House Price Prediction Using Regression in Machine Learning

The house pricing projection is crucial to the financial services industry and real estate sector. People are researching market techniques and attempting to purchase a new property within their means. However, the primary drawback of the current method is that it does not determine a home's price based on the essential forecast of future market trends, which results in an increase in price. Therefore, our paper's goal is to accurately forecast a home's price with minimal loss. For predicting house prices several factors need to be considered including location, size, amenities and economic indicators. There are very great techniques that machine learning offers for modelling and house price prediction. Regression algorithms are really valuable for predicting continuous values, like house prices by computing the association between data features and target values. The base tool in this direction is simply a basic linear approach, which fits best when discovering simple, linear trends. Decision trees and random forests add flexibility for modelling much more complicated and non-linear relationships. More advanced techniques like gradient boosting and XGBoost improve the accuracy in computing multiple models together to correct each other's errors iteratively. All these methods together provide a set of approaches for solving tasks in the price prediction problem under various accuracy and complexity requirements.