Summary of Enhancing Retail Sales Forecasting with Optimized Machine Learning Models, by Priyam Ganguly and Isha Mukherjee
Enhancing Retail Sales Forecasting with Optimized Machine Learning Models
by Priyam Ganguly, Isha Mukherjee
First submitted to arxiv on: 17 Oct 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 This paper proposes an innovative approach to improve retail sales forecasting using machine learning (ML) techniques. By leveraging Random Forest (RF), Gradient Boosting (GB), Support Vector Regression (SVR), and XGBoost, the study demonstrates that ML models can outperform traditional methods like LR in predicting future sales. The proposed RF model is optimized through hyperparameter tuning using randomized search cross-validation, allowing it to capture complex patterns in the data that traditional methods miss. The optimized RF model achieves an impressive R-squared value of 0.945, significantly higher than the initial RF model and traditional LR. This research highlights the importance of advanced ML techniques in predictive analytics, offering a significant improvement over traditional methods and contemporary models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about using special computer programs to make better predictions about how much stuff will be sold at stores in the future. Right now, these predictions aren’t very accurate because they don’t take into account things like changes in weather or what people are buying and selling online. The researchers used some advanced math tools to create a new program that can predict sales more accurately than usual. They tested it and found that it was way better at guessing how much stuff would be sold than the old methods. This could help stores make better decisions about what products to stock and when, which could save them money and time in the long run. |
Keywords
» Artificial intelligence » Boosting » Hyperparameter » Machine learning » Random forest » Regression » Xgboost
