Performance Analysis of Customer Attrition Trend in Telecommunication Sectors using Machine Learning and Deep Learning
Bibliographic record
Abstract
Customer churn poses a significant challenge to telecommunication sectors, and making correct predictions is crucial for effective resource allocation and profitability of the company. Finding new customers is a more challenging task than retaining the old ones, highlighting the importance of proactive churn management. This research work investigates machine learning algorithms to analyze performances of different algorithms. We leverage 8 widely used supervised algorithms for classification. These are Adaboost, XGBoost and LightGBM gradient boosting techniques, Random Forest (RF), k-Nearest Neighbors (KNN), Support Vector Machines(SVM), Naïve Bayes(NB), Logistic Regression(LR), and Deep learning to build predictive models capable of identifying customers’ churn. Each model is trained using a telecom dataset encompassing various customer behavioral and demographic features. The assessment of the models is done using a confusion matrix to compute accuracy scores, allowing for a comparative analysis of the algorithms' effectiveness. The results provide ideas about advantages and limitations of the algorithms in telecommunication sector churn, informing the selection of optimal models for practical deployment and contributing to the application of more efficient customer retention strategies to take measures by the company beforehand. This research is used to enhance customer relationship management in the telecommunications sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".