Prediction of Bank Customer Potential Using Creative Marketing Based on Exploratory Data Analysis and Decision Tree Algorithm
Bibliographic record
Abstract
Today's bank marketing uses traditional methods, using standard and boring ads to target prospects. Of course, this can impact the banking process, reducing the number of potential banking customers. Banks need to think outside the box and apply creative marketing ideas to drive their profitable marketing development and marketing success potential. Most consumers consider banking a daily necessity, and it is best to avoid it. If they can take a creative approach to bank marketing, that idea can change. Especially when bank marketing integrates creative bank marketing ideas such as gamification, automation, chatbots, and rewards to encourage potential customers to use banking services, therefore; this study uses a decision tree algorithm with the best trash old decisions to perform a classification process on kaggle.com's bank marketing dataset. The classification process uses Python 3 to find the accuracy value of the decision tree algorithm calculation using K-Fold and scale data. This survey achieved classification results with 82% accuracy, 84% recognition, and 85% accuracy with recommendations and creative marketing solutions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".