A/B Testing and the Unsupervised and Supervised Learning in Machine Learning

Open-Ended Questions Coursework
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Question 1 Response
Testing is a fundamental statistical approach for comparing two distinct aspects of marketing. As a marketing director at NYIT, Koning et al. (2019) argue there is a need to distinguish two markets and evaluate the behaviors and characteristics of every group. The best marketing approach to enroll new students is advertising what the program offers, its affordability, and the campus locality. On the other hand, current students' interests may differ; thus, there is a need to advertise extracurricular activities, course offerings, and resources on campus. Through critical analysis of the preferences and needs of every market, I will develop various marketing techniques and utilize A/B testing to ascertain the most effective version, as discussed above.
A/B testing has advantages, including its capacity to offer data-based insights and maximize marketing strategies considering real-time outcomes. Moreover, the A/B testing process is critical in mitigating the risk of capitalizing on a marketing approach irrelevant to the audience (Koning et al., 2019). However, there are disadvantages to using A/B testing, like time consumption, the cost of conducting various tests, and the requirement of carefully controlling variables for accurate results.
Question 2 Response
As an epidemiologist at the New York City Health Department, it is crucial to evaluate the extent of the outbreak to design a suitable response. Cluster analysis is the most appropriate approach in assessing whether the attack is localized compared to regression analysis. Cluster analysis, unsupervised learning, entails grouping observations with similar characteristics. On the other hand, regression analysis, supervised learning, involves predicting the results variable while considering predictor variables (Schonlau & Zou, 2020).
The significant differences between the two machine learning methods are labeled data availability and type of performed analysis. In comparison, unsupervised learning seeks to point out similarities and patterns in the labeled data without predefined results, while supervised learning uses labeled data to predict results variables considering predictor variables (Schonlau & Zou, 2020). Also, supervised learning is used for problems such as regression and classification, while unsupervised is for dimensionality reduction and clustering.
Question 3 Response
The best practices which resonate with me include the following:
* Using concise and clear labels: The audience will understand the message clearly and more manageable if the data labels chart title, the axes, and legend using relevant and descriptive information (Hehman & Xie, 2021).
* Choosing the suitable...