I. J. of Electrical & Electronics Research
Research Article | 
Free Hold Price Predictor Using Machine Learning
Author(s) : Shivesh Singh1, Shaurya Singh2, Sudeept Singh Yadav3and Avneesh Kumar4
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 2 , Special Issue on RDCTML
Publisher : FOREX Publication
Published : 22 May 2022
e-ISSN : 2347-470X
Page(s) : 138-143
Abstract
People who want to buy a new home tend to save more on their budgets and market strategies. The current system includes real estate calculations without the necessary forecasts for future market trends and inflation. The housing market is one of the most competitive in terms of pricing and the same has varied greatly in terms of many factors. Asset pricing is an important factor in decision- making for both buyers and investors in supporting budget allocation, acquisition strategies and deciding on the best plans as a result, it is one of the most important areas in which machine learning ideas can be used to maximize and accurately anticipate prices. As a result, in this paper, we present the different significant factors that we employ to accurately anticipate property values. To reduce residual errors, we can utilize regression models with a range of characteristics. Some engineering aspects are required when employing features in the regression model for improved prediction. To improve model fit, a set of multi-regression elements or a polynomial regression (with a set of varying strengths in the elements) is frequently utilized. In these models, it is expected to be significantly affected by the slope of the spine used to reduce it. Therefore, it directs the best use of regression models over other strategies to maximize the effect. This paper's goal is to predict free hold prices for free hold consumers based on their budgets and goals. Prospective prices can be forecast by evaluating past market trends and price levels, as well as future developments.
Keywords: House price, Regression Analysis, Linear Regression, Supervised Learning, Healthcare, Machine Learning
Shivesh Singh, School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Email: sshivesh@gmail.com
Shaurya Singh, School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Email: shaurya00700@gmail.com
Sudeept Singh Yadav, School of Computing Science and Engineering, Galgotias University, Greater Noida, India, India; Email: sudeept999@gmail.com
Avneesh Kumar, School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Email: avneesh.avn119@gmail.com
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Shivesh Singh, Shaurya Singh, Sudeept Singh Yadav and Avneesh Kumar (2022), Free Hold Price Predictor Using Machine Learning. IJEER 10(2), 138-143. DOI: 10.37391/IJEER.100215.