• Dr.K N S Lakshmi
• Maravathu Narendra

One of the key areas of real estate is house price forecasting. The literature makes an effort to extract practical knowledge from historical real estate market data. In order to find models that are helpful to home buyers and sellers, machine learning techniques are used to examine previous real estate transactions. revealed the significant disparity in housing costs between the most costly and least expensive suburbs of cities. Experiments also show that the Linear Regression is a viable strategy. In the real estate sector, predicting house prices is essential since it aids buyers, sellers, and investors in making wise choices. This abstract describes a study on the use of linear regression, a popular machine learning algorithm for regression analysis, to predict home prices. The goal of this research is to create a model that can precisely calculate residential property prices based on a variety of factors .The real estate data included in this study includes information on location, square footage, the number of bedrooms and bathrooms, and accessibility to amenities. These characteristics are regarded as crucial elements that affect home pricing.

House price prediction , Machine Learning , Linear Regression , Real estate prediction

"House Price Prediction Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.a218-a224, June-2023, Available :http://www.jetir.org/papers/JETIR2306030.pdf

"House Price Prediction Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppa218-a224, June-2023, Available at : http://www.jetir.org/papers/JETIR2306030.pdf