Risk Decision and Predicting of Customer Churn Based on Principal Component Analysis
Bibliographic record
Abstract
This study will establish a predictive analytics model that uses churn prediction models to anticipate customer churn by evaluating their risk of churn. These models are successful in focusing customer retention marketing activities on the fraction of the customer base that is most prone to churn because they generate a short, prioritized list of probable defectors. We will start with exploratory data analysis in this paper. We will get a quick summary of the data using this way. The data is then further analyzed using feature engineering and feature selection. Finally, the target variables will be visualized using Principal Component Analysis (PCA). Using the Kolmogorov-Smirnov (KS) score test, features are picked after calculating the churn detection rate and comparing it to the average churn rate. The best part is then identified using Cross-Validated Recursive Feature Elimination. The accuracy, auc, and ks of each model were assessed after training, and the Gaussian naive Bayes, Logistic regression, and Neural network were finally picked by comparison. Model stacking is a technique for comparing model performance and ultimately deciding which model to utilize.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".