Multi-model telecom churn prediction

Customer turnover is likely to be significant in the telecom business due to its dynamic and competitive nature. Traditional measures of performance are inadequate in such a fluid environment to accurately portray organisational objectives. The reason behind this is because the performance measurements are not in line with the company goals. A multi-model telecom churn prediction (MMTCP) with minority upliftment techniques is presented in this work. It can handle data imbalance successfully and has a loss function that separates loss into two parts: loss due to incorrect prediction and loss due to unavoidable loss. First, utilising a set of training data and a number of diverse base learners, MMTCP generates predictions at the first level; second, using these predictions as a starting point, it uplifts the minority in the model. Gradient-boosted trees and naïve bayes make up the first stage, while one-class SVM is the basis of the second combiner stage. As compared to both current classifier models and the state-of-the-art churn prediction methods from literature, the experimental findings suggest that the MMTCP model exhibits 1 to 7% greater churn prediction levels and 1.3 to 1.7 times decreased loss levels.