4. CONCLUSIONS
This paper aims to describe and define the concept of artificial neural networks along with big
data analytics in order to predict accuracy of data. Big data analytics
is promising right
direction for predicting the accuracy of huge assortment of data. As there is an increase in
demand of big data analytics in which the information is been collected from different sources
which to overcome the uncertainty of data in prediction. The predictive analysis of data is
done by selection, sampling in dataset format and then modeling
the data for refine the
information. This paper provides with a concept of large volume of data which is to collected
from different sources and been loaded in the form of data sets. The data set which is further
classified into trained data set and evaluation data set which loaded in the form of CSV
format. There after it is loaded into matrix which is to be processed in order to find the
accuracy of data. There exists huge volume of data which can produce uncertainty in the
results which leads inaccuracy. The huge assortment of data is considered in the form of data
set is represented as plain text which leads to multiple rows that can extend to millions, thus
which become congestion for processing the data efficiently. By
using the concept of paper
Ch. Mamatha, P. Buddha Reddy, M.A. Ranjit Kumar and Shrawan Kumar
http://iaeme.com/Home/journal/IJCIET
215
editor@iaeme.com
we set threshold limit in order to process the accurate results the prediction should not extend
beyond the origin which results in accurate prediction.
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