Případová studie na dolování z dat v jazyce Python

This thesis focuses on basic concepts and techniques of the process known as knowledge discovery from data. The goal is to demonstrate available resources in Python, which enable to perform the steps of this process. The thesis addresses several methods and techniques focused on detection of unusual observations, based on clustering and classification. It discusses data mining task for data with the limited amount of inspection resources. This inspection activity should be used to detect unusual transactions of sales of some company that may indicate fraud attempts by some of its salespeople.

KDD, knowledge discovery in databases, data mining, data analysis, outlier detection, anomaly detection, outlier analysis, detecting fraudulent transactions, unsupervised learning, supervised learning, semi-supervised learning, classification, Naive Bayes, Local Outlier Factor, Isolation Forest, data preprocessing, data cleaning