House Price Prediction using Machine Learning Algorithm - The Case of Karachi City, Pakistan

机译：采用机器学习算法的房屋价格预测 - 巴基斯坦卡拉奇市的情况

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House prices are a significant impression of the economy, and its value ranges are of great concerns for the clients and property dealers. Housing price escalate every year that eventually reinforced the need of strategy or technique that could predict house prices in future. There are certain factors that influence house prices including physical conditions, locations, number of bedrooms and others. Traditionally predictions are made on the basis of these factors. However such prediction methods require an appropriate knowledge and experience regarding this domain. Machine Learning techniques have been a significant source of advanced opportunities to analyze, predict and visualize housing prices. In this paper, Gradient Boosting Model XGBoost is utilized to predict housing prices. Publicly available dataset containing 38,961 records of Karachi city is attained from an Open Real Estate Portal of Pakistan. Lot of work has been done in predicting house prices across many countries, however very limited amount of work has been done for predicting house prices in Pakistan. Our proposed house price prediction model is able to predict 98% accuracy.

机译：房价是经济的重要印象，其价值范围对客户和物业经销商有着极大的担忧。房价每年都会升级，最终加强了需要预测未来房价的战略或技术的必要性。有一定的因素会影响房价，包括物理条件，地点，卧室数量等。传统上是根据这些因素进行的预测。然而，这种预测方法需要关于该领域的适当知识和经验。机器学习技术是分析，预测和可视化房价的高级机会的重要来源。在本文中，利用梯度提升模型XGBoost来预测房价。纳入巴基斯坦的开放房地产门户网站，纳入了包含38,961次记录的公开数据集。在许多国家预测房价进行了很多工作，然而，在巴基斯坦预测房价的情况下已经有限。我们所提出的房屋价格预测模型能够预测98％的准确性。

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《International Arab Conference on Information Technology》|2020年|1-5|共5页
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Maida Ahtesham; Narmeen Zakaria Bawany; Kiran Fatima;
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Urban areas; Predictive models; Machine learning algorithms; Machine learning; Prediction algorithms; Numerical models; Data models;

机译：城市地区;预测模型;机器学习算法;机器学习;预测算法;数值模型;数据模型;

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House Price Prediction using Machine Learning Algorithm - The Case of Karachi City, Pakistan

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