This repository contains my first data analysis project using Python, where I explored a telecom customer churn dataset to uncover patterns and trends in customer behavior.
The goal of this exploratory data analysis (EDA) was to identify key factors that contribute to customer churn and provide actionable insights for retention strategies.
This project was completed using the following tools:
| Tool | Description |
|---|---|
| Python | Programming language for analysis |
| Pandas | Data manipulation and preprocessing |
| NumPy | Numerical computing |
| Matplotlib | Data visualization |
| Seaborn | Statistical data visualization |
| Jupyter Notebook | Interactive coding environment |
| VS Code | Interactive coding environment |
- Customers on month-to-month contracts are over 4x more likely to churn than those with two-year contracts.
- Senior citizens and customers without support services (tech support, security) show significantly higher churn.
- Payment methods like electronic checks and services like fiber optic internet correlate with higher churn rates.
- Customers with low tenure and high monthly charges are more likely to leave.
As this was my first Python project, I learned:
- How to structure a data analysis notebook
- Cleaning and transforming real-world datasets
- Extracting insights using grouped statistics and visualizations
- Communicating results clearly through storytelling and graphs