Botswana Bank Customer Churn Dataset

Dataset Overview

This synthetic dataset simulates customer data for a fictional bank in Botswana, specifically designed to model customer churn behavior. It includes a comprehensive set of customer demographics, financial data, product usage, and behavioral indicators that could influence whether a customer decides to leave the bank. The dataset is generated using the Python Faker library, ensuring realistic but entirely fictional data points for educational, testing, and modeling purposes.

Dataset Highlights

Number of Records: 115,640 customers
Churn Rate: Determined by a calculated churn risk score based on several customer attributes
Geographical Focus: Botswana
Data Structure: The dataset is organized in a tabular format, with each row representing a unique customer

Use Cases

This dataset is ideal for the following applications:

Churn Prediction Modeling: Building and evaluating machine learning models to predict customer churn.
Customer Segmentation: Analyzing customer profiles and segmenting them based on various demographics and financial attributes.
Product Analysis: Understanding which products are most associated with customer retention or churn.
Educational Purposes: Teaching data science and machine learning concepts using a realistic dataset.