Work detail

Performance Analysis of Credit Scoring Models on Lending Club Data

Author: Mgr. Michal Polena
Year: 2017 - summer
Leaders: doc. PhDr. Petr Teplý Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 100
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/185577/
Abstract: 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.

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