📊 Predictive Customer Analytics (EDA)

Exploratory Data Analysis (EDA) of customer churn data using Python, Pandas, and Seaborn.

📌 Overview

This repository contains a streamlined analysis simulating customer churn data to identify underlying attrition patterns. By leveraging data visualization and statistical summaries, the project explores the relationship between churn and demographic/financial factors (e.g., age, balance, gender).

🚀 Key Insights

• Age Correlation: Higher churn rates observed in specific age brackets, suggesting a need for age-tailored retention strategies.
• Balance Impact: Customer balance levels show a significant relationship with retention probability.
• Gender Trends: Analysis reveals nuances in churn behavior across different demographic segments.

🛠️ Tech Stack

• Languages: Python (Jupyter Notebook)
• Data Manipulation: NumPy, Pandas
• Visualization: Matplotlib, Seaborn

📂 Project Structure

• churn_analysis.ipynb: The main notebook containing the full EDA and visualization pipeline.
• README.md: Project documentation.