3 publications
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Neural Network Machine Learning Analysis for Noisy Data: R ProgrammingBy Yagyanath Rimal
Abstract: Abstract: This review paper clearly discusses the compression between Neural Network Machine Learning Analysis for Noisy Data: R Programming. Although there is large gap between data analysis to analyze overfitting and multicollinearity problems in data sets. Its primary purpose is to explain the machine learning procedures using neural network whose data structure were cross validation using R software whose outputs were sufficiently explain with various intermediate output and graphical interpretation …
Published in Recent Trends in Programming languages · Vol. 6, Issue 3, 2019 · pp. 1–10 Read article →
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Machine Learning Cluster Analysis for Large Categorical Data Using R ProgrammingBy Yagyanath Rimal
Abstract: AbstractThis review paper clearly discusses the compression between various types of cluster analysis of large categorical data sets. Although there is large gap between the choice of cluster analysis for large data in research design. Its primary purpose is to explain the simplest way of clustering analysis whose data structure were wide scattered using R software whose outputs were sufficiently explain with various intermediate output and graphical interpretation to reach …
Published in Recent Trends in Programming languages · Vol. 6, Issue 2, 2019 · pp. 23–34 Read article →
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Regression Analysis of Research Data using R ProgrammingBy Yagyanath Rimal
Abstract: ABSTRACTThe goal of the review paper is discussing the relationship between various types of regression analysis whose output was sufficiently analyzed using R programming. The primary purpose of this paper is to explain the relationship of linear, multiple, quantile and polynomial regression models to achieve final conclusion with different data sets. Therefore, this paper presents the easiest way of regression analysis commands and R programming strengths for of data analysis. …
Published in Recent Trends in Programming languages · Vol. 6, Issue 2, 2019 · pp. 42–50 Read article →