Predicting House Price Values Using Linear Regression with Ridge Regularization Approach

Abstract

The valuation of real estate is the central tenet of all business. The term valuation is defined  as  the  analytical  process  of  influential  the  current  worth  of  an  assert or a company. However, there is wide range of purpose for which valuations are required. But here valuations are done for effective way to calculate the selling price of a entity. To develop a real estate valuation model which predicts the value of a property using the domain of Machine Learning. The algorithmic approach involves usage ridge regression on top of linear regression approach(Supervised Learning). The selling price is estimates using  by  considering various  parameters such as population rate in particular area, distance to roadways, property age etc. The dataset collection is taken from a standard source such that 80 parameters along with 1000’s of test and training data are considered for  property  valuation and  separate  dataset  is considered for testing and training a model. For further improvement of accuracy, Ridge regularization is applied on top of linear regression so that data are regularized with increase in model accuracy. Users who are going to sell the property can get the accurate values based on this regression prediction. Users requires no intermediate person (broker) to sell in the entity. The python language with its standard libraries are utilized for model expectations dependent on dataset  esteem. Since end-user can't run this model each and every time by utilizing python  idle  there  comes  the  usability lab. To overcome this as well as for powerful utilization of this model by end-users a separate site page is structured with the goal that clients can legitimately pass esteems  from site to python code and get the exact value for the entity.