Job Applicant’s Profile Evaluator using KNN

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.
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.

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

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

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.

FUTURE WORK:

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