research-article

Product marketing prediction based on XGboost and LightGBM algorithm

Authors:

Jiyu Wu,

Zhenzhang LiAuthors Info & Claims

AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

Pages 150 - 153

https://doi.org/10.1145/3357254.3357290

Published: 16 August 2019 Publication History

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Abstract

The XGboost and LightGBM algorithm performs predictive analysis of sales volume in the product sales data set. The principle of XGboost and LightGBM algorithm is studied, the predicted objects and conditions are fully analyzed, and the algorithm parameters and data set characteristics are compared. The results show that n_estimators have a small effect on the prediction of model XGboost, while gamma has a large effect on the prediction of model XGboost. Learning_rate has a small impact on LightGBM prediction, while n_estimators have a large impact on LightGBM prediction. Finally, the optimal parameters were obtained, and the sales volume from January to October 2015 was predicted based on the optimal parameters, and RMSE values of the two algorithms were obtained. Statistical analysis shows that there is no significant difference between the two algorithms in the optimal prediction results after adjusting their own parameters.

References

[1]

E. P. Kao and M. S. Smith, "Discounted and per unit net revenues and costs of product warranty: The case of phase-type lifetimes," Manage. Sci., vol. 39, no. 10, pp. 1246--1254, 1993.

Digital Library

Google Scholar

[2]

Wei Xie, Zhi-Sheng Ye, Aggregate Discounted Warranty Cost Forecast for a New Product Considering Stochastic Sales, IEEE Transactions on Reliability, 65(2016) 486 -- 497.

Crossref

Google Scholar

[3]

S. S. Ja, V. G. Kulkarni, A. Mitra, and J. G. Patankar, "Warranty reserves for nonstationary sales processes," Naval Res. Logistics, vol. 49, no. 5, pp. 499--513, 2002.

Crossref

Google Scholar

[4]

Breiman, L. (1996a). "Bagging Predictors." Machine Learning, 26, 2, 123--140.

Google Scholar

[5]

(1996b). "Heuristics of Instability and Stabilization in Model Selection." The Annals of Statistics, 24, 2350--2383.

Google Scholar

[6]

Hoeting, J. A., Madigan, D., Raftery, A., and Volinsky, C. T. (1999). "Bayesian Model Averaging: A Tutorial (with discussion)." Statistical Science, 14, 4, 382--117.

Google Scholar

[7]

L. Torlay. M. Perrone-Bertolotti. E. Thomas., M. Baciu" Machine learning-XGBoost analysis of language networks to classify patients with epilepsy", Brain Informatics (2017) 4:159--169.

Google Scholar

[8]

Mesut Gumus and Mustafa S. Kiran, "Crude Oil Price Forecasting Using XGBoost", (UBMK'17) 2nd International Conference on Computer Science and Engineering.

Google Scholar

[9]

Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang and Chih-Jen Lin, "LIBLINEAR: A Library for Large Linear Classification" Journal of Machine Learning Research 9 (2008) 1871--1874.

Digital Library

Google Scholar

[10]

Feldman, David, and Shulamith Gross. "Mortgage default: classification trees analysis." The Journal of Real Estate Finance and Economics 30.4 (2005): 369--396.

Crossref

Google Scholar

[11]

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.-Y., . LightGBM: A highly efficient gradient boosting decision tree. Adv. Neur. Infor. Process. Sys.2017, 30, 3146--3154

Google Scholar

[12]

Xu ZG. Complex production process prediction model based on EMD-XGBOOST-RLSE. Proceedings of 2017 9th International Conference on Modelling, Identification and Control (ICMIC 2017). Kunming, China. 2017. 8.

Crossref

Google Scholar

[13]

https://www.kaggle.com/c/competitive-data-science-predict-future-sales/data

Google Scholar

Cited By

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Peng JPan FLi JDuan Y(2024)Research on Training Performance Prediction Model Based on LightGBMTransactions on Computer Science and Intelligent Systems Research10.62051/848177395(1470-1475)Online publication date: 12-Aug-2024
https://doi.org/10.62051/84817739
Nguyen KTran TNguyen H(2024)Research on the Influence of Hyperparameters on the LightGBM Model in Load ForecastingEngineering, Technology & Applied Science Research10.48084/etasr.826614:5(17005-17010)Online publication date: 9-Oct-2024
https://doi.org/10.48084/etasr.8266
Jayashree RVenkata Subramanian J(2024)Optimized Deep Learning-Based Intrusion Detection Using WOA With LightGBMInnovative Machine Learning Applications for Cryptography10.4018/979-8-3693-1642-9.ch005(91-104)Online publication date: 12-Apr-2024
https://doi.org/10.4018/979-8-3693-1642-9.ch005
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Index Terms

Product marketing prediction based on XGboost and LightGBM algorithm
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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Published In

AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 2019

198 pages

ISBN:9781450372299

DOI:10.1145/3357254

Conference Chairs:
Li Ma
North China University of Technology, China
,
Xu Huang
University of Canberra, Australia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 16 August 2019

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AIPR 2019: 2019 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 16 - 18, 2019

Beijing, China

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Peng JPan FLi JDuan Y(2024)Research on Training Performance Prediction Model Based on LightGBMTransactions on Computer Science and Intelligent Systems Research10.62051/848177395(1470-1475)Online publication date: 12-Aug-2024
https://doi.org/10.62051/84817739
Nguyen KTran TNguyen H(2024)Research on the Influence of Hyperparameters on the LightGBM Model in Load ForecastingEngineering, Technology & Applied Science Research10.48084/etasr.826614:5(17005-17010)Online publication date: 9-Oct-2024
https://doi.org/10.48084/etasr.8266
Jayashree RVenkata Subramanian J(2024)Optimized Deep Learning-Based Intrusion Detection Using WOA With LightGBMInnovative Machine Learning Applications for Cryptography10.4018/979-8-3693-1642-9.ch005(91-104)Online publication date: 12-Apr-2024
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LightGBM: An Effective miRNA Classification Method in Breast Cancer Patients

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