Do you think the analytics team can utilize this data set to help the airline company to achieve its goal? If yes, explain why you think it is helpful and then further discuss which variables and how many observations the analytics team should include in the analysis. Justify your choices. If no, explain why you do not think it is helpful in detail.

There are five questions  in this assignment. The minimum increment is 0.5 point. Solve them and fill the answers in the blank space.

1. Breakfast Cereals. Find the dataset HW2_Cereals.csv on Blackboard. The table below describes the variables in the dataset. Write a Python code to explore and summarize the data as follows.

a. Use appropriate graphs to detect if shelf is relevant to rating. Attach that graph in the space below. Based on your judgment, are they relevant? Explain your answer.
b. Attach the heat map of the correlation matrix in the space below. Which pair of variables is most strongly positively correlated? Which is most strongly negatively correlated?

Submit your Python notebook file with the filename [DM2022] HW2_Q1_YOURFULLNAME. ipynb .

2. Several new airports have just opened in major cities, opening the market for potential new routes. Hereafter we refer to those major cities as M cities. A major airline  has a goal to predict average ticket fare on these potential new routes. The analytics team has found the following dataset in the company’s data warehouse . They consist of the variables listed in the following table. Note that the set of M cities does not contain any city included in the CSV file.
Table DESCRIPTION OF VARIABLES FOR AIRFARE EXAMPLE
S_CODE Starting airport’s code
S_CITY Starting city
E_CODE Ending airport’s code
E_CITY Ending city
COUPON Average number of coupons for that route
VACATION Whether (Yes) or not (No) a vacation route
SW Whether (Yes) or not (No) Southwest Airlines serves that route
S_INCOME Starting city’s average personal income
E_INCOME Ending city’s average personal income
S_POP Starting city’s population
E_POP Ending city’s population
SLOT Whether or not either endpoint airport is slot controlled
GATE Whether or not either endpoint airport has gate constraints
DISTANCE Distance between two endpoint airports in miles
PAX Number of passengers on that route during period of data collection
FARE Average fare on that route

Do you think the analytics team can utilize this data set to help the airline company to achieve its goal? If yes, explain why you think it is helpful and then further discuss which variables and how many observations the analytics team should include in the analysis. Justify your choices. If no, explain why you do not think it is helpful in detail. .

3. Suppose the sample dataset you retrieved from the IT department is the following. Identify the missing values in the table by labeling them. Write down your answers. For each missing value, write down how you would handle it. Note that you only need to write down the treatment you want to give and do not have to compute the specific value for imputation. You can put your answer in a batch mode if you believe a group of labeled missing values shall receive the same kind of treatment.

4. Briefly discuss why we need to standardize numerical variables and code categorical variables in general.

5. Use Microsoft Excel to standardize the following two variables in the table. Next, find out the exchange rate between USD and Euro on the day you do this homework question. Use the rate to convert Income from USD to Euro. And then standardize the Income in Euro. Are the standardized values different between using Income in Euro and using Income in USD? Show all calculations.
Submit your Excel spreadsheet with the filename
Age Income (USD $)
26 50,000
55 155,000
64 98,000
31 191,000
40 38,000
48 56,000

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