What are the statement problem associated with the analysis of customer retention using ensemble ML models?
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The analysis of customer retention using ensemble machine learning models faces several challenges. Firstly, the issue of customer churn is critical due to rising competition and customer migration, leading to the need for effective retention strategies . Secondly, the performance of predictive models is significantly impacted by imbalanced real-world datasets, making model selection a challenging task . Additionally, the high cost of acquiring new customers emphasizes the importance of retaining existing ones, making accurate churn prediction crucial for long-term profitability . Finally, while ensemble learners like bagging, boosting, and stacking show promise in improving prediction accuracy, the selection of the most suitable model remains a complex task due to varying performance metrics and dataset characteristics .
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22 Jan 2021 13 Citations | The paper addresses the problem of customer churn in banks by utilizing ensemble learning algorithms like Catboost, Lightgbm, and Random Forest to predict churn with high accuracy. |
1 Citations | The problem statement involves enhancing customer retention strategies in financial organizations by utilizing ensemble machine learning models to predict churn and identify key features for improved accuracy. |
| The primary problem addressed is the challenge of selecting the best predictive model due to imbalanced datasets, impacting customer retention analysis using ensemble ML models. | |
| The main issue addressed is customer churn prediction in banks due to competition and customer migration, with a focus on using ensemble classifiers for higher performance in predicting customer behavior. | |
| The main problem addressed is customer churn in banks due to competition, leading to the use of ensemble classifiers like random forest and gradient boosting for improved prediction performance. |
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