BUSI $652$ Predictive Analytics

Assignment:

The provided dataset $$Franchises Dataset$$ contains data collected from different $100$ franchises.

The data contains the net profit $($million $$)$ for each franchise, the counter sales $($million $$),$ the

drive$-$through sales $($million $$),$ the number of customers visiting the business daily, the type of

the franchise, and the location of the franchise.

Address the following questions:

a$)$ Develop a decision tree model for the net profit. Assess the accuracy of the model.

b$)$ Simulate the decision tree and visualize and interpretate the impact of the descriptive

features.

c$)$ Develop a Random Forest $($RF$)$ prediction model for the net profit.

d$)$ Rationalize the selected structure of the model.

e$)$ Simulate the model parameters and visualize and interpretate the impact of the

descriptive features.

f$)$ What is the forecast of the net profit, if the counter sales are $500,000$ $$,$ drive$-$through

sales are $700,000$$$,$ and the franchise is a pizza store located in Richmond, using both

models $($Decision Tree and Random Forest$).$ Comment on the forecasted value.

g$)$ What are the roles of the $$max$\_$feature$$ and the $$n$\_$estimators$$ parameters in the random

forest.

h$)$ What are the assumptions and limitations of the models?

Submit a PDF file for your answers as well as the excel sheet. Include the Python code in the

PDF$.$