This project presents an exploratory data analysis (EDA) of a telecom customer dataset with the goal of understanding customer churn — the phenomenon where customers discontinue their service.

Customer churn represents a critical business challenge in the telecommunications industry, as retaining existing customers is generally more cost-effective than acquiring new ones. This analysis aims to identify patterns and customer characteristics associated with churn, providing insights that can support data-driven retention strategies.

Objectives Explore the structure and quality of the dataset Analyze the distribution of customer churn Identify relationships between churn and key variables such as: contract type customer tenure monthly charges Generate insights to help reduce customer attrition

Dataset Source: Telecom customer dataset (JSON format) Size: 7,267 customers × 21 features Target variable: Churn

Data Preparation The dataset was loaded directly from a JSON file and inspected for quality and consistency. No missing values were found in the dataset Column names and data types were preserved as provided No transformations or feature engineering were applied at this stage This approach ensures transparency and allows the analysis to reflect the original data.

Exploratory Data Analysis The exploratory analysis focused on understanding how churn is distributed among customers and how it relates to key features. Main analyses include: Churn distribution across the customer base Churn vs. contract type Churn vs. customer tenure Churn vs. monthly charges Visualizations were created using matplotlib to highlight trends and patterns.

Key Insights Customers on month-to-month contracts show significantly higher churn rates. Newer customers are more likely to churn than long-term customers. Customers with higher monthly charges tend to churn more frequently. Churn is not randomly distributed and is influenced by pricing and contract structure.

Reco Based on the findings, the following actions are suggested:

Encourage long-term contracts through incentives or loyalty benefits. Strengthen onboarding and engagement during the first months of service. Review pricing strategies for customers with higher monthly charges. Use the insights from this analysis as a foundation for future churn prediction models.

Tools & Technologies: Python, Google Colab. pandas, matplotlib