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based authentication with Knox.\r\n\r\nThen I add Machine Learning features for demostrate the full workflow of the data mining, including the four stage corresponding to four pages:\r\n1. data management\r\n2. data explore\r\n3. model train\r\n4. prediction\r\n\r\nThe data set is the classic iris data, which is only for demo, and this project is from my interest. so you can reference, but the quality is not assured.\r\n\r\n## features\r\n\r\n* authentication functions\r\n\u003e login from login page\r\n\u003e register your account\r\n\u003e logout from inner page\r\n\r\n* data management\r\n\u003e input iris items\r\n\u003e edit iris items\r\n\u003e delete iris items\r\n\r\n* data explore\r\n\u003e inspect attribute distribution through histogram\r\n\u003e inspect sepal distribution through scatter graph\r\n\u003e inspect petal distribution through scatter graph\r\n\r\n* model train\r\n\u003e input cluster number\r\n\u003e train a cluster model using sklearn-kmeans library\r\n\u003e inspect cluster result through sepal and petal scatter\r\n\r\n* prediction\r\n\u003e input iris sepal and petal attributes\r\n\u003e predict iris cluster\r\n\r\n## technology stack\r\n\r\n\r\ncategory | name | comment\r\n---------|----------|---------\r\n frontend | reactjs | frontend framework\r\n frontend | redux | state management\r\n frontend | react-C3JS | D3 based graph tool\r\n frontend | react-bootstrap | style component library\r\n frontend | data-ui | react data visualization tool\r\n backend | django | backend framework\r\n backend | django-rest-knox | authentication library\r\n backend | djangorestframework | restful framework\r\n backend | sklearn | machine learning tool\r\n\r\n## Quick Start\r\n\r\n```bash\r\n# Install dependencies\r\ncd ./frontend\r\nnpm install\r\n\r\n# Build for production\r\nnpm run build\r\n\r\n\r\n# Install dependencies\r\ncd ../backend\r\npip3 install pipenv\r\npipenv install\r\n\r\n# Serve API on localhost:8000\r\npipenv run python manage.py runserver\r\n```\r\n\r\n### deploy on production environment\r\n\r\n#### run on wsgi server(uwsgi)\r\n\r\n```\r\npipenv run uwsgi --http :9090 --wsgi-file config/wsgi.py --check-static ../frontend/dist/\r\n```\r\n\r\n#### run on nginx + uwsgi\r\n\r\nthere are two points needed to modified:\r\n(1) the root path of default file\r\nroot /root/win10/mine/machine_learning_system/frontend/dist/;\r\n(2) the user role of nginx.conf file\r\nuser root;\r\n\r\n```\r\napt update\r\napt install nginx\r\n\r\npipenv run uwsgi --socket :9090 --wsgi-file config/wsgi.py\r\n\r\ncp -f ./config/default /etc/nginx/sites-available/default\r\ncp -f ./config/nginx.conf /etc/nginx/nginx.conf\r\nnginx\u0026\r\n\r\n```\r\n\r\n## snapshot\r\n\r\n### login page\r\n![avatar](./snapshot/loginpage.png)\r\n\r\n### model train page\r\n![avatar](./snapshot/train.png)\r\n\r\n### prediction page\r\n![avatar](./snapshot/predict.png)\r\n\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffanqingsong%2Fmachine_learning_system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffanqingsong%2Fmachine_learning_system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffanqingsong%2Fmachine_learning_system/lists"}