Employee Salary Prediction Using Machine Learning

Project Overview

• Built a regression-based machine learning system to predict annual employee salary
• Objective: analyze salary trends and identify key factors influencing employee pay
• Uses historical hiring and salary data for exploratory analysis and prediction

Dataset

• train_salary.csv
• Contains employee details including:
  • Name
  • JobTitle
  • Agency
  • AgencyID
  • HireDate
  • AnnualSalary
  • GrossPay

Data Preprocessing & Feature Engineering

• Removed unnecessary columns such as GrossPay
• Cleaned column names by stripping extra spaces
• Removed rows with missing hire dates
• Converted salary from string to numeric format
• Extracted Month, Day, and Year from HireDate
• Removed extreme salary outliers (AnnualSalary > 140000)
• Dropped non-informative columns such as employee name

Exploratory Data Analysis

• Analyzed most common job titles and hiring agencies
• Visualized salary distribution and outliers
• Identified top 10 hiring jobs and highest paying roles
• Examined hiring trends across months and years
• Performed correlation analysis using heatmaps and pair plots

Categorical Encoding

• Applied mean encoding to categorical variables:
  • JobTitle
  • Agency
  • AgencyID
• Replaced categorical values with their corresponding average salary

Model Building & Evaluation

• Train-test split: 80% training, 20% testing
• Scaled features using StandardScaler
• Models used:
  • Linear Regression
  • Decision Tree Regressor
  • Random Forest Regressor
  • XGBoost Regressor
• Evaluation metric: R² score
• Ensemble models showed better predictive performance

How to Run the Repository

1. Clone the repository

    git clone https://github.com/Gem793/Salary_Prediction_Model
    cd Employee_Salary_Predictor
   

2. Install required dependencies

    pip install pandas numpy matplotlib seaborn scikit-learn xgboost
   

3. Ensure train_salary.csv is present in the project directory

4. Launch Jupyter Notebook jupyter notebook

5. Run the notebook cells

Technologies Used

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn
• XGBoost

Conclusion

• Salary prediction can be effectively modeled using regression techniques
• Job title, agency, and hiring year are strong salary predictors
• Demonstrates an end-to-end machine learning workflow from EDA to modeling