📊 Telecom Customer Churn Analysis
📝 Project Overview This project analyzes customer churn data from a telecommunications company to understand the factors influencing customer retention and churn. The primary goal is to identify characteristics and behaviors associated with customer churn to inform strategies that can reduce it.
📂 Dataset The dataset contains information on 300 customers, including:
🔹 Demographic data (gender, age, etc.)
🔹 Account information (tenure, contract type, payment method, etc.)
🔹 Service details (Internet, Online Security, Tech Support, etc.)
🔹 Monthly and Total Charges
🔹 Churn status (indicating whether the customer has left the service)
📊 Analysis Steps
🔍 Data Exploration: Understanding data types, checking for missing values, and identifying unique values.
📈 Data Visualization: Exploring churn distribution based on customer attributes, including:
💵 Monthly and Total Charges
📅 Tenure (length of service)
🛠️ Add-on services (Internet Service, Tech Support, etc.)
📉 Correlation Analysis: Examining relationships between numeric variables to identify potential factors influencing churn.
🔍 Key Insights
💸 Higher Monthly Charges: Customers with higher monthly charges are more likely to churn.
🕒 Shorter Tenures: Customers with shorter service durations (tenure) have a higher churn rate.
🔒 Add-On Services: Services such as Online Security and Tech Support are associated with lower churn rates.
🔗 Loyalty Over Time: Long-term customers are less likely to churn, suggesting loyalty builds over time.
🎯 Conclusion To reduce churn, the company should focus on:
🌟 Improving early customer experiences
💡 Considering flexible pricing for cost-sensitive customers
📢 Promoting value-added services
🎁 Implementing loyalty programs for long-term customers and targeting at-risk customers with personalized retention strategies
⚙️ Requirements
🐍 Python (Pandas, Seaborn, Matplotlib)
📒 Jupyter Notebook
🚀 How to Run
📥 Clone the repository.
📦 Install required packages.
📂 Open and