House Pricing Using K-means Clustering

Abstract

In todays world our resources are limted and our demand is increasing everyday. Humans are social animals who live in group and need shelter. Housing is a basic demand of every human being. So we  have done an analysis of housing cost based on K-means clustering. We have used R programming language to implement our concepts. Here we  are predicting the house price using machine learning algorithm. By using some attributes like lotarea , street , alley , lotshape ,  landcontour , utilities ,  lotconfig , landslope the house price should be intended on . In this we use machine learning algorithm K-means clustering to determined the price of house. Here we have house pricing data which is collected from kaggle.com .  The house pricing data considered number of rows are 1460 and number of colums are 82. By using this data attributes such as lotarea , street , alley and lotshape we predicted the price of house.