V.R. Siva
R. Durga
- Student Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai Tamil Nadu India
- Associate Professor Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai Tamil Nadu India
Abstract
A key component of forecasting home prices and rental patterns is real estate market analysis. Data science, data mining methodologies, and statistical models are some of the strategies that have been created in recent years to solve this problem. A few problems are still required to be resolved, such as the obstacles caused by the availability and quality of the data; the presence of outliers, missing values, and inconsistent formats in housing datasets might have a negative impact on prediction models’ performance; and ensuring data accuracy and coverage necessitates working with government agencies and data providers in conjunction with meticulous data preparation processes. A number of major issues impede the accuracy and dependability of current systems, like only a little amount of thorough and current data is readily available, market dynamics are complicated, and effective.
Keywords: Data science, forecasting home prices, real estate market analysis, data mining methodologies, key component, rental patterns, market analysis
[This article belongs to Recent Trends in Parallel Computing(rtpc)]
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Recent Trends in Parallel Computing
| Volume | 11 |
| Issue | 01 |
| Received | February 16, 2024 |
| Accepted | March 2, 2024 |
| Published | April 4, 2024 |