Determining House Price Using Regression

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Anirban Chakraborty


The purpose of this article is to estimate the purchasing and sale opportunities of houses on the market by Machine
learning techniques. For financial stability, the housing sector is quite critical. People seeking to purchase a new house appear to
be more cautious in their expectations and sales tactics analyzing historical industry patterns and pricing levels, as well as
potential changes. The index of our method consists of the price of the house and its efficiency metrics, such as the amount of
renovation, the distance from the city center, the construction programs, the height of the property, the floor and the location of the
apartment in the home, and there are several other criteria. Service includes a database that recognizes the preferences of its
clients and then integrates machine learning software. The program will enable consumers invest in real estate without
approaching brokers. It, therefore, reduces the uncertainties inherent with the deal. The program has a login ID and a pin. At the
same time, when the user searches for an attribute, the value of the original attribute and the value of the predicted attribute are

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How to Cite
Chakraborty, A. (2020). Determining House Price Using Regression. International Organization of Research & Development, 8(1), 5. Retrieved from