https://github.com/isathyam31/income_prediction_using_machine_learning
This Machine Learning project predicts an adult's income and is deployed using Flask, Docker, and CI/CD pipelines.
https://github.com/isathyam31/income_prediction_using_machine_learning
adult-income cicd dockers flask machine-learning
Last synced: about 2 months ago
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This Machine Learning project predicts an adult's income and is deployed using Flask, Docker, and CI/CD pipelines.
- Host: GitHub
- URL: https://github.com/isathyam31/income_prediction_using_machine_learning
- Owner: iSathyam31
- Created: 2025-01-06T17:50:34.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-06T20:23:37.000Z (over 1 year ago)
- Last Synced: 2025-08-08T13:36:39.071Z (11 months ago)
- Topics: adult-income, cicd, dockers, flask, machine-learning
- Language: Jupyter Notebook
- Homepage: https://hub.docker.com/repository/docker/sattu31/flask-income-app
- Size: 8.59 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## 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.