90, colors=['skyblue', 'yellow'])
-
-    # Add title
+    # Generate pie chart
+    df['Gender'].value_counts().plot(
+    kind='pie',
+    autopct='%1.1f%%',
+    startangle=90,
+    colors=['skyblue', 'yellow']
+)
+     # Add title
     plt.title('Gender Distribution')
-
-    # Display the chart
+    # Adjust layout and display the chart
     plt.tight_layout()
     plt.show()
     st.markdown('''**Insights:**
-    - **Uneven Distribution**: The distribution is not even across price buckets, indicating certain price ranges are more common.
-    - **"Very High" Dominance**: The "Very High" bucket contains the most products, indicating a focus on premium items.
-    - **Lower Representation in "Very Low"**: The "Very Low" bucket has the fewest items, suggesting fewer budget-friendly products.
-    - **Balanced Mid-Range**: The "Low", "Medium", and "High" buckets have relatively similar counts.
+    
     ''')
     
     # Age Distribution and Binning