This project aims to analyze customer churn in the banking sector using a dataset of bank customers. By identifying the factors contributing to customer churn, this analysis provides insights for improving customer retention strategies.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook
The dataset contains information about 10,000 bank customers and includes the following features:
- Age
- Salary
- Marital Status
- Credit Card Limit
- Credit Card Category
- Churn Rate (16.07%)
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/koke3/Bank_Churners_analysis.git
- Navigate to the project directory:
cd Bank_Churners_analysis - Install the required libraries:
pip install -r requirements.txt
- Open the Jupyter Notebook in the project directory:
jupyter notebook
2.Run the analysis cells in the notebook to explore the data and generate insights.
The analysis provides visualizations and findings on customer behavior, highlighting key factors influencing churn. Insights derived from this analysis can be used to inform business strategies aimed at reducing churn.
Contributions are welcome! If you have suggestions or improvements, please create a pull request or open an issue.
This project is licensed under the MIT License - see the LICENSE file for details.