Market basket analysis of drug store in quarter period since 2018 to 2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many firms' distribution strategies have had to adapt due to the impact of the strong competition in the business of selling planting equipment as a result of the Covid-19 scenario, and individuals are more likely to plant trees. As a result, there are more competitors in the tree planting market. Our company is one of the indoor planting equipment suppliers. In addition to increasing sales and profit, I'll use Method Market Basket Analysis to investigate client buying behavior. To increase the likelihood of selling products by changing the marketing plan in light of the study conclusions.The purpose of this research is to use Market Basket Analysis to adjust customer behavior in order to determine which products customers buy together. Then, retailer can manage the winning products to set the promotion or marketing activity.For the result from 3 years (2018 , 2019 and 2020) the winning product that should to drive the sale and set promotion or campaign is H034&H036,H034&H003 and H034&B001. And retailer should to use this method to analyzed customer behavior which product that customer buy it’s together in many times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.325 | 0.003 |
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 it