Update pages/3_EDA_and_Feature_Engineering.py

pages/3_EDA_and_Feature_Engineering.py

@@ -596,8 +596,128 @@ Customers who exited (especially "Inactive - Exited") tend to have a lower age r
 
     ''')
 
+st.write('# Multivariate Analysis')
+    st.write("### Correlation Heatmap with Multiple Features")
+    
+    # Calculate the correlation matrix for more columns
+    correlation_matrix = df[['age', 'tenure', 'estimatedsalary', 'creditscore', 'balance', 'hascrcard']].corr()
+    
+    # Create the heatmap
+    fig, ax = plt.subplots(figsize=(8, 6))  # Set the figure size
+    sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', annot_kws={'size': 12}, ax=ax)
+    
+    # Set the title
+    ax.set_title('Correlation Heatmap', fontsize=14)
+    
+    # Display the plot in Streamlit
+    st.pyplot(fig)
+    st.markdown('''
+    ### Insights from heatmap:
+
+- **Independence of Variables:** Variables like age, balance, creditscore, and tenure show minimal correlation, suggesting they don't significantly influence each other.
+
+- **Weak Relationships:** For example, hascard has almost no relationship with other variables, meaning it may not directly impact features like balance or estimatedsalary.
+    ''')
+    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({
+        'creditscore': 'mean',
+        'balance': 'mean',
+        'estimatedsalary': 'mean',
+        'age': 'mean'
+    }).reset_index()
+    
+    # Display the grouped data in a table
+    st.dataframe(grouped_data)
+    st.write('''
+    ### Insights for Grouped Data by Geography and Exiting Category
+
+1. **Geography Influence on Customer Behavior**:
+   - The **creditscore**, **balance**, **estimated salary**, and **age** of customers may vary significantly across different geographies. For example, if one geography has a higher average **balance** and **estimated salary**, it could indicate wealthier customers in that region.
+
+2. **Exiting Category Patterns**:
+   - The **exiting_category** can show patterns of customer churn. For instance, if the average **creditscore** is significantly lower for customers who exited, it could indicate that customers with lower scores are more likely to leave.
+   - Similar insights can be made for **balance** and **estimated salary**. Customers with lower balances or salaries might be more prone to churn.
+
+3. **Age Distribution**:
+   - By analyzing the **age** column, you might find that younger or older customers are more likely to exit. If younger customers have a lower average age in the exited category, businesses may need to focus on improving retention strategies for that demographic.
+
+4. **Geography and Exiting Correlation**:
+   - If you notice certain geographies have higher **exiting_category** proportions (e.g., more exits in one region), it could indicate regional issues or dissatisfaction that need to be addressed.
+   
+5. **Targeted Strategies Based on Grouping**:
+   - Businesses can develop **targeted marketing or retention strategies** based on this grouping. For example, focusing on high-creditscore customers in a specific region to reduce churn, or providing more incentives for customers in low-income areas.
+
+### Actionable Strategies Based on Insights:
+
+1. **Customer Segmentation**: Use the insights to segment customers based on their geography and behavior (e.g., exited vs. retained), and tailor marketing or retention efforts for each group.
+2. **Improvement of Customer Retention Programs**: If exiting customers tend to have lower balances or salaries, businesses could offer more targeted retention strategies (e.g., loyalty rewards, personalized offers).
+3. **Localized Marketing Campaigns**: If certain geographies show higher churn, localized campaigns addressing the concerns specific to those areas can be launched.
+
+    ''')
+    
+    st.write("### Average Balance by Geography and Exiting Category")
+    
+    # Create the pivot table
+    pivot_table = dfc.pivot_table(
+        index='geography', columns='exiting_category',
+        values='balance', aggfunc='mean'
+    )
+    
+    # Create the heatmap
+    fig, ax = plt.subplots(figsize=(8, 6))  # Set the figure size
+    sns.heatmap(pivot_table, annot=True, fmt=".2f", cmap="YlGnBu", ax=ax)
+    
+    # Set the title
+    ax.set_title("Average Balance by Geography and Exiting Category")
+    
+    # Display the plot in Streamlit
+    st.pyplot(fig)
+    st.markdown('''
+    **1. Geography Insights:**
+
+**Germany:**
+- Customers in Germany have the highest average balances across all categories (~119,000–120,000).
+- This suggests German customers may hold larger accounts or higher financial engagement with the bank.
+
+**France:**
+- French customers have lower average balances (~59,000–72,000) compared to Germany, with some variation between Active/Exited and Exited/Not Exited categories.
 
+**Spain:**
+- Spanish customers fall in the mid-range, with average balances between ~56,000–77,000, slightly higher than France but significantly lower than Germany.
 
+**2. Customer Status Insights**
+
+**Exited Customers:**
+- Exited customers in all geographies (France, Germany, Spain) tend to have slightly lower balances than their active counterparts.
+  Exception: In France, exited customers have balances slightly higher than active ones in the "Exited" categories, which could indicate retention issues with higher-value customers.
+
+**Not Exited Customers:**
+- Customers who have not exited generally hold higher balances, particularly in Germany and Spain.
+  Suggests that customers with higher balances are more likely to remain loyal.
+
+**3. Geography-Specific Observations:**
+
+**France**: 
+- Active and non-exited customers have higher balances (72,622) compared to their exited counterparts (59,780–61,009).
+
+**Germany**: 
+- **Insight**: Little variation in average balances across all categories (~119,000–120,000). This indicates a consistently high-value customer base regardless of status.
+
+**Spain**: 
+- **Insight**: The largest variation is seen in Spain. Non-exited customers (77,529) have much higher balances than exited customers (56,480).
+  - This suggests a significant difference in engagement between customer segments in Spain, indicating that non-exited customers may be more engaged or satisfied with the service compared to exited customers.
+
+**Key Takeaways**:
+- **Germany** holds the most high-value customers with consistently high balances.
+- **France** shows retention issues, as exited customers can still hold moderate balances.
+- **Spain** shows the largest difference in balances between exited and non-exited customers, indicating potential for targeted retention efforts.
+
+**Retention Opportunity**: 
+- Focus on retaining high-value customers in **Spain** and **France** to improve loyalty.
+
+    ''')