Statistička analiza podataka u maloprodaji

master's thesis

• Title (english): Statistical analysis of data in retail
• Author: Mihaela Lekić
• Mentor: Tomislav Stipančić (mentor)
• Granter: University of Zagreb; Faculty of Mechanical Engineering and Naval Architecture; Zagreb
• Defense date and country: 2023-12-07, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mechanical Engineering

Abstract

Fokus rada je analiza maloprodajnih podataka online platformi kako bi se utvrdila opća kretanja i korelacije zavisne varijable, predvidjela buduća potražnja te predložila optimalna lokacija skladišta tvrtke. Korišteni su stvarni podaci poslovanja maloprodajne platforme Plac.hr, ali i komplementarne baze podataka za koronavirus (Hrvatski zavod za javno zdravstvo), demografiju stanovništva (Državni zavod za statistiku, GeoPortal Zagrebačke infrastrukture prostornih podataka i dr.) i poslovanje pravnih subjekata (Financijska agencija i Poslovna Hrvatska). Podaci su u obliku vremenskih nizova (eng. time series) od 2020. do 2023. godine, a metodologija uključuje korištenje alata Google Colab za analizu i vizualizaciju podataka u programskom jeziku Python. Cilj rada je pružiti dubinsku analizu vremenskih nizova maloprodajne tvrtke, identificirati trendove, sezonalnosti i korelacije, te primijeniti modele za poboljšanje operativne učinkovitosti e-trgovine. Poseban je osvrt dan utjecaju pandemije COVID-19 na potrošačke navike.

Abstract (english)

The focus of the work is the analysis of retail data from online platforms to determine general trends and correlations of the dependent variable, predict future demand, and propose the optimal location of a company's warehouse. The main dataset relied on real-life business data of the retail platform Plac.hr, as well as complimentary databases on the coronavirus pandemic (Croatian Institute of Public Health), population demography (State Statistical Office, GeoPortal of the Zagreb Spatial Data Infrastructure, etc.), business operations of legal entities (Financial Agency and Poslovna Hrvatska). The data are in the form of time series from 2020 to 2023, and the methodology includes the use of Google Colab for data analysis and visualization in the Python programming language. The work aims to provide an in-depth analysis of time series data of a retail company, identify trends, seasonality and correlations, and apply models to improve the operational efficiency of e-commerce. A special review is given to the impact of the COVID-19 pandemic on consumer habits.

• Keywords: maloprodajna analitika; vremenski nizovi; e-trgovine; COVID-19; API
• Keywords (english): retail analytics; time series; e-commerce; COVID-19; API
• Language: croatian
• URN:NBN: urn:nbn:hr:235:228786
• Study programme: Title: Mechanical Engineering; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka strojarstva (magistar/magistra inženjer/inženjerka strojarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Embargoed access; Embargo expiration date: 2028-12-01
• Created on: 2023-12-01 16:04:45