Summary of Unlocking Your Sales Insights: Advanced Xgboost Forecasting Models For Amazon Products, by Meng Wang et al.
Unlocking Your Sales Insights: Advanced XGBoost Forecasting Models for Amazon Products
by Meng Wang, Yuchen Liu, Gangmin Li, Terry R.Payne, Yong Yue, Ka Lok Man
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solution leverages the XGBoost model to predict sales for consumer electronics products on Amazon, a crucial factor in shaping corporate operations and decision-making processes. Initially, predicting sales volume yielded unsatisfactory results, but replacing sales volume data with sales range values improved accuracy. Compared to traditional models, XGBoost demonstrates superior predictive performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting the future transaction volume is vital for businesses. This study uses a special algorithm called XGBoost to forecast sales of electronic products on Amazon. The team tried predicting exact sales numbers but got disappointing results. However, when they used ranges instead (e.g., “10-20 units sold”), their model performed much better. XGBoost outperformed traditional methods in this task. |
Keywords
* Artificial intelligence * Xgboost
