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- W3144097188 abstract "The purpose of this article is to estimate the purchasing and sale opportunities of houses on the market by Machinelearning techniques. For financial stability, the housing sector is quite critical. People seeking to purchase a new house appear tobe more cautious in their expectations and sales tactics analyzing historical industry patterns and pricing levels, as well aspotential changes. The index of our method consists of the price of the house and its efficiency metrics, such as the amount ofrenovation, the distance from the city center, the construction programs, the height of the property, the floor and the location of theapartment in the home, and there are several other criteria. Service includes a database that recognizes the preferences of itsclients and then integrates machine learning software. The program will enable consumers invest in real estate withoutapproaching brokers. It, therefore, reduces the uncertainties inherent with the deal. The program has a login ID and a pin. At thesame time, when the user searches for an attribute, the value of the original attribute and the value of the predicted attribute aredisplayed." @default.
- W3144097188 created "2021-04-13" @default.
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- W3144097188 date "2020-08-01" @default.
- W3144097188 modified "2023-09-23" @default.
- W3144097188 title "Determining House Price Using Regression" @default.
- W3144097188 doi "https://doi.org/10.5281/zenodo.3956571" @default.
- W3144097188 hasPublicationYear "2020" @default.
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