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| Performing Customer Churn Analysis and Classification Using Machine Learning
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DA Ajiputra, M Arfan, M Somantri
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Abstract:
Customer Churn Analysis and Classification with the ultimate goal of finding out what factors cause customer churn and how to overcome them. The methodology used in this research project is the Cross Industry Standard Process for Data Mining or CRISP-DM. method, with four classification models, namely: Logistic Regression, Decision Tree, Randomforest without Hyperparameter Tuning, and Random Forest with Hyperparameter Tuning. the author takes the learning path 'Accelerated Machine Learning Program. Customer Churn Analysis and Classification with the ultimate goal of finding out what factors cause customer churn and how to overcome them. The methodology used in this research is the Cross Industry Standard Process for Data Mining or CRISP-DM. method, with four classification models, namely: Logistic Regression, Decision Tree, Randomforest without Hyperparameter Tuning, and Random Forest with Hyperparameter Tuning. The research project that the author is doing is Customer Churn Analysis and Classification with the ultimate goal of finding out what factors cause customer churn and how to overcome them. The methodology used in research is the Cross Industry Standard Process for Data Mining or CRISP-DM. method, with four classification models, namely: Logistic Regression, Decision Tree, Randomforest without Hyperparameter Tuning, and Random Forest with Hyperparameter Tuning. Customer Churn Analysis and Classification with the ultimate goal of finding out what factors cause customer churn and how to overcome them. The methodology used in this research is the Cross Industry Standard Process for Data Mining or CRISP-DM method, with four classification models, namely: Logistic Regression, Decision Tree, Randomforest without Hyperparameter Tuning, and Random Forest with Hyperparameter Tuning.
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