Customer Churn Analysis & Power BI Dashboard

Analyzed 10K+ customer records to understand customer behavior and identify churn patterns and high-risk customer segments.

The project included:

• Data cleaning and exploratory data analysis using Python

• Data quality checks and analysis using SQL Server

• Customer churn analysis by geography, gender, age, tenure, balance, credit score, and account activity

• Calculation and visualization of key churn KPIs

• Development of an interactive Power BI dashboard using Power Query and DAX

• Identification of customer segments with higher churn rates

The analysis found an overall churn rate of 20.4%, with Germany having the highest churn rate at 32.4%.

Tools: Power BI, Power Query, DAX, Excel, SQL Server, Python, Pandas, NumPy, Matplotlib, Seaborn