Over the last year, you have been working as a product manager in a technology company, experimenting with launching new digital products. You introduced different types of software applications (productivity, gaming, finance, and health) in various markets (North America, Europe, and Asia). You also considered the company's scale (startup or enterprise) and different marketing strategies (organic growth vs. aggressive paid marketing). After a year, you gathered all the results (see the table below) and analyzed whether each product launch was successful. Now, you would like to build a decision tree that will help guide future product launches. 5 Your task is to construct the most effective decision tree based on the dataset below and explain why it is the best one. In this exercise, you should experiment with two metrics: first, use the Gini impurity metric, and then build another tree, but this time using entropy. While working out these decision trees, you might encounter ambiguous situations - explain the ambiguities and how you handled them. Finally, compare the results of the results obtained from using Gini impurity and entropy. Hint: The order of columns in the table does not necessarily lead to an optimal decision tree. ID Product Type 1 Finance Market Europe Company Size Marketing Strategy Success Startup Organic NO 2 Gaming North America Enterprise Paid YES 3 Health Asia Startup Paid YES 4 Finance Europe Enterprise Organic NO 5 Productivity North America Startup Organic NO 6 Health Europe Enterprise Paid YES 7 Gaming Asia Enterprise Organic YES 8 Health Asia Startup Paid YES 9 Health North America Enterprise Organic YES 10 Productivity North America Startup Organic NO 11 Finance Asia Enterprise Paid YES 12 Health Europe Startup Organic NO

Over the last year, you have been working as a product manager in a technology company, experimenting with launching new digital products. You introduced different types of software applications (productivity, gaming, finance, and health) in various markets (North America, Europe, and Asia). You also considered the company's scale (startup or enterprise) and different marketing strategies (organic growth vs. aggressive paid marketing). After a year, you gathered all the results (see the table below) and analyzed whether each product launch was successful. Now, you would like to build a decision tree that will help guide future product launches. 5 Your task is to construct the most effective decision tree based on the dataset below and explain why it is the best one. In this exercise, you should experiment with two metrics: first, use the Gini impurity metric, and then build another tree, but this time using entropy. While working out these decision trees, you might encounter ambiguous situations - explain the ambiguities and how you handled them. Finally, compare the results of the results obtained from using Gini impurity and entropy. Hint: The order of columns in the table does not necessarily lead to an optimal decision tree. ID Product Type 1 Finance Market Europe Company Size Marketing Strategy Success Startup Organic NO 2 Gaming North America Enterprise Paid YES 3 Health Asia Startup Paid YES 4 Finance Europe Enterprise Organic NO 5 Productivity North America Startup Organic NO 6 Health Europe Enterprise Paid YES 7 Gaming Asia Enterprise Organic YES 8 Health Asia Startup Paid YES 9 Health North America Enterprise Organic YES 10 Productivity North America Startup Organic NO 11 Finance Asia Enterprise Paid YES 12 Health Europe Startup Organic NO