A Machine Learning Model for House Price Predictions House Price prediction

Main Article Content

Article Sidebar

Published Sep 8, 2021
impana v


Real estate is one of the transparent industry
in our ecosystem. Housing prices keeps on changing based on various factors. In the existing system, house
prices are calculated without the necessary prediction
about the future price increase and market trends.
Prediction of house prices based on the real factors is
one of the main aim of the project. This project involves
functioning of a website which accepts customer
specifications combining the applications of linear
regression algorithm. The results obtained are not the
solo determination of one technique, it involves the
weighted mean of various techniques which yields
maximum accuracy and minimum number of errors
than the algorithm which is applied individually.

Machine Learning techniques are applied to predict
the sale prices for the homes in Banglore. In this
approach the dataset consists of the information about
location, square feet, number of floors and rooms.
Python library is used for representing the data,
Regression techniques such as multiple linear regression
and support vector regressions are used here to build a
predictive model comparing them on the various
metrics such as Mean Absolute Error(MAE),Mean
Squared Error(MSE),R-Squared Value, Root Mean
Squared Error(RMSE) .Here, the attempt is to build a
model that evaluates the house prices based on various

How to Cite

v, impana. (2021). A Machine Learning Model for House Price Predictions: House Price prediction. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/189
Abstract 8 |

Article Details


Houseprice, MultipleLinearregression, Support vector regression

[1] Risse M. & Kern M. Forecasting house-price growth in the
Euro area with dynamic model averaging. North American
Journal of Economics and Finance,2016.
[2] Ahmad I., Hussain M., Alghamdi, & Alelaiwi A..
Enhancing SVM performance in intrusion detection using
optimal feature subset selection based on genetic principal
components. Neural computing and applications(2014).
[3] Yu, H., Chen, R., & Zhang, G. (2014). A SVM stock
selection model within PCA. Procedia computer science, 31,
[4] Jing, C., & Hou, J. (2015). SVM and PCA based fault
classification approaches for complicated industrial process.
[5] Bork L. & Moller S, Forecasting house prices in the
50 states using Dynamic Model Averaging and Dynamic Model
Selection. International Journal of Forecasting 2015.
[6] Balcilar, M., Gupta, R., & Miller, S. M. (2015). The out
-of sample forecasting performance of nonlinear models of regional
housing prices in the US. Applied Economics.
[7] Park & Bae J. K. Using machine learning algorithms for
housing price prediction: The case of Fairfax County, Virginia
housing data. Expert Systems with Applications (2015).
GE3- Computers & Information Technology