Understanding public employee compensation is vital for ensuring transparency, accountability, and effective budgeting in government operations. The dataset contains more than 300k employee records found in San Francisco from 2011 to 2018. This project explores a salary dataset containing information about job titles, experience levels, company sizes, employment types, and work locations. The goal is to analyze how different factors affect salaries across industries and regions.
This dataset typically includes:
- Employee_details- EmployeeName, JobTitle, Year
- Salary_details- BasePay, OvertimePay, Benefits, TotalPay
- Cleaned and processed real-world Olympic dataset
- Data preprocessing using Pandas and NumPy
- Visualizations using Matplotlib and Seaborn
- Top 10 Highest Paying Job Titles
- Average Salary Analysis
- Correlation Heatmap
- Applying multiple predictive algorithms to analyze salaries
| Tools & Technologies | Description |
|---|---|
| Python | Core language used for scripting and analysis |
| Pandas | Used for data cleaning, transformation, and manipulation |
| Matplotlib | Used to create charts, graphs, and heatmaps for better insights |
| Scikit-learn | Used for model building, training, testing, and evaluation |
-
README.md
- Contains an overview of the project, including dataset information, features, tech stack, and usage instructions.
-
average_pay.py
- Analyzes and visualizes the average salary across different job roles and organizations to identify pay trends.
-
heatmap.py
- Generates a correlation heatmap to explore relationships between key numerical features like salary, experience, and company size.
-
top_10_job.py
- Extracts and visualizes the top 10 highest-paying job titles, helping to highlight the most lucrative positions.
-
total.py
- Implements and compares multiple machine learning algorithms for salary prediction. Evaluates their accuracy and performance to identify the most effective model.