Recency-Frequency-Monetary Analysis and Recommendation System using Apriori Algorithm on E-Commerce Sales Data
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
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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