HOUSE COST ESTIMATION OF BANGLORE REGION USING FEATURE SELECTION ALGORITHM OF MACHINE LEARNING
Keywords:cost estimation, machine learning, support vector machine, logistic regression
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.
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