An ABC bank wants to perform customer churn analytics based on the data, to find out the potential customers who have exited the bank, based on the following analytics.

Customer Churn Analytics – Banking (Databricks)

Overview This project implements an end‑to‑end data pipeline using Databricks and Delta Lake to perform customer churn analytics for a bank. The solution follows the Medallion Architecture (Bronze, Silver, Gold) to transform raw CSV data into business‑ready KPI datasets and dashboards.

Architecture

Bronze – Raw data ingestion and staging (Delta tables) Silver – Data cleansing, deduplication, and standardization Gold – Business KPIs and analytical datasets

Data Sources

customer.csv – Customer demographics, tenure, credit score, churn status account.csv – Account balance and product details

Pipeline Summary

Ingest raw CSV data into Bronze layer Clean and standardize data in Silver layer Build churn‑related KPIs in Gold layer Visualize insights using Databricks dashboards

KPIs Implemented

High‑balance customers who exited Retained customers with good credit and multiple products Active high‑value female customers Long‑tenure customers by geography and gender Geography with most inactive customers Active young customers (≤ 35) by geography Active customers with zero balance High‑salary customers (> 12L annually)

Technologies Used

Databricks Apache Spark (PySpark) Delta Lake Medallion Architecture Databricks Dashboards