{"id":28639373,"url":"https://github.com/anikchand461/salaryai","last_synced_at":"2025-07-26T09:33:33.830Z","repository":{"id":294259991,"uuid":"986419952","full_name":"anikchand461/SalaryAi","owner":"anikchand461","description":"SalaryAi is a machine learning-powered web application that predicts employee salaries based on input features like age, gender, education level, job title, and years of experience. 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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.\n\n\u003e ⚠️ **Note:** This application is based on salary data from the **United States**. Predictions are aligned with typical U.S. salary ranges.\n\n---\n\n## 🚀 Features\n\n* 🧠 Machine learning-powered salary predictions\n* 🌐 Fast and lightweight API built with FastAPI\n* 🎨 Responsive and user-friendly frontend interface\n* ⬇️ Dynamic dropdowns populated directly from training dataset values\n* 📦 Model and preprocessing pipeline stored using `joblib`\n\n---\n\n## 💠 Tech Stack\n\n| Category         | Tools Used                           |\n| ---------------- | ------------------------------------ |\n| Backend          | Python, FastAPI                      |\n| Frontend         | HTML, CSS, JavaScript                |\n| Machine Learning | Pandas, Scikit-learn, Joblib         |\n| API Testing      | FastAPI Docs (Swagger UI)            |\n| Deployment       | Uvicorn (Locally) / Render           |\n\n---\n\n## 📊 Dataset Fields\n\n* `Age` (Float)\n* `Gender` (Dropdown: e.g., Male, Female, Other)\n* `Education Level` (Dropdown: e.g., Bachelor's, Master's, PhD)\n* `Job Title` (Dropdown: 190+ options from dataset)\n* `Years of Experience` (Float)\n\n---\n\n## 🖼️ Screenshot\n\n![SalaryAi Screenshot](screenshot.png) \u003c!-- Replace with actual path to screenshot --\u003e\n\n---\n\n## 🔧 How to Run Locally\n\n### 1. Clone the Repository\n\n```bash\ngit clone https://github.com/yourusername/SalaryAi.git\ncd SalaryAi\n```\n\n### 2. Install Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n### 3. Run the App\n\n```bash\nuvicorn main:app --reload\n```\n\n### 4. Access the Interface\n\n* Visit the frontend page at: `http://localhost:8000`\n* Or use the Swagger API at: `http://localhost:8000/docs`\n\n---\n\n## 📂 Project Structure\n\n```\nSalaryAi/\n├── main.py                # FastAPI backend\n├── predict_salary.pkl     # Trained ML pipeline      \n├── static/\n│   ├── index.html         # CSS styling, JavaScript logic\n├── requirements.txt       # Python dependencies\n└── README.md              # Project documentation\n```\n\n---\n\n## 📌 Future Improvements\n\n* Add more countries and currencies\n* Authentication for user-specific history\n* Visualize trends with graphs (experience vs salary, etc.)\n* Host the model API on cloud\n\n---\n\n## 🧑‍💻 Author\n\n**Anik Chand**\n[LinkedIn](https://www.linkedin.com/in/anikchand) • [GitHub](https://github.com/yourusername)\n\n---\n\n## 📄 License\n\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanikchand461%2Fsalaryai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanikchand461%2Fsalaryai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanikchand461%2Fsalaryai/lists"}