This paper provides an overview about how to predict house costs utilizing different regression methods with the assistance of python libraries. The proposed technique considered the more refined aspects used for the calculation of house price and provided the more accurate prediction. It also provides a brief about various graphical and numerical techniques which will be required to predict the price of a house. This paper contains what and how the house pricing model works with the help of machine learning and which dataset is used in our proposed mode. The proposed method is based on the linear regression. This project is proposed to predict house prices and to get better and accurate results. The data for the house prediction is collected from the publicly available sources. In validation, training is performed on 50% of the dataset and the rest 50% is used for testing purposes. This technique splits the dataset into several subsets. At that point, it has been attempted for preparing on the entirety of the subsets; however, leave one (k-1) subset for the assessment of the prepared model. This strategy emphasizes k times with an alternate subset turned around for the preparation reason each time. Nowadays the real estate market is a standout amongst the most focused regarding pricing and keep fluctuating. People are looking to buy a new home with their budgets and by analyzing market strategies. But main disadvantage of current system is to calculate a price of house without necessary prediction about future market trends and result is price increase. So, the main aim of our project is to predict accurate price of house without any loss. There are many factors that have to be taken into consideration for predicting house price and try to predict efficient house pricing for customers with respect to their budget as well as also according to their priorities.



Software And Hardware