This research is concerned with examining market segments and regional variations associated with winery visitors in the state of Virginia. The tourism literature published by the state of Virginia for wineries indicates that there are five wine regions. In this research, data were collected from interviews conducted at wineries in each of the five wine regions. The first phase of analysis sought to create market segments using a factor-cluster approach. Segments were created using cluster analysis and multiple discriminant analysis. Three distinct market segments based on benefits sought by the visitor emerged from these data. Regional variations were examined in the second part of this study. The data were classified into individual regions based on the locations of the wineries examined. Distinct differences in the regional profiles were revealed. Weak significant relationships among the segments and regions were also revealed through analysis indicating a spatial component to the segments. This research proposes the use of three regions for market research purposes, while retaining the five existing regions for promoting an organized structure to visiting wineries in the state. / Master of Science

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Stress detection based on non-EEG physiological data can be useful for monitoring drivers, pilots, and also for monitoring of people in ordinary situation, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature recorded for 3 type of stress alternated with relax state. Two final models were created in this thesis. First model for Binary classification stress/relax, second for classification of 4 different type of psychical state. Best results were reached using model created by decision tree algorithm with 8 features for binary classification and with 8 features for classification of 4 psychical state. Accuracy of final models is aproximately 95 % for binary model and 99 % for classification of 4 psychical state. All algorithms were implemented in Python.