R-Logistic-Regression

The analysis in R involved logistic regression on the 'Telco-Customer-Churn' dataset to predict customer churn. Initial models were refined to address non-significant variables and multicollinearity. The final model achieved 81.17% accuracy and 67.53% sensitivity but had a low AUC of 0.145, indicating limited effectiveness in distinguishing churn cases. Significant predictors included tenure, contract type, and billing options.