The customer churn dataset is a collection of customer data that focuses on predicting customer churn, which refers to the tendency of customers to stop using a company's products or services. The dataset contains various features that describe each customer, such as their credit score, country, gender, age, tenure, balance, number of products, credit card status, active membership, estimated salary, and churn status. The churn status indicates whether a customer has churned or not. The dataset is used to analyze and understand factors that contribute to customer churn and to build predictive models to identify customers at risk of churning. The goal is to develop strategies and interventions to reduce churn and improve customer retention.
This dataset is for ABC Multistate bank with following columns:
- customer_id, unused variable.
- credit_score, used as input.
- country, used as input.
- gender, used as input.
- age, used as input.
- tenure, used as input.
- balance, used as input.
- products_number, used as input.
- credit_card, used as input.
- active_member, used as input.
- estimated_salary, used as input.
- churn, used as the target. 1 if the client has left the bank during some period or 0 if he/she has not.
Aim is to Predict the Customer Churn for ABC Bank.
- What is the overall churn rate in the dataset?
- How does the churn rate vary by different customer demographics, such as gender and country?
- Are there any specific age groups more prone to churn?
- What is the average tenure of customers who churned compared to those who didn't?
- Do customers with higher credit scores tend to churn less?
- Is there a correlation between account balance and churn rate?
- Does the credit card status impact churn rate significantly?
Obs: This dataset is taken from: https://www.kaggle.com/datasets/gauravtopre/bank-customer-churn-dataset