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Smart city article details
| Title | A Comparative Study Of Machine Learning Models For House Price Prediction And Analysis In Smart Cities |
|---|---|
| ID_Doc | 801 |
| Authors | Kansal M.; Singh P.; Shukla S.; Srivastava S. |
| Year | 2023 |
| Published | Communications in Computer and Information Science, 1888 CCIS |
| DOI | http://dx.doi.org/10.1007/978-3-031-43940-7_14 |
| Abstract | Developing any precise or exact prediction of house prices is an unsettled task for many years. It is the social as well as economic need for the welfare & comfort of the citizens. During the Covid-19 outbreak policy reforms were introduced and various businesses scaled down their workforce so prospective buyers needed to wait for the decision about the purchase of Properties. Thus it became important to provide accurate and accessible solutions to the buyers to mould their decision. The objective and aim of our research work are to provide digital-based solutions to real estate prices because of the increasing growth in online platforms that provide virtual tours. We performed a detailed study to understand the pertinent attributes and the most efficient model built to perform forecasting of the expected price. The results of this analysis verified the use of models like Linear Regression, Random forest Regression, XG Boost, and Voting Regressor as some efficient models. The model that performed fairly well as compared to other models is Random Forest with an accuracy of (98.207) while others with an accuracy of (73.12) for Linear Regression, an accuracy of (95.41) for XG Boost, the accuracy of (94.44) for Voting Regression. Our Findings in this research have advocated the idea that prices of any real estate property are governed by 2 major factors: Its Locality and Construction Composition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | bagging and boosting techniques; Gradient Descent; Linear Regression; Multicollinearity; Random Forest Regression; Repo rates; Voting technique; XG Boost Regression |
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