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Construction of Training Data for Price Prediction of a Real Estate from Internet Ads
Abstract:
The paper presents a model for constructing a data set aimed at predicting a price of a real estate (houses and flats) from the standard Internet ads. The model for predicting a real estate price includes, in addition to standard real estate's features (area, number of bedrooms, etc.) appearing in ad, attrac- tiveness of a real estate location as well as information on some additional interior facilities (e.g., refrigerator, dish-washing machine, stove, etc.). The proposed training set construction model uses OpenStreetMap's Overpass API for determining attractiveness of a real estate's location, and a convolu- tion neural network for detecting interior facilities from real estate photos.
CITATION:
IEEE format
M. Vidović, I. Radosavljević, A. Mitrović, Z. Konjović, “Construction of Training Data for Price Prediction of a Real Estate from Internet Ads,” in Sinteza 2019 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2019, pp. 388-393. doi:10.15308/Sinteza-2019-388-393
APA format
Vidović, M., Radosavljević, I., Mitrović, A., Konjović, Z. (2019). Construction of Training Data for Price Prediction of a Real Estate from Internet Ads. Paper presented at Sinteza 2019 - International Scientific Conference on Information Technology and Data Related Research. doi:10.15308/Sinteza-2019-388-393
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