/minimizing-churn-rate

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.

Primary LanguagePython

This repository is not active