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Pls help in writing python code for this poorly formed and written question where nothing is clear. Question : Product Optimization Exercise The problem statement

Pls help in writing python code for this poorly formed and written question where nothing is clear.
Question :
Product Optimization Exercise
The problem statement for this homework: You are producing beverage mugs and are trying
to identify the best price-feature-vector. Assume the following attributes and attribute levels:
Price: $30, $10, $5
Time Insulated: 0.5 hrs,1 hr,3 hrs
Capacity: 12 oz,20 oz,32 oz
Cleanability: Difficult (7 min), Fair (5 min), Easy (2 min)
Containment: Slosh resistant, Spill resistant, Leak resistant
Brand: A, B, C
Assume the following as the "proposed market scenario", i.e. the scenario with the current
competitors and our proposed candidate.
Incumbents
1: $30,3 hrs,20 oz, Clean Easy, Leak Resistant, Brand A
2: $10,1 hrs,20 oz, Clean Fair, Spill Resistant, Brand B
Our proposed candidate
3: $ 30,1 hrs,20 oz, Clean Easy, Leak Resistant, Brand C
Assume the following cost structure:
Time Insulated: 0.5 hrs costs $0.5,1 hr costs $1,3 hrs costs $3
Capacity: 12 oz costs $1.00,20 oz costs $2.6,32 oz costs $2.8
Cleanability: Difficult (7 min) costs $1, Fair (5 min) costs $2.2, Easy (2 min) costs
$3.0
Containment: Slosh resistant costs $0.5, Spill resistant costs $0.8, Leak resistant costs
$1
You are given data on the preference parameters of 311 consumers in mugs-preferenceparameters-full.xlsx. The CSV version of this file is also provided as mugs-preferenceparameters-full.csv.
Discrete Optimization: Consider each of the three levels for each of the five attributes
and enumerate all the possibilities in lexical order with Price as the leftmost attribute
changing slowest and taking levels sequentially $30, $10, $5, then Time Insulated as
the left-second-most attribute changing second-slowest and taking values sequentially
0.5 hrs,1 hr,3 hrs and so on. You will have 243 product candidates. (The lexical order
produces indices as shown in mugs-products-lexical-order.csv. Please make sure
you list your products in that exact same order. The lexical order of products is
obtained by looping as shown in mugs-products-lexical-order-loop.R, which has
close similarity in python. Again, using the compensatory rule, compute and report
the following four columns of numbers for each candidate: the share, the cost, the
margin and the expected profit per person (all under the current competition
incumbents). The table that you submit needs to have all 243 rows. Hint to check your
answer: product candidate 230 has a negative expected profit per customer of between
-1.75 and -1.85, and product candidate 106 has an expected profit per customer of
between 0.7 and 0.8.
There is no clarity anywhere , multiple datasets and large barrage of values .

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