Abstract
Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur
Highlights
House is one of the most essential basic needs in human life, along with other basic needs such as food and water
It is important to examine the correlation between house prices and housing attributes and identify significant variables that are essential to the use of the machine learning (ML) techniques in the real estate industry, involving pre-processing and exploration of the datasets obtained
This paper presents an exploration of ML algorithms for house price prediction by focusing on Kuala Lumpur housing data
Summary
House is one of the most essential basic needs in human life, along with other basic needs such as food and water. The ML model that provides the best prediction results will be beneficial for researchers, home buyers, property investors, and house builders in terms of gaining a lot of knowledge and information of the house price values in the present sector. It is important to examine the correlation between house prices and housing attributes and identify significant variables that are essential to the use of the ML techniques in the real estate industry, involving pre-processing and exploration of the datasets obtained As various factors such as location and property demand could affect house prices, most parties involved including buyers and investors, housebuilders, and real estate market may want to know the exact attributes or the main factors affecting house prices to assist investors in making decision and to facilitate house builders in setting house prices [2], [10], [11].
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