The cohesive thread of the following seminar thesis is the presentation and comparison of different methods of machine learning to identify a "good borrower", according to the popular online lending platform Lending Club (acronym LC). The paper presents the results of the findings of an article entitled Risk assessment in social lending via random forests by Milad Malekipirbazari and Vural Aksakalli, who, with the help of WEKA program, showed that the random forests method in identifying a "good borrower" is better than FICO score and LC grades. These methods for identifying a "good borrower" are used by today's agencies to identify the credit rating of an individual for social lending. In the conclusion of the thesis's seminar, a reconstruction and confirmation of results
of the article is presented, based on results I have obtained after running the simulations in WEKA program first-hand. Machine learning methods presented in this thesis's seminar are: random forests,
logistic regression, support vector machines, and k-nearest neighbours method.
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