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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM21058

RETAIL PRICE PREDICTION USING LINEAR REGRESSION

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM21058

Volume:

7 |

5 |

May 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MMB

MS. M BANUPRIYA

CK

CHETANAPPRIYA K.L

MM

MAHESWARAN M

MK

MAYURI K.B

RK

RAJARATNAM KAWSHIKA

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Abstract

Machine Learning plays a vital role from past years in normal speech command, product recommendation as well as in the medical field. It also provides better customer services and a safer automobile system. This shows that ML is a trending technology in almost all the fields. People are looking forward to buy a new home and car with their budgets by analyzing the market. By using certain Machine learning algorithms, a price predicting model can be implemented. This model will help people to put resources into a bequest without moving towards a broker. This project demonstrates the usage of machine learning algorithm in the prediction of prices using dataset downloaded from Kaggle. This model provides a review of the usage of existing machine learning algorithms on a dataset and tries to implement this prediction engine for real-life usage by users. The findings are obtained using linear regression algorithm. Machine learning Algorithms are used because they provide us with continuous value as output and not a categorized value. Because of which it is possible to predict the actual price rather than the price range of a car or house.

KEYWORDS

Machine learning price prediction linear regression regression analysis feature

Cite This Article

MS. M BANUPRIYA, CHETANAPPRIYA K.L, MAHESWARAN M, MAYURI K.B, RAJARATNAM KAWSHIKA, "RETAIL PRICE PREDICTION USING LINEAR REGRESSION," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 5, May 2023. https://doi.org/10.55041/IJSREM21058

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Publication Date

5/15/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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