Reference no: EM133185717

Question 1: Give one example of how you can use supervised machine learning at work to solve a problem? In your example you must include:
- A brief problem statement.
- The size of the prize i.e. if you solve the problem what is bottom line $ impact.
- Any assumptions made on the success of your model.
- An example dataset (makeup a fake one if needed). Clearly mention what is target variable an how you get that.
- How will you collect the dataset? Are there any challenges there?
- Are there any ethical implications of your project? If yes, please describe them.

Question 2: You are not the CEO of you favorite S&P 100 company, complete the following questions.
- What company is this and what do they do?
- How can your company use machine learning in each of the scenarios below:
a. Improve/Integrate into your products/services
b. Improve internal processes (supply chain, finance, marketing, etc.)
c. Understand your company's customers.

Question 3: A false positive is an outcome where the model incorrectly predicts the positive class. Describe the implications of a false positive in the following scenarios:
- Predicting customer churn
- Early detection of Cancer
- Credit Card Fraud detection

TIP: Think about consequences i.e. what will happen if you predict a patient with cancer and it turns out to be a false prediction. What could go wrong? Can there be any guardrail processes to mitigate the impact of a false positive such as doctor screening cases where the model is less confident?

Question 4: Professor needs your help. He is looking for a few ways to improve student experience and course delivery for his courses at the University. A friend of the professor told him that Natural Language Processing (NLP) could be helpful. Please give one detailed example of a NLP use-case tht can improve student experience and course delivery.