House Price Analysis Using Machine Learning |
Author(s): |
| Shruti Sontakke , Atharva College of Engineering; Dakshu Salame, Atharva College of Engineering; Dhruvi Khasia, Atharva College of Engineering; Prof. Shika Malik, Atharva College of Engineering |
Keywords: |
| House Prices, Machine Learning, Linear Regression, Random Forest, Gradient Booster Regression, XGBoost Regressor |
Abstract |
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The phenomenon of the falling or rising of the house prices has attracted interest from the researcher as well as many other interested parties. There have been many previous researches that used various regression techniques to address the question of the changes house price. This project 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 box-cox transformation techniques. The performance of the machine learning techniques is measured by the following parameters of accuracy, precision, specificity and sensitivity. The work considers discrete values 0 and 1 as respective classes. If the value of the class is 0 then we contemplate that the price of the house has decreased and if the value of the class is 1 then the price of the house has increased. |
Other Details |
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Paper ID: IJSRDV9I30245 Published in: Volume : 9, Issue : 3 Publication Date: 01/06/2021 Page(s): 209-213 |
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