Model For Predicting London House Prices

Abstract

With the growth of population, housing prices have become one of the important indicators to reflect the economic performance. Forecasting prices around the world will be part of social and economic development. Through the analysis of various factors affecting the market housing price, it is helpful to make a more accurate assessment of the future housing price trend. In a broad sense, accurate housing price forecast is helpful to the country's macro-control of the market housing price trend, and the prediction of future housing price is part of the strategic planning of enterprises. For consumers, the housing price forecast has a positive effect on the rational planning of individual economy. Since house prices are related to many factors, and there is a linear relationship between house prices and some factors that affect house prices, there are many linear graphs to study this problem. The research direction of this paper is to obtain data from Kaggle and conduct exploratory analysis on these data sets. The main method used in this article is linear regression, and no other forecasting methods will be added. What this work needs to do is use the right machine learning methods to predict home values to create a complex property model under generally stable conditions, which is this work aims to do in this study. Through many data sets show that the multiple linear regression model does achieve a relatively excellent housing price prediction effect.