The Investigation and Prediction of Influencing Factors of Australian Housing Price Based on Machine Learning Models

With the ever-changing economic environment in recent years, people pay more and more attention to the real estate market, and different house configurations affect house prices. In this paper, house features such as bedroom count and bathroom count are analyzed as factors influencing house prices. Analyze the relationship between influencing factors and real estate prices and predict the trend of housing prices. First, constant values and values that have no impact on the research results should be selected and deleted, and effective influencing factors such as bedroom count and bathroom count should be left. Then, Exploratory Data Analysis (EDA) analysis was carried out on these effective influencing factors, and range, mean, standard deviation and quartiles of the data were read. Then the scatter plot is made to observe whether there is a correlation between the influencing factors and real estate prices. In the part of linear regression, the least squares method was first used to establish Ordinary Least Squares (OLS) model for R square, F-statistic and other calculations, observe the values, and further understand the correlation between influencing factors and housing prices. Through the comprehensive analysis of the above EDA and OLS models, it is concluded that there is a correlation between the influencing factors and real estate prices. Finally, plot is used to plot the linear relationship between them. It can be seen from the image that with the increase of the number of influencing factors, the housing price also increases, and the two are in a positive correlation.