Exploratory data analysis on 10,000+ banking records to identify customer churn patterns and attrition drivers.
Banks need to proactively identify customers likely to churn to improve retention strategies and reduce revenue loss.
- Python (Pandas, NumPy, Matplotlib, Seaborn)
- Jupyter Notebook
- Identified top churn drivers: credit score, tenure, product count
- Customers with 1 product and low balance had highest attrition
- Generated business insights for targeted retention campaigns
pip install pandas numpy matplotlib seaborn jupyter notebook Bank_Attrition_Analysis.ipynb