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CHAID and logistic regression
Novák, Jaroslav ; Čabla, Adam (advisor) ; Matějka, Martin (referee)
The aim of this thesis is to introduce logistic regression and method dedicated to construction of decision trees called CHAID, compare these two methods with regard to interpretation of their outputs. In order to accomplish the set goals application of these methods will be presented on real dataset. Statistical software will be used to obtain outputs. The outputs will be interpreted and conclusions on their bases will be presented. This thesis will also introduce possibilities of interpretation of these outputs and pros and cons that are connected with them.
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Increasing the efficiency of advertising on Facebook by using decision trees
Randus, Jan ; Vraná, Lenka (advisor) ; Jašek, Pavel (referee)
This bachelor thesis aims to increase the efficiency of Facebook marketing campaigns, having used advanced statistical methods. For this purpose, I gathered data from Facebook marketing campaign of a fashion online store. In the theoretical part, the thesis addresses methods which were used for data analysis (decision trees) and also covers sampling methods which were used for model adjustments. In addition, the thesis describes the field of online performance marketing. The practical part is devoted to the application of decision trees and the creation of decision model on the data gathered in the beginning. Finally, the thesis answers the question how to advertise on Facebook as efficiently as possible, based on the output from my decision model.
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A combination of real options, simulation and decision trees for investment decisions
Pavlovská, Tereza ; Dlouhý, Martin (advisor) ; Dlouhá, Zuzana (referee)
This thesis is concerned with the evaluation of real options whose value represents a certain flexibility of the firm to decide about company´s assets in the future. In addition to classic models which were developed for option rating, such as binomial and Black-Scholes model, which have advantages and disadvantages, there is introduced a possible combination of decision trees and simulation Monte Carlo which runs directly inside the tree. This combination can erase the disadvantages which these methods have when they are used separately for option evaluation. In this thesis there can be found an application example inspired by a real situation and there are described different possibilities of usage of the mentioned combination and there is also demonstrated an unambiguous advantage of this method and that is a bigger amount of information which is provided in comparison with standard models. It allows the company to access much more complex image of the investment. The result is also various option values according to the used technique.
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Creation, Utilization and Optimization of Decision Trees
Selement, Pavel ; Bína, Vladislav (advisor) ; Váchová, Lucie (referee)
Decision trees are one of the main methods for solving decision problems. The goal of this thesis is to introduce their properties and basic conditions for use. The main contribution of this work is located in linking decision trees research in the decision theory and in the field of machine learning. The goal is not meant to be a comprehensive list of available methods but rather points out the overlooked connection between those two science disciplines. It is shown, both in theory and by an example, how to use the methods originally from machine learning for decision trees in the decision theory and thus in management practice. At the end there are several variants introduced to explain how to simplify decision trees.
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Data Mining and use of decision trees by creation of Scorecards
Straková, Kristýna ; Witzany, Jiří (advisor) ; Fičura, Milan (referee)
The thesis presents a comparison of several selected modeling methods used by financial institutions for (not exclusively) decision-making processes. First theoretical part describes well known modeling methods such as logistic regression, decision trees, neural networks, alternating decision trees and relatively new method called "Random forest". The practical part of thesis outlines some processes within financial institutions, in which selected modeling methods are used. On real data of two financial institutions logistic regression, decision trees and decision forest are compared which each other. Method of neural network is not included due to its complex interpretability. In conclusion, based on resulting models, thesis is trying to answers, whether logistic regression (method most widely used by financial institutions) remains most suitable.
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Using data mining methods in the analysis of credit risk data
Tvaroh, Tomáš ; Witzany, Jiří (advisor) ; Matejašák, Milan (referee)
This thesis focuses on comparison of selected data mining methods for solving classification tasks with the method of logistic regression. First part of the thesis briefly introduces data mining as a scientific discipline and classification task is shown in the context of knowledge data discovery. Next part explains the principle of particular methods amongst which, along with logistic regression, artificial neural networks, classification decision trees and Support Vector Machine method were selected. Together with mathematical background of each algorithm, demonstration of how the classification functions for new examples is mentioned. Analytical part of this thesis tests decribed methods on real-world data from the Lending Club company and they are compared based on classification accuracy. Towards the end, an evaluation of logistic regression is made in terms of whether its majority position is due to historical reasons or for its high classification accuracy compared to other methods.
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CHAID - decision tree construction technique
Freyvald, Michal ; Vild, Jiří (advisor) ; Čabla, Adam (referee)
CHAID analysis (Chi-squared Automated Interaction Detection) is a construction technique of decision trees, which is especially suitable for categorical data and is based on Paerson's chi-square statistical test of independence of categorical fields. Important characteristics of decision trees are summarized and structure of CHAID analysis, particular phases of analysis construction, advantages, disadvantages and recommended settings for a high- quality analysis are described in this paper. On exemplary sample from customers segment we're observing usage of CHAID in practise and with the help of IBM SPSS PASW Statistics v18.0 software we're proving usefulness of CHAID analysis in target marketing.
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Design and implementation of Data Mining model with MS SQL Server technology
Peroutka, Lukáš ; Maryška, Miloš (advisor) ; Smutný, Zdeněk (referee)
This thesis focuses on design and implementation of a data mining solution with real-world data. The task is analysed, processed and its results evaluated. The mined data set contains study records of students from University of Economics, Prague (VŠE) over the course of past three years. First part of the thesis focuses on theory of data mining, definition of the term, history and development of this particular field. Current best practices and meth-odology are described, as well as methods for determining the quality of data and methods for data pre-processing ahead of the actual data mining task. The most common data mining techniques are introduced, including their basic concepts, advantages and disadvantages. The theoretical basis is then used to implement a concrete data mining solution with educational data. The source data set is described, analysed and some of the data are chosen as input for created models. The solution is based on MS SQL Server data mining platform and it's goal is to find, describe and analyse potential as-sociations and dependencies in data. Results of respective models are evaluated, including their potential added value. Also mentioned are possible extensions and suggestions for further development of the solution.
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Managerial Decision-Making in a Sports Organization
Rampír, Vojtěch ; Váchová, Lucie (advisor) ; Zelená, Veronika (referee)
Bachelor thesis describes operating of the sport club HBC Kolin and by using decision trees creates optimal strategy of propagation of upcoming play-off matches. Thesis contains basics of manager decision making, types of decisions and manager characters.Thesis describes basics of sponzoring too, types of sponzoring and sponzors, their roles in clubs and gets more specific about sport sponzoring.
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Comparisons of discriminant analysis and classification trees
Dlabač, Jaroslav ; Vilikus, Ondřej (advisor) ; Stecenková, Marina (referee)
This bachelor thesis compares two methods to discrimination and classification of data in multivariate statistics analysis. While discriminant analysis represents the classical statistical method for discrimination and subsequent classification data method, CART is a new procedure in data-minig, which uses artificial intelligence. The first half of this work is devoted to theoretical description and comparison of these two methods. The second half is the demonstration of both methods on practical example. At the end, the results of both methods are compared and evaluated.
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