PayPredict : Salary Estimation Using Regression Models
Project Overview
This project focuses on predicting a person’s salary based on features like years of experience, education level, job role, and location. It uses supervised regression techniques to build models that estimate salary ranges for professionals in different industries.
Objective
To build a regression model that predicts salary based on job-related and demographic features, helping understand pay trends and expectations.
Key Skills Applied
- Data Cleaning and Normalization
- Feature Encoding (Categorical to Numerical)
- Regression Techniques (Linear, Polynomial, Decision Tree Regressor)
- Model Evaluation (R² Score, MAE, RMSE)
Tools & Libraries Used
Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
Learning Outcomes
- Learned to apply regression models to real-world salary datasets
- Understood the influence of variables like experience, location, and education
- Practiced data visualization for salary trends
- Built models that can support HR tech and career platforms
Result
Developed a regression model with [e.g., R² score of 0.82], capable of estimating salary with reasonable accuracy for data analyst and tech-related roles.