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ML-Ready LinkedIn Jobs Dataset
Data Science and Analytics
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Free
About
Job-related information scraped from LinkedIn postings over a two-day period. It includes key features such as company details, job specifics like title and description, and salary information. The data has been specifically reformatted to enhance its compatibility with various machine learning algorithms, making it valuable for exploring the factors that influence the characteristics of job postings.
Columns
- Co_Nm: Company Name (Object)
- Co_Pg_Lstd: Company Page Listed (Boolean)
- Emp_Cnt: Company Employee Count (Integer)
- Flw_Cnt: Company Follower Count (Integer)
- Job_Ttl: Job Title (Object)
- Job_Desc: Job Description (Object)
- Is_Supvsr: Indicates if the position is for a supervisor (Boolean)
- max_sal: Maximum Salary (Float)
- med_sal: Median Salary (Float)
- min_sal: Minimum Salary (Float)
- py_prd: Pay Period (Categorical: Not Listed, YEARLY, HOURLY, MONTHLY, Unpaid, WEEKLY, ONCE)
- py_lstd: Indicates if pay was listed (Boolean)
- wrk_typ: Work Type (Categorical: Full-time, Contract, Part-time, Temporary, Internship, Other, Volunteer)
- loc: Job Location (Object)
- st_code: Job State Code (Object)
- is_remote: Indicates if the job is remote (Boolean)
- views: Number of Posting Views (Integer)
- app_typ: Application Type (Categorical: Offsite Apply, SimpleOnSiteApply, ComplexOnSiteApply)
- app_is_off: Indicates if the application is offsite (Boolean)
- xp_lvl: Experience Level (Categorical: Mid-Senior level, Not Listed, Entry level, Associate, Director, Internship, Executive)
- domain: Posting Domain (Object)
- has_post_domain: Indicates if a posting domain is present (Boolean)
- is_sponsored: Indicates if the posting is sponsored (Boolean)
- base_comp: Indicates if base compensation is present (Boolean)
Distribution
The data is provided in CSV format. The specific number of rows or records is not detailed in the available information.
Usage
This data is ideal for machine learning projects, particularly for analysing job market trends, predicting salary ranges, and understanding the factors that influence job posting engagement. It can also be used for natural language processing on job descriptions and for exploring employment patterns.
Coverage
The data was collected over a two-day period from LinkedIn job postings. The specific geographic scope is not defined, but it includes job location and state code information where available. There is no information provided about the specific time range or demographic scope of the data.
License
CC BY-SA 4.0
Who Can Use It
- Data Scientists: For building predictive models related to salary, job demand, and required skills.
- Recruiters and HR Professionals: To analyse market trends and optimise job posting content.
- Job Seekers: To understand salary benchmarks and market demand for specific roles.
- Academic Researchers: For studies on labour markets, career paths, and employment trends.
Dataset Name Suggestions
- LinkedIn Job Postings for Machine Learning
- ML-Ready LinkedIn Jobs Dataset
- Career and Employment Data from LinkedIn
- Job Market Analytics Dataset
Attributes
Original Data Source: ML-Ready LinkedIn Jobs Dataset
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