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Imagine you are the decision-maker for a trucking firm that is trying to decide the number of trucks to purchase. You have access to past sales data, the cost of trucks, the forecasted demand for next year, and the predicted supply and demand for the product that you are moving/transporting for the next 10 years. Unfortunately, this data is rather complex. You will need to sort through this data and determine what is important. Describe the data analytics methods that you will use to address this purchasing problem. In your original post, answer the following:
- Describe the data analytics methods that you will use to address this purchasing problem.
- Do you think this constitutes "big data"? Why or why not?
- What do you think is the importance of mining this data? How can data mining help solve this dilemma?
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In response to the threat of Covid-19, UPSA has successfully migrated course content and delivery to an online learning management system.
Confusion Matrix and ROC Curve first, develop a matrix for this scenario and calculate accuracy, true negative rate, and true positive rate.
The best way to get a good negotiaton result is:
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