Pranjali1049/Salary_Prediction
This salary prediction model leverages machine learning techniques, including Random Forest, Decision Tree, and Linear Regression, to estimate salaries based on individual attributes such as age, gender, education level, job title, and years of experience. The Random Forest model outperforms the others, achieving the highest R-squared score.
This project helps HR managers and compensation analysts estimate salaries based on individual attributes like age, gender, education, job title, and experience. You input a person's characteristics and receive a predicted salary range. It is designed for professionals involved in compensation planning, recruitment, or salary benchmarking.
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Use this if you need to quickly estimate a fair salary for a new hire or for internal compensation adjustments based on common employee attributes.
Not ideal if you need a real-time, highly granular salary prediction for a global workforce across a wide range of highly specialized roles not covered by typical datasets.
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Last pushed
Oct 04, 2023
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