HOUSE COST ESTIMATION OF BANGLORE REGION USING FEATURE SELECTION ALGORITHM OF MACHINE LEARNING
Keywords:
cost estimation, machine learning, support vector machine, logistic regressionAbstract
AI assumes a significant part from past years in picture recognition, spam redesign, typical discourse order, item suggestion and clinical determination. Present AI calculation helps us in improving security alarms, guaranteeing public wellbeing and improves clinical upgrades. AI framework likewise gives better client assistance and more secure vehicle frameworks. In the present paper we examine about the forecast of future lodging costs that is produced by AI calculation. For the determination of forecast strategies we look at and investigate different forecast techniques. The housing market is a champion among the most engaged in regards to estimating and continues to change. It is one of the superb fields to apply the thoughts of machine learning on the most proficient method to upgrade and predict the expenses with high exactness. The target of the paper is the expectation of the market estimation of a land property. This investigation uses AI calculations as an exploration technique that creates lodging cost expectation models. We make a lodging cost expectation model in perspective on AI calculation models for instance, XGBoost, rope relapse and neural framework on take a gander at their request exactness execution. We in that point suggest a lodging cost expectation model to help a house seller or a land specialist for better data dependent on the valuation of house. Those assessments display that rope relapse calculation, in perspective on precision, dependably beats substitute models in the execution of lodging cost expectation.
References
Alfiyatin, A.N., Febrita, R.E., Taufiq, H., Mahmudy, W.F. (2017): Modeling house price prediction using regression analysis and particle swarm optimization. – International Journal of Advanced Computer Science and Applications 8(10): 323-326.
Au, T.C. (2018): Random forests, decision trees, and categorical predictors: The “Absent Levels” problem. – Journal of Machine Learning Research 19: 30p.
Banerjee, D., Dutta, S. (2017): Predicting the housing price direction using machine learning techniques. – In IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) 3p.
Bogin, A.N., Doerner, W.M. (2019): Property renovations and their impact on house price index construction. – Journal of Real Estate Research 41(2): 249-284.
Ghosalkar, N.N., Dhage, S.N. (2018): Real estate value prediction using linear regression. – In Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) 5p.
Jiang, L., Li, C. (2010): An empirical study on attribute selection measures in decision tree learning. – Journal of Computational Information Systems 6(1): 105-112.
Lu, S., Li, Z., Qin, Z., Yang, X., Goh, R.S.M. (2017): A hybrid regression technique for house prices prediction. – In IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) 5p.
Pow, N., Janulewicz, E., Liu, L. (2014): Applied Machine Learning Project 4 Prediction of real estate property prices in Montréal. – Course project, COMP-598, Fall/2014, McGill University 8p.
Ray, S. (2019): A quick review of machine learning algorithms. – In International conference on machine learning, big data, cloud and parallel computing (COMITCon) 4p.
Sampathkumar, V., Santhi, M.H., Vanjinathan, J. (2015): Forecasting the land price using statistical and neural network software. – Procedia Computer Science 57: 112-121.
Tan, K.C., Teoh, E.J., Yu, Q., Goh, K.C. (2009): A hybrid evolutionary algorithm for attribute selection in data mining. – Expert Systems with Applications 36(4): 8616-8630.
Varma, A., Sarma, A., Doshi, S., Nair, R. (2018): House price prediction using machine learning and neural networks. – In Second International Conference on Inventive Communication and Computational Technologies (ICICCT) 4p.
Xiao-zhu, D., Ling-wei, K. (2013): The land prices and housing prices—Empirical research based on panel data of 11 provinces and municipalities in Eastern China. – In International Conference on Management Science and Engineering 20th Annual Conference Proceedings 6p.
