dm project report

Project Report

Version 1.1

May 6, 2010

WKU Job Applicant’s Profile Evaluator using KNN

Vijayeandra Parthepan

Mohnish Thallavajhula

Professor: Dr. Huanjing Wang

Submitted in partial fulfillment

Of the requirements of

CS565 Data Mining

Western Kentucky University

Project Report04/12/23

Table of Contents

1.0. Introduction..................................................................................................................................................3

2.0. Motivation......................................................................................................................................................3

3.0. Dataset Description....................................................................................................................................3

4.0. Approaches..................................................................................................................................................4

5.0. Future Work..................................................................................................................................................5

5.0. Results...........................................................................................................................................................5

6.0. Conclusion....................................................................................................................................................6

7.0. Referrences..................................................................................................................................................6

2


1.0. INTRODUCTION:

K Nearest Neighbor (KNN) is the supervised data mining pattern recognition algorithm. It

classifies objects based on closest training exam-nearest neighbor algorithm. It is amongst the simplest of

all machine learning algorithms. An object is classified by a majority vote of its neighbors. K is small

positive integer and it is usually previously set.

WKU job applicant’s profile evaluator using KNN 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.

2.0. MOTIVATION:

The potential employee’s who wish to find some jobs in the university are not sure which jobs

they are most likely to get and hence they may end up applying to jobs which may not suit their profile. So,

in order to make their job search more accurate, we are going to compare their profile with already existing

employee’s and provides them the job suggestions. We are going to analyze the status of WKU employees

using KNN. The KNN algorithm classifies the new employee to a particular class based on the existing

records. 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.

3.0. DATASET DESCRIPTION:

Training data is the existing assignments of the jobs.

Sample Training Data:

A G 3.0 CS 2

B UG 2.5 ANY 3

C G 3.0 MPH 5

Test data is the details of the Job Applicant.

Sample Test Data:

G 3.5 CS 5

3


Test Data 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

Training Data Description:

Qualification in 1st column

GPA in 2nd column

Department in 3rd column

Years of experience in 4th column

4.0. APPROACHES:

After calculating the group to which the Job Applicant belongs to, the list of jobs that the Job

applicant can apply is displayed.

The algorithm of the k-nearest neighbor that we apply in our project is as follows,

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.

We are classifying the job applicants based on their details into different classes of jobs.

Group A: {Graduate Assistant, Research Assistant}

Group B: {Lab Assistant, Desk Clerk, Night Clerk}

Group C: {Shuttle driver, Receptionist}

The application has been developed using C# .NET.

4


5.0. FUTURE WORK:

Convert the Windows implementation into Web Application.

Provide direct application process to the jobs by taking the applicant’s details.

6.0. RESULTS:

Screen shot of the help menu:

5


Screen shot of the main menu:

7.0. 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.

8.0. REFERRENCES:

http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm

6

dm project report

Technology