Thesis Details
Dolování z dat v jazyce Python
The main goal of this thesis was to get acquainted with the phases of data mining, with the support of the programming languages Python and R in the field of data mining and demonstration of their use in two case studies. The comparison of these languages in the field of data mining is also included. The data preprocessing phase and the mining algorithms for classification, prediction and clustering are described here. There are illustrated the most significant libraries for Python and R. In the first case study, work with time series was demonstrated using the ARIMA model and Neural Networks with precision verification using a Mean Square Error. In the second case study, the results of football matches are classificated using the K - Nearest Neighbors, Bayes Classifier, Random Forest and Logical Regression. The precision of the classification is displayed using Accuracy Score and Confusion Matrix. The work is concluded with the evaluation of the achived results and suggestions for the future improvement of the individual models.
Data mining, Python, R, Data Preprocessing, Clustering, Prediction, Classification, Case studies, ARIMA, comparison Python and R, Bayes, knn.
Bartík Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
Křivka Zbyněk, Ing., Ph.D. (DIFS FIT BUT), člen
Švéda Miroslav, prof. Ing., CSc. (DIFS FIT BUT), člen
Veselý Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
Zeman Václav, doc. Ing., Ph.D. (UTKO FEEC BUT), člen
@mastersthesis{FITMT20060, author = "Jakub \v{S}enovsk\'{y}", type = "Master's thesis", title = "Dolov\'{a}n\'{i} z dat v jazyce Python", school = "Brno University of Technology, Faculty of Information Technology", year = 2017, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/20060/" }