House Price Prediction using Machine Learning Algorithm

Houses are one of the fundamental needs in human society. Good and peaceful surroundings where people feel comfortable are called houses. Hence, to have a good and happy living, people need to choose a good house model. This article focuses on accurately predicting house prices by using machine learning algorithms. This model helps people in selecting the house that is suitable for their living. The main parameter people look for will be the surrounding area, house type, price, location, and some other amenities. All these factors are considered to find out the required house for living. As people are very concerned about their budgets for buying a house, the prediction of house prices should be very precise. It also helps people in choosing houses based on their budgets, which do not affect their financial state in the future. The main outcome of this model is to predict the price of a house accurately as per the user requirements. This study has attempted to implement various machine learning algorithms like Linear Regression (LR), Gradient Boosting Regressor (GBR), Histogram Gradient Boosting Regressor, and Random Forest (RF) Regressor algorithms. Finally, the algorithm that generates high accuracy is considered for predicting the house price.