The main purpose of the paper is to show the use of linear regression to estimate the house prices prediction in Hyderabad a city in Telangana state India. Nowadays, the home is a basic amenity for most human beings. The biggest dream of all middle-class people is to have their own home. But today, the prices of plots, flats, and homes have become so high that the general public could not afford them. On the coin second side, the sellers are unable to find genuine buyers and the prediction of house prices is a nightmare for middle-class people, these all issues are created because land brokers and land brokers sell land or homes or land for more money and take more commission. The applications like magic bricks and no broker are providing data to the public, that data is to estimate the current prices of any locality. In this article, the prediction of house prices is taken as a problem. A home price forecasting method that collects past home prices and predicts the current price of the house. Machine learning techniques are used in the analysis to estimate the prices of the future. The Python language is used to analyze the problem and various regression models to ensure accurate predictions. The Home Price Index usually represents the sum of price fluctuations in residential real estate. However, to predict the price of a home, based on location, house type, size, year of construction, local amenities, and some other factors that may affect the supply and demand of the house. We need a more accurate method in this analysis we developed a more accurate method by applying various algorithms.