Adult Income Prediction

This project predicts whether an individual's income exceeds $50K/year based on census data. The machine learning model is developed, serialized into a pickle file, and deployed using a Flask web application. The deployment process is streamlined with Docker and CI/CD pipelines.

Project Structure

INCOME
│
├── .github/workflows/
│   └── dockers.yml          # CI/CD pipeline configuration for automated deployment
│
├── app/
│   ├── static/
│   │   └── style.css        # CSS for styling the Flask application
│   ├── templates/
│   │   └── index.html       # HTML template for the web application
│   ├── app.py               # Flask application to serve predictions
│   ├── model.pkl            # Serialized best machine learning model
│   └── model.py             # Script to train and save the best model as a pickle file
│
├── assets/
│   └── flask-app.png        # Project screenshot or logo (optional)
│
├── data/
│   └── adult.csv            # Adult Income dataset for training and testing
│
├── Notebook/
│   └── Code.ipynb           # Jupyter notebook for data exploration, preprocessing, and model evaluation
│
├── venv/                    # Virtual environment (optional for local development)
│
├── .gitignore               # Specifies files and directories to ignore in Git
├── Dockerfile               # Configuration file to build the Docker container
├── README.md                # Project documentation
├── requirements.txt         # Python dependencies

Features

1. Data Analysis and Preprocessing:

    * Data exploration, cleaning, and feature engineering are performed in `Code.ipynb` using the `adult.csv` dataset.

2. Model Training:

    * The `model.py` script: 
    * Makes the pickle(model.pkl) of the model which is trained best in the notebook.

3. Flask Web Application:

    * The web app is implemented in `app.py` and consists of:
    * A user-friendly interface (`index.html` and `style.css`) to input data.    
    * Backend logic to load the pickle file (`model.pkl`) and return predictions.  

4. Deployment:

    * The project is containerized using Docker (`Dockerfile`). 
    * Automated deployment is managed using CI/CD pipelines configured in `dockers.yml`.  

Getting Started

Prerequisites

• Python 3.8 or later
• Docker installed on your machine
• (Optional) Virtual environment tools like venv or conda

Installation

1. Clone the repository:

git clone https://github.com/your-repo/Income_Prediction_Using_Machine_Learning.git
cd Income_Prediction_Using_Machine_Learning

2. Set up a virtual environment (optional but recommended):

conda create -p venv python==3.x -y
conda activate venv

3. Install dependencies:

pip install -r requirements.txt

4. Train and Serialize the Model: Run model.py to train the model and generate the model.pkl file:

python app/model.py

5. Run the Flask Application:

python app/app.py

6. Access the App:

• Open your browser and navigate to http://127.0.0.1:5000.

Deployment with Docker

1. Build the Docker Image:

docker build -t flask-income-app .

2. Run the Docker Container:

docker run -p 5000:5000 adult-income-prediction

3. Access the App:

• Open your browser and navigate to http://127.0.0.1:5000.

CI/CD Pipeline

• The project uses GitHub Actions for CI/CD.
• The dockers.yml file automates:
  1. Building and testing the Docker image.
  2. Deploying the app to your Docker environment.

Technologies Used

• Programming Language: Python
• Libraries: Pandas, Scikit-learn, Flask
• Frontend: HTML, CSS
• Containerization: Docker
• CI/CD: GitHub Actions

Dataset

• Source: UCI Machine Learning Repository - Adult Dataset
• Description: * Predicts whether income exceeds $50K/year based on census data. * Contains features like age, education, occupation, and hours worked.

Future Work

• Add advanced preprocessing techniques for better feature engineering.
• Deploy the model on a cloud platform like AWS, GCP, or Azure.

Acknowledgments

• Thanks to the UCI Machine Learning Repository for providing the dataset.
• Inspired by various open-source projects and community contributions.