Forestalling House Prices Using Linear Regression in Machine Learning

Abstract

Predicting house prices is a fundamental problem in the field of real estate and economics.The accurate estimation of house prices is crucial for various stakeholders including homeowners, buyers, sellers, and policymakers.In this paper, we explore the application of linear regression, a simple yet effective machine learning technique, to predict house prices.We discuss the methodology, data preprocessing, feature selection, model training, and evaluation metrics.We also highlight the strengths, limitations, and potential improvements of the linear regression approach in the context of house price prediction.In this paper, we focus on using linear regression to predict house prices, discussing the dataset, preprocessing steps, model training, and evaluation.