House Price Prediction using Machine Learning in Python

N. Kalra, Nidhika Uppal, Prerna Pathak, Muskan Nandkani, Garima Sharma
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Abstract

The business of buying and selling of house continues to grow every year due to population growth and migration to other cities for their financial purposes. Real estate is a very emerging field in everyone’s day to day life. The prices of houses are regularly changing on daily basis and are sometimes fired rather than based on actual estimates. Foreseeing property costs by actual components is a main criterion of this research paper. Our basic aim is to take all the actual and primary features to determine the result of our system. We have used regression models like decision tree classifier, random forest, and multiple linear regression classifier for prediction to get better results and for upgraded accuracy. This paper will give information that how we will predict the home price with the help of different features and python with its libraries. The main objective of this research paper is the estimation of the market worth of a land, house, property which will help customers to buy and sell property without moving to a specialist.
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在Python中使用机器学习进行房价预测
由于人口增长和为了经济目的向其他城市迁移,房屋买卖业务每年都在持续增长。房地产在每个人的日常生活中都是一个新兴领域。房屋的价格每天都在变化,有时会被解雇,而不是根据实际估计。以实际组成部分预测物业成本是本研究的主要准则。我们的基本目标是采用所有实际的和主要的特征来确定我们系统的结果。我们使用决策树分类器、随机森林和多元线性回归分类器等回归模型进行预测,以获得更好的结果和更高的精度。本文将给出如何利用不同的特性和python及其库来预测房价的信息。本研究论文的主要目的是估计土地,房屋,财产的市场价值,这将有助于客户购买和出售财产,而无需转移到专家。
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