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Peer-to-peer (P2P) lending platforms have grown rapidly over the past decade as the network infrastructure has improved and the demand for personal lending has grown. Such platforms allow users to create peer-to-peer lending relationships without the help of traditional financial institutions. Assessing the borrowers' credit is crucial to reduce the default rate and benign development of P2P platforms. Building a personal credit scoring machine learning model can effectively predict whether users will repay loans on the P2P platform. And the handling of data outliers and sample imbalance problems can affect the final effect of machine learning models. There have been some studies on balanced sampling methods, but the effect of outlier detection methods and their combination with balanced sampling methods on the effectiveness of machine learning models has not been fully studied. In this paper, the influence of using different outlier detection methods and balanced sampling methods on commonly used machine learning models is investigated. Experiments on 44,487 Lending Club samples show that proper outlier detection can improve the effectiveness of the machine learning model, and the balanced sampling method only has a good effect on a few machine learning models, such as MLP.

This paper introduces the UI interface design and its application based on the iOS platform of comprehensively informationized teaching system. On the basis of typical examples of interfaces, results of user’s questionnaire and relevant theories, this paper attempts to present the UI interface in detail through the stages of discussion, design and implement. Meanwhile, in order to find balance between theoretical strictness and practical effectiveness, this paper will make every effort to point out the improvement methods and put forward solutions to the defect, according to the analysis of design under usability experience criterion.