Napoved širjenja gripe z uporabo algoritmov podatkovnega rudarjenja

magistrsko delo

Teja Rupnik (Author), Marjetka Krajnc (Mentor)

Abstract

Natančna napoved širjenja gripe lahko izdatno pripomore tako pri preventivi kot pri zajezitvi ob izbruhu. V magistrski nalogi si bomo ogledali algoritme podatkovnega rudarjenja na primeru napovedi širjenja gripe. Predstavili bomo modela LASSO in naključni gozd ter na obeh uporabili standardno napoved in tekočo napoved. Za napoved bomo uporabili podatke iz socialnega omrežja Twitter, podatke zbrane v spletnem brskalniku Google, vremenske podatke in zgodovinske podatke o številu pacientov okušenih z gripo. Ugotovili bomo, kako različni nabori podatkov vplivajo na napoved in kateri model nam da najboljšo napoved.

Keywords

LASSO;naključni gozd;napovedi;podatkovno rudarjenje;

Data

Language:	Slovenian
Year of publishing:	2022
Typology:	2.09 - Master's Thesis
Organization:	UL FMF - Faculty of Mathematics and Physics
Publisher:	[T. Rupnik]
UDC:	519.2
COBISS:	97483779
Views:	819
Downloads:	94
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Flu spreading prediction using data mining algorithms
Secondary abstract:	Exact flu spreading prediction can be helpfull in prevention and intervention in case of influenza outbreak. In this work we will describe algorithms of data mining on the case of flu spreading prediction. We will present two models: LASSO and random forest. For each model we will observe a regular forecast and a rolling forecast. For prediction we will use data from social network Twitter, data gathered in Google search queries, history of total number of influence patients and weather data, including temperature and humidity. We will find optimal model for flu spreading prediction and show which set of data gives the best results.
Secondary keywords:	least absolute shrinkage and selection operator;random forest;predictions;data mining;
Type (COBISS):	Master's thesis/paper
Study programme:	0
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. v Ljubljani, Fak. za matematiko in fiziko, Oddelek za matematiko, Finančna matematika - 2. stopnja
Pages:	IV, 55 str.
ID:	14519286