Multi-Model Optimization for Telecom Churn Prediction: A Complete Data Science Approach from Theory to Python Implementation

Customer behavior analysis remains a cornerstone of strategic decision-making in the telecommunications industry. In this study, we present a complete, Python-based data science pipeline focused on predicting customer dependency status a proxy indicator for household-related churn or service needs. Using a real-world telecom dataset, our approach covers the full data lifecycle: from data cleaning and preprocessing to supervised classification and unsupervised segmentation. We evaluate a diverse set of machine learning models, including Linear and Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and XGBoost. Each model is carefully assessed through accuracy metrics, confusion matrices, and ROC curves to ensure both robustness and interpretability. Additionally, we apply K-Means clustering to explore customer segmentation patterns and reveal underlying group structures within the data. Our results indicate that ensemble-based models, particularly Random Forest and XGBoost, consistently outperform simpler classifiers in predictive accuracy. The integration of interpretability tools and feature importance analyses further highlights the relevance of variables such as tenure and monthly charges in customer behavior modeling. This work provides a hands-on and reproducible guide for telecom analysts and data scientists aiming to translate raw customer data into actionable business intelligence using well-established machine learning practices.