This repository contains a collection of end-to-end machine learning projects, each demonstrating the full lifecycle of developing, evaluating, and deploying real-world ML models. Each project is self-contained with its own data, modeling pipeline, and deployment solution.

├── Data Science Salary/
├── data/
│   ├── raw/
│   │   └── [Raw data files]
│   └── processed/
│       └── [Processed data files]
├── deployment/
│   └── [Deployment scripts]
│   modeling/
│  └── [Trained model files]
├── models/
│   └── [model-specific files]
├── notebooks/
│   └── [notebooks]
├── reports/
│   └── [Project reports and presentations]
├── scripts/
│  └──[scripts]
├── requirements.txt
├── setup.py
└── README.md

Each project directory contains its own code, data, and documentation.

• Programming Languages: Python
• Frameworks: Streamlit, Gradio, FastAPI
• Libraries: scikit-learn, pandas, NumPy, Plotly, Flask

1. Clone the repository:

   git clone <your-repo-url>
   cd End-2-End

2. Navigate to a project directory:

   cd "Data Science Salary"

3. Set up a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

4. Install dependencies:

   pip install -r requirements.txt

5. Run the application:

   • See the project’s README.md for specific instructions (e.g., streamlit run app.py or python app.py).

• Data Science Salary: Predicts data science salaries using Gradio for deployment.
• Health Care: ML solutions for healthcare data.
• Loan Status: Loan approval prediction web app using Streamlit.
• User Behavior Analyzes and predicts user behavior patterns.

We welcome contributions!

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes.

4. Commit your changes:

   git commit -m "Add feature"

5. Push to your branch:

   git push origin feature-branch

6. Open a pull request.

This repository is licensed under the MIT License.