Abstract
The purpose of this research was to build a linear model for predicting the price of houses. The price of the house could be approximated without knowing the price of every house. In the process of the experiment, the data from real estate markets would be analyzed for the supervised study. A linear model would be utilized to predict the price. Different values of learning rate would be compared, and the most efficient value according to the cost function would be chosen. Finally, the prediction model with learning rate 2 would be chosen and used by people who would like to know the price of houses without spending a long time. The price of the house can be successfully accessed by inputting the values of features -numbers of bedrooms, bathrooms, area, and floors- to the model.
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