In the machine learning, we have to think about what could be the feature that can affect the accuracy during the prediction.

We will focus on the “feature selection” tools that we are going to use in machine learning.

When predicting salary using machine learning, here are some common features that can be useful:

  1. Education Level: The level of education attained by an individual, such as high school, bachelor’s degree, master’s degree, or Ph.D.
  2. Years of Experience: The number of years of work experience the individual has in a relevant field.
  3. Job Title: The specific job title or position held by the individual.
  4. Industry: The industry or sector in which the individual works, such as technology, finance, healthcare, etc.
  5. Company Size: The size of the company the individual is employed in, such as small, medium, or large.
  6. Location: The geographical location of the job, as salaries can vary significantly based on location.
  7. Skills: The specific skills possessed by the individual that are relevant to the job or industry, such as programming languages, data analysis, management skills, etc.
  8. Certifications: Any professional certifications or qualifications the individual holds, which can indicate specialized knowledge or expertise.
  9. Performance Metrics: Objective performance metrics or evaluations that reflect the individual’s past achievements or contributions in their role.
  10. Benefits Package: The benefits and perks offered by the employer, such as health insurance, retirement plans, stock options, etc.
  11. Negotiation Skills: The ability to negotiate and secure higher compensation packages.
  12. Gender or Diversity Factors: Although controversial and subject to legal considerations, certain studies have shown that gender or diversity factors can influence salary discrepancies.

These features can vary depending on the context and specific dataset available. It is important to carefully analyze the data and domain expertise to identify the most relevant features for your particular salary prediction problem. Additionally, feature engineering techniques can be applied to create new features or derive meaningful insights from the existing ones to enhance the model’s predictive accuracy.

The Data School
Author: The Data School