Applying Machine Learning Methods in the Field of Housing and Communal Services

Student: Rakhimov Sevastyan

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2019

Every person leading an independent life has a long list of mandatory monthly expenses: food, transportation, telephone, Internet, taxes, housing and communal services. The system that calculates the amount of services spent (and hence the calculation of the total cost) is very complicated. The purpose of the research is to find out the most appropriate machine learning methods to understand the features and factors affecting the cost of housing and communal services. The study focuses on analysis of search data, feature engineering and predictive modeling using regression methods.

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