| Autor: |
Shaniba Ibrahim, Sunitha Cheriyan, Saju Mohanan, Susan Treesa |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE). |
| Popis: |
Intelligent Decision Analytical System requires integration of decision analysis and predictions. Most of the business organizations heavily depend on a knowledge base and demand prediction of sales trends. The accuracy in sales forecast provides a big impact in business. Data mining techniques are very effective tools in extracting hidden knowledge from an enormous dataset to enhance accuracy and efficiency of forecasting. The detailed study and analysis of comprehensible predictive models to improve future sales predictions are carried out in this research. Traditional forecast systems are difficult to deal with the big data and accuracy of sales forecasting. These issues could be overcome by using various data mining techniques. In this paper, we briefly analyzed the concept of sales data and sales forecast. The various techniques and measures for sales predictions are described in the later part of the research work. On the basis of a performance evaluation, a best suited predictive model is suggested for the sales trend forecast. The results are summarized in terms of reliability and accuracy of efficient techniques taken for prediction and forecasting. The studies found that the best fit model is Gradient Boost Algorithm, which shows maximum accuracy in forecasting and future sales prediction. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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