This document describes an implementation of the k-nearest neighbors algorithm to classify job applicants into different job groups based on their details. The algorithm calculates the distance between a test applicant and training data of existing job assignments. It then finds the k closest training records and assigns the test applicant to the majority class of the k nearest neighbors. The implementation was developed as a Windows application called the Job Applicant's Status Analyzer (JASA) to simplify the job application process. Future work could include converting JASA to a web application and allowing direct application submission.

1. WKU Job Applicant’s
Status Analyzer
k – Nearest Neighbor
Algorithm Implementation
presented by
Mohnish Thallavajhula
Vijayeandra Parthepan

2. Introduction
 Classify Job Applicants based on their
details into Classes of Jobs.
Ex:
Group A: {Graduate Assistant, Research
Assistant}
Group B: {Lab Assistant, Desk Clerk, Night
Clerk}
 Use data from existing data and analyze
the appropriate jobs for the applicant.

3. Algorithm [k – Nearest Neighbor]
 1. Calculate the “distance” from the test record
to the training records.
 2. Find the “k - nearest” training records.
 3. Check the majority class from the k – nearest
training records.
 4. The class label for the training record is
predicted as the class with the majority
votes/weight among the k – nearest training.

4. About Job Applicant’s
Status Analyzer (JASA)
 It
analyzes the status of the current job
applicant based on the applicant’s
details and classifies the applicant to the
Group of jobs that the applicant can
apply.
 The application has been developed
using C# .NET

5. Implementation
 Test data is the details of the Job Applicant.
 Training data is the existing assignments of
the jobs.
 The k – “nearest” details of the existing job
assignments will be considered and the job
applicant will be classified into which group
the applicant belongs to.
 The list of jobs available will then be shown.

6. Training Data description
Sample Training Data:
A G 3.0 CS 2
B UG 2.5 ANY 3
C G 3.0 MPH 5
Sample Test Data:
G 3.5 CS 5
Description:
Training data has:
Class Name in 1st column
Qualification in 2nd column
GPA in 3rd column
Department in 4th column
Years of experience in 5th column

7. Test Data description
Description:
Test data has:
Qualification in 1st column
GPA in 2nd column
Department in 3rd column
Years of experience in 4th column

8. JASA

10. Result
 Aftercalculating the group to which the
Job Applicant belongs to, the list of jobs
that the Job applicant can apply are
displayed.

11. Future work
 Convert
the Windows implementation into
Web Application
 Provide direct application process to the
jobs by taking the applicant’s details.

12. Conclusion
 By implementing k – NN, the applicant is
classified into a particular group of jobs.
 Thus, the job application process is simplified.
 Since we have implemented k – NN, the
implementation is much simpler than it’s
counter parts i.e. Decision Trees, Naïve Bayes,
Support Vector Machines.

13. Thank You