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Forecasting Bank Products Set of Potential Client

Student: Kozhevina Anna

Supervisor: Boris Demeshev

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Year of Graduation: 2019

The problem of forecasting customer preferences has been significant since the market economy appeared. It is especially crucial for areas where there are not so many potential consumers so every offer made is important for future profit. The solution of this problem enables the seller to produce and supply relevant number goods or service and understand what product to offer to a particular buyer. The paper presents two methods to reveal which bank products would be interesting for a particular consumer in business-to-business services market. The first method at first clusters clients of the bank on their product sets. Then the decision tree forecasting consumers clusters is trained on their features found in opened sources. Training is optimized using gradient boosting algorithm. The second method uses non-negative matrix factorization to interpret how the features of a potential client affect it's future choice of products.

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