Update README.me

README.me

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 license: mit
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+# 🏦 Bank Customer Churn Analysis
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+![Python](https://img.shields.io/badge/Python-3.8%2B-blue?style=for-the-badge&logo=python&logoColor=white)
+![Pandas](https://img.shields.io/badge/Pandas-Data%20Analysis-150458?style=for-the-badge&logo=pandas&logoColor=white)
+![Scikit-Learn](https://img.shields.io/badge/scikit--learn-Machine%20Learning-F7931E?style=for-the-badge&logo=scikit-learn&logoColor=white)
+![Status](https://img.shields.io/badge/Status-Completed-success?style=for-the-badge)
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 ## 🎥 Project Walkthrough (Video)
 
 Click the image below to watch the full explanation of the data analysis:
@@ -8,12 +16,8 @@ Click the image below to watch the full explanation of the data analysis:
 [![Watch the Video](https://img.youtube.com/vi/Syatg5hxqLc/0.jpg)](https://www.youtube.com/watch?v=Syatg5hxqLc)
 
 > **In this video:** I explain the data cleaning process, outlier handling, and the key insights about customer churn.
-# 🏦 Bank Customer Churn Analysis
 
-![Python](https://img.shields.io/badge/Python-3.8%2B-blue?style=for-the-badge&logo=python&logoColor=white)
-![Pandas](https://img.shields.io/badge/Pandas-Data%20Analysis-150458?style=for-the-badge&logo=pandas&logoColor=white)
-![Scikit-Learn](https://img.shields.io/badge/scikit--learn-Machine%20Learning-F7931E?style=for-the-badge&logo=scikit-learn&logoColor=white)
-![Status](https://img.shields.io/badge/Status-Completed-success?style=for-the-badge)
+---
 
 ## 📌 Project Overview
 This project is an **end-to-end data science analysis** of a bank customer dataset. The primary objective is to investigate the factors contributing to customer attrition (churn) and to identify patterns that distinguish customers who leave from those who stay.
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 > 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.
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-![Age Distribution by Churn Status](plot_age_density.png)
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-> **💡 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.
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-## 🛠️ Tech Stack
-* **Language:** Python
-* **Libraries:** Pandas, Matplotlib, Seaborn, Scikit-learn
-* **Environment:** Jupyter Notebook
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