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Data mining project

Data mining project

Published in: Business, Technology
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Transcript

  • 1. WKU Job Applicant’s Status Analyzer k – Nearest NeighborAlgorithm Implementationpresented by Mohnish Thallavajhula Vijayeandra Parthepan
  • 2. Introduction Classify Job Applicants based on their details into Classes of Jobs.Ex:Group A: {Graduate Assistant, ResearchAssistant}Group B: {Lab Assistant, Desk Clerk, NightClerk} 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’sStatus 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 descriptionSample Training Data:A G 3.0 CS 2B UG 2.5 ANY 3C G 3.0 MPH 5Sample Test Data:G 3.5 CS 5Description:Training data has:Class Name in 1st columnQualification in 2nd columnGPA in 3rd columnDepartment in 4th columnYears of experience in 5th column
  • 7. Test Data descriptionDescription:Test data has:Qualification in 1st columnGPA in 2nd columnDepartment in 3rd columnYears of experience in 4th column
  • 8. JASA
  • 9. Result Aftercalculating the group to which the Job Applicant belongs to, the list of jobs that the Job applicant can apply are displayed.
  • 10. Future work Convert the Windows implementation into Web Application Provide direct application process to the jobs by taking the applicant’s details.
  • 11. 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.
  • 12. Thank You

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