final minor II eval notes

[Amitpatil215]

https://github.com/arszen123/offer-notification-application https://betterprogramming.pub/building-an-offer-notification-service-on-aws-99faad5d2806

https://github.com/allen-tran/drop-it]

https://github.com/Vennify-Inc/DoogleGrive

age gender skills salery

Age 0-10 m,f,o (popular skills) salery 11-20 21-30

What we have done till mid evaluation?

• Resume preprocessing
  • binning of salaries, merging columns of framework and lang, database
  • ommiting na values
• Built KG on neo4j
  • having nodes and relationship in b/w id,domain,age & gender

What we did now? Divided into 3 parts

• Analysis
  • Skill destribution across age and gender
  • Domain destribution across age and gender
  • Slighlty more women in 25-35 age group
  • slighlty salery increase due to increase in experience
  • Association Rule Mining using FP grwoth on skill and domain
  • Popular skill, domain
  • Rare skill and domain
  • Heat map between skill and domain
• Building Knowledge Graph
  • for resume
  • for Job Description
• Recomendation System
  • Manipulation Scripts
    • Add, delete, relation and nodes
  • Basic Script
    • Finding co resumes
    • get job id which ask for sql skills
    • list all skills a resume has
  • Graph info
    • jobid and resume id node counts
  • Recomendation
    • using skills in between
    • using skills in between & priortizing
    • using empty relationship & priortizing
    • using skill and domain & priorizing them
    • Adamic adar verification
  • Analytics
    • Resume having max skills count
  • Link prediction
    • predict link b/w JD and resume

Future Scope?

• extending KG to more dimensions like location, salery
• Using unstructured dataset
• Native language support

Actually we also thinking of writing research paper for IC3, so in that part also this analysis would be helpful

Resumes -> 19K -> Kaggle stackoverflow servey JD -> 22K dataworld 2017 -> dice job board of US 2017