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Occupation, Salary, and Likelihood of Automation

pszyarto

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Last edited Sep 04, 2025
Created on Sep 04, 2025

Dataset 2: Occupation, Salary, and Likelihood of Automation Source: Kaggle – Occupation, Salary, and Likelihood of Automation https://www.kaggle.com/datasets/andrewmvd/occupation-salary-and-likelihood-of-automation?utm_source=chatgpt.com Description This dataset links U.S. occupational data with automation probability scores, offering insights into how vulnerable different professions are to AI and automation. It also provides employment numbers and median salary estimates. This makes it valuable for exploring the tradeoff between wages, employment size, and automation risk. Attributes (VAD Framework) Attribute Type Notes Occupation Categorical Example: Cashier, Radiologist, Lawyer Automation Probability Quantitative Probability score (0–1) that the occupation can be automated Employment Quantitative Number of employed workers in that occupation Median Annual Wage (USD) Quantitative Median salary for the role Education Level Ordered Ordinal (e.g., High School < Bachelor’s < Master’s < Doctorate)

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