An Improved Gradient Boosted Algorithms Based Solutions Predictive Model (Trade)

Authors

  • P. Senthil Associate Professor in MCA Computer Science, Kurinji College of Arts and Science, Tiruchirappalli, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajms-2016.5.1.1198

Abstract

In this paper, we describe a general process on how to integrate different types of predictive models within an organization to fully leverage the benefits of predictive modeling. The three major predictive modeling applications discussed in this paper are marketing, pricing, and GBAAlgorithm models. These applications have been well applied and published over the past several years for the Property and Casualty Manufacturing Industry, but this paper and discussions focused on their individual application. We believe that significant value can be realized if they are fully integrated, offering manufacturing companies the opportunity to take an enterprise wide view of managing their business through analytics. Therefore, the paper will discuss a general process on how they can be integrated and how the integrated result can assist insurance companies with managing the complex insurance business, such as minimizing the GBA(Gross Building Area) Algorithm cycle and achieving profitable growth and reacting to external market forces faster than their competition.

References

Dan Gabriel Cacuci, & Aurelian Florin Badea. (2015). Predictive modeling methodology for obtaining optimally predicted results with reduced uncertainties: Illustrative application to a simulated solar collector facility. Solar Energy, 119, 486-506.

Cacuci, D. G., & Badea, A. F. (2013). Predictive model for the growth kinetics of Staphylococcus aureus in raw pork developed using Integrated Pathogen Modeling Program (IPMP). Meat Science, 107, 20-25.

Radha krishnan, K. et. al., (2015). Evaluation and predictive modeling the effects of spice extracts on raw chicken meat stored at different temperatures. Journal of Food Engineering, 166, 29-37.

Radha Krishnan, K., Babuskin, S., Azhagu Saravana Babu, P., Sivarajan, M., & Sukumar, M. (2015). A supervised machine-learning approach towards geochemical predictive modelling in archaeology. Journal of Archaeological Science, 59, 80-88.

Nanukuttan, S. V., Basheer, P. A. M., McCarter, W. J., Tang, L., Holmes, N., Chrisp, T. M., Starrs, G., & Magee, B. (2015). A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings. Energy and Buildings, 97, 86-97.

Huang, H., Chen, L., & Hu, E. (2015). Network-based approach for predictive accident modelling. Safety Science, 80, 274-287.

Baksh, A. A., Khan, F., Gadag, V., & Ferdous, R. (2015). Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms. Energy, 86, 393-402.

Zeng, Y., Zhang, Z., & Kusiak, A. (2015). Fuzzy modeling and predictive control of superheater steam temperature for power plant. ISA Transactions, 56, 241-251.

Wu, X., Shen, J., Li, Y., & Lee, K. Y. (2015). Minimal important change (MIC) based on a predictive modeling approach was more precise than MIC based on ROC analysis. Journal of Clinical Epidemiology, In Press, Corrected Proof, Available online 28 March 2015.

Terluin, B., Eekhout, I., Terwee, C. B., & de Vet, H. C. W. (2015). A summary of fault modelling and predictive health monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 60–61, 252-272.

Downloads

Published

08-04-2016

How to Cite

Senthil, P. (2016). An Improved Gradient Boosted Algorithms Based Solutions Predictive Model (Trade). Asian Journal of Managerial Science, 5(1), 30–40. https://doi.org/10.51983/ajms-2016.5.1.1198