ANALYSING HOUSE PRICE PREDICTIONS ACCORDING TO LIVING STANDARDS BASED ON MACHINE LEARNING METHODS

K. Mahboob, Nida Khalil, Saniah Rehan
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Abstract

There is a lack of reliable economical methods for forecasting house prices for those who wish to buy a house according to their living standards. This paper presents details of predictive analytics for house pricing in three different towns of Karachi, Pakistan according to different living standards based on machine learning (ML) methods. The purpose of this study is to determine which data set features contribute greatly to the accuracy of the predictions when experimenting with selected predictive techniques. The house price value has been analysed using five different ML methods. A model selection has been made by comparing the accuracy of the techniques based on some performance metrics and the best technique was used to predict the house price value.
基于机器学习方法,根据生活水平分析房价预测
对于那些希望根据自己的生活水平买房的人来说,目前还缺乏可靠的经济方法来预测房价。本文介绍了基于机器学习(ML)方法,根据不同生活水平对巴基斯坦卡拉奇三个不同城镇的房价进行预测分析的细节。本研究的目的是确定哪些数据集特征在使用选定的预测技术进行实验时对预测的准确性有很大贡献。使用五种不同的ML方法分析了房价价值。通过比较几种技术在预测房价价值方面的准确性,对模型进行了选择,并将最佳技术用于预测房价价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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