I would really appreciate if you can provide the email so that I can send you the whole dataset. Below are the questions

1. Before you proceed further, write down what your expectations would be on how the following variables relate to whether the firm is likely to have zero debt and why. For each variable, make sure to make it clear whether you predict a positive estimate or a negative estimate in a regression model with zerolev as an output variable.
   1. Firm size
   2. Firm age
   3. Operating income
   4. Cash flow volatility

In explaining why, best answers should borrow from capital structure theories and other concepts that you have covered in this class and other finance classes.

2. Generate a scatterplot matrix and the correlation table for zero leverage, firm age, firm size, operating income, and the volatility of cash flows. Describe the relations you see between leverage and other variables.
3. Fit a logistic regression model with zero leverage as an output variable and firm size, firm age, operating income, and cash flow volatility as input variable. Use 2010-2017 as the training dataset to fit the data. Describe what you observe, including
   1. What is the relation between leverage with the four inputs? Which estimates are significant and which are not? In other words, what type of companies have higher probability of having zero leverage?
   2. Are these results consistent with what you expected from Question 2?
4. Based on the model from 4, come up with a predicted probability of having zero leverage for a company that is 5 years old, has a firm size of $200 million, operating income of 0.01, and cash flow volatility of 0.2, along with the confidence intervals. Is this firm likely to have zero leverage or no?
5. There are many other variables in the dataset other than the ones we used above. In addition to the four inputs already used, pick three additional inputs you believe should be an important factor in explaining which firms should have zero leverage versus which firms do not. Explain why.
6. Now fit a logistic regression model again, but this time, with the four inputs you used in Question 4, and three more inputs you chose in Question 6. So there should be total of seven inputs. Again use the 2010-2017 data as the training dataset.
   1. Do the three new inputs show results that you expected to see? Again, what type of companies have zero leverage?
   2. Did any of the estimates on the existing inputs change?
7. Based on the model estimates from Question 7, come up with a confusion matrix using the testing dataset (2018 only).
   1. Describe what you see here. What kind of error do you see?
   2. Did adding the additional three inputs improve the errors?
   3. If you believe that the precision of the model when the models prediction is 1 is important (i.e., the precision of the model is important), how would you change the 0.5 probability threshold to improve the precision? How do the errors look like if you change the threshold?
   4. If you believe that identifying all possible cases of zero leverage firm is important (recall score), how would you change the 0.5 probability threshold to improve recall? How do the errors look like if you change the threshold?
   5. Based on your observations from parts (c) and (d), what can you say about the relation between precision and recall?
8. Try various alternative methods (LDA, QDA, and KNN) using the seven inputs above, and report on your findings. Do you obtain better results? If so, in what sense?

The question posted earlier the sample data attached below. It's just sample data actual data is really huge.

US_Zerodebt sample data