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Demand Forecasting by Machine Learning Methods

Student: Mikhail Senkevich

Supervisor: Alexander Skorobogatov

Faculty: St.Petersburg School of Economics and Management

Educational Programme: Applied Economics and Mathematical Methods (Master)

Year of Graduation: 2018

This paper presents a detailed review of machine learning methods in the context of demand forecasting on durable goods, namely cars. We constructed both quantitative and qualitative models of the demand forecasting on new cars in Russian Federation and estimated the influence of economic indicators of Russia on the volume of demand. We also developed an optimal technique of the forecasting and marked the most effective methods. Finally, we analyzed and compared the models obtained.

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