Linear Regression-Based Prediction Model For Real Estate Properties in India

The real estate market is one of the most competitive in terms of price, and it is also one of the few that is subject to change on a consistent basis. It is one of the most significant areas where the principles of machine learning are applied to improve and increase the accuracy of cost predictions. The price of a home depends on three factors: its physical condition, its design, and its location. The current method of estimating property prices does not account for fluctuations in market prices or the rate of inflation. The objective of this paper is to provide Indian customers with a forecast of residential prices that takes their needs and budgets into account. To enhance the overall performance of the prediction models, the mean target encoding is also incorporated into the methodology. According to the findings of this study, the estimation error that is produced by linear regression is the lowest and accuracy of the implemented system is 98.6%.