WKU Job Applicant’s Status Analyzer k-NN Implementation
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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.
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WKU Job Applicant’s Status Analyzer k-NN Implementation
- 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