📊 Telco Customer Churn Analysis

Created by: Rehana | IBM Data Analyst Certificate Candidate | April 2026

🎯 Project Overview

This project analyzes customer churn for a fictional telecommunications company. The goal is to identify key factors driving customer churn and provide actionable business recommendations.

📈 Key Findings

• Overall churn rate: 26.5%
• Month-to-month contracts: 42.7% churn (vs 2.8% for 2-year contracts)
• Electronic check payments: 41.2% churn (vs 15.5% for automatic payments)
• New customers (first year): 44.5% churn (vs 8.5% for 5+ year customers)

🛠️ Tools Used

• Python: pandas, numpy, matplotlib, seaborn
• SQL: SQLite for database queries
• Visualization: Matplotlib, Seaborn
• Dashboard: Python matplotlib dashboard

📁 Files in This Repository

File	Description
01_data_cleaning.ipynb	Data cleaning and preparation
02_eda_churn.ipynb	Exploratory data analysis & charts
03_sql_queries.ipynb	SQL analysis queries
04_dashboard.py	Dashboard generation script
churn_analysis_queries.sql	Standalone SQL file
churn_dashboard.png	Dashboard screenshot
telco_cleaned.csv	Cleaned dataset

📊 Dashboard Preview

💡 Business Recommendations

1. Convert month-to-month customers to annual contracts with incentives
2. Promote automatic payments with small discounts
3. Improve onboarding for new customers (first 12 months)
4. Investigate fiber optic service issues (higher churn than DSL)

🚀 How to Run This Project

# Clone the repository
git clone https://github.com/rihhanna/telco-customer-churn-analysis.git

# Install requirements
pip install pandas numpy matplotlib seaborn

# Run Jupyter notebooks in order
jupyter notebook

## 👩‍💻 Author
**Rehana Hassan**

🔗 GitHub: [github.com/rihhanna](https://github.com/rihhanna)

📧 Email: hrihhana@gmail.com

🎓 IBM Data Analyst Certificate 

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*Feel free to connect with me for collaboration or opportunities!*