SalaryAi is an AI-powered salary prediction web application built using FastAPI, Machine Learning, HTML, CSS, and JavaScript. It enables users to input professional details such as age, gender, education level, job title, and years of experience — and instantly get a predicted salary based on trained data.
⚠️ Note: This application is based on salary data from the United States. Predictions are aligned with typical U.S. salary ranges.
- 🧠 Machine learning-powered salary predictions
- 🌐 Fast and lightweight API built with FastAPI
- 🎨 Responsive and user-friendly frontend interface
- ⬇️ Dynamic dropdowns populated directly from training dataset values
- 📦 Model and preprocessing pipeline stored using
joblib
| Category | Tools Used |
|---|---|
| Backend | Python, FastAPI |
| Frontend | HTML, CSS, JavaScript |
| Machine Learning | Pandas, Scikit-learn, Joblib |
| API Testing | FastAPI Docs (Swagger UI) |
| Deployment | Uvicorn (Locally) / Render |
Age(Float)Gender(Dropdown: e.g., Male, Female, Other)Education Level(Dropdown: e.g., Bachelor's, Master's, PhD)Job Title(Dropdown: 190+ options from dataset)Years of Experience(Float)
git clone https://github.com/yourusername/SalaryAi.git
cd SalaryAipip install -r requirements.txtuvicorn main:app --reload- Visit the frontend page at:
http://localhost:8000 - Or use the Swagger API at:
http://localhost:8000/docs
SalaryAi/
├── main.py # FastAPI backend
├── predict_salary.pkl # Trained ML pipeline
├── static/
│ ├── index.html # CSS styling, JavaScript logic
├── requirements.txt # Python dependencies
└── README.md # Project documentation
- Add more countries and currencies
- Authentication for user-specific history
- Visualize trends with graphs (experience vs salary, etc.)
- Host the model API on cloud
This project is licensed under the MIT License.