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This thesis aims to extend an existing Open Educational Resource (OER), which is available as a GitHub repository, and provide an organized introduction to basic machine learning (ML) concepts and algorithms. Further models, followed by structured metadata for each object, will be included while adhering to the contribution guidelines of the OER and following the CC license. The Machine-Learning-OER Basics repository intends to provide a wide range of benefits by enabling diverse users to apply and distribute machine learning algorithms. The goal of this digital collection is to fill the existing gap for instructional material on using machine learning in OER as well as make it easier to learn ML concepts effectively. These ML models are developed using the programming language Python and the library scikit-learn, among other standard libraries. Jupyter Notebook will make it straightforward for the user to explore the code. In order to apply the models to various practical scenarios, a non-specific data set is selected. This work is considered a solution approach in that it includes adding classification models.
A performance comparison of the models is conducted. This comparative analysis evaluates the efficiency of each model. The examination includes various metrics for measurement. This work serves as a written extension, providing comprehensive background information on the algorithms utilized within the repositories and the performance comparison.
This paper examines different business models of companies dealing with (earmarked) remittances and sheds light on the associated challenges of the industry, specifically, remittances for health, based on the model of the fintech startup GloryHealthCare. The work "Business Model Generation" by Osterwalder and Pigneur (2010) is used as a method for the analysis, as this is often used as a basis for the business models of startups. The study focuses regionally on Europe and Africa, as Germany and Ghana are the start-up's first target markets. Among other things, the industry's processes, pricing, and existing competition are examined. The SWOT analysis methodology clarifies the individual companies' opportunities and risks and makes a competitive position visible. Meanwhile, network effects of the diverse business models are made visible based on the paper "Digital Economy and Network Effects" by Frank Linde (2020). Network effects play a crucial role in the reach, influence, and competitiveness of existing and new businesses in the remittance industry. The study also emphasizes the importance of knowledge and networks, which are more important than financial resources. The previous aspects considered a basis for developing a new concept as an alternative to the Business Model Canvas: the iBusiness Model. The results of this study provide insights into the design of efficient business models and support companies in the remittance industry in developing strategies to overcome challenges and take advantage of opportunities.