An Analysis on the Performance of Rapid Miner and R Programming Language as Data Pre-processing Tools for Unsupervised Form of Insurance Claim Dataset

Authors

  • Thomas ss
  • Sheshasaayee a

Keywords:

Data analytics, data pre-processing, noise removal, clean data, , Rapid Miner, R programming

Abstract

Data Science has emerged as a super science in almost all the sectors of analytics. Data Mining is the key runner and the pillar stone of data analytics. The analysis and study of any form of data has become so relevant in todays’ scenario and the output from these studies give great societal contributions and hence are of great value. Data analytics involves many steps and one of the primary and the most important one is data pre-processing stage. Raw data has to be cleaned, stabilized and processed to a new form to make the analysis easier and correct. Many pre-processing tools are available but this paper specifically deals with the comparative study of two tools such as Rapid Miner and R programming language which are predominantly used by data analysts. The output of the paper gives an insight into the weightage of the particular tool which can be recommended for better data pre-processing

References

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Published

2025-11-25

How to Cite

[1]
S. S. Thomas and A. Sheshasaayee, “An Analysis on the Performance of Rapid Miner and R Programming Language as Data Pre-processing Tools for Unsupervised Form of Insurance Claim Dataset”, Int. J. Comp. Sci. Eng., vol. 7, no. 5, pp. 1–4, Nov. 2025.

Issue

Vol. 7 No. 5 (2019): IJCSE Special Issue Mar Edition

Section

Research Article

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