Contemporary accession for GST using K-means algorithm and data mining
Abstract & Details
Research Area
Computer Engineering
Keywords
GST (Goods and service tax)
K-Means clustering
Abstract
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.
License
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Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Dnyanesh Jindam | Marathwada Mitra Mandal's Institute of Technology |
| 2 | Akshay Gaikwad | Marathwada Mitra Mandal's Institute of Technology |
| 3 | Aishvarya Patil | Marathwada Mitra Mandal's Institute of Technology |
| 4 | Namrata Masih | Marathwada Mitra Mandal's Institute of Technology |
| 5 | Swapnil Chaudhari | Marathwada Mitra Mandal's Institute of Technology |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Jindam, Dnyanesh, Gaikwad, Akshay, Patil, Aishvarya, Masih, Namrata, & Chaudhari, Swapnil (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.
MLA Style
Jindam, Dnyanesh, et al. "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, pp. 537-541.
IEEE Style
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, 2019.
Vancouver Style
Jindam Dnyanesh, Gaikwad Akshay, Patil Aishvarya, Masih Namrata, Chaudhari Swapnil. Contemporary accession for GST using K-means algorithm and data mining. International Journal of Advance Research and Innovative Ideas In Education. 2019;5(1):537-541.
Harvard Style
Jindam, Dnyanesh, Gaikwad, Akshay, Patil, Aishvarya, Masih, Namrata, & Chaudhari, Swapnil (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. 537-541.
Chicago Style
Jindam, Dnyanesh, et al. "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.
Turabian Style
Jindam, Dnyanesh, et al. "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.
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