1) Data Introduction • The Salary data aims to determine whether individuals earn less than or more than $50,000 annually based on their employment, education, and demographic information. It is used widely in analyses that seek to understand income disparities and economic factors influencing earnings.

2) Data Utilization (1) Salary data has characteristics that: • The dataset includes factors such as age, education, job type, hours worked per week, and other socio-economic variables that contribute to predicting salary categories. (2) Salary data can be used to: • Workforce Analysis: Useful for employers and policymakers to understand wage structures and adjust compensation plans accordingly. • Economic Research: Helps researchers analyze economic mobility and the impact of education and employment on income levels.

Salary Dataset with two columns , We can do Salary prediction using Simple linear regression. It has also been used in Machine Learning A to Z course.

Columns 1. Years of Experience 2. Salary

Dataset

This dataset was created by Jihan Kristal Yasmin

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Dataset

This dataset was created by Enejan H

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Dataset

This dataset was created by Austin Otutu

Released under Data files © Original Authors

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Analysis of ‘Engineering Graduate Salary Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/manishkc06/engineering-graduate-salary-prediction on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Engineering is the use of scientific principles to design and build machines, structures, and other items, including bridges, tunnels, roads, vehicles, and buildings. The discipline of engineering encompasses a broad range of more specialized fields of engineering, each with a more specific emphasis on particular areas of applied mathematics, applied science, and types of application. https://images.pexels.com/photos/414579/pexels-photo-414579.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500" alt="eng"> Engineering is a broad discipline that is often broken down into several sub-disciplines. Although an engineer will usually be trained in a specific discipline, he or she may become multi-disciplined through experience. Engineering is often characterized as having four main branches: chemical engineering, civil engineering, electrical engineering, and mechanical engineering. [Reference: Wikipedia]

Engineering Graduates in India India has a total 6,214 Engineering and Technology Institutions in which around 2.9 million students are enrolled. Every year on an average 1.5 million students get their degree in engineering, but due to lack of skill required to perform technical jobs less than 20 percent get employment in their core domain. [source of information: BWEDUCATION]

Objective

A relevant question is what determines the salary and the jobs these engineers are offered right after graduation. Various factors such as college grades, candidate skills, the proximity of the college to industrial hubs, the specialization one have, market conditions for specific industries determine this. On the basis of these various factors, your objective is to determine the salary of an engineering graduate in India.

Data Description

• ID: A unique ID to identify a candidate
• Salary: Annual CTC offered to the candidate (in INR)
• Gender: Candidate's gender
• DOB: Date of birth of the candidate
• 10percentage: Overall marks obtained in grade 10 examinations
• 10board: The school board whose curriculum the candidate followed in grade 10
• 12graduation: Year of graduation - senior year high school
• 12percentage: Overall marks obtained in grade 12 examinations
• 12board: The school board whose curriculum the candidate followed
• CollegeID: Unique ID identifying the university/college which the candidate attended for her/his undergraduate
• CollegeTier: Each college has been annotated as 1 or 2. The annotations have been computed from the average AMCAT scores obtained by the students in the college/university. Colleges with an average score above a threshold are tagged as 1 and others as 2.
• Degree: Degree obtained/pursued by the candidate
• Specialization: Specialization pursued by the candidate
• CollegeGPA: Aggregate GPA at graduation
• CollegeCityID: A unique ID to identify the city in which the college is located in.
• CollegeCityTier: The tier of the city in which the college is located in. This is annotated based on the population of the cities.
• CollegeState: Name of the state in which the college is located
• GraduationYear: Year of graduation (Bachelor's degree)
• English: Scores in AMCAT English section
• Logical: Score in AMCAT Logical ability section
• Quant: Score in AMCAT's Quantitative ability section
• Domain: Scores in AMCAT's domain module
• ComputerProgramming: Score in AMCAT's Computer programming section
• ElectronicsAndSemicon: Score in AMCAT's Electronics & Semiconductor Engineering section
• ComputerScience: Score in AMCAT's Computer Science section
• MechanicalEngg: Score in AMCAT's Mechanical Engineering section
• ElectricalEngg: Score in AMCAT's Electrical Engineering section
• TelecomEngg: Score in AMCAT's Telecommunication Engineering section
• CivilEngg: Score in AMCAT's Civil Engineering section
• conscientiousness: Scores in one of the sections of AMCAT's personality test
• agreeableness: Scores in one of the sections of AMCAT's personality test
• extraversion: Scores in one of the sections of AMCAT's personality test
• nueroticism: Scores in one of the sections of AMCAT's personality test
• openess_to_experience: Scores in one of the sections of AMCAT's personality test

**Note: **To give you more context AMCAT is a job portal.

Acknowledgemet

I would like to thank ‘Aspiring Minds Research’ for making this dataset available publicly.

Inspiration

The data can be used not only to make an accurate salary predictor but also to understand what influences salary and job titles in the labour market. It’s up to you to explore things.

This Dataset is also available at DPhi

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Austin Otutu

Released under Data files © Original Authors

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Dataset

This dataset was created by Abdul Rehman Amer

Released under MIT

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Based on professional technical analysis and AI models, deliver precise price‑prediction data for This will make ur monthly salary on 2025-08-11. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Wages in the United States increased 4.78 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset

This dataset was created by Abdelrhman Tarek

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Explore the progression of average salaries for graduates in Predictive Analytics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Predictive Analytics relative to other fields. This data is essential for students assessing the return on investment of their education in Predictive Analytics, providing a clear picture of financial prospects post-graduation.

Forecast: Wages and Salaries in IT Services in the US 2022 - 2026 Discover more data with ReportLinker!

Explore the progression of average salaries for graduates in Data Science Predictive Analytics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Data Science Predictive Analytics relative to other fields. This data is essential for students assessing the return on investment of their education in Data Science Predictive Analytics, providing a clear picture of financial prospects post-graduation.

Forecast: Wages and Salaries in Publishing in the US 2023 - 2027 Discover more data with ReportLinker!

Income is a primary determinant of social mobility, career progression, and personal happiness. It has been shown to vary with demographic variables like age and education, with more oblique variables such as height, and with behaviors such as delay discounting, i.e., the propensity to devalue future rewards. However, the relative contribution of each these salary-linked variables to income is not known. Further, much of past research has often been underpowered, drawn from populations of convenience, and produced findings that have not always been replicated. Here we tested a large (n = 2,564), heterogeneous sample, and employed a novel analytic approach: using three machine learning algorithms to model the relationship between income and age, gender, height, race, zip code, education, occupation, and discounting. We found that delay discounting is more predictive of income than age, ethnicity, or height. We then used a holdout data set to test the robustness of our findings. We discuss the benefits of our methodological approach, as well as possible explanations and implications for the prominent relationship between delay discounting and income.

Forecast: Wages and Salaries in Programming and Broadcasting in the US 2023 - 2027 Discover more data with ReportLinker!

1) Data Introduction • The LinkedIn Job Postings (2023 - 2024) Dataset contains job posting information collected from LinkedIn. It is structured to support analysis of labor market trends and hiring dynamics.

2) Data Utilization (1) Characteristics of the LinkedIn Job Postings Dataset: • The dataset includes time-related information (e.g., posting date, expiration date, number of views), enabling applications such as time-series analysis, salary prediction, and industry-level demand analysis. • With clearly defined features such as job type, employment format, location, and experience level, the dataset is well-suited for analyzing corporate talent demands and sector-specific hiring patterns.

(2) Applications of the LinkedIn Job Postings Dataset: • Hiring trend analysis and job matching AI model development: The dataset can be used to develop NLP-based job classifiers, salary predictors, and skill-experience matching systems. • Corporate talent strategy analysis: It can also be used to build business intelligence (BI) tools that analyze employment strategies by evaluating factors such as job demand, benefits offerings, and remote work availability.

This dataset provides values for WAGES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Forecast: Wages and Salaries in Healthcare in the US 2022 - 2026 Discover more data with ReportLinker!