diplomska naloga
Peter Konda (Avtor), Marko Bajec (Mentor)

Povzetek

Izvedba podatkovnega rudarjenja v bančništvu z uporabo metodologije CRISP-DM

Ključne besede

poslovno podatkovno rudarjenje;metodologija CRISP-DM;SQL Server 2008;Analysis Services;Weka;računalništvo;univerzitetni študij;diplomske naloge;

Podatki

Jezik: Slovenski jezik
Leto izida:
Tipologija: 2.11 - Diplomsko delo
Organizacija: UL FRI - Fakulteta za računalništvo in informatiko
Založnik: [P. Konda]
UDK: 004(043.2)
COBISS: 7466580 Povezava se bo odprla v novem oknu
Št. ogledov: 1291
Št. prenosov: 349
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Angleški jezik
Sekundarni naslov: [ǂThe ǂuse of CRISP-DM methodology for data mining in banking]
Sekundarni povzetek: Data mining has been recognized as an independent field of research for more than a decade. Introduced in 2000 CRISP-DM is considered the first formal methodology that fully covers the process of data mining. Large companies now seek to incorporate this technology into their existing systems. This thesis describes the uses of data mining in a bank. NLB, d. d. like most enterprizes in Slovenia established a data warehousing system. Using OLAP the employees can perform business analysis with ease, but may have problems finding complex patterns in the data. Therefore data mining represents a possible upgrade over existing systems. The first few chapters introduce data mining and its place in modern science. Since data mining deals with data I included a brief history of data storage development. The next chapters contain a full description of CRISP-DM methodology and techniques for solving common business problems. The research part covers the data mining process in practice. The objective is to calculate a propensity score for each customer. This was done iteratively using the SQL Server 2008 database platform with strong emphasis on data loading and analysis. I compared the accuracy of different classification models using graphic representation and cross-validation.
Sekundarne ključne besede: data mining in customer relationship management;CRISP-DM methodology;SQL Server 2008;Analysis Services;Weka;computer science;diploma;
Vrsta datoteke: application/pdf
Vrsta dela (COBISS): Diplomsko delo
Komentar na gradivo: Univerza v Ljubljani, Fakulteta za računalništvo in informatiko
Strani: 56 str.
ID: 23914181