Open AccessPrediction of House Price Using Machine Learning
Author(s) -
Mr. Piyush Chordia,
Mr. Pratik Konde,
Ms. Supriya Jadhav,
Hrutik Pandhare,
Prof. Shikha Pachouly
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40466
Subject(s) - outlier , value (mathematics) , variance (accounting) , econometrics , computer science , machine learning , artificial intelligence , work (physics) , class (philosophy) , feature (linguistics) , feature selection , economics , engineering , mechanical engineering , linguistics , philosophy , accounting
The trend of the sudden drop or constant rising of housing prices has attracted interest from the researcher as well as many other interested people. There have been various research works that use different methods and techniques to address the question of the changing of house prices. This work considers the issue of changing house price as a classification problem and discuss machine learning techniques to predict whether house prices will rise or fall using available data. This work applies various feature selection techniques such as variance influence factor, Information value, principle component analysis, and data transformation techniques such as outlier and missing value treatment as well as different transformation techniques. The performance of the machine learning techniques is measured by the four parameters of accuracy, precision, specificity, and sensitivity. The work considers two discrete values 0 and 1 as respective classes. If the value of the class is 0 then we consider that the price of the house has decreased and if the value of the class is 1 then we consider that the price of the house has increased.