iterative/demo-bank-customer-churn
Demo DVC project training a classification model on tabular data
This helps banks and financial institutions predict which customers are likely to close their accounts. By analyzing customer data like age, credit score, and account balance, it provides a probability score for each customer indicating their likelihood of leaving the bank. Customer relationship managers or marketing teams can use this to proactively retain valuable customers.
No commits in the last 6 months.
Use this if you need to identify at-risk bank customers to implement targeted retention strategies.
Not ideal if you're looking for a solution for non-financial industries or need to predict different customer behaviors beyond churn.
Stars
41
Forks
48
Language
Jupyter Notebook
License
MIT
Category
Last pushed
May 11, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/iterative/demo-bank-customer-churn"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
retentioneering/retentioneering-tools
Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web...
junfengn-ctrl/uplift-modeling-customer-retention
End-to-end uplift modeling pipeline for customer retention using T-Learner and X-Learner with...
gattsu001/Telecom-Churn-Predictor
Predicts which telecom customers are likely to churn with 95% accuracy using engineered features...
himanshu-03/Customer-Churn-Prediction
The Customer Churn table contains information on all 7,043 customers from a Telecommunications...
Pegah-Ardehkhani/Customer-Churn-Prediction-and-Analysis
Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy)...