Telecom Customer Churn Analysis

This project presents an exploratory data analysis (EDA) of customer churn behavior in a telecom company using Python. The goal is to uncover patterns and trends that help understand what drives customer churn and support retention strategies.

For key findings and business recommendations, please refer to the Executive Summary document.

Key Techniques Used

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Visualizations using Seaborn and Matplotlib
• Chi-Square tests for categorical relationships (scipy.stats)
• Calculating churn rates for different customer groups (e.g., by contract type, payment method, tenure)

Tools & Libraries

• Python
• Pandas
• Matplotlib
• Seaborn
• SciPy (scipy.stats)
• Jupyter Notebook