https://github.com/rohitinu6/salary-prediction
This project predicts salaries using machine learning models based on factors such as country, race, and demographic details.
https://github.com/rohitinu6/salary-prediction
data-science eda machine-learning machine-learning-algorithms python salary-prediction visualization
Last synced: 3 months ago
JSON representation
This project predicts salaries using machine learning models based on factors such as country, race, and demographic details.
- Host: GitHub
- URL: https://github.com/rohitinu6/salary-prediction
- Owner: rohitinu6
- Created: 2025-02-05T01:19:15.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-06T02:49:58.000Z (over 1 year ago)
- Last Synced: 2025-11-12T21:03:01.025Z (8 months ago)
- Topics: data-science, eda, machine-learning, machine-learning-algorithms, python, salary-prediction, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.82 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Salary Prediction based on Country and Race
## 📌 Project Overview
This project predicts salaries using machine learning models based on factors such as country, race, and demographic details. The goal is to analyze salary disparities and identify key predictors of income.
## 🚀 Features
- Data preprocessing and exploratory data analysis (EDA)
- Feature engineering and selection
- Machine learning model implementation
- Model evaluation and performance analysis
- Visualization of salary trends
## 🛠 Tech Stack
- Python
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
- Jupyter Notebook
## 📚 Dataset
The dataset includes variables such as:
- **Country**
- **Race**
- **Education Level**
- **Years of Experience**
- **Job Title**
- **Salary**
## 📊 Machine Learning Models Used
- Linear Regression
- Random Forest Regressor
- Gradient Boosting Regressor
- Support Vector Machine (SVM)
## 🔥 Results
Models are evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared values. The best-performing model provides insights into salary variations.
## 📁 Repository Structure
```
📚 Salary-Prediction
|--📂 data (Dataset & processed data)
|--📂 notebooks (Jupyter Notebooks)
|--📂 models (Trained models)
|--📂 images (Code and Results Screenshots)
|--📄 README.md (Project documentation)
```
## 🎨 Code and Results
Include images of code and results in the `images` folder. Example:
## 📚 How to Run the Project
1. Clone the repository:
```bash
git clone https://github.com/rohitinu6/Salary-Prediction.git
```
2. Navigate to the project folder:
```bash
cd Salary-Prediction
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Jupyter Notebook or Python scripts to train and test models.
## 📞 Links
- **GitHub Repository:** [Salary Prediction](https://github.com/rohitinu6/Salary-Prediction.git)
- **Portfolio:** [Rohit Dubey](https://tinyurl.com/dubeyrohit)
- **GitHub Profile:** [rohitinu6](https://github.com/rohitinu6)
- **LinkedIn:** [Rohit Dubey](https://www.linkedin.com/in/rohit-dubey-d/)
- **Twitter/X:** [@rohitdubey003](https://x.com/rohitdubey003)
## 🐖 Tags
`Machine Learning` `Salary Prediction` `Data Science` `Python` `EDA`
## 📝 License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
---
💡 **For any queries or collaboration opportunities, feel free to connect!** 🚀