{"id":25473586,"url":"https://github.com/isathyam31/income_prediction_using_machine_learning","last_synced_at":"2026-05-15T21:32:01.238Z","repository":{"id":271295064,"uuid":"912940508","full_name":"iSathyam31/Income_Prediction_Using_Machine_Learning","owner":"iSathyam31","description":"This Machine Learning project predicts an adult's income and is deployed using Flask, Docker, and CI/CD pipelines.","archived":false,"fork":false,"pushed_at":"2025-01-06T20:23:37.000Z","size":9007,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-08T13:36:39.071Z","etag":null,"topics":["adult-income","cicd","dockers","flask","machine-learning"],"latest_commit_sha":null,"homepage":"https://hub.docker.com/repository/docker/sattu31/flask-income-app","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/iSathyam31.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-06T17:50:34.000Z","updated_at":"2025-01-06T20:26:22.000Z","dependencies_parsed_at":"2025-01-06T21:29:26.282Z","dependency_job_id":null,"html_url":"https://github.com/iSathyam31/Income_Prediction_Using_Machine_Learning","commit_stats":null,"previous_names":["isathyam31/income_prediction_using_machine_learning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/iSathyam31/Income_Prediction_Using_Machine_Learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iSathyam31%2FIncome_Prediction_Using_Machine_Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iSathyam31%2FIncome_Prediction_Using_Machine_Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iSathyam31%2FIncome_Prediction_Using_Machine_Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iSathyam31%2FIncome_Prediction_Using_Machine_Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iSathyam31","download_url":"https://codeload.github.com/iSathyam31/Income_Prediction_Using_Machine_Learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iSathyam31%2FIncome_Prediction_Using_Machine_Learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33080777,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-15T20:25:35.270Z","status":"ssl_error","status_checked_at":"2026-05-15T20:25:34.732Z","response_time":103,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["adult-income","cicd","dockers","flask","machine-learning"],"created_at":"2025-02-18T10:35:11.599Z","updated_at":"2026-05-15T21:32:01.216Z","avatar_url":"https://github.com/iSathyam31.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Adult Income Prediction\nThis 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.\n\n### Project Structure\n```\nINCOME\n│\n├── .github/workflows/\n│   └── dockers.yml          # CI/CD pipeline configuration for automated deployment\n│\n├── app/\n│   ├── static/\n│   │   └── style.css        # CSS for styling the Flask application\n│   ├── templates/\n│   │   └── index.html       # HTML template for the web application\n│   ├── app.py               # Flask application to serve predictions\n│   ├── model.pkl            # Serialized best machine learning model\n│   └── model.py             # Script to train and save the best model as a pickle file\n│\n├── assets/\n│   └── flask-app.png        # Project screenshot or logo (optional)\n│\n├── data/\n│   └── adult.csv            # Adult Income dataset for training and testing\n│\n├── Notebook/\n│   └── Code.ipynb           # Jupyter notebook for data exploration, preprocessing, and model evaluation\n│\n├── venv/                    # Virtual environment (optional for local development)\n│\n├── .gitignore               # Specifies files and directories to ignore in Git\n├── Dockerfile               # Configuration file to build the Docker container\n├── README.md                # Project documentation\n├── requirements.txt         # Python dependencies\n```\n\n\n### Features\n#### 1. Data Analysis and Preprocessing:\n        * Data exploration, cleaning, and feature engineering are performed in `Code.ipynb` using the `adult.csv` dataset.\n\n#### 2. Model Training:\n        * The `model.py` script: \n        * Makes the pickle(model.pkl) of the model which is trained best in the notebook.\n\n#### 3. Flask Web Application:\n        * The web app is implemented in `app.py` and consists of:\n        * A user-friendly interface (`index.html` and `style.css`) to input data.    \n        * Backend logic to load the pickle file (`model.pkl`) and return predictions.  \n\n#### 4. Deployment:\n        * The project is containerized using Docker (`Dockerfile`). \n        * Automated deployment is managed using CI/CD pipelines configured in `dockers.yml`.  \n\n\n### Getting Started\n#### Prerequisites\n* Python 3.8 or later\n* Docker installed on your machine\n* (Optional) Virtual environment tools like venv or conda           \n\n#### Installation\n1. Clone the repository:\n```\ngit clone https://github.com/your-repo/Income_Prediction_Using_Machine_Learning.git\ncd Income_Prediction_Using_Machine_Learning\n```\n2. Set up a virtual environment (optional but recommended):\n```\nconda create -p venv python==3.x -y\nconda activate venv\n```\n3. Install dependencies:\n```\npip install -r requirements.txt\n```\n4. Train and Serialize the Model:\nRun model.py to train the model and generate the model.pkl file:\n```\npython app/model.py\n```\n5. Run the Flask Application:\n```\npython app/app.py\n```\n6. Access the App:\n* Open your browser and navigate to `http://127.0.0.1:5000`.\n\n\n### Deployment with Docker\n1. Build the Docker Image:\n```\ndocker build -t flask-income-app .\n```\n2. Run the Docker Container:\n```\ndocker run -p 5000:5000 adult-income-prediction\n```\n3. Access the App:\n* Open your browser and navigate to `http://127.0.0.1:5000`.\n\n\n### CI/CD Pipeline\n* The project uses GitHub Actions for CI/CD.\n* The `dockers.yml` file automates:\n   1. Building and testing the Docker image.\n   2. Deploying the app to your Docker environment.\n\n\n### Technologies Used\n* **Programming Language**: Python\n* **Libraries**: Pandas, Scikit-learn, Flask\n* **Frontend**: HTML, CSS\n* **Containerization**: Docker\n* **CI/CD**: GitHub Actions\n\n## Dataset\n* **Source**: UCI Machine Learning Repository - Adult Dataset\n* **Description**:\n       * Predicts whether income exceeds $50K/year based on census data.\n       * Contains features like age, education, occupation, and hours worked.\n\n\n### Future Work\n* Add advanced preprocessing techniques for better feature engineering.\n* Deploy the model on a cloud platform like AWS, GCP, or Azure.\n\n\n### Acknowledgments\n* Thanks to the UCI Machine Learning Repository for providing the dataset.\n* Inspired by various open-source projects and community contributions.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisathyam31%2Fincome_prediction_using_machine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fisathyam31%2Fincome_prediction_using_machine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisathyam31%2Fincome_prediction_using_machine_learning/lists"}