Customer Churn Analysis and Prediction: From Data Exploration to Streamlit Real-Time Application

This project is an example of an end-to-end data science workflow focusing on customer churn prediction, covering the entire process from data exploration, feature engineering, classification model comparison to Streamlit real-time interactive application, demonstrating the core skills of data analysts and machine learning engineers.

Customer churn is a challenge for business operations, directly affecting revenue and customer acquisition costs. Identifying customers at risk of churn in advance and taking measures is key to improving customer lifetime value. This project provides a complete workflow to help master the skills needed for practical work.

The data exploration phase reveals churn patterns through analysis of demographics, service usage patterns, and financial indicators: young customers have a high tendency to churn, recent decline in activity is a warning sign, and customers with auto-renewal have high retention rates; feature engineering includes techniques such as category encoding, numerical standardization, feature combination, time extraction, and missing value handling.

Compare models such as logistic regression, decision tree/random forest, gradient boosting machine, and support vector machine; use stratified K-fold cross-validation, focus on metrics such as accuracy, precision, recall, F1 score, and AUC-ROC, and emphasize precision-recall curve analysis (because the cost of missing churn customers is higher in business).

Build an interactive application with Streamlit, featuring single customer real-time prediction, batch prediction, SHAP model interpretation, historical data analysis view, and model performance dashboard; technically adopt practices such as modular design, configuration management, model persistence (joblib), and exception handling.

Provide a complete workflow template for learners; serve as a prototype of a churn prediction system for enterprises; provide business analysts with hypothesis analysis capabilities to help formulate customer maintenance strategies.

Possible improvement directions include introducing time series features, trying deep learning models (LSTM/attention mechanism), integrating AutoML tools, and adding model monitoring functions (data drift alerts).