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House Prices Forecasting Using Advanced Machine Learning Techniques

Student: Olga Sukhobok

Supervisor: Nikolay Pilnik

Faculty: Faculty of Economic Sciences

Educational Programme: Applied Economics (Master)

Final Grade: 8

Year of Graduation: 2019

A model of machine learning (XGBoost), developed in this article, predict the house’s prices in Moscow. The model has a high predictive power and the chosen method of interpretation (SHAP) allows to evaluate both the general effect of each feature on the model’s forecast and to calculate the magnitude of the effect of every feature for every specific observation. Moreover, for each attribute, the magnitude of the effect was analyzed depending on the value of this attribute. The key factors affecting the model's forecast are the total area of the apartment, the distance to the metro, the distance of the district from the city center, the CPI and others.

Full text (added May 30, 2019)

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