Smart City Gnosys
Smart city article details
| Title | Machine Learning Algorithms For Predicting House Prices In A Smart City Using Its Real-Time Data |
|---|---|
| ID_Doc | 35875 |
| Authors | Joel M.R.; Navaneethakrishnan M.; Sriram K.P.; Manonmani S.P. |
| Year | 2023 |
| Published | Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 |
| DOI | http://dx.doi.org/10.1109/ICEARS56392.2023.10085090 |
| Abstract | Housing prices are an important economic indicator, wherein the property values are important to both buyers and sellers. One of the least transparent industries across the world is real estate. In order to conduct the research, real-time housing data has been collected from Chennai City. Sine this study deals with the continuous outcome variable, regression techniques are highly preferred. The major goal of this research study is to predict housing values by using real-time information. This study performs various house price evaluation process based on various factors. Several regression models like Lasso and Ridge are utilized and their performances are compared in order to select the best model for resolving a specific issue. This study intends to develop an effective machine learning model to predict the price of a property based on input parameters and display the information in a website to reach out to people. © 2023 IEEE. |
| Author Keywords | Lasso; Linear Regression; Machine Learning; Random Forest; Real-time Data |
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