Detail práce

Performance Analysis of Credit Scoring Models on Lending Club Data

Autor: Mgr. Michal Polena
Rok: 2017 - letní
Vedoucí: doc. PhDr. Petr Teplý Ph.D.
Konzultant:
Typ práce: Diplomová
Finance, finanční trhy a bankovnictví
Jazyk: Anglicky
Stránky: 100
Ocenění:
Odkaz: https://is.cuni.cz/webapps/zzp/detail/185577/
Abstrakt: In our master thesis, we compare ten classification algorithms for credit scoring.
Their prediction performances are measured by six different classification
performance measurements. We use a unique P2P lending data set with more
than 200,000 records and 23 variables for our classifiers comparison. This data
set comes from Lending Club, the biggest P2P lending platform in the United
States. Logistic regression, Artificial neural network, and Linear discriminant
analysis are the best three classifiers according to our results. Random forest
ranks as the fifth best classifier. On the other hand, Classification and regression
tree and k-Nearest neighbors are ranked as the worse classifiers in our ranking.

Partneři

ČSOB
Deloitte
McKinsey & Company

Sponzoři

CRIF
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