Predictive Analytics for Customer Retention: A CatBoost Model for Churn Detection
Bibliographic record
Abstract
Competitive marketplaces make customer retention a priority since businesses spend more on acquiring new customers than on keeping current ones. The research uses CatBoost as its algorithm because it represents a gradientboosting model that excels at processing categorical data to improve churn prediction accuracy. An analysis of the 440,834entry customer churn dataset used our team to complete thorough preprocessing alongside feature engineering and model training activities. The CatBoost model demonstrated superior performance when compared to logistic regression, random forest and XGBoost because it achieved higher accuracy levels and lower computational resources and overfitting. The examine of metrics shows outstanding success where the training accuracy matched validation accuracy at 99.89 % and 99.88 % respectively and precision scored 100 % and the F1-score achieved 99.90 %. CatBoost's powerful categorical feature abilities together with its automated hyperparameter execution maintained a validation loss at a minimal 0.71 %. This paper demonstrates why CatBoost functions well with real-life customer retention problems since it delivers tangible solutions that help companies prevent customer attrition. The research contributes to academic analysis of machine learning models along with real-world CRM strategies due to optimized predictive processes and superior accuracy achievement.
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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 | 0.000 | 0.000 |
| 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".