House Price Prediction Using Machine Learning Algorithms

Abstract

Now-a-days, it is possible through machine learning algorithms to predict approximate prices
of things very conveniently. House prices prediction systems are one of those things which
provides us the ease of predicting the house prices based on certain features and location. They
have the ability to extract all the relevant information from the available unanalyzed data,
which helps us estimate the house prices and factors that affect the house prices the most.
Previous studies also have shown, variations in house prices many times affect households and
the housing industry to a great extent. Therefore, in this literature, analysis of the prominent
factor and the most effective models for house prices prediction have been experimented and
discussed. XGBoost, Random Forest, Support vector regression are some commonly applied
prediction algorithms in this field and the same will also be discussed in this paper. Factors
which play a prominent role to determine the house prices are the location, living area and no.
of bedrooms. This research will help the researchers and property developers in finding the
important factors for determining house prices and to identify the most accurate and precise
ML algorithm that can be applied for predicting the house prices.