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What is Churn Prediction in B2B Services about?

This thesis examines using machine learning models to predict customer churn for a subscription-based business-to-business service provider in the financial administration sector. The study compares the performance of ensemble learning models like XGBoost and Random Forest against a single model like Naive Bayes. Since the dataset is imbalanced with more non-churning customers, sampling methods like SMOTE, SMOTEENN, and RandomUnderSampler are evaluated to balance the data. The results show that ensemble mod

Author

Dershaye Mulugeta

Language

EN