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ABSTRACT
Investing in real estate is a huge asset, which brings back huge returns in the future.
Hence planning the finances well ahead for this investment is crucial and beneficial. This
paper will provide an overview of a model which is built to predict property prices. The
model takes into account some features for the property like the availability of car parking
spaces, type of drainage present, name of the area etc and uses supervised machine
learning techniques to predict the price of the property. A couple of machine learning
algorithms are compared to find that algorithm which shows the least and error and
highest accuracy. Calculations like Mean square error, Mean absolute error, R2 error etc
is done on all the algorithms in order to find the most efficient one. This report examines
in detail the construction and working of this model.
