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Title: :  Contemporary accession for GST using K-means algorithm and data mining
PaperId: :  9487
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 5 Issue 1 2019
DUI:    16.0415/IJARIIE-9487
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Dnyanesh JindamMarathwada Mitra Mandal's Institute of Technology
Akshay GaikwadMarathwada Mitra Mandal's Institute of Technology
Aishvarya PatilMarathwada Mitra Mandal's Institute of Technology
Namrata MasihMarathwada Mitra Mandal's Institute of Technology
Swapnil ChaudhariMarathwada Mitra Mandal's Institute of Technology

Abstract

Computer Engineering
GST (Goods and service tax), K-Means clustering
GST application is an electronic application which is utilized to make mindfulness in the general public about the GST standards and the different taxes which have been actualized in each product. Goods and services tax which is typically called as GST is a taxing system executed by the present government with the goal of diminishing the tax complexity in the system. The goods and service tax is aimed at making a single unified marketplace that is intended to make a valuable situation for both the corporate and public sector. The tax replaced existing multiple cascading taxes levied by the central and state governments. Goods and services are partitioned into five expense sections for collection of tax - 0%, 5%, 12%, 18% and 28%. However, Petroleum products, alcoholic drinks, electricity, are not taxed under GST and rather are taxed independently by the individual state governments, according to the past expense routine. There is an uncommon rate of 0.25% on rough precious and semi-precious stones and 3% on gold. In addition a cess of 22% or other rates on top of 28% GST applies on couple of things like aerated drinks, luxury cars and tobacco products. Pre-GST, the statutory assessment rate for most merchandise was about 26.5%, Post-GST, most merchandise are relied upon to be in the 18% tax range.The tax rates, rules and regulations are governed by the GST Council which consists of the finance ministers of centre and all the states. The system uses K Means Clustering method which is a type of data analysis technique. Cluster an analysis does the task of grouping a set of the element in a way that similar types of elements are placed in clusters. The algorithm works sequentially to assign data point to one of the K groups.

Citations

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IJARIIE Dnyanesh Jindam, Akshay Gaikwad, Aishvarya Patil, Namrata Masih, and Swapnil Chaudhari. "Contemporary accession for GST using K-means algorithm and data mining" International Journal Of Advance Research And Innovative Ideas In Education Volume 5 Issue 1 2019 Page 537-541
MLA Dnyanesh Jindam, Akshay Gaikwad, Aishvarya Patil, Namrata Masih, and Swapnil Chaudhari. "Contemporary accession for GST using K-means algorithm and data mining." International Journal Of Advance Research And Innovative Ideas In Education 5.1(2019) : 537-541.
APA Dnyanesh Jindam, Akshay Gaikwad, Aishvarya Patil, Namrata Masih, & Swapnil Chaudhari. (2019). Contemporary accession for GST using K-means algorithm and data mining. International Journal Of Advance Research And Innovative Ideas In Education, 5(1), 537-541.
Chicago Dnyanesh Jindam, Akshay Gaikwad, Aishvarya Patil, Namrata Masih, and Swapnil Chaudhari. "Contemporary accession for GST using K-means algorithm and data mining." International Journal Of Advance Research And Innovative Ideas In Education 5, no. 1 (2019) : 537-541.
Oxford Dnyanesh Jindam, Akshay Gaikwad, Aishvarya Patil, Namrata Masih, and Swapnil Chaudhari. 'Contemporary accession for GST using K-means algorithm and data mining', International Journal Of Advance Research And Innovative Ideas In Education, vol. 5, no. 1, 2019, p. 537-541. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Contemporary_accession_for_GST_using_K_means_algorithm_and_data_mining_ijariie9487.pdf (Accessed : 22 May 2020).
Harvard Dnyanesh Jindam, Akshay Gaikwad, Aishvarya Patil, Namrata Masih, and Swapnil Chaudhari. (2019) 'Contemporary accession for GST using K-means algorithm and data mining', International Journal Of Advance Research And Innovative Ideas In Education, 5(1), pp. 537-541IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Contemporary_accession_for_GST_using_K_means_algorithm_and_data_mining_ijariie9487.pdf (Accessed : 22 May 2020)
IEEE Dnyanesh Jindam, Akshay Gaikwad, Aishvarya Patil, Namrata Masih, and Swapnil Chaudhari, "Contemporary accession for GST using K-means algorithm and data mining," International Journal Of Advance Research And Innovative Ideas In Education, vol. 5, no. 1, pp. 537-541, Jan-Feb 2019. [Online]. Available: https://ijariie.com/AdminUploadPdf/Contemporary_accession_for_GST_using_K_means_algorithm_and_data_mining_ijariie9487.pdf [Accessed : 22 May 2020].
Turabian Dnyanesh Jindam, Akshay Gaikwad, Aishvarya Patil, Namrata Masih, and Swapnil Chaudhari. "Contemporary accession for GST using K-means algorithm and data mining." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 5 number 1 (22 May 2020).
Vancouver Dnyanesh Jindam, Akshay Gaikwad, Aishvarya Patil, Namrata Masih, and Swapnil Chaudhari. Contemporary accession for GST using K-means algorithm and data mining. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2019 [Cited : 22 May 2020]; 5(1) : 537-541. Available from: https://ijariie.com/AdminUploadPdf/Contemporary_accession_for_GST_using_K_means_algorithm_and_data_mining_ijariie9487.pdf
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