Bibliographic record
Abstract
Research This research applies the K-Means Clustering method to identify groups of hotel guests based on their room preferences, duration of stay and level of satisfaction. The results of this research have great potential to help hotel management improve service quality and marketing strategies. The main focus of the research is on guest satisfaction and resource optimization. Using K-Means Clustering data analysis, this research aims to uncover common patterns among hotel guests, enabling management to allocate resources more efficiently. By understanding guest preferences regarding rooms and length of stay, management can better customize their hotel services and facilities. Apart from that, this research also aims to increase guest satisfaction by identifying factors that influence satisfaction levels. With a deeper understanding of guest needs and preferences, management can take appropriate steps to improve the guest experience. The results of this research can also have significant marketing implications. By understanding different guest profiles, hotels can design more effective marketing strategies and target specific promotions to specific groups of guests based on their characteristics. Overall, this research has the potential to help hotels improve guest satisfaction and operational efficiency through better understanding guest preferences and grouping them based on certain factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".