# Liberty University Statistics K-Nearest Neighbor Classification Essay

k-Nearest Neighbor Classification

## The purpose of this assignment is to perform k-Nearest Neighbor classification, interpret the results, and analyze whether or not the information generated can be used to address a specific business problem.

For this assignment, you will use the “Adult Incomes” data set from the Topic Materials.

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ABC Survey Company collects data via surveys that it then sells to marketing departments. Marketing departments typically do not like missing data. Since survey takers typically do not like to answer questions regarding their salary, the one question usually missing from the survey results is, “Is your annual salary \$50,000 or more?”

You are the analyst who has been tasked with finding a way to impute (i.e., fill-in) the answer to the question, “Is your annual salary \$50,000 or more?” This information can best be imputed based upon how individuals answer other survey questions related to their marital status, educational level, occupation, and familial relationship status. If this important question can be accurately imputed, then the worth of the survey data provided by ABC Survey Company increases dramatically.

Question 1:Using only “Marital_Status,” “Education,” “Occupation,” and “Relationship” variables, find the number of neighbors (k) that minimizes the error rate. Use a range of k between 3 and 10. Include the “k Selection Error Log” output when submitting the answer.

Question 2:Using the same variables and the k selected in Question 1, rerun the nearest neighbor model using the feature selection option in the IBM SPSS Modeler. What is the set of variables that minimize the error rate? Include the “Predictor Selection Error Log” output when submitting the answer.

Question 3:Using the value of k and the set of variables that minimizes the error rate, rerun the k-Nearest Neighbor model. What is the classification table? Include the pivot table output when submitting the answer.

Question 4:Consider the following individual: Marital_Status=Never-married, Education=Masters, Occupation=Sales, and Relationship=Not-in-family. Based on the k-Nearest Neighbor model from Question 3, how would this individual be classified? Provide the predicted income level (“>50K” or “<=50K") and explain the process that you used to determine the income level. Include the table illustrating the data when submitting the answer.

Question 5:Describe the model building process you used to determine whether or not a particular survey taker earned an annual salary of \$50,000 or more. Include discussion of the accuracy of the k-Nearest Neighbor model and how it can be used in practice to impute the answer to the question, “Is your annual salary \$50,000 or more?”

Course Code
MIS-655
Class Code
MIS-655-O500
Criteria
Content
Percentage
100.0%
k-Nearest Neighbor Classification Questions 1-4
30.0%
Charts, Graphs, and Calculations
30.0%
Model Building Process and Accuracy
30.0%
Mechanics of Writing (includes spelling,
punctuation, grammar, language use)
10.0%
Total Weightage
100%
Assignment Title
k-Nearest Neighbor Classification
Unsatisfactory (0.00%)
Answers to k-Nearest Neighbor questions are not included.
Charts, graphs, and calculations to support and communicate
visual representations of data are not included.
Description of the model building process and model
accuracy is not included.
Surface errors are pervasive enough that they impede
communication of meaning. Inappropriate word choice or
sentence construction is used.
Total Points
95.0
Less than Satisfactory (65.00%)
Answers to k-Nearest Neighbor questions are incomplete or
incorrect.
Charts, graphs, and calculations to support and communicate
visual representations of data are incomplete or incorrect.
Description of the model building process and model
accuracy is incomplete or incorrect.
Frequent and repetitive mechanical errors distract the
reader. Inconsistencies in language choice (register) or word
choice are present. Sentence structure is correct but not
varied.
Satisfactory (75.00%)
Answers to k-Nearest Neighbor questions are included but
contain some errors in the classification as evidenced by
associated pivot charts, tables, and outputs.
Charts, graphs, and calculations to support and communicate
visual representations of data are mostly included and mostly
correct.
Description of the model building process and model
accuracy is included but lacks relevant supporting details.
Some mechanical errors or typos are present, but they are
not overly distracting to the reader. Correct and varied
sentence structure and audience-appropriate language are
employed.
Good (85.00%)
Answers to k-Nearest Neighbor questions are complete and
supported by associated pivot charts, tables, and outputs that
are generally accurate.
Charts, graphs, and calculations to support and communicate
visual representations of data are complete and correct.
Description of the model building process and model
accuracy is complete and includes relevant supporting details.
Prose is largely free of mechanical errors, although a few may
be present. The writer uses a variety of effective sentence
structures and figures of speech.
Excellent (100.00%)
Answers to k-Nearest Neighbor questions are expertly crafted
and supported by pivot charts, tables, and outputs that are
completely accurate.
Charts, graphs, and calculations to support and communicate
visual representations of data are expertly crafted.
Description of the model building process and model
accuracy is extensive and includes numerous relevant
supporting details.
Writer is clearly in command of standard, written, academic
English.
Points Earned
ID
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
34
36
38
49
47
54
29
41
39
29
22
44
31
61
35
54
29
22
29
50
32
70
26
32
55
38
25
51
17
83
52
37
35
43
25
19
44
21
30
22
28
54
36
50
46
34
Age_Category
25-34
35-44
35-44
45-54
45-54
45-54
25-34
35-44
35-44
25-34

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