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This project develops a customer attrition prediction model using machine learning to identify customers at risk of leaving. The workflow includes data cleaning, exploratory data analysis (EDA), feature engineering, data visualization, Logistic Regression modeling, and model evaluation using accuracy, precision, recall, F1-score, and ROC-AUC. The project concludes with actionable business recommendations to improve customer retention and reduce churn.