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Smart city article details
| Title | Enhancing Real Estate Price Prediction In Smart Cities: A Comparative Analysis Of Machine Learning Techniques |
|---|---|
| ID_Doc | 23910 |
| Authors | Kansal M.; Singh P.; Agarwal U.; Singhal K.; Arora K.; Dixit M. |
| Year | 2024 |
| Published | Lecture Notes in Networks and Systems, 1015 LNNS |
| DOI | http://dx.doi.org/10.1007/978-981-97-3523-5_6 |
| Abstract | The property market is renowned for its lack of transparency and volatile nature, often leading to inflated prices and challenges in accurately valuing houses. This research paper aims to address this issue by offering a practical solution for individuals seeking suitable houses in six major metropolitan cities in India, including Bangalore, Kolkata, Mumbai, Delhi, Hyderabad, and Chennai. Our objective is to provide accurate predictions of property market values, considering the diverse needs and preferences of potential buyers. To achieve this goal, we employ a range of machine learning techniques to predict real estate prices based on essential house characteristics and attributes such as the number of bedrooms, area, and proximity to amenities like Wi-Fi, power backup, gas connection, and car parking. Through comprehensive analysis, we evaluate the effectiveness of both linear regression and random forest regression, which utilizes decision trees, in predicting real estate prices. However, it is important to consider the advantages and disadvantages of each technique. Furthermore, our study emphasizes the significance of utilizing high-quality datasets to ensure accurate predictions. The findings highlight the effectiveness of machine learning in real estate price prediction, providing a feasible solution for individuals searching for their ideal homes. This research contributes to the existing knowledge by showcasing the practical application of machine learning in the real estate market. By improving transparency and enabling informed decision-making, our work aims to empower buyers and enhance the overall efficiency of the housing market. Future research should focus on refining and expanding these methodologies to cater to the unique dynamics of different housing markets and incorporate additional relevant variables for even more accurate predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. |
| Author Keywords | ElasticsNet linear regression; Lasso linear regression; Machine learning; Random forest regression |
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