{'fraction_non_alphanumeric': 0.01644398766700925, 'fraction_numerical': 0.0, 'mean_word_length': 5.0875, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Importance of data pre-processing in credit scoring models based on data mining approaches

Data Mining has become essential tool for discovery of hidden patterns and information in databases. However, for a Data Mining model to be meaningful and effective, data pre-processing is one of the key factors in successful model preparation. In this paper, we have investigated how data pre-processing affects real dataset when applying Data Mining technique for the purpose of predicting default clients in a micro-financing institution. Therefore, several data pre-processing techniques have been described and applied to the dataset. Results are shown and compared for both of the cases with Generalized Linear Model and Decision Tree being the two Data Mining classification algorithms used for Credit Scoring model. It is concluded that Credit Scoring Model is much more accurate and efficient when it is executed on data that has been carefully prepared and pre-processed.