I found this data on kaggle and I use it to predict the salary baisd on some features and here are the explnation that the owner provided.
This file contains detailed information about data professionals, including their salaries, designations, departments, and more. The data can be used for salary prediction, trend analysis, and HR analytics.
FIRST NAME: First name of the data professional (String)
LAST NAME: Last name of the data professional (String)
SEX: Gender of the data professional (String: 'F' for Female, 'M' for Male)
DOJ (Date of Joining): The date when the data professional joined the company (Date in MM/DD/YYYY format)
CURRENT DATE: The current date or the snapshot date of the data (Date in MM/DD/YYYY format)
DESIGNATION: The job role or designation of the data professional (String: e.g., Analyst, Senior Analyst, Manager)
AGE: Age of the data professional (Integer)
SALARY: Annual salary of the data professional (Float)
UNIT: Business unit or department the data professional works in (String: e.g., IT, Finance, Marketing)
LEAVES USED: Number of leaves used by the data professional (Integer)
LEAVES REMAINING: Number of leaves remaining for the data professional (Integer)
RATINGS: Performance ratings of the data professional (Float)
PAST EXP: Past work experience in years before joining the current company (Float)
Provenance
The dataset was compiled from internal HR records of a hypothetical company. Each record represents a unique data professional with various attributes collected from their employment history. The data spans from 2009 to 2016, capturing a snapshot as of January 7, 2016. Data Organization: The data has been organized chronologically by the date of joining (DOJ). Each row represents an individual data professional. Various attributes such as designation, department, and performance ratings have been included to enable comprehensive analysis.