Q1. This assignment requires understanding the concepts explained in data mining, predictive
analytics and machine learning sections.
(a) For this exercise, your goal is to build a model to identify inputs or predictors that differentiate risky
customers from others (based on patterns pertaining to previous customers) and then use those inputs
to predict new risky customers. This sample case is typical for this domain. The sample data to be used
in this exercise is CreditRisk.xlsx .
The data set has 425 cases and 15 variables pertaining to past and current customers who have borrowed
from a bank for various reasons. The data set contains customer-related information such as financial
standing, reason for the loan, employment, demographic information, and the outcome or dependent
variable for credit standing, classifying each case as good or bad, based on the institution’s past
experience.
Take 400 of the cases as training cases and set aside the other 25 for testing. Build a decision tree model
to learn the characteristics of the problem. Test its performance on the other 25 cases. Report on your
model’s learning and testing performance. Prepare a report that identifies the decision tree model and
training parameters, as well as the resulting performance on the test set.
You can use either R (and related packages e.g., rattle Package) or a GUI-based software Weka.
To use Weka go through Learning Resource for Weka decision tree
See R resources posted in the blackboard.
(b) Using the same dataset also develop a Neural Network (NN) model using either R or Weka
(Multilayer Perceptron)
(c) Compare and evaluate the model performances of decision tree and NN. (use 10-fold cross
validation and Leave-one-out for classification assessment). Also generate ROC plots. Explain and
discuss the results.
(d) How can you improve the prediction accuracy? What are the pre-processing or post- processing
steps required to improve the accuracy? Finally, implement them to show that they really improve
accuracy?
Report everything in a .pdf file with descriptions of preprocessing steps, model development, model
output (including plots) interpretation, explanations, and validations of the models and their
significance.
Q2. In this exercise you'll use R package tidyverse (see chapter 4 of Introduction to Data Science Data
Analysis and Prediction Algorithms with R by Rafael A. Irizarry. You need to go through chapter 4 before
attempting the following questions. Also, see my lecture video in the blackboard.
Using dplyr functions (i.e., filter, mutate ,select, summarise, group_by etc. ) and "murder" dataset
(available in dslab R package) and write appropriate R syntax to answer the followings:
a. Calculate regional total murder excluding the OH, AL, and AZ
b. Display the regional population and regional murder numbers.
c. How many states are there in each region?
d. What is Ohio's murder rank in the Northern Central Region (Hint: use rank(), row_number())
e. How many states have murder number greater than its regional average.
f. Display 2 least populated states in each region
Use pipe %>% operator for all the queries. Show all the output results.
Q3.
The solutions to the following problems require understanding the concept of mathematical optimization
and linear programming and Goal Seek described in chapter 8 (page 477 in Section 8.6). You need to use
Excel Solver Add-in to develop the model for some of the problems. See related resources in the
blackboard.
If Excel Solver is not already loaded, follow these steps to load Solver. (Only for PC, not Mac).
Use separate Excel worksheet to develop a model for each problem. Rename the worksheets to
reflect the problem number. Upload one single Excel file in the Blackboard.
Problem a.
At a chip manufacturing plant, four technicians (A, B, C, and D) produce three products
(products 1, 2, and 3). The chip manufacturer can sell 80 units of product 1 this month, 50 units
of product 2, and at most 50 units of product 3. Technician A can make only products 1 and 3.
Technician B can make only products 1 and 2. Technician C can make only product 3.
Technician D can make only product 2. For each unit produced, the products contribute the
following profit: product 1, $6; product 2, $7; product 3, $10. The time (in hours) each technician
needs to manufacture a product is as follows:
Product Technician A Technician B Technician C Technician D
1 2 2.5 Cannot do Cannot do
2 Cannot do 3 Cannot do 3.5
3 3 Cannot do 4 Cannot do
Each technician can work up to 120 hours per month. How can the chip manufacturer
maximize its monthly profit?
Problem b.
Suppose that each day, northern, central, and southern California each use 100 billion gallons of
water. Also assume that northern California and central California have available 120 billion
gallons of water, while southern California has 40 billion gallons of water available. The cost of
shipping one billion gallons of water between the three regions is as follows:
Northern Central Southern
Northern $5,000 $7,000 $10,000
Central $7,000 $5,000 $6,000
Southern $10,000 $6,000 $5,000
We will not be able to meet all demand for water, so we assume that each billion gallons of
unmet demand incurs the following shortage costs:
Northern Central Southern
Storage cost/billion
gallon short $6,000 $5,000 $9,000
How should California's water be distributed to minimize the sum of shipping and shorta
Problem c.
A student is trying to determine how many hours of studying to devote to each of his subjects to
maximize his overall grade point average this semester. To do so, the student predicts the grade
average he will receive for studying different amounts of time in each of his classes. The table
below displays his predictions.
Hours per Week Calculus Chemistry Physics Economics
1-5 75 76 65 85
6-10 84 87 81 92
11-15 93 94 91 97
The student wants to study no more than a total of 35 hours per week. He estimates that the
amount of time he should study physics is at least double the amount of time he should study
economics, and the amount of time he should study calculus is in between those two values.
Formulate this problem as an optimization model and use the solver to find the optimal
solution. (Hint: Use Excel’s VLOOKUP function; Select “Evolutionary” for solving method in
Solver)
Problem d.
The growth of Internet users is given in the following table:
Year Number of Users (In Million)
1995 16
1996 36
1997 70
1998 147
1999 248
2000 361
2001 531
2002 584
2003 719
2004 817
2005 1018
2006 1093
2007 1316
2008 1574
2009 1804
2010 1971
2011 2267
2012 2497
2013 2802
Fit a nonlinear growth model π = π² (π+πΆπ−π·π) (where, y = number of Internet users, t = time; K, πΆ, πππ β are model parameters) of the growth of the Internet by estimating the model parameter alpha, beta and K using Excel Solver by minimizing the overall error term. Also, plot the scatterplot graph of the actual growth and the fitted curve (continuous).
Upload one single pdf file with the answers of Q1 & Q2. For Q3, just upload the Excel file.
This is a group assignment. Do not share or interact between the groups
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