Prediction of House Prices Using Machine Learning

Abstract

Abstract: Economy of the country is greatly driven by the prices of houses in that country. Both buyers and sellers depend on the pricing strategies. Ask an emptor to explain the factors they think are considered for pricing the house at that price and that they probably start with railways and end with various attributes. Over here it proves that more factors will be applied on the pricing strategies of the house. The aim of the project is to predict the house prices with various regression models. Nowadays Machine Learning is a booming technology. Data is the heart of Machine Learning. AI and Machine Learning holds the key position in the technological market. All industries are moving towards automation. So we have considered ML as a main predicting subject in our project and worked using it. These days everything fluctuates. Starting with crypto and various business models varies day by day which includes real estate as well so in this project house prediction depends on real estate data and ML techniques. Many people want to buy a good house within the budget. But the disadvantage is that the present system doesn’t calculate the house predictions so well and end up in loss of money. So, the goal of our project is to reduce money loss and buy good house. Many factors are there to be considered in order to predict the house price which includes budget factors and fewer house modifications according to the buyer. So, we are considering all of those factors and predicted using various machine learning techniques like SVR, KNN, SGB regression, CatBoost regression, Random forest regression