Recency-Frequency-Monetary Analysis and Recommendation System using Apriori Algorithm on E-Commerce Sales Data
Bibliographic record
Abstract
The Recommendation Systems and Operation Analysis at Amazon.com account for a significant portion, specifically 35%, of the company’s revenue. By providing product recommendations during online shopping, these systems play a crucial role in increasing the average order value, click-through rates, and email conversions. This is achieved through intelligent predictions that anticipate what items customers are likely to purchase next. With the help of big data and data mining, this research focuses on building an online product recommendation engine which predicts products a customer is most likely to buy based on the customer’s shopping history as well as browsing data. In this paper, we aim to discuss recent research and practical applications related to RFM, Association Rule, Apriori Algorithm, and Streamlit. Our goal is to build a full stack recommendation engine using Python, Streamlit, Recency Frequency Monetary (RFM) Analysis, and the Association Rule (Apriori Algorithm).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.004 |
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".