Which machine learning algorithm was used for the case study described in the videos?

The correct answer is Decision Tree Classification, which is often chosen for its ability to handle both categorical and numerical data, providing an intuitive and interpretable model. In the context of a case study, decision trees can effectively visualize the decision-making process, allowing stakeholders to understand how different features lead to specific outcomes.

Decision trees operate by splitting the data into subsets based on feature values, thus creating a model that resembles a tree structure, which is easy to follow. This characteristic makes them particularly suitable for scenarios where interpretability is crucial, allowing users to not only see the predictions but also the reasoning behind them.

The other algorithms listed have their strengths and are suitable for specific types of tasks. Logistic regression is driven by probabilities, making it ideal for binary classifications but less interpretable in complex scenarios. Support Vector Machines are powerful in high-dimensional spaces but can be less interpretable due to their reliance on hyperplanes in a multidimensional space. k-Nearest Neighbor, while straightforward, can struggle with larger datasets and is less effective for high-dimensional data, making it less desirable compared to Decision Tree Classification for case studies requiring clear insights.

Thus, Decision Tree Classification stands out as the most appropriate choice for a case study focused on transparency and decision-making.