I completed a decision tree analysis for MIS 2502 with Konstantin Bauman. I selected a data set detailing information about car sales, including the age, gender and income of prospective buyers and the outcome of their decision. By building the decision tree, patterns in the data were identified, allowing the prediction of whether a person will buy a car or not based on identifying features. While creating the decision tree, I tested various combinations of minimus split and depth settings to provide the most effective results and reduce the effects of over and underfitting.
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