Performance of Ridge Regression in Comprehensive Analysis of Residential Property Pricing

Performance of Ridge Regression in Comprehensive Analysis of Residential Property Pricing

Shilpa T H, Bhuvika A M, Manasa H, Deeksha H, Prof. Pankaj Kumar

Abstract— The value of the world's economy is rising daily. The country's economic strength immediately raises the cost of real estate there. The adoption of a residential property pricing model is required to forecast residential property values, which enables customers to purchase homes at lower costs and in accordance with their needs. The bedrooms, location, type, and availability of the area, as well as the overall square feet, are all factors used in this model. This model calculates the price of a property using a variety of these attributes and their values, enabling the consumer to know the most accurate pricing.  The most precise method for predicting the price of a residential property is to use ridge regression and linear regression with a lasso (least absolute shrinkage and Selection operator). The most reliable way to anticipate the value of a residential property is to know its pricing. The model is specifically made to calculate a dwelling price using pre-loaded data.

Keywords- machine learning techniques include ridge regression, least absolute shrinkage and selection operator, and linear regression.