🎯 Adult Income Prediction ML

A comprehensive machine learning pipeline that predicts whether an individual's income exceeds $50K/year based on census data. Built with Python and deployed as a web application using Flask.

video

ML.Project.-.Made.with.Clipchamp.1.1.1.mp4

🏗️ Project Structure

Adult-Income-Prediction-ML/
├── 📄 app.py                          # Flask web application
├── 📊 adult.csv                       # Dataset
├── 📓 ML_project_live_class.ipynb     # Jupyter notebook for analysis
├── 📝 problem_statement.txt           # Project requirements
├── 📚 readme.md                       # Project documentation
├── 📋 requirements.txt                # Python dependencies
├── ⚙️ setup.py                        # Package setup
├── 📂 artifacts/                      # Generated model artifacts
│   ├── 📥 data_ingestion/
│   ├── 🔄 data_transformation/
│   └── 🤖 model_trainer/
├── 🌐 env/                            # Virtual environment
├── 📝 logs/                           # Application logs
├── 📓 notebook/                       # Jupyter notebooks
│   └── 📊 data/
├── 🔧 src/                            # Source code
└── 🎨 templates/                      # HTML templates

🚀 Getting Started

Prerequisites

• Python 3.7+
• pip (Python package manager)
• Git (for cloning the repository)

📦 Installation

1. Clone the repository

   git clone <repository-url>
   cd Adult-Income-Prediction-ML

2. Create a virtual environment

   python -m venv env

3. Activate the environment

   Windows:

   .\env\Scripts\activate

   Linux/Mac:

   source env/bin/activate

4. Install dependencies

   pip install -r requirements.txt

🎮 Usage

Web Application

Start the Flask web application:

python app.py

Navigate to http://localhost:5000 in your browser to use the prediction interface.

Jupyter Notebook

For interactive data exploration and model development:

jupyter notebook ML_project_live_class.ipynb

🔧 Project Components

📥 Data Ingestion

• Loads and validates the raw census data (adult.csv)
• Handles missing values and data quality checks
• Splits data into training and testing sets

🔄 Data Transformation

• Feature engineering and preprocessing
• Categorical variable encoding
• Feature scaling and normalization
• Data pipeline creation

🤖 Model Training

• Multiple algorithm evaluation
• Hyperparameter tuning
• Model selection and validation
• Performance metrics calculation

🌐 Deployment

• Flask web application
• User-friendly prediction interface
• Real-time prediction capabilities
• Input validation and error handling

📊 Dataset Features

The model uses the following features for prediction:

• Age
• Work Class
• Education Level
• Marital Status
• Occupation
• Relationship
• Race
• Sex
• Capital Gain/Loss
• Hours per Week
• Native Country

🎯 Model Performance

The trained model achieves:

• Accuracy: High prediction accuracy on test data
• Precision: Reliable positive predictions
• Recall: Good coverage of actual positive cases
• F1-Score: Balanced performance metric

📝 Logging

Comprehensive logging system:

• Location: logs/ directory
• Features: Error tracking, performance monitoring, debugging information
• Format: Structured logs with timestamps and severity levels

🛠️ Development

Project Setup

# Install in development mode
pip install -e .

# Run tests
python -m pytest

# Check code quality
flake8 src/

Adding New Features

1. Create feature branch
2. Implement changes
3. Add tests
4. Update documentation
5. Submit pull request

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Sourav Upadhyay

🙏 Acknowledgments

• Census Bureau for providing the dataset
• Open source community for amazing tools
• Contributors and supporters

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