Churn Prediction Analysis in the Telecom Industry Using PCA and Bagging Techniques
Bibliographic record
Abstract
Customer churn prediction remains an important task for telecom service providers aiming to strengthen customer retention and reduce service attrition.In this study, a predictive framework is proposed by combining Principal Component Analysis (PCA) for dimensionality reduction with several bagged ensemble models, including Random Forest, Decision Tree, SVM, and LightGBM.The proposed models were trained using a real telecom dataset that reflects customer usage trends, service choices, and subscription histories.PCA serves to compress the feature space and eliminate data redundancy, while bagging contributes to performance stability, particularly when dealing with imbalanced classes.Among the evaluated models, LightGBM delivered the most promising results, achieving an accuracy of 80.13% and an AUC of 0.9069.These results demonstrate that PCA-supported ensemble techniques can identify meaningful churn indicators; however, the work does not compare performance against non-PCA or non-bagged variants, and therefore no superiority claims are made.The framework can assist telecom providers in understanding churn tendencies and may be further improved by integrating time-based behavioral patterns, customer feedback data, or real-time prediction capabilities in future studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".