Gold Price Prediction Using Naïve Bayes Regression Algorithm and ANN

Authors

  • Krishna Gandhi
  • Miss. Niti Shah

DOI:

https://doi.org/10.37591/joosd.v4i2.1012

Abstract

Abstract

In this day and age, the amount of stored data has been immensely expanding step by step which is by and large in the organized or unstructured shape and cannot be utilized for any processing to remove valuable data, so a few systems, for example, synopsis, arrangement, grouping, data extraction, expectation and perception are accessible for the same, which goes under the classification of content examination. Forecast is the branch of content investigation. Forecast is the procedure of examining the verifiable information and contemplating future occasions. The global market has as of late, pulled in a great deal of consideration, and the cost of gold is moderately higher than its authentic pattern. Gold value fluctuation drift forecast is an imperative issue in the financial world. Indeed, even little changes in prescient execution can make bunches of profits. This research work gives the correlation between Naïve Bayes regression and artificial neural network for predict the gold cost. According to result, we can state that Naïve Bayes regression has better forecast according to their error rate and execution.

Keywords: Gold price, prediction, Naïve Bayes regression

Cite this Article

Krishna Gandhi, Niti Shah. Gold Price Prediction Using Naïve Bayes Regression Algorithm and ANN. Journal of Open Source Developments. 2017; 4(2): 6–9p.


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Published

2017-07-18

Issue

Vol. 4 No. 2 (2017)

Section

Research Articles

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