Executive Summary: This paper is a demonstration of how machine learning can be used to optimize pursuing leads in sales. In a bank’s phone marketing campaign, whether a sale was closed or not is predicted using a Random Forest based on customer data like history with the company, occupation and age. The final model correctly identifies a closed lead 69% of the time (True Positive Rate), and misidentifies a lead that doesn't close as one that does 17% of the time (False Positive Rate).
2019-05-20
Analysis of a Likert survey in R
Which university departments have a more favorable view of Wikipedia usage?
Executive Summary: This is an analysis of a Likery survey on university faculty perceptions and practices of using Wikipedia as a teaching resource. In particular, departments will be clustered by how favorably they view Wikipedia usage according to 5 Likert scale questions. Categorical Principal Component Analysis was used to aggregate the questions, and then a Kruskal Wallis test and Dunn post hoc test were on the first principal component to cluster the departments. Two groups are distinguished from each other: Sciences, Engineering & Architecture and Arts & Humanities view Wikipedia more favorably than Health Sciences and Law & Politics.
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