Determining relationships between data and creating Bayesian network

In this thesis, the relationships between the data were determined by filtering according to the desired conditions, a Bayesian Network was created, and the accuracy of the results was calculated. Naive Bayes function and Hill Climb Algorithm were used while determining the relationships between the data and creating the Bayes Network. Then, statistical information was obtained from the data set. The probabilistic calculation of the realization of the result based on the properties of a data, and the conditional probabilities of the data with each other are explained. In the following chapters, the required techniques and the methods used are explained, the probability calculation of the relationships between the data is found with the Hill Climb Algorithm and the Bayes Network structure of the relations is graphed. These techniques and methods consisting of mathematical calculations are shown with the RStudio working environment and lines of code written in R language. The graph to be created is shown with lines of code using the RStudio library. Thus, the relationships between data are shown numerically and visually. The conditional probabilities of each of the properties will be shown in tabular form. In other words, the probability of occurrence of the features according to the attribute to which they depend is calculated and this calculated value is obtained according to the quality called class. Then, conditional probability values ​​of the properties with each other were calculated and the results were shown. As a result, the creation of the Bayes Network with an example data set is explained with its findings.