Built an end-to-end Customer Churn Prediction System using ML, achieving 80%+ accuracy with XGBoost. Project includes complete data cleaning, feature engineering, model comparison, & performance evaluation.Key churn drivers such as tenure, monthly charges, internet service, & contract duration were identified through EDA & PowerBI visual insights.
flask machine-learning exploratory-data-analysis business-intelligence data-analytics feature-engineering data-science-portfolio predictive-analytics powerbi xgboost-model classification-models ml-pipeline customer-churn-analysis data-cleaning-and-preprocessing model-evaluation-and-comparison data-driven-decision-making data-analytics-portfolio-project
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Updated
Dec 3, 2025 - Jupyter Notebook