Applied Logistic Regression to a dataset of customers’ financial habits artificially created from real life case studies. Achieved test accuracy of 62.9%. Used Undersampling to balance the dataset, k-fold cross validation to improve accuracy and Recursive Feature Elimination to reduce chances of overfitting.
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Applied Logistic Regression to a dataset of customers’ financial habits artificially created from real life case studies. Achieved test accuracy of 62.9%. Used Undersampling to balance the dataset, k-fold cross validation to improve accuracy and Recursive Feature Elimination to reduce chances of overfitting.
HimalayPatel/minimizing-churn-rate
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Applied Logistic Regression to a dataset of customers’ financial habits artificially created from real life case studies. Achieved test accuracy of 62.9%. Used Undersampling to balance the dataset, k-fold cross validation to improve accuracy and Recursive Feature Elimination to reduce chances of overfitting.
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