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Predictive Analytics - Process and Development of Predictive Models
Praus, Ondřej ; Pour, Jan (advisor) ; Mrázek, Luboš (referee)
This master's degree thesis focuses on predictive analytics. This type of analysis uses historical data and predictive models to predict future phenomenon. The main goal of this thesis is to describe predictive analytics and its process from theoretical as well as practical point of view. Secondary goal is to implement project of predictive analytics in an important insurance company operating in the Czech market and to improve the current state of detection of fraudulent insurance claims. Thesis is divided into theoretical and practical part. The process of predictive analytics and selected types of predictive models are described in the theoretical part of the thesis. Practical part describes the implementation of predictive analytics in a company. First described are techniques of data organization used in datamart development. Predictive models are then implemented based on the data from the prepared datamart. Thesis includes examples and problems with their solutions. The main contribution of this thesis is the detailed description of the project implementation. The field of the predictive analytics is better understandable thanks to the level of detail. Another contribution of successfully implemented predictive analytics is the improvement of the detection of fraudulent insurance claims.
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Modul pro vyhledávání nevhodných obrázků
Žurek, Aleš
This work is focused on classifying photos which are uploaded on dating service Lidé.cz. Pictures are classified into two categories based on whether they contain pornographic content or not. Convolutional neural networks are used for classification and these neural networks are taught by using Caffe framework. The results of this work fulfilled all requirements from Seznam.cz, a.s. company. Classification accuracy of the best model on created testing dataset with 5643 photos was 93,64 % and the time for classification of photography is low enough to perform classification in real time. The first part contains an analysis of the current approaches for image classification. The second part focuses on the analysis and draft of the solution and the third part describes the implementation of the solution and the testing of neural networks models.
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Webová aplikace pro testování obchodních strategií a predikci časových řad
Matyáš, Michal
This diploma thesis deals with the creation and testing of trading strategies based on technical analysis and prediction of time series using neural networks. The the-oretical part introduces the reader to basic methods of market analysis, especially with technical analysis. In the practical part there are analyzed available options for testing trading strategies and designed functions and structure, which are used to implement web application.
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Automatic recognition of the electrometer status from picture
HANZLÍK, Ondřej
This thesis deals with problems of recognition of an electrometer´s state from sensing image. It is tangibly about electrometer´s scanning by a mobile phone´s camera. There is a surface with an electrometer´s dial which is detected and on this surface the particular numbers are detected consequently. The numbers are recognized via neural network. For more information from this image there are used some techniques of image segmentation to check the status. For the classification of the segmentation´s outputs are used classification tools, especially a support vector machine (SVM) and neural networks. Problems of image segmentations are solved by using OpenCV library. OpenCV is used for the implementation of the vector machine either. Application is on Android platform. Part of the thesis is concerned in a creation of a desktop application which is instrumental towards testing of neural network. The thesis also describes how to save the necessary data gathering in the course of the recognition which are used for working with neural network. The part of the thesis also deals with running web which will be evolved for the opportunity to participate in the further development of the system. There is available a public repository with source codes created during implementation.
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Aplikace metod strojového učení na dolování znalosti z dat
Kraus, Jan
The diploma thesis deals with the area of data mining applied to large collections of textual data. Specifically the thesis is focused on sentiment analysis based on the user's subjective verbal assessment in natural language. The first part of the diploma thesis introduces the reader to basic terms of machine learning and data mining applied particularly to large textual data collections. Following is the description of textual data preprocessing methods and principles of machine learning algorithms. In the practical part of this thesis there are experiments designed and subsequently executed using the SPSS Modeler tool. The experimental part is focused especially on identification of significant attributes and recongnition of relationships between them. The emphasis is put especially on thorough interpretation of the results obtained.
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Character recognition system
HANZLÍK, Ondřej
"The thesis proposes a system for recognition of printed text (OCR), which uses neural network for recognizing letters. The neural network is implemented using the program RapidMiner. To control the neural network is using the processes created by program RapidMiner. These processes are run directly from a Java application. RapidMiner is implemented into the java application and using its libraries is started directly from java application." directly from a Java application. RapidMiner is implemented into the java application and using its libraries is started directly from java application."
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The Theoretical Background of Neuronal Networks
ROKŮSEK, Zdeněk
In focus of this dissertation is the theoretical background of the neural networks with a description of basic models of these neural networks and their application in practice. Furthermore, this work provides a concise historical overview of the exploration on field of the neural calculations. It also clarifies neuro-physiological motivation that conducts to the mathematical model of the neuron just like the neural networks. The number of options of neural networks and ways how to apply them is high. The neural networks can be used to define figures or to compress data, and so on. Among the most familiar models of a neural network is the multilayer neural network, the model of MADALINE, the associative network or the Hopfield´s network.
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Comparison of approaches to creating credit scoring models
Hofman, Elena ; Šedivý, Jan (advisor)
This work is focused on the management of a credit risk related to the traditional bank lending business to individuals. The paper deals with a theory of measuring risk with help of PD (Probability of Default) parameter when different scoring models are used. The goal is to outline an issue with the credit risk and its management in general, attention is paid to details of a process of creating scoring models. There are three specific modeling techniques listed, namely logistic regression, decision trees and neural networks. Methods are explained in detail and are given possibilities of mutual comparison. The application part is devoted to the evaluation and comparison of credit scoring models based on these methods.
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