Many Americans seek to purchase a new home or to list it on market and sell it for a reasonable price. If you come across any random home buyer questioning them about their dream house, then there are high chances that their descriptions would not start off describing the various aspects of house like the height of basement ceiling or the nearness to a commercial building.
Generally, assessors apply their experience and common knowledge to gauge a home based on its various characteristics like its location, commodities and its dimensions. But, regression analysis comes up with another approach which provides much better home prices with reliable predictions.
In this project I examined a comprehensive housing dataset from the city of Ames in Iowa, USA (source).
The Ames Housing Dataset is an open source dataset that contains information on residentials home sales in Ames, Iowa in the USA between 2006 and 2010. The data contains attributes that every home buyer would like to know as neighborhood in which house is located, year when the house was built, garage area and so on.