29. What is the ROC curve and the meaning of sensitivity, specificity, confusion matrix?
30. Why is dimensionality reduction important?
31. What are hyperparameters, how to tune them, how to test and know if they worked for the particular problem?
32. How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession, and gender? Define a machine learning algorithm for this.
33. How will you inspect missing data and when are they important for your analysis?
34. How will you design the heatmap for Uber drivers to provide recommendation on where to wait for passengers? How would you approach this?
35. What are time series forecasting techniques?
36. How does a logistic regression model know what the coefficients are?
37. Explain Principle Component Analysis (PCA) and it’s assumptions.