HOUSING PRICE PREDICTION USING MACHINE LEARNING

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Zende Prachi Shankar; Mohini Gaikwad; Vibha Bhor

HOUSING PRICE PREDICTION USING MACHINE LEARNING

Authors: Zende Prachi Shankar, Mohini Gaikwad, Vibha Bhor

Unique Paper ID: 149160
Volume: 6
Issue: 11
PageNo: 118-122

Keywords: Python Spyder Data integrity Prediction Data Modeling.

Abstract:
House price forecasting is an important topic of real estate. The literature attempts to drive useful knowledge from historical data of property markets. Machine learning techniques are applied to analyze historical property transaction to discover useful models for house buyers and sellers. Revealed is the high discrepancy between house prices in the most expensive and most affordable suburbs. Moreover, experiments demonstrate that the combination of stepwise and support vector machine that is based on mean squared error measurement is a competitive approach. The goal of the study is through analyzing a real historical transactional dataset to derive valuable insight into the housing market. It seeks useful models to predict the value of a house given a set of its characteristics. Effective model could allow home buyers or real estate agents to make better decisions.

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Cite This Article

ISSN: 2349-6002
Volume: 6
Issue: 11
PageNo: 118-122

HOUSING PRICE PREDICTION USING MACHINE LEARNING

Available:https://ijirt.org/article?manuscript=149160

14 Mar, 2020

HOUSING PRICE PREDICTION USING MACHINE LEARNING

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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