# Work in excel | Management homework help

BUA 6315: Business Analytics for Decision Making

Module 5 Assignment Handout:

Supervised Data Mining

Overview:
In this assignment, you will learn how to apply supervised data mining techniques in business cases.

Prompt:
For this assignment, you will analyze the three case studies below and address the questions associated
with each.

For all the cases, you will first partition data sets into 50% training, 30% validation, and 20% test and use
12345 as the default random seed. If the predictor variable values are in the character format, then treat the
predictor variable as a categorical variable. Otherwise, treat the predictor variable as a numerical variable.

Case 1:
For this case, first download the data: Social Media data (available in Blackboard).

Next review the following case study:

A social media marketing company is conducting consumer research to see how the income level and age
might correspond to whether or not consumers respond positively to a social media campaign. Aliyah
Turner, a new college intern, is assigned to collect data from the past marketing campaigns. She compiled
data on 284 consumers who participated in the marketing campaigns in the past, including income (in
\$1,000s), age, and whether or not each individual responded to the campaign (1 if yes, 0 otherwise).

Then complete the actions below and record your answers in a Microsoft Word document.

Note​: For step-by-step instructions on how to use Excel and Analytic Solver to estimate and predict with
KNN method and how to interpret results, refer to the following videos from Lesson 1: Supervised Data
Mining – KNN Algorithm:

● ​K – Nearest Neighbors (KNN) – Introduction​ (7:51)
● ​KNN Model with Analytic Solver​ (12:57)

1. Perform KNN analysis to estimate a classification model for the social media campaign using the

data. What is the optimal value of k?
2. Report the overall accuracy, specificity, sensitivity, and precision rates for the test data set (for

Analytic Solver) or validation dataset (for R) using the cutoff value of 0.50. Explain each of them with
one sentence.

3. What is the area under the ROC curve(or the AUC value)?
4. Comment on the performance of the KNN classification model. Is the KNN method an effective way

to predict whether or not a consumer responds positively? ​Note: Interpret the results of ROC curve,
lift chart, and decile-wise lift chart.

5. What is the predicted outcome for the first new consumer record?

1

BUA 6315: Business Analytics for Decision Making

Case 2:
For this case, first download the data: Mobile Banking data (available in Blackboard).

Next review the following case study:

Sunnyville Bank wants to identify customers who may be interested in its new mobile banking app. The
worksheet called Mobile Banking Data contains 500 customer records collected from a previous marketing
campaign for the bank’s mobile banking app. Each observation in the data set contains the customer’s age
(Age), gender (Male/Female), education level (Edu, ranging from 1 to 3), income (Income in \$1,000s),
whether the customer has a certificate of deposit account (CD), and whether the customer downloaded the
mobile banking app (App equals 1 if downloaded, 0 otherwise). Create a classification tree model for

Then complete the actions below and record your answers in a Microsoft Word document.

Note​: For step-by-step instructions on how to use Excel and Analytic Solver to estimate and predict with a
classification tree, and how to interpret results, refer to the following video from Lesson 2: Supervised Data
Mining – Decision Trees: ​Using Analytic Solver to Build a Classification Tree​ (8:17).

1. How many leaf nodes are in the best-pruned tree? What are the predictor variables and split value
for the root node of the best-pruned tree?

2. What are the accuracy rate, specificity, sensitivity, and precision of the best-pruned tree on the test
data? Explain each of them with one sentence.

3. Generate the ROC curve. What is the area under the ROC curve?
4. Score the 20 customers in the data set you have downloaded using the best-pruned tree. How many

Case 3:
For this case, first download the data: Electricity data (available in Blackboard).

Next review the following case study:

Kyle Robson, an energy researcher for the U.S. Energy Information Administration, is trying to build a model
for predicting annual electricity retail sales for states. Kyle has compiled a data set for the 50 states and the
District of Columbia that contains average electricity retail price (Price in cents/kWh), per capita electricity
generation (Generation), median household income (Income), and per capita electricity retail sales (Price in
MWh). Create a regression tree model for predicting per capita electricity retail sales (Sales).

Then complete the actions below and record your answers in a Microsoft Word document.

Note​: For step-by-step instructions on how to use Excel and Analytic Solver to estimate and predict with a
regression tree and how to interpret results, refer to the following video from Lesson 2: Supervised Data
Mining – Decision Trees: ​Using Analytic Solver to Build a Prediction Tree​ (5:53).

2

BUA 6315: Business Analytics for Decision Making

1. How many leaf nodes are in the best-pruned tree and minimum error tree?
2. What are the predictor variables and split value for the first split of the best-pruned tree? What are

the rules that can be derived from the root node?
3. What are the RMSE and MAD of the best-pruned tree on the test data?
4. What is the predicted per capita electricity retail sales for a state with the following values: Price =

11, Generation = 25, and income = 65,000?

Submission Guidelines:
Your completed assignment must be submitted as a Microsoft Word document, 1-2 pages in length, double
spacing, 12-point Times New Roman font, and 1-inch margins. The submission must be accompanied by
three Microsoft Excel spreadsheets showing your work. Only the Word document will be assessed for
Note​: No tables or charts need to be included in your Word document for this assignment.

This assignment will be assessed based on the accuracy of your responses to each question in the
worksheet.

3

Basic features
• Free title page and bibliography
• Unlimited revisions
• Plagiarism-free guarantee
• Money-back guarantee
On-demand options
• Writer’s samples
• Part-by-part delivery
• Overnight delivery
• Copies of used sources
Paper format
• 275 words per page
• 12 pt Arial/Times New Roman
• Double line spacing
• Any citation style (APA, MLA, Chicago/Turabian, Harvard)

# Our guarantees

We value our customers and so we ensure that what we do is 100% original..
With us you are guaranteed of quality work done by our qualified experts.Your information and everything that you do with us is kept completely confidential.

### Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

### Zero-plagiarism guarantee

The Product ordered is guaranteed to be original. Orders are checked by the most advanced anti-plagiarism software in the market to assure that the Product is 100% original. The Company has a zero tolerance policy for plagiarism.

### Free-revision policy

The Free Revision policy is a courtesy service that the Company provides to help ensure Customer’s total satisfaction with the completed Order. To receive free revision the Company requires that the Customer provide the request within fourteen (14) days from the first completion date and within a period of thirty (30) days for dissertations.

The Company is committed to protect the privacy of the Customer and it will never resell or share any of Customer’s personal information, including credit card data, with any third party. All the online transactions are processed through the secure and reliable online payment systems.

### Fair-cooperation guarantee

By placing an order with us, you agree to the service we provide. We will endear to do all that it takes to deliver a comprehensive paper as per your requirements. We also count on your cooperation to ensure that we deliver on this mandate.

## Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
\$26
The price is based on these factors: