A Machine Learning Model for House Price Predictions House Price prediction

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Published Sep 8, 2021
impana v

Abstract

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
factors.

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

Keywords

Houseprice, MultipleLinearregression, Support vector regression

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Section
GE3- Computers & Information Technology