Classification of customer call details records using Support Vector Machine (SVMs) and Decision Tree (DTs)

On a daily basis, telecom businesses create a massive amount of data. Decision-makers underlined that acquiring new customers is more difficult than maintaining current ones. Further, existing churn customers' data may be used to identify churn consumers as well as their behavior patterns. This study provides a churn prediction model for the telecom industry that employs SVMs and DTs to detect churn customers. The suggested model uses classification techniques to churn customers' data, with the Support Vector Machine (SVMs) method performing well 98.36 % properly categorized instances) and the Decision Tree (DTs) approach performing poorly 33.04 % and the decision tree algorithm deliver outstanding results

Predictive analytics of Churn Customers Calling Details Records using Classification by Clustering (CBC) dealing with Supervised Machine Learning Algorithms

Telecom companies generate enormous amounts of data regularly. The telecom Decision makers that obtaining new customers is more challenging than sustaining existing ones. Furthermore, data from existing churn customers may be utilized to detect churn clients and their patterns of behavior. This research develops a model of churn prediction for the telecommunication business, which uses NB, SVM, DT, and RDF to detect churn clients. The proposed model churns customers' data using classification techniques, with the Random Forest (RDF) method performing well (95.94 % correctly categorized instances), the Decision Tree (DTs) providing classification accuracy (95.40 %), the Naïve Bayes (NB) provided classification accuracy (89.58 %), and the Support Vector Machine (SVMs) provided classification accuracy (71.08 %). The four different classification algorithms' predictions and observations are compared, with a percentage of 71 percent equality and 29 percent variation