Comparison of modern methods using the python programming language in mass housing valuation
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Abstract
Multiple methods are used in the mass housing valuation. With the developing technology, modern methods have gained speed. In this study, a systematic study was conducted with machine learning to estimate the final price of housing property. The studied dataset contains 73 samples and 18 arguments. In this study with the Python programming language, NumPy, Pandas, Scikit–learn, Matplotlib and Seaborn, which are the basic libraries of Python, were used. Multiple linear regression (MLR) and decision tree regression method were used to perform the study. The adjusted determination coefficient (r2) was used to measure the performance of the estimation accuracy of applications. As a result of applications, the multiple linear regression model showed better results than the decision tree model.
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References
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