mystic-trooper / job_recomendation_kg Goto Github PK

1.github repo
2.Project Synopsis
3. Graphical Analysis/mining
4. Presentation (10 min slides)

Mid - eval

1. Data Cleaning
   1.1 Remove replace null values in following columns

2. Age,salery,Marrid/Unmarried, Gender,skills
   => Build a credibility score out of 10

   ++ how gender, skills, dependency,dev_type, salery related to each other -> graphical represntaion
   2.1 Highest paid skills & highest salery group by skill
   2.2 Highest paid dev_type
   2.3 Age with salery
   2.4 Find salery wise job_satisfaction group by skills

For Final Eval

1. Build homogenous graph's as in resume-skills, resume-location, resume-dev_type(backend/frontend),
   after that take most popular nodes and build a heterogenous knowledge graph

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