0zean / bank_churn_ml_dashboard

Explaining Bank Customer Churn

• Dashboard Link

Description

One important metric that banks consider for assessing their lifetime value attributed to customers is whether or not a specific customer would stop being a customer. This is referred to generally as churn and is often modeled in a mathematical way. In this project, I pose churn for bank customers in a machine learning project which involves binary classification and the use of tree-based models such as Catboost and LightGBM for getting high quality predictions and being able to explain them using ideas in XAI such as SHAP and permutation importance. In order for stakeholders to get a bird's eye view into the insights of this project, I created a dashboard with all the important classification metrics, visualizations, what-if scenarios, and explainable AI concepts using a framework known as ExplainerDashboard.

Quick Glance

bank_churn_ml_dashboard's Issues

Update README.md

Tasks

• Write a short paragraph about what the project is about including the use of ExplainerDashboard.
• Include the GIF for a quick glance on what people can see on the deployed app.
• Link to the deployed application.