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Title:Napovedovanje prodaje z dodatnimi odprtimi podatkovnimi viri
Authors:ID Kolman, Denis (Author)
ID Kljajić Borštnar, Mirjana (Mentor) More about this mentor... New window
Files:.pdf MAG_Kolman_Denis_2021.pdf (2,29 MB)
MD5: 3B36A153BCB899EE08E4F04F532B37DB
PID: 20.500.12556/dkum/b14a8d2a-4f58-46d4-8f3f-7523011c08a5
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FOV - Faculty of Organizational Sciences in Kranj
Abstract:Rešitev, predstavljena v nalogi, se osredotoča na izboljšanje procesa napovedovanja prodajnih rezultatov izdelkov ali storitev s pomočjo vpeljave dodatnih odprtih virov podatkov. Z nalogo želimo poudariti, kako enostavno lahko pridobimo podatke in jih zelo dragoceno uporabimo v procesu poslovanja. Za končno primerjavo in vrednotenje smo najprej postavili dva modela v različnih sistemih za napovedovanje prodajnih rezultatov na podlagi enega vira, to so zgodovinski podatki prodaje. V nadaljevanju smo napovedni model s historičnimi podatki nadgradili. Dodali smo še podatke o številu zabeleženih bolezni, ki smo jih pridobili preko spletnega portala z javno objavljenimi podatki (odprti podatki). Korelacija med omenjenimi podatki obstaja, vendar je povezovanje te vrste podatkov kompleksno, zato je interpretacija rezultatov po eni strani lahko zelo zahtevna, po drugi strani pa povsem logična in zanimiva. Cilj, ki smo si ga zadali, je torej pokazati tri različne modele za napovedovanje, jih primerjati in ugotoviti, kakšen doprinos prinesejo odprti podatki. Rezultati naloge so pokazali, da smo zgradili zelo dobro osnovo za nadaljnji razvoj rešitve in enostavno implementacijo v uporabo.
Keywords:Odprti (javni) podatki, napovedovanje, podatkovno rudarjenje, model CRISP-DM.
Place of publishing:Maribor
Year of publishing:2021
PID:20.500.12556/DKUM-80846 New window
COBISS.SI-ID:88108035 New window
Publication date in DKUM:07.12.2021
Views:661
Downloads:65
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:FOV
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:27.10.2021

Secondary language

Language:English
Title:Sales predictions with additional open data source
Abstract:The solution presented in the thesis, focuses on improving the process of sales prediction results for products or services, with additional open data sources. With this paper, we want to emphasize how easy it is to obtain data and use it very valuable in the business process. For the final comparison and evaluation, we first set up two models in different systems, for forecasting sales results based on one source, i.e. historical sales data. In next step, we upgraded the second prediction model based on historical data with additional open data. We added data about the number of recorded diseases, which we obtained through the web portal with public published data. There is a correlation between these two data types, but connecting this data is complex in many cases. Because of that, the interpretation of the results can be very demanding from one prospective, but also completely logical and interesting from the other. The goal was therefore to show three different prediction models, to compare them and determine what contribution open data makes on it. The results of the task showed that we have built a very good basis for further development of the solution and easy implementation for proper usage.
Keywords:Open data, prediction, data mining, CRISP-DM model.


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