https://github.com/derrickbaruga7/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.
https://github.com/derrickbaruga7/r-logistic-regression
data-science logistic-regression r
Last synced: over 1 year ago
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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.
- Host: GitHub
- URL: https://github.com/derrickbaruga7/r-logistic-regression
- Owner: derrickbaruga7
- Created: 2024-07-26T13:57:02.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-26T13:59:59.000Z (almost 2 years ago)
- Last Synced: 2025-02-08T10:20:35.500Z (over 1 year ago)
- Topics: data-science, logistic-regression, r
- Language: R
- Homepage:
- Size: 3.91 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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.