Comparative Study of Machine Learning Algorithms for Telecom Customer Churn Prediction

Abstract

Machine learning has become an indispensable tool for predictive analysis across various domains, including business, healthcare, and finance. This study aims to compare and evaluate the performance of four prominent machine learning algorithms, namely Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN), for the specific application of predicting customer churn in a telecom company.

The study utilizes a dataset of customer churn within a telecom company. Prior to training and testing the algorithms, data preprocessing techniques, such as data cleaning, feature selection, and normalization, were applied to the dataset. The performance of the algorithms was meticulously assessed using a suite of metrics, including accuracy, precision, recall, and F1-score.

The results demonstrated that Random Forest and SVM surpassed both KNN and ANN in terms of accuracy and F1-score. However, SVM exhibited a higher precision but lower recall compared to Random Forest. KNN consistently displayed the lowest performance amongst the four algorithms. The study also delved into the feature importance of the algorithms, uncovering that the most significant factors contributing to customer churn were contract length, monthly charges, and total charges.

Overall, the study concludes that Random Forest and SVM emerge as the most suitable algorithms for predictive analysis of customer churn within a telecom company. Nonetheless, the selection of the most appropriate algorithm may vary depending on the specific problem and dataset. The findings of this study hold valuable insights for businesses and researchers, empowering them to make well-informed decisions when choosing the ideal machine learning algorithm for predictive analysis.

Keywords

Machine Learning, Predictive Analysis, Customer Churn, Telecom, Random Forest, Support Vector Machines, SVM, K-Nearest Neighbors, KNN, Artificial Neural Networks, ANN, Feature Importance

Comparative Study of Machine Learning Algorithms for Telecom Customer Churn Prediction