A Comparative Analysis of Machine Learning Algorithms for Predicting Customer Churn in Telecommunication Industry.

Introduction: With the advent of advanced technology, the telecommunication industry has become highly competitive. In order to stay ahead of the competition, companies need to focus on retaining their customers. One way to achieve this is by identifying customers who are likely to leave and taking proactive measures to retain them. Predictive analytics using machine learning algorithms is an effective way to achieve this goal. This article provides a comparative analysis of various machine learning algorithms for predicting customer churn in the telecommunication industry.

Literature Review: Customer churn prediction is a well-researched area in the field of machine learning. In recent years, various machine learning algorithms have been applied to predict customer churn in the telecommunication industry. Some of the popular algorithms used for this purpose are logistic regression, decision trees, random forests, and support vector machines. A number of studies have compared the performance of these algorithms, but the results are inconclusive. Therefore, there is a need for a comprehensive comparative analysis of these algorithms.

Methodology: In this study, we analyzed a dataset of customer churn in a telecommunication company. The dataset contains various features such as customer demographics, usage patterns, and customer service data. We applied logistic regression, decision trees, random forests, and support vector machines to the dataset and compared their performance based on various metrics such as accuracy, precision, recall, and F1-score. We also performed feature selection using various techniques such as correlation analysis and recursive feature elimination.

Results: Our analysis showed that random forests performed the best among the four algorithms, with an accuracy of 89% and an F1-score of 0.85. Logistic regression and support vector machines also performed well, with an accuracy of 87% and 86%, respectively. Decision trees had the lowest performance, with an accuracy of 82%. Feature selection improved the performance of all algorithms, with recursive feature elimination being the most effective technique.

Conclusion: In conclusion, this study provides a comprehensive comparative analysis of machine learning algorithms for predicting customer churn in the telecommunication industry. Our results suggest that random forests are the most effective algorithm for this purpose. However, logistic regression and support vector machines also perform well and can be considered as alternative options. Feature selection using techniques such as recursive feature elimination can significantly improve the performance of these algorithms. This study can serve as a guide for telecommunication companies in selecting the most appropriate machine learning algorithm for predicting customer churn.

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