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Solution of unemployment of young people through closer connestion of their vocational training with labor market requirements
Steindlberger, Martin ; Kotrusová, Miriam (advisor) ; Hiekischová, Michaela (referee)
This diploma thesis deals with the relationship between the vocational training of young people and the requirements of the labour market. The thesis is focused on secondary vocational education. The problem, that has been examined, is the lack of readiness of school graduates, to move to the free labour market. In order to improve this, it is necessary to try to reconcile the content and outputs of vocational training with the needs of the labour market. If graduates are poorly prepared for employers' needs, these graduates may become unemployed. The problem is getting worse with the longer duration of unemployment. The aim of this diploma thesis is to find out and describe who participates in vocational training and how their mutual cooperation is implemented. In the thesis the author will try to find out the strengths and weaknesses of this cooperation. He will also try to find out in what kind of way the training of young people to the needs of the labour market is adapted. The thesis is a case study focused on the South Bohemian Region, which is briefly presented by selected indicators characteristic of the selected region. To obtain the necessary data, there are used figures and statistics from the former researches, as well as information from selected participants involved in the solution...
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Předpovídání trendů akciového trhu z novinových článků
Serebryannikova, Anastasia ; Kuboň, Vladislav (advisor) ; Vidová Hladká, Barbora (referee)
In this work we made an attempt to predict the upwards/downwards movement of the S&P 500 index from the news articles published by Bloomberg and Reuters. We employed the SVM classifier and conducted multiple experiments aiming at understanding the shape of the data and the specifics of the task better. As a result, we established the common evaluation settings for all our subsequent experiments. After that we tried incorporating various features into the model and also replicated several approaches previously suggested in the literature. We were able to identify some non-trivial dependencies in the data which helped us achieve a high accuracy on the development set. However, none of the models that we built showed comparable performance on the test set. We have come to the conclusion that whereas some trends or patterns can be identified in a particular dataset, such findings are usually barely transferable to other data. The experiments that we conducted support the idea that the stock market is changing at random and a high quality of prediction may only be achieved on particular sets of data and under very special settings, but not for the task of stock market prediction in general. 1
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Artificial neural networks for macroeconomic data analysis
Padrón Peňa, Ildefonso ; Mrázová, Iveta (advisor) ; Kuboň, David (referee)
The analysis and prediction of macroeconomic time-series is a factor of great interest to national policymakers. However, economic analysis and forecast- ing are not simple tasks due to the lack of a precise model for the economy and the influence of external factors, such as weather changes or political decisions. Our research is focused on Spanish speaking countries. In this thesis, we study dif- ferent types of neural networks and their applicability for various analysis tasks, including GDP prediction as well as assessing major trends in the development of the countries. The studied models include multilayered neural networks, recur- sive neural networks, and Kohonen maps. Historical macroeconomic data across 17 Spanish speaking countries, together with France and Germany, over the time period of 1980-2015 is analyzed. This work then compares the performances of various algorithms for training neural networks, and demonstrates the revealed changes in the state of the countries' economies. Further, we provide possible reasons that explain the found trends in the data.
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Web Application for Making Predictions of a Call Centre
Mička, David ; Hynek, Jiří (referee) ; Bartík, Vladimír (advisor)
The goal of this thesis is to create a web application for creation of call centre predictions. The app should be able to replace current solutions that are in use in the daily operation of Kiwi.com s.r.o. The app should be more intuitive and easier to use and maintain than Verint or the spreadsheet solution of doing predictions. It should also have enough options for creation of tactical forecasts that allow the company to react on upcoming situations and should help set realistic expectations for the management of our customer centre.
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Prediction of Protein Solubility
Marušiak, Martin ; Martínek, Tomáš (referee) ; Hon, Jiří (advisor)
Protein solubility is closely related to the usability of proteins in industrial use and research. The successful prediction of solubility would therefore lead to a significant saving of financial resources. This work presents new solubility predictor Solpex based on machine learning that achieved better performance on independent test set than any comparable solubility prediction tool. The predictor implementation was preceded by a study of the biological nature of solubility, evaluation of existing solubility prediction approaches, datasets building, many experiments with novel features and selection of the best features for the predictor. As the most important step in machine learning is the datasets building, this work mainly benefits from own rigorous processing of the main source of solubility data - the TargetTrack database.
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Machine Learning Optimization of KPI Prediction
Haris, Daniel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.
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Machine learning with applications to finance
Mešša, Samuel ; Hurt, Jan (advisor) ; Večeř, Jan (referee)
The impact of data driven, machine learning technologies across a wide variety of fields is undeniable. The financial industry, which relies heavily on predictive modeling being no exception. In this work we summarize two widely used machine learning models: support vector machines and neural networks, discuss their limitations and compare their performance to a more traditionally used method, namely logistic regression. Evaluation was done on two real world datasets, which were used to predict default of loan applicants and credit card holders formulated as a binary classification task. Neural networks and support vector machines either outperformed or showed comparable results to logistic regression with performance measured in receiver operator characteristic area under curve. In the second task neural networks outperformed both other models by a significant margin.
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