Telecom Customer Churn Analysis

Python • Pandas • Matplotlib • Jupyter Notebook

Overview Cleaned and analyzed customer-level telecom data to identify patterns associated with customer churn across contracts, tenure, pricing, service adoption, and reported churn reasons.

What I Built Using Python and Pandas, I investigated missing values and data quality issues, prepared the dataset for analysis, performed exploratory analysis, and created visualizations to communicate the most important patterns.

Rather than simply removing unusual or incomplete records, data-quality decisions were validated against related fields and documented to preserve the integrity of the analysis.

Key Findings Overall churn was approximately 27% • Month-to-month customers had substantially higher churn than customers with longer contracts • Churned customers had higher average monthly charges • Internet customers without Online Security showed higher churn • Competitor-related factors represented a major group of reported churn reasons

Tools: Python • Pandas • Matplotlib • Jupyter Notebook

Want to see the analysis? View the complete notebook and technical walkthrough on GitHub → https://github.com/jamietran8818/business-data-analytics-portfolio/blob/main/python-customer-churn-analysis/README.md