Due to growing market saturation and cheap switching costs, client retention has become a crucial concern in today's fiercely competitive telecom sector. For telecom service providers, reliable Forecast of customer attrition is crucial because staying up to date customers is more cost-effective than recruiting fresh ones . A comparison of many strategies for learning machines to telecom client retention is presented in this article. Using a telecom customer dataset, the study assesses well-known classification techniques as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors [1]. Model efficacy is evaluated and compared using key performance indicators like as accuracy, precision, recall, F1-score, and ROC-AUC [2]. To increase prediction accuracy, processing of information methods comparable managing Lacking values, a characteristic selection, and class imbalance correction are used. The final results of the experiment suggest that collaboration based prototypes provide superior generalization along with dependability than standard algorithms when it comes to forecasting customer attrition. The results of this study can help telecom firms choose appropriate machine learning models to create retention tactics that work, lower churn rates, and enhance customer happiness [3]. Since keeping current customers is far more cost-effective than recruiting new ones, telecommunications providers must be able to estimate customer attrition accurately. Using an operational telecom the database, which is this study compares a number of machine learning algorithms for telecom client retention. To ascertain their prediction efficacy, popular classification methods such as K-Nearest Neighbors, Random Forest, Decision Tree, Support Vector Machines, and and Logistic Regression serve a purpose and assessed. To achieve a thorough comparison, especially when class imbalance is present, model performance is evaluated utilizing critical assessment measures like accuracy, precision, recall, F1-score, and ROC-AUC. To improve model resilience and forecast performance, data preparation techniques are used, such as choosing characteristics, unbalanced correction, while participating handling the absence of values.
Telecom Customer Retention, Customer Churn Prediction[4], Machine Learning Models, Comparative Analysis, Supervised Learning, Classification Algorithms, Predictive Analytics, Customer Behavior Analysis, Data Mining, Telecom Industry [5].
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