This computer science report delves into the analysis of the TOR browser and the Dream Market, a darknet marketplace, focusing on data mining techniques and the identification of NPS (New Psychoactive Substances) drugs. The report begins with background information on the TOR browser, its features, and the emergence of marketplaces like Dream Market. It then poses research questions regarding the demographics of sales, vendor trustworthiness, and optimal data mining methods. The study addresses the challenges of excessive data and the need for data preprocessing, outlining the objectives of providing qualitative and quantitative information, ensuring timely information, and removing redundant data. The research employs various data mining techniques, including classification, prediction, decision trees, clustering, and ensemble methods, with a focus on decision trees and ensemble methods for improved performance. The scope of the research includes the application of descriptive and quasi-experimental methods, specifically focusing on filtering mechanisms and mining illegal products. The report highlights the significance of the research in understanding data flows, privacy, and the importance of TOR browser over other browsers. The informatics artifacts include RAM capacity and page file analysis. The study aims to identify patterns in NPS drug transactions and the trustworthiness of vendors over time, using data collected from the Dream Market. The report also provides a detailed analysis of the NPS drugs identification in Dream Market, the effects of NPS drugs, and the overall aim of exploring illicit NPS drug transactions in Dream Market. The report also provides a comprehensive overview of the problem statement, objectives, significance, and scope of the research, along with the informatics artifacts used.