Geographical Factors Affecting Grubhub’s Business amid COVID-19 Pandemic
Bibliographic record
Abstract
During the COVID-19 outbreak, the food delivery market in the United States began to thrive. However, Grubhub, one of the largest food delivery platforms, did not capitalize on this opportunity and experienced severe net losses and a significant decline in market share. Despite the popularity of research on the demographic factors affecting the food delivery market, geographic factors were poorly concerned. In this paper, more attention was paid to reveal the geographical factors that led to the recession of Grubhub under the pandemic. Four machine learning models, namely Linear Regression, Support Vector Regression, Bayesian Ridge Regression, and Elastic Net, were applied to identify the unusual decrease in the net income of Grubhub using Python. This paper then explore the geographical factors by visualizing the business and demographic data. The predicted results show that Grubhub's performance was far below its average over the past two years. Furthermore, by data visualization, it is found that a major geographical factor preventing Grubhub from capturing opportunities is its lack of business expansion into suburban and rural areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".