Update README.me

README.me

@@ -63,4 +63,19 @@ We focused on two main research questions to derive business insights.
 > Geography is a strong predictor. While France has the highest volume of customers, **Germany exhibits a significantly higher churn rate**. This suggests potential issues with local competition, customer service, or market fit specifically in the German region.
 
 ### 👴 Q2: What is the relationship between Age and Churn?
-*
+*Investigation:* Using Kernel Density Estimation (KDE) to compare age distributions of retained vs. churned customers.
+
+![Age Distribution by Churn Status](plot_age_density.png)
+
+> **💡 Insight:**
+> Age is a critical factor.
+> * **Stayed (0):** Peak distribution around **30-40 years old**.
+> * **Churned (1):** Distinct peak around **45-55 years old**.
+> * **Action Item:** The bank is losing its mature, middle-aged customer base. Retention campaigns should specifically target the 45-60 age demographic.
+
+---
+
+## 🛠️ Tech Stack
+* **Language:** Python
+* **Libraries:** Pandas, Matplotlib, Seaborn, Scikit-learn
+* **Environment:** Jupyter Notebook