The main goal of this analysis study is predicting the sales price and compare the algorithm which algorithm provide high accuracy. Finally select the best algorithm to predict the sales price at early stage.
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. 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 Support Vector Model Algorithm, which
shows better result in sales prediction.
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