Johan Wijaya
ABSTRACT
Because of too high expectations or having a wrongly segmented target market, the developer hasn't received a good response from the target market. Developers need a new marketing tool that is based on data. The use of machine learning systems as marketing tools to help solve the problems in house price prediction is an important topic in the real estate industry. The design of machine learning will use CRISP-DM as a framework and to analyze using linear regression and random forest as the best possible accuracy. Besides that, to find a potential market, we will use K-Means as a clustering method. The modeling and experiments to design a machine learning engine that can predict a range of selling prices using linear regression can give maximum accuracy and analyze the target market. The research focusing on different attributes will bring different dominant attributes to the table too.
- M. Galinium, “Integrating data mining technique and AHP in market analysis to propose new product development in real estate,” in IOP Conference Series: Materials Science and Engineering, 2017, vol. 166, no. 1, p. 12030.Google Scholar
- P. Wazurkar, R. S. Bhadoria, and D. Bajpai, “Predictive analytics in data science for business intelligence solutions,” in 2017 7th International Conference on Communication Systems and Network Technologies (CSNT), 2017, pp. 367–370.Google ScholarCross Ref
- M. Thamarai and S. P. Malarvizhi, “House Price Prediction Modeling Using Machine Learning,” Int. J. Inf. Eng. Electron. Bus., vol. 12, no. 2, pp. 15–20, 2020, doi: 10.5815/ijieeb.2020.02.03.Google Scholar
- C. Pascal, S. Ozuomba, and C. kalu, “Application of K-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services,” Int. J. Adv. Res. Artif. Intell., vol. 4, no. 10, pp. 40–44, 2015, doi: 10.14569/ijarai.2015.041007.Google ScholarCross Ref
- Y. Mulyano, R. A. Rahadi, and U. Amaliah, “Millennials Housing Preferences Model in Jakarta,” Eur. J. Bus. Manag. Res., vol. 5, no. 1, 2020.Google ScholarCross Ref
- K. Y. Ngiam and W. Khor, “Big data and machine learning algorithms for health-care delivery,” Lancet Oncol., vol. 20, no. 5, pp. e262–e273, 2019.Google Scholar
- D. Faggella, “What is Machine Learning?,” Emerj., 2020.Google Scholar
- Z. Sun, H. Zou, and K. Strang, “Big data analytics as a service for business intelligence,” in Conference on e-Business, e-Services and e-Society, 2015, pp. 200–211.Google ScholarDigital Library
- R. Y. K. Lau, S. S. Y. Liao, K.-F. Wong, and D. K. W. Chiu, “Web 2.0 environmental scanning and adaptive decision support for business mergers and acquisitions,” MIS Q., pp. 1239–1268, 2012.Google ScholarCross Ref
- J. Lopes, T. Guimarães, and M. F. Santos, “Predictive and prescriptive analytics in healthcare: a survey,” Procedia Comput. Sci., vol. 170, pp. 1029–1034, 2020.Google Scholar
- N. W. Grady, J. A. Payne, and H. Parker, “Agile big data analytics: AnalyticsOps for data science,” in 2017 IEEE international conference on big data (big data), 2017, pp. 2331–2339.Google ScholarCross Ref
- J. Schleier-Smith, “An architecture for agile machine learning in real-time applications,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 2015-Augus, pp. 2059–2068, 2015, doi: 10.1145/2783258.2788628.Google ScholarDigital Library
- C. Kurniawan, L. C. Dewi, W. Maulatsih, and W. Gunadi, “Factors Influencing Housing Purchase Decisions of Millennial Generation in Indonesia,” Int. J. Manag., vol. 11, no. 4, 2020.Google Scholar
- T. Wambsganss, N. Molyndris, and M. Söllner, “Unlocking transfer learning in argumentation mining: A domain-independent modelling approach,” Proc. 15th Int. Conf. Bus. Inf. Syst. 2020 "Developments, Oppor. Challenges Digit. WIRTSCHAFTSINFORMATIK 2020, no. February, 2020, doi: 10.30844/wi_2020_c9.Google ScholarCross Ref
Index Terms
- DESIGN AN AGILE OF MACHINE LEARNING TO PREDICTIVE HOUSE PRICING AND TARGETING SEGMENTED MARKET
Recommendations
Sustainable Stock Market Prediction Framework Using Machine Learning Models
Prediction of stock prices is a challenging task owing to its volatile and constantly fluctuating nature. Stock price prediction has sparked the interest of various investors, data analysists, and researchers because of high returns on their ...
Under-Resourced Machine Learning for Stock Market Prediction
ICCIR '22: Proceedings of the 2022 2nd International Conference on Control and Intelligent RoboticsIn recent years, machine learning has made remarkable achievements in many fields. The stock market forecast is a vital application scenario. However, stock market prediction in real situations usually has relatively little data available for machine ...
Machine learning approaches in stock market prediction: A systematic literature review
AbstractPredicting the stock market has been done for a long time using traditional methods by analyzing fundamental and technical aspects. With machine learning, stock market predictions are made more accessible and more accurate. Various machine learn- ...
Comments