{"id":23592721,"url":"https://github.com/tushar2704/wine_quality","last_synced_at":"2026-04-07T09:31:24.871Z","repository":{"id":188734811,"uuid":"679248796","full_name":"tushar2704/Wine_Quality","owner":"tushar2704","description":"This project aims to predict the quality of wines using various machine learning algorithms. 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It utilizes the MLflow platform to manage the end-to-end machine learning lifecycle, including data preprocessing, model training, hyperparameter tuning, and deployment.\n\n## Features\n\n- Data preprocessing pipeline for cleaning and transforming the dataset.\n- Support for multiple machine learning algorithms for wine quality prediction.\n- Hyperparameter tuning using grid search or random search.\n- Tracking and logging experiments with MLflow for easy comparison.\n- REST API endpoint for making predictions using the trained model.\n- Dockerized environment for seamless deployment.\n\n\n## Project Structure\n```\n├── data/\n│   ├── wine-quality.csv\n│   └── ...\n├── models/\n│   ├── model.pkl\n│   └── ...\n├── notebooks/\n│   ├── data_exploration.ipynb\n│   ├── model_experimentation.ipynb\n│   └── ...\n├── .gitignore\n├── Dockerfile\n├── preprocess_data.py\n├── train.py\n├── predict_api.py\n└── README.md\n```\n\n## Getting Started\n\n### STEPS:\n\nClone the repository\n\n```bash\nhttps://github.com/tushar2704/Wine_Quality\n```\n### STEP 01- Create a conda environment after opening the repository\n\n```bash\nconda create -n ml python=3.11 -y\n```\n\n```bash\nconda activate ml\n```\n\n\n### STEP 02- install the requirements\n```bash\npip install -r requirements.txt\n```\n\n\n```bash\n# Finally run the following command\npython app.py\n```\n\nNow,\n```bash\nopen up you local host and port\n```\n\n\n\n## MLflow\n\n[Documentation](https://mlflow.org/docs/latest/index.html)\n\n\n##### cmd\n- mlflow ui\n\n### dagshub\n[dagshub](https://dagshub.com/)\n\nMLFLOW_TRACKING_URI=https://dagshub.com/tushar27/Wine_Quality.mlflow \\\nMLFLOW_TRACKING_USERNAME=tushar27 \\\nMLFLOW_TRACKING_PASSWORD=\"\" \\\npython main.py\n\nRun this to export as env variables:\n\n```bash\n\nexport MLFLOW_TRACKING_URI=https://dagshub.com/tushar27/Wine_Quality.mlfloww\n\nexport MLFLOW_TRACKING_USERNAME=tushar27 \n\nexport MLFLOW_TRACKING_PASSWORD=\"\"\n\n```\n\n\n\n# AWS-CICD-Deployment-with-Github-Actions\n\n## 1. Login to AWS console.\n\n## 2. Create IAM user for deployment\n\n\t#with specific access\n\n\t1. EC2 access : It is virtual machine\n\n\t2. ECR: Elastic Container registry to save your docker image in aws\n\n\n\t#Description: About the deployment\n\n\t1. Build docker image of the source code\n\n\t2. Push your docker image to ECR\n\n\t3. Launch Your EC2 \n\n\t4. Pull Your image from ECR in EC2\n\n\t5. Lauch your docker image in EC2\n\n\t#Policy:\n\n\t1. AmazonEC2ContainerRegistryFullAccess\n\n\t2. AmazonEC2FullAccess\n\n\t\n## 3. Create ECR repo to store/save docker image\n    - Save the URI: 566373416292.dkr.ecr.ap-south-1.amazonaws.com/mlproj\n\n\t\n## 4. Create EC2 machine (Ubuntu) \n\n## 5. Open EC2 and Install docker in EC2 Machine:\n\t\n\t\n\t#optinal\n\n\tsudo apt-get update -y\n\n\tsudo apt-get upgrade\n\t\n\t#required\n\n\tcurl -fsSL https://get.docker.com -o get-docker.sh\n\n\tsudo sh get-docker.sh\n\n\tsudo usermod -aG docker ubuntu\n\n\tnewgrp docker\n\t\n# 6. Configure EC2 as self-hosted runner:\n    setting\u003eactions\u003erunner\u003enew self hosted runner\u003e choose os\u003e then run command one by one\n\n\n# 7. Setup github secrets:\n\n    AWS_ACCESS_KEY_ID=\n\n    AWS_SECRET_ACCESS_KEY=\n\n    AWS_REGION = us-east-1\n\n    AWS_ECR_LOGIN_URI = demo\u003e\u003e  566373416292.dkr.ecr.ap-south-1.amazonaws.com\n\n    ECR_REPOSITORY_NAME = simple-app\n\n## Contact Information\n\nIf you have any questions, feedback, or collaboration opportunities, please feel free to reach out to me. You can contact me via email at [info@tushar-aggarwal.com](mailto:info@tushar-aggarwal.com) or connect with me on LinkedIn at [Tushar Aggarwal](https://www.linkedin.com/in/yourname).\n\nThank you for visiting my Data Analysis Portfolio! I hope you find my projects informative and insightful.\n\n\n\n## Author\n- [\u003cins\u003e\u003cb\u003e©2023 Tushar Aggarwal. All rights reserved\u003c/b\u003e\u003c/ins\u003e](https://www.tushar-aggarwal.com/)\n- \u003cb\u003e[LinkedIn](https://www.linkedin.com/in/tusharaggarwalinseec/)\u003c/b\u003e\n- \u003cb\u003e[Medium](https://medium.com/@tushar_aggarwal)\u003c/b\u003e \n- \u003cb\u003e[Tushar-Aggarwal.com](https://www.tushar-aggarwal.com/)\u003c/b\u003e\n- \u003cb\u003e[New Kaggle](https://www.kaggle.com/tagg27)\u003c/b\u003e \n\n\n\n\n\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftushar2704%2Fwine_quality","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftushar2704%2Fwine_quality","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftushar2704%2Fwine_quality/lists"}