Broadening the Scope: Exploring Best Machine Learning Algorithms for Customer Churn Prediction - Gupta, Megha; Patil, Anisha; Tyagi, Ansh; Singhal, Deepanshi (International Journal of Computer Sciences and Engineering, 2023)
As businesses strive to maintain a competitive edge in today`s dynamic market, understanding and mitigating customer churn has become a critical imperative. This study explores the application of machine learning algorithms in Python for predicting customer churn, providing valuable insights to empower businesses in customer retention strategies. Leveraging a comprehensive dataset encompassing customer behavior, transaction history, and demographic information. Our methodology incorporates a diverse set of machine learning techniques, encompassing K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Random Forest, Logistic Regression, Decision Tree Classifier, AdaBoost Classifier, Gradient Boosting Classifier, and Voting Classifier. The outcomes reveal that the machine learning models demonstrate auspicious predictive capabilities, presenting businesses with a proactive means of identifying and mitigating potential churn risks. The discoveries from this investigation contribute valuable insights to the expanding realm of knowledge in customer relationship management, offering actionable guidance for businesses seeking to enhance customer retention strategies through the implementation of machine learning techniques in Python.