Advanced International Journal for Research

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Volume 7 Issue 5
September-October 2026

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Customer Churn Prediction Using a Multilayer Feedforward Neural Network with Behavioral and Service-Usage Features

Author(s) Ms. Shabista Khan, Prof. Dipti Sharma
Country India
Abstract This paper reconstructs a customer-churn classification experiment from a supplied 64,374-record CSV, MATLAB source file, and stored training figures. Ten demographic, relationship, usage, support, payment, subscription, contract, spend, and interaction variables are used; CustomerID is excluded. The executable script trains a feedforward fitting network on records 1–10,000 and evaluates rounded outputs on records 10,001–13,001, a 3,001-record external holdout. The network has five hidden layers with ten neurons each. Stored MATLAB artefacts identify Levenberg–Marquardt backpropagation, internal random train/validation/test division, termination at epoch 24, best validation mean-squared error of 0.032864 at epoch 18, final training performance of 0.00902, and an all-development regression coefficient of 0.93989. The external figure reports 95.77% accuracy, corresponding to 4.23% binary classification error under the script’s calculation. A source variable labels this percentage as MAPE, but the code computes no percentage-error denominator; the correct interpretation is classification error. No active particle-swarm-optimization routine is present. The work contributes an implementation-aligned model description, a full-file data audit, separation of internal validation from external holdout evaluation, and an explicit statement of unavailable evidence. Because the prediction vector, random seed, MAT-file, and trained object are absent, confusion-matrix metrics, AUC, calibration, and exact rerun claims are not fabricated. The manuscript closes with a protocol for a reproducible comparative experiment.
Keywords customer churn; feedforward neural network; multilayer perceptron; Levenberg–Marquardt; behavioral features; reproducibility
Field Engineering
Published In Volume 7, Issue 5, September-October 2026
Published On 2026-09-07
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E-ISSN 3048-7641
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