Predicting House Prices using Linear Regression

Analysing the relationship between a home’s size, number of bedrooms, location, age, and other characteristics and the price at which it is expected to sell is the process of applying linear regression to predict home prices. A supervised learning technique called linear regression fits a line to a dataset of past home sales in order to model this relationship. By minimising the discrepancy between the anticipated and actual sale prices, this line—also known as the regression line—is produced. Simple linear regression can be used to forecast the price for a single feature, such size.wn as the regression line—is produced. Simple linear regression can be used to forecast the price for a single feature, such size. Multiple linear regression can be used to capture how each feature affects price. To reduce the discrepancy between expected and real prices, the model is trained by varying weights, which indicate the impact of each feature on the price. The model is a useful tool for real estate forecasting and pricing strategies since it can forecast prices for new data once it has been trained.