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The future of credit scoring modelling using advanced techniques
Čermáková, Jolana ; Krištoufek, Ladislav (advisor) ; Geršl, Adam (referee)
Machine learning is becoming a part of everyday life and has an indisputable impact across large array of industries. In the financial industry, this impact lies particularly in predictive modelling. The goal of this thesis is to describe the basic principles of artificial intelligence and its subset, machine learning. The most widely used machine learning techniques are outlined both in a theoretical and a practical way. As a result, four models were assembled within the thesis. Results and limitations of each model were discussed and these models were also mutually compared based on their individual per- formance. The evaluation was executed on a real world dataset, provided by Home Credit company. Final performance of machine learning methods, measured by the KS and GINI metrics, was either very comparable or even worse than the performance of a traditional logistic regression. Still, the problem may lie in an insu cient dataset, in the improper data prepara- tion, or in inappropriately used algorithms, not necessarily in the models themselves.
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Comparison of Dyna-Clue and land change modeler software for predictive modelling in the suburban area of Prague
Indrová, Magdalena ; Kupková, Lucie (advisor) ; Grill, Stanislav (referee)
Comparison of Dyna-CLUE and Land Change Modeler software for predictive modelling in the suburban area of Prague Abstract The objective of this work was to predict the development of the suburban area of Prague, using Dyna- CLUE and Land Change Modeler (LCM) software, and based on the results compare the capabilities of both these software tools. In this work I used time series of land cover data obtained by the department of applied geoinformatics and cartography, local plans of the municipalities, and information about soil protection provided by the Research Institute for Soil and Water Conservation. Based on these data, a predicted land cover map for 2020 was created in both software tools. The results were then compared and showed that the models respect the restriction of development in predetermined areas. In accordance with local plans, new residential development was properly allocated. For commercial development, the requirements were not completely fulfilled. It is evident that both models are able to create a correct map of future land cover based on specified requirements. However, the instability of LCM and the necessity of using other software while working with Dyna- CLUE somewhat complicated the work. Keywords: Dyna-CLUE, Land Change Modeler, predictive modelling, land cover, residential...
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