Python Visuals|Data Analysis using Python

Moosa Ansari

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Data Visualizer

Data Analyst

Python

📊 Customer Churn Analysis

📌 Project Overview

This project aims to analyze customer churn in a telecommunications company using data-driven insights. The dataset includes 7,043 customer records with demographic details, service usage patterns, and payment methods.
Through data visualization and statistical analysis, we identify key factors influencing customer attrition and propose actionable strategies to improve retention.

📂 Dataset Information

The dataset consists of 21 columns and covers the following key attributes:
Customer Demographics (Gender, Senior Citizen, Partner, Dependents)
Account Information (Tenure, Contract Type, Billing & Payment Method)
Service Usage (Internet Service, Online Security, Tech Support, Streaming Services)
Churn Status (Whether the customer left the service)

🔍 Key Insights

Overall Churn Rate: 26.54% of customers have churned (1,869 out of 7,043).
Contract Type Influence: Customers with month-to-month contracts churn at 42%, while those with 2-year contracts churn at only 3.9%.
Payment Method Impact: Electronic check users have the highest churn rate at 46%, compared to 16% for credit card users.
Tenure & Churn Relationship: Customers with a tenure of 1-2 months churn at nearly 60%, while long-term customers (5+ years) churn at just 16%.
Service-Based Retention: Customers without Tech Support or Online Security churn at 41-42%, while those who subscribe to these services have a much lower churn rate (~14-15%).
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Posted Feb 16, 2025

Contribute to mohdmoosa03/Python_customer_churn_analysis development by creating an account on GitHub.

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Data Visualizer

Data Analyst

Python

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