{"id":316795,"date":"2021-01-22T15:01:07","date_gmt":"2021-01-22T15:01:07","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=316795"},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-29T21:00:00","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=316795","title":{"rendered":"\u0420\u0430\u0437\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0435\u043c \u0438 \u0440\u0430\u0437\u0432\u0451\u0440\u0442\u044b\u0432\u0430\u0435\u043c \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u0443\u044e \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0443 \u0418\u0418 \u0441 Python \u0438 Django"},"content":{"rendered":"\n<div class=\"post__text post__text-html post__text_v1\" id=\"post-content-body\">\u0412\u0437\u043b\u0451\u0442 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 \u043f\u0440\u0438\u0432\u0451\u043b \u043a 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\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440 H2O \u0438\u043b\u0438 KNIME. \u041c\u043d\u043e\u0433\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u0438 \u0434\u0430\u043d\u043d\u044b\u0445, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0445\u043e\u0442\u044f\u0442 \u0441\u044d\u043a\u043e\u043d\u043e\u043c\u0438\u0442\u044c \u0432\u0440\u0435\u043c\u044f, \u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442\u0441\u044f \u044d\u0442\u0438\u043c\u0438 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u043c\u0438, \u0447\u0442\u043e\u0431\u044b \u0431\u044b\u0441\u0442\u0440\u043e \u043f\u0440\u043e\u0442\u043e\u0442\u0438\u043f\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0438 \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438, \u0430 \u043f\u043e\u0437\u0436\u0435 \u0440\u0435\u0448\u0430\u044e\u0442, \u0431\u0443\u0434\u0443\u0442 \u043b\u0438 \u0438\u0445 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href=\"https:\/\/skillfactory.ru\/ml-programma-machine-learning-online?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_ML&amp;utm_term=regular&amp;utm_content=200121\">\u043a\u0443\u0440\u0441\u0430 \u043f\u043e \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u043c\u0443 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044e<\/a> \u0432 \u044d\u0442\u043e\u043c \u043f\u043e\u0441\u0442\u0435 \u043f\u043e\u043a\u0430\u0436\u0435\u043c, \u043a\u0430\u043a \u0431\u044b\u0441\u0442\u0440\u043e \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u0443\u044e \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0443 ML, \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u0443\u044e \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0442\u044c \u0441\u0430\u043c\u044b\u0435 \u043f\u043e\u043f\u0443\u043b\u044f\u0440\u043d\u044b\u0435 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u044b \u043d\u0430 \u043b\u0435\u0442\u0443.<\/p>\n<p>  <a href=\"https:\/\/habr.com\/ru\/company\/skillfactory\/blog\/538320\/\"><\/p>\n<div style=\"text-align:center;\"><img decoding=\"async\" src=\"https:\/\/habrastorage.org\/webt\/dk\/j5\/bv\/dkj5bv6jt0-rnpc3kva4ixvaasa.jpeg\"><\/div>\n<p><\/a><br \/>  <i>\u041f\u043e\u0440\u0442\u0440\u0435\u0442 \u041e\u0440\u043d\u0435\u043b\u043b\u044b \u041c\u0443\u0442\u0438 \u0414\u0436\u043e\u0437\u0435\u0444\u0430 \u0410\u0439\u0435\u0440\u043b\u0435 (\u0444\u0440\u0430\u0433\u043c\u0435\u043d\u0442), \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u043d\u044b\u0439 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430.<\/i><br \/>  <a name=\"habracut\"><\/a><\/p>\n<hr>\n<p>  \u041f\u0440\u0435\u0436\u0434\u0435 \u0432\u0441\u0435\u0433\u043e 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id=\"600938a9c373282e340f4789\" src=\"https:\/\/embedd.srv.habr.com\/iframe\/600938a9c373282e340f4789\"><\/iframe><\/div>\n<p>  \u041a\u0430\u043a \u0432\u0438\u0434\u0438\u0442\u0435, \u0432\u044b \u0432\u044b\u0431\u0438\u0440\u0430\u0435\u0442\u0435 \u0441\u0430\u043c\u044b\u0435 \u043f\u043e\u043f\u0443\u043b\u044f\u0440\u043d\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0441 \u043a\u043e\u043d\u0442\u0440\u043e\u043b\u0438\u0440\u0443\u0435\u043c\u044b\u043c \u0438 \u043d\u0435\u043a\u043e\u043d\u0442\u0440\u043e\u043b\u0438\u0440\u0443\u0435\u043c\u044b\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435\u043c \u0438 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u0442\u0435 \u0438\u0445 \u0432\u0441\u0435\u0433\u043e \u043e\u0434\u043d\u0438\u043c \u0449\u0435\u043b\u0447\u043a\u043e\u043c \u043c\u044b\u0448\u0438. \u0415\u0441\u043b\u0438 \u0445\u043e\u0447\u0435\u0442\u0441\u044f \u043f\u043e\u043f\u0440\u043e\u0431\u043e\u0432\u0430\u0442\u044c \u0442\u0435 \u0436\u0435 \u043d\u0430\u0431\u043e\u0440\u044b \u0434\u0430\u043d\u043d\u044b\u0445, \u0447\u0442\u043e \u0438 \u0432 \u0432\u0438\u0434\u0435\u043e, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 <a href=\"https:\/\/drive.google.com\/drive\/folders\/12G4tPHZyHEYErSqATW-KRVcCeL80ztzb?usp=sharing\">\u0441\u043a\u0430\u0447\u0430\u0442\u044c \u0438\u0445 \u0437\u0434\u0435\u0441\u044c.<\/a>\u00a0<\/p>\n<p>  \u041a\u0440\u0443\u0442\u043e, \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a \u044d\u0442\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c! \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u043e, \u0432\u044b \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442\u0435 \u043d\u0430 \u043d\u043e\u0443\u0442\u0431\u0443\u043a\u0435 \u0438\u043b\u0438 \u043d\u0430 \u043e\u0431\u043b\u0430\u0447\u043d\u043e\u043c \u0441\u0435\u0440\u0432\u0435\u0440\u0435, \u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b <a href=\"https:\/\/www.digitalocean.com\/\">\u0441\u0435\u0440\u0432\u0435\u0440 Digital Ocean<\/a>, \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043d\u0430 Nginx \u0438 Gunicorn, \u0438 \u044f \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u043e \u0440\u0430\u0441\u0441\u043a\u0430\u0436\u0443 \u043e \u0442\u043e\u043c, \u043a\u0430\u043a \u0440\u0430\u0437\u0432\u0451\u0440\u0442\u044b\u0432\u0430\u0442\u044c \u0435\u0433\u043e: \u044d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u043f\u043e\u043b\u0435\u0437\u043d\u043e, \u043a\u043e\u0433\u0434\u0430 \u043d\u0443\u0436\u043d\u043e \u0440\u0430\u0437\u0432\u0451\u0440\u0442\u044b\u0432\u0430\u0442\u044c \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u044f \u043d\u0430 Linux-\u0441\u0435\u0440\u0432\u0435\u0440\u0435 \u043a\u0430\u0436\u0434\u044b\u0439 \u0434\u0435\u043d\u044c.<\/p>\n<p>  \u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u043d\u0430\u0447\u043d\u0451\u043c \u043d\u0430\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u0442\u044c \u043d\u0430\u0448\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0441 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043e\u043a:<\/p>\n<pre><code class=\"bash\">sudo apt-get update sudo apt-get install python3-pip python3-dev libpq-dev postgresql postgresql-contrib nginx # install python postgresql and nginx # now install a postgresql database sudo -u postgres psql  #log in to psql CREATE DATABASE myproject;   #give a name to your database project  CREATE USER myprojectuser WITH PASSWORD 'password';   # create user and password ALTER ROLE myprojectuser SET client_encoding TO 'utf8'; ALTER ROLE myprojectuser SET default_transaction_isolation TO 'read committed'; ALTER ROLE myprojectuser SET timezone TO 'UTC'; \\q # log out  # cd to the folder where you want to keep your environment sudo -H pip3 install env virtualenv env # create a virtual environment for this project source myprojectenv\/bin\/activate pip install django gunicorn psycopg2 #install django and gunicorn  # now cd to the directory where you want to create your project, in my case cd \/www\/var django-admin.py startproject trainyourmodel #cd trainyourmodel django-admin startapp portal # create an app for the views of this app #cd trainyourmodel again and nano settings.py <\/code><\/pre>\n<p>  <\/p>\n<pre><code class=\"python\"># now we configure our settings.py file  DATABASES = {     'default': {         'ENGINE': 'django.db.backends.postgresql_psycopg2',         'NAME': 'myproject',         'USER': 'myprojectuser',         'PASSWORD': 'password',         'HOST': 'localhost',         'PORT': '',     } }   STATIC_URL = '\/static\/' STATIC_ROOT = os.path.join(BASE_DIR, 'static\/') <\/code><\/pre>\n<p>  \u041f\u043e\u0441\u043b\u0435 \u044d\u0442\u043e\u0433\u043e \u0432\u0441\u0451, \u0447\u0442\u043e \u0412\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c, \u2014 \u044d\u0442\u043e \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0412\u0430\u0448\u0443 \u043a\u043e\u043d\u0444\u0438\u0433\u0443\u0440\u0430\u0446\u0438\u044e \u0434\u043b\u044f \u0441\u0435\u0440\u0432\u0438\u0441\u0430 Gunicorn:\u00a0<\/p>\n<pre><code class=\"bash\">sudo nano \/etc\/systemd\/system\/gunicorn.service<\/code><\/pre>\n<p>  <\/p>\n<pre><code class=\"plaintext\">[Unit] Description=gunicorn daemon After=network.target  [Service] User=root Group=www-data WorkingDirectory=\/var\/www\/trainyourmodel ExecStart=\/opt\/env\/bin\/gunicorn --access-logfile - --workers 3 --bind unix:\/var\/www\/trainyourmodel\/trainyourmodel.sock trainyourmodel.wsgi:application<\/code><\/pre>\n<p>  <\/p>\n<pre><code class=\"bash\">sudo systemctl start gunicorn sudo systemctl enable gunicorn sudo systemctl status gunicorn  gunicorn --bind 0.0.0.0:8000 trainyourmodel.wsgi <\/code><\/pre>\n<p>  \u0418 \u0434\u043b\u044f Nginx:\u00a0<\/p>\n<pre><code class=\"bash\">sudo nano \/etc\/nginx\/sites-available\/trainyourmodel<\/code><\/pre>\n<p>  <\/p>\n<pre><code class=\"nginx\">server {     listen 80;     server_name server_domain_or_IP;      location = \/favicon.ico { access_log off; log_not_found off; }     location \/static\/ {         root \/var\/www\/trainyourmodel;     }      location \/ {         include proxy_params;         proxy_pass http:\/\/unix:\/var\/www\/trainyourmodel\/trainyourmodel.sock;     } } #copy the same file also in sudo nano \/etc\/nginx\/sites-enabled\/trainyourmodel <\/code><\/pre>\n<p>  <\/p>\n<pre><code class=\"bash\">sudo systemctl restart nginx sudo systemctl status nginx<\/code><\/pre>\n<p>  \u0422\u0435\u043f\u0435\u0440\u044c \u0432\u0441\u0451 \u0433\u043e\u0442\u043e\u0432\u043e, \u0438 \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u043f\u0440\u043e\u0441\u0442\u043e \u043a\u043b\u043e\u043d\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043a\u043e\u0434 \u0438\u0437 \u043c\u043e\u0435\u0433\u043e <a href=\"https:\/\/github.com\/dirusali\/trainyourmodel\/tree\/release-1.2\">GitHub-\u0440\u0435\u043f\u043e\u0437\u0438\u0442\u043e\u0440\u0438\u044f<\/a> (\u0442\u0435\u043a\u0443\u0449\u0430\u044f \u0432\u0435\u0442\u043a\u0430 \u2014 releasee-1.2) \u0438 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c \u0432\u0441\u0435 \u043d\u0443\u0436\u043d\u044b\u0435 \u043f\u0430\u043a\u0435\u0442\u044b, \u043f\u0440\u043e\u0441\u0442\u043e \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0443:\u00a0<\/p>\n<pre><code class=\"bash\">pip -install -r requirements.txt<\/code><\/pre>\n<p>  \u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435 \u0442\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0445\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0438 \u0432\u0430\u0448\u0435\u0439 \u0441\u0438\u0441\u0442\u0435\u043c\u044b, \u0447\u0442\u043e\u0431\u044b \u0443\u0431\u0435\u0434\u0438\u0442\u044c\u0441\u044f, \u0447\u0442\u043e \u043e\u043d\u0430 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u044f\u043c (\u0441\u0442\u043e\u044f\u0442 \u043d\u0443\u0436\u043d\u044b\u0435 \u0432\u0435\u0440\u0441\u0438\u0438 Python, Django \u0438 \u0442. \u0434.). \u041f\u043e\u0441\u043b\u0435 \u044d\u0442\u043e\u0433\u043e \u0432\u044b \u0443\u0432\u0438\u0434\u0438\u0442\u0435 \u0442\u0438\u043f\u0438\u0447\u043d\u044b\u0435 \u043f\u0430\u043f\u043a\u0438 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u043f\u0440\u043e\u0435\u043a\u0442\u0430 Django, \u0430 \u0438\u043c\u0435\u043d\u043d\u043e:<\/p>\n<ul>\n<li>\u041f\u0430\u043f\u043a\u0430 \u043f\u0440\u043e\u0435\u043a\u0442\u0430 <code>trainyourmodel<\/code> \u0441 \u0444\u0430\u0439\u043b\u043e\u043c <code>settings.py<\/code>.<\/li>\n<li>\u041f\u0430\u043f\u043a\u0430 \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u044f \u043f\u043e\u0434 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0435\u043c Portral c \u0444\u0430\u0439\u043b\u043e\u043c <code>views.py<\/code>.<\/li>\n<li>\u041f\u0430\u043f\u043a\u0430 \u0448\u0430\u0431\u043b\u043e\u043d\u043e\u0432 \u0434\u043b\u044f \u043a\u043e\u0434\u0430 .html.<\/li>\n<\/ul>\n<p>  \u0415\u0441\u043b\u0438 \u0445\u043e\u0447\u0435\u0442\u0441\u044f \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u043a\u0440\u0430\u0441\u0438\u0432\u044b\u043c, \u043d\u0443\u0436\u043d\u043e \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u043f\u0430\u043f\u043a\u0443 \u0441\u043e \u0441\u0442\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u043c\u0438 \u0444\u0430\u0439\u043b\u0430\u043c\u0438: \u0444\u0430\u0439\u043b\u0430\u043c\u0438 \u043e\u0444\u043e\u0440\u043c\u043b\u0435\u043d\u0438\u044f \u0444\u0440\u043e\u043d\u0442\u0435\u043d\u0434\u0430 \u0432\u0430\u0448\u0435\u0433\u043e \u0448\u0430\u0431\u043b\u043e\u043d\u0430, \u043d\u043e \u044d\u0442\u043e \u0432\u044b\u0445\u043e\u0434\u0438\u0442 \u0437\u0430 \u0440\u0430\u043c\u043a\u0438 \u0441\u0442\u0430\u0442\u044c\u0438. \u0415\u0441\u043b\u0438 \u0432\u044b \u043d\u0435 \u0437\u043d\u0430\u043a\u043e\u043c\u044b \u0441 Django, \u0432\u0430\u043c \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0437\u043d\u0430\u0442\u044c, \u0447\u0442\u043e \u044d\u0442\u043e\u0442 \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a \u0440\u0430\u0437\u0434\u0435\u043b\u044f\u0435\u0442 \u0444\u0430\u0439\u043b \u0431\u044d\u043a\u0435\u043d\u0434\u0430 \u0441 \u0442\u0430\u0431\u043b\u0438\u0446\u0430\u043c\u0438 \u0432\u0430\u0448\u0435\u0439 \u0431\u0430\u0437\u044b \u0434\u0430\u043d\u043d\u044b\u0445 (\u044d\u0442\u0438 \u0442\u0430\u0431\u043b\u0438\u0446\u044b \u043d\u0430\u0437\u044b\u0432\u0430\u044e\u0442\u0441\u044f \u043c\u043e\u0434\u0435\u043b\u044f\u043c\u0438), \u0438 \u0444\u0430\u0439\u043b\u044b \u0444\u0440\u043e\u043d\u0442\u0435\u043d\u0434\u0430, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043d\u0430\u0437\u044b\u0432\u0430\u044e\u0442\u0441\u044f \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u044f\u043c\u0438 \u0438 \u0448\u0430\u0431\u043b\u043e\u043d\u0430\u043c\u0438.\u00a0<\/p>\n<p>  \u042f \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u043b \u0431\u0430\u0437\u0443 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u0442\u043e\u0442 \u0441\u043b\u0443\u0447\u0430\u0439, \u0435\u0441\u043b\u0438 \u0437\u0430\u0445\u043e\u0447\u0443 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0435\u0451 \u0432 \u0431\u0443\u0434\u0443\u0449\u0435\u043c, \u043d\u043e \u043f\u043e\u043a\u0430 \u043c\u044b \u043d\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c \u043d\u0438\u043a\u0430\u043a\u0443\u044e \u0431\u0430\u0437\u0443, \u0442\u0430\u043a \u043a\u0430\u043a \u0431\u0443\u0434\u0435\u043c \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438 \u043d\u0430 \u043b\u0435\u0442\u0443, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u044f \u043f\u043e\u043c\u0435\u0441\u0442\u0438\u043b \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0432 \u043f\u0430\u043f\u043a\u0443 \u043f\u043e\u0434 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0435\u043c <code>portal<\/code>, \u0438 \u0442\u0443\u0434\u0430 \u043c\u044b \u0440\u0430\u0437\u043c\u0435\u0441\u0442\u0438\u043c \u0432\u0435\u0441\u044c \u043a\u043e\u0434, \u0437\u0430\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0439 \u043d\u0430\u0448\u0438\u043c\u0438 \u043c\u043e\u0434\u0435\u043b\u044f\u043c\u0438.\u00a0<\/p>\n<p>  \u041f\u0440\u0435\u0436\u0434\u0435 \u0432\u0441\u0435\u0433\u043e \u043c\u044b \u0438\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u0443\u0435\u043c \u0432\u0441\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043d\u0430\u043c \u043f\u043e\u043d\u0430\u0434\u043e\u0431\u044f\u0442\u0441\u044f \u0434\u043b\u044f \u043d\u0430\u0448\u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f \u043a\u0440\u0430\u0441\u0438\u0432\u044b\u0445 \u0433\u0440\u0430\u0444\u0438\u043a\u043e\u0432 \u043e\u043d\u043b\u0430\u0439\u043d (Pandas, NumPy, sci-kit-learn, TensorFlow, Keras, Seaborn, Plotly \u0438 \u0442. \u0434.).\u00a0<\/p>\n<pre><code class=\"python\"># This Python file uses the following encoding in format utf-8 import plotly import plotly.express as px import plotly.graph_objects as go from django.core.mail import send_mail, BadHeaderError, EmailMessage  import io import csv from io import BytesIO import base64 import functools import operator import random import unicodedata import datetime import pandas as pd import seaborn as sns import numpy as np from efficient_apriori import apriori  from sklearn import metrics from sklearn.cross_validation import train_test_split from sklearn.metrics import classification_report, confusion_matrix  from sklearn.ensemble import RandomForestClassifier from sklearn.cluster import KMeans from sklearn.svm import SVC from sklearn.grid_search import GridSearchCV from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.linear_model import LinearRegression from sklearn.naive_bayes import GaussianNB  import tensorflow as tf from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout from tensorflow.keras.optimizers import RMSprop  from django.template.loader import get_template, render_to_string from django.shortcuts import render from django.views.generic import TemplateView from django.views.generic.detail import DetailView from django.views import View from django.db.models import Count from django.shortcuts import redirect  from portal.models import Subscribe  from el_pagination.views import AjaxListView from django.http import Http404, JsonResponse, HttpResponse, HttpResponseRedirect, HttpResponseServerError from django.db.models import Q from django.contrib.staticfiles.templatetags.staticfiles import static<\/code><\/pre>\n<p>  \u0422\u0435\u043f\u0435\u0440\u044c \u043c\u044b \u0433\u043e\u0442\u043e\u0432\u044b \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e, \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u044f \u043d\u0430\u0437\u0432\u0430\u043b <code>upload_csv<\/code>, \u043e\u043d\u0430 \u0431\u0435\u0440\u0451\u0442 CSV-\u0444\u0430\u0439\u043b, \u0437\u0430\u0433\u0440\u0443\u0436\u0435\u043d\u043d\u044b\u0439 \u0432 \u043d\u0430\u0448\u0438 \u0448\u0430\u0431\u043b\u043e\u043d\u044b, \u0438 \u0441\u043e\u0437\u0434\u0430\u0441\u0442 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u043d\u044b\u0445 Pandas. \u041c\u044b \u0431\u0443\u0434\u0435\u043c \u0434\u0435\u043b\u0430\u0442\u044c \u0441\u0430\u043c\u044b\u0435 \u0440\u0430\u0437\u043d\u044b\u0435 \u0432\u0435\u0449\u0438 \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u044d\u0442\u043e\u0433\u043e \u0444\u0430\u0439\u043b\u0430, \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044f. \u041a\u043e\u0433\u0434\u0430 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044c \u043a\u043b\u0438\u043a\u0430\u0435\u0442 \u043f\u043e \u043e\u043f\u0446\u0438\u044f\u043c \u0444\u0440\u043e\u043d\u0442\u0435\u043d\u0434\u0430, \u043c\u044b \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u043c \u043e\u043f\u0446\u0438\u0438 \u0432 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439, \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f, \u0432 \u0441\u0432\u043e\u044e \u043e\u0447\u0435\u0440\u0435\u0434\u044c, \u043c\u043e\u0436\u0435\u0442 \u043f\u0440\u0438\u043d\u0438\u043c\u0430\u0442\u044c \u044d\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f:<\/p>\n<ul>\n<li><code>_plot<\/code> \u2014 \u0441\u0442\u0440\u043e\u0438\u043c \u0433\u0440\u0430\u0444\u0438\u043a \u0440\u0430\u0441\u0441\u0435\u044f\u043d\u0438\u044f \u0438\u043b\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443\u044e \u043a\u0430\u0440\u0442\u0443.<\/li>\n<li><code>_cate<\/code> \u2014 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u0410\u043f\u0440\u0438\u043e\u0440\u0438 \u0434\u043b\u044f \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u0430\u043b\u044c\u043d\u044b\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445.<\/li>\n<li><code>_unsuper<\/code> \u2014 \u0432\u044b\u0431\u0438\u0440\u0430\u0435\u043c \u043e\u0434\u0438\u043d \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c ML \u0431\u0435\u0437 \u0443\u0447\u0438\u0442\u0435\u043b\u044f.<\/li>\n<li><code>_super<\/code> \u2014 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u0441 \u0443\u0447\u0438\u0442\u0435\u043b\u0435\u043c.<\/li>\n<\/ul>\n<p>  \u041a\u043e\u0433\u0434\u0430 \u043c\u044b \u043a\u043b\u0438\u043a\u0430\u0435\u043c \u043f\u043e \u043e\u043f\u0446\u0438\u0438, \u0442\u0438\u043f \u043a\u043e\u043d\u0442\u0440\u043e\u043b\u0438\u0440\u0443\u0435\u043c\u043e\u0433\u043e \u0438\u043b\u0438 \u043d\u0435\u043a\u043e\u043d\u0442\u0440\u043e\u043b\u0438\u0440\u0443\u0435\u043c\u043e\u0433\u043e \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 \u0445\u0440\u0430\u043d\u0438\u0442\u0441\u044f \u0432 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 &#8216;algo&#8217;, \u0437\u0430\u0442\u0435\u043c \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0442\u043e\u0433\u043e, \u043a\u0430\u043a\u043e\u0439 \u0442\u0438\u043f \u0432\u044b\u0431\u0440\u0430\u043d, \u043c\u044b \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u0434\u0440\u0443\u0433\u0443\u044e \u043c\u043e\u0434\u0435\u043b\u044c \u0438\u0437 \u043e\u043f\u0446\u0438\u0439 Scikit-Learn, \u0432\u0441\u0451 \u044d\u0442\u043e \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0442\u0430\u043a:\u00a0<\/p>\n<pre><code class=\"python\">def upload_csv(request):\t \tif request.method == &quot;POST&quot;: \t    file = request.FILES['csv_file'] \t    df = pd.read_csv(file) \t    t = [] \t    names = list(df.head(0)) \t    df_target = df \t    grafica = request.POST['graficas'] \t    if request.POST['submit'] == '_plot':  \t        if grafica == &quot;scatter&quot;: \t\t        fig = px.scatter_matrix(df_target) \t\t        graph_div = plotly.offline.plot(fig, auto_open = False, output_type=&quot;div&quot;) \t\t        context = {'graph_div': graph_div} \t\t        return render (request, &quot;plottings.html&quot;, context) \t        if grafica == &quot;heatmap&quot;: \t\t        fig = go.Figure(data=go.Heatmap(z = df.corr(),x=names,y=names)) \t\t        graph_div = plotly.offline.plot(fig, auto_open = False, output_type=&quot;div&quot;) \t\t        context = {'graph_div': graph_div} \t\t        return render (request, &quot;heatmap.html&quot;, context) \t    if request.POST['submit'] == '_cate': \t\t    h = list(df.head(0)) \t\t    t.append(list(h)) \t\t    for i in range(0, len(df)): \t\t\t    a = list(df.iloc[i]) \t\t\t    t.append(a) \t\t    itemsets, rules = apriori(t, min_support=0.2,  min_confidence=1) \t\t    context = {'rules': rules}           \t   \t\t    return render(request, &quot;apriori.html&quot;, context)\t \t    lon = len(list(df.head(0))) \t    header = list(df[0:lon]) \t    target = header[lon-1] \t    y = np.array(df[target]) \t    df.drop(target,axis=1,inplace=True) \t    X = df.values \t    Z = X \t    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=101)  \t    graph_div = '' \t    pred = '' \t    algo = request.POST['algoritmo']\t \t    grafica = request.POST['graficas'] \t    clasi = request.POST['clasi'] \t \t    if request.POST['submit'] == '_unsuper': \t\t\t    Nc = range(1, 20) \t\t\t    kmeans = [KMeans(n_clusters=i) for i in Nc] \t\t\t    score = [kmeans[i].fit(Z).score(Z) for i in range(len(kmeans))] \t\t\t    fig = go.Figure(data=go.Scatter(y=score, x=list(Nc), mode='markers')) \t\t\t    fig.update_xaxes(title=&quot;Number of Clusters&quot;) \t\t\t    fig.update_yaxes(title=&quot;Error&quot;) \t\t\t    fig.update_layout(autosize=False, width=800,height=500) \t\t\t    graph = plotly.offline.plot(fig, auto_open = False, output_type=&quot;div&quot;)            \t\t\t    nclusters = int(request.POST['clusters']) \t\t\t    kmeans = KMeans(n_clusters=nclusters) \t\t\t    model = kmeans.fit(Z) \t\t\t    labels = kmeans.labels_ \t\t\t    resultados = [] \t\t\t    for i in labels: \t\t                    resultados.append(i)\t \t\t\t    with open('\/var\/www\/trainyourmodel\/static\/labels.csv', 'w', ) as myfile: \t\t\t\t    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL) \t\t\t\t    for word in resultados: \t\t\t\t\t    wr.writerow([word])\t \t\t\t    context = {'graph': graph, 'labels': labels}            \t\t\t    return render(request, &quot;kmeans.html&quot;, context)\t\t\t\t\t \t    if request.POST['submit'] == '_super': \t \t\t    if algo == 'Linear Regression': \t\t\t    lm = LinearRegression() \t\t\t    model = lm.fit(X_train,y_train) \t\t\t    pred = lm.predict(X_test) \t\t\t    mae = metrics.mean_absolute_error(y_test,pred) \t\t\t    mse = metrics.mean_squared_error(y_test,pred) \t\t\t    rmse = np.sqrt(metrics.mean_squared_error(y_test,pred)) \t\t\t    coef = pd.DataFrame(lm.coef_, df.columns, columns=['Coefficient'])\t \t\t\t    fig = go.Figure(data=go.Scatter(x=y_test, y=pred, mode='markers')) \t\t\t    fig.update_xaxes(title=&quot;Test Sample&quot;) \t\t\t    fig.update_yaxes(title=&quot;Predictions&quot;) \t\t\t    fig.update_layout(autosize=False, width=800,height=500) \t\t\t    scatter = plotly.offline.plot(fig, auto_open = False, output_type=&quot;div&quot;) \t\t\t    resultados = [] \t\t\t    for i in pred: \t\t                    resultados.append(i)\t \t\t\t    with open('\/var\/www\/trainyourmodel\/static\/pred.csv', 'w', ) as myfile: \t\t\t\t    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL) \t\t\t\t    for word in resultados: \t\t\t\t\t    wr.writerow([word])\t \t\t\t    context = {'pred':pred, 'scatter': scatter, 'mae': mae, 'mse': mse, 'rmse': rmse, 'coef': coef}            \t\t\t    return render(request, &quot;scatter.html&quot;, context)\t \t\t    if algo == 'Support Vector Machine': \t\t\t    param_grid = {'C':[0.1,1,10,100,1000],'gamma':[1,0.1,0.01,0.001,0.0001]} \t\t\t    grid = GridSearchCV(SVC(),param_grid,verbose=3) \t\t\t    model = grid.fit(X_train,y_train) \t\t\t    pred = grid.predict(X_test) \t\t\t    mae = metrics.mean_absolute_error(y_test,pred) \t\t\t    mse = metrics.mean_squared_error(y_test,pred) \t\t\t    rmse = np.sqrt(metrics.mean_squared_error(y_test,pred)) \t\t\t    matrix = print(confusion_matrix(y_test,pred)) \t\t\t    report = print(classification_report(y_test,pred)) \t\t    if algo == 'K-Nearest Neighbor': \t\t\t    knn = KNeighborsClassifier(n_neighbors=1) \t\t\t    model = knn.fit(X_train,y_train) \t\t\t    pred = knn.predict(X_test) \t\t\t    mae = metrics.mean_absolute_error(y_test,pred) \t\t\t    mse = metrics.mean_squared_error(y_test,pred) \t\t\t    rmse= np.sqrt(metrics.mean_squared_error(y_test,pred)) \t\t\t    matrix = confusion_matrix(y_test,pred) \t\t\t    report = classification_report(y_test,pred)\t \t\t    if algo == 'Naive Bayes': \t\t\t    gnb = GaussianNB() \t\t\t    pred = gnb.fit(X_train, y_train).predict(X_test) \t\t\t    mae = metrics.mean_absolute_error(y_test,pred) \t\t\t    mse = metrics.mean_squared_error(y_test,pred) \t\t\t    rmse = np.sqrt(metrics.mean_squared_error(y_test,pred)) \t\t\t    matrix = confusion_matrix(y_test,pred) \t\t\t    report = classification_report(y_test,pred)\t \t\t    if algo == 'Decision Trees': \t\t\t    dtree = DecisionTreeClassifier() \t\t\t    model = dtree.fit(X_train,y_train) \t\t\t    pred = dtree.predict(X_test) \t\t\t    mae = metrics.mean_absolute_error(y_test,pred) \t\t\t    mse = metrics.mean_squared_error(y_test,pred) \t\t\t    rmse = np.sqrt(metrics.mean_squared_error(y_test,pred)) \t\t\t    matrix = confusion_matrix(y_test,pred) \t\t\t    report = classification_report(y_test,pred) \t\t    if algo == 'Random Forest': \t\t\t    forest = RandomForestClassifier(n_estimators=200) \t\t\t    model = forest.fit(X_train,y_train) \t\t\t    pred = forest.predict(X_test) \t\t\t    mae = metrics.mean_absolute_error(y_test,pred) \t\t\t    mse = metrics.mean_squared_error(y_test,pred) \t\t\t    rmse = np.sqrt(metrics.mean_squared_error(y_test,pred)) \t\t\t    matrix = confusion_matrix(y_test,pred)\t \t\t\t    report = classification_report(y_test,pred)\t \t\t    resultados = [] \t\t    for i in pred: \t\t\t    resultados.append(i)\t \t\t    with open('\/var\/www\/trainyourmodel\/static\/pred.csv', 'w', ) as myfile: \t\t\t    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL) \t\t\t    for word in resultados: \t\t\t            wr.writerow([word])\t \t\t    context = {'matrix00': matrix[0][0], 'matrix01': matrix[0][1], 'matrix10': matrix[1][0], 'matrix11': matrix[1][1], 'mae': mae, 'mse': mse, 'rmse': rmse, 'f1': report[0:52], 'f2': report[54:106], 'f3': report[107:159], 'f4': report[160:213]}            \t\t    return render(request, &quot;upload_csv.html&quot;, context)\t<\/code><\/pre>\n<p>  \u041d\u0430\u043a\u043e\u043d\u0435\u0446, \u0435\u0441\u043b\u0438 \u0432\u044b \u043d\u0430\u0436\u043c\u0451\u0442\u0435 \u043d\u0430 \u043a\u043d\u043e\u043f\u043a\u0443 \u00abdeep learning\u00bb \u0432 \u0432\u0435\u0440\u0445\u043d\u0435\u0439 \u0447\u0430\u0441\u0442\u0438 \u0441\u0430\u0439\u0442\u0430, \u0442\u043e \u043f\u0435\u0440\u0435\u0439\u0434\u0451\u0442\u0435 \u043d\u0430 \u0434\u0440\u0443\u0433\u043e\u0439 \u044d\u043a\u0440\u0430\u043d, \u0432\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u043d\u0430 \u044d\u0442\u043e\u043c \u044d\u043a\u0440\u0430\u043d\u0435 \u0431\u0435\u0440\u0443\u0442\u0441\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u0435\u0439 \u043f\u043e\u0434 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0435\u043c <code>neural<\/code>, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0431\u0443\u0434\u0435\u0442 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0441 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u043c\u0438 \u0441\u0435\u0442\u044f\u043c\u0438. \u0415\u0441\u0442\u044c \u0434\u0432\u0430 \u0442\u0438\u043f\u0430 \u0441\u0435\u0442\u0435\u0439 \u043d\u0430 \u0432\u044b\u0431\u043e\u0440: \u00abmean-squared-error\u00bb \u0438\u043b\u0438 \u00abbinary cross-entropy\u00bb. \u041a\u0430\u043a \u0442\u043e\u043b\u044c\u043a\u043e \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u043f\u0440\u043e\u0432\u0435\u0440\u044f\u0435\u0442 \u0442\u0438\u043f \u0441\u0435\u0442\u0438 \u0434\u043b\u044f \u0437\u0430\u043f\u0443\u0441\u043a\u0430, \u043e\u043d\u0430 \u0431\u0435\u0440\u0451\u0442 \u044d\u043f\u043e\u0445\u0438, \u043f\u0430\u043a\u0435\u0442, \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043f\u043b\u043e\u0442\u043d\u043e\u0441\u0442\u0438 \u0438 \u0432\u044b\u0432\u043e\u0434\u0438\u0442 \u043d\u0430 \u044d\u043a\u0440\u0430\u043d \u043c\u043e\u0434\u0435\u043b\u044c Keras, \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u044f \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432, \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442\u0441\u044f \u0447\u0442\u043e-\u0442\u043e \u0432\u0440\u043e\u0434\u0435 \u044d\u0442\u043e\u0433\u043e:<\/p>\n<pre><code class=\"python\">def neural(request):\t \tif request.method == &quot;POST&quot;: \t    file = request.FILES['file'] \t    df = pd.read_csv(file) \t    df_target = df \t    lon = len(list(df.head(0))) \t    dim = len(list(df.head(0))) -1\t \t    header = list(df[0:lon]) \t    target = header[dim] \t    y = np.array(df[target]) \t    df.drop(target,axis=1,inplace=True) \t    X = df.values \t    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=101)  \t    graph_div = '' \t    pred = '' \t    red = request.POST['red'] \t    dense = int(request.POST['dense'])\t \t    epochs = int(request.POST['epochs']) \t    batch = int(request.POST['batch']) \t    #nodes = int(request.POST['categories']) \t    if red == 'Binary Cross-Entropy': \t\t\t    model= Sequential() \t\t\t    model.add(Dense(dense, input_dim=dim, activation='relu')) \t\t\t    model.add(Dense(dim, activation='relu')) \t\t\t    model.add(Dense(1, activation='sigmoid')) \t\t\t    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) \t\t\t    model.fit(x=X_train,y=y_train,batch_size=batch, epochs=epochs,shuffle=True) \t\t\t    pred = model.predict(X_test)\t \t\t\t    _, accuracy = model.evaluate(X_test, y_test)\t\t\t     \t\t\t    accu = (accuracy*100) \t\t\t    resultados = [] \t\t\t    for i in pred: \t\t                    resultados.append(i[0])\t \t\t\t    with open('\/var\/www\/trainyourmodel\/static\/pred.csv', 'w', ) as myfile: \t\t\t\t    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL) \t\t\t\t    for word in resultados: \t\t\t\t\t    wr.writerow([word])\t \t\t\t    context = {'accu': accu, 'pred': pred}            \t\t\t    return render(request, &quot;neural.html&quot;, context)\t\t\t\t\t\t \t    if red == 'Mean Squared Error': \t\t\t    model = Sequential() \t\t\t    model.add(Dense(dense, input_dim=dim, activation='relu')) \t\t\t    model.add(Dense(1, activation='linear')) \t\t\t    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy']) \t\t\t    model.fit(x=X,y=y,batch_size=batch, epochs=epochs,shuffle=True) \t\t\t    pred = model.predict(X_test) \t\t\t    _, accuracy = model.evaluate(X_test, y_test) \t\t\t    resultados = [] \t\t\t    for i in pred: \t\t                    resultados.append(i[0])\t \t\t\t    with open('\/var\/www\/trainyourmodel\/static\/pred.csv', 'w', ) as myfile: \t\t\t\t    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL) \t\t\t\t    for word in resultados: \t\t\t\t\t    wr.writerow([word]) \t\t\t    accu = (accuracy*100)\t \t\t\t    context = {'accu': accu, 'pred': pred}            \t\t\t    return render(request, &quot;neural.html&quot;, context)<\/code><\/pre>\n<p>  <font color=\"09b744\"><\/p>\n<h2>\u0417\u0430\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435<\/h2>\n<p><\/font><br \/>  \u0412 \u044d\u0442\u043e\u043c \u043f\u0440\u043e\u0435\u043a\u0442\u0435 \u043c\u044b \u0443\u0432\u0438\u0434\u0435\u043b\u0438, \u043a\u0430\u043a \u0441\u0440\u0430\u0432\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u043b\u0435\u0433\u043a\u043e \u0432\u043d\u0435\u0434\u0440\u0438\u0442\u044c \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043d\u0430 \u0441\u0435\u0440\u0432\u0435\u0440\u0435 Linux \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e Django, \u043c\u044b \u043c\u043e\u0433\u043b\u0438 \u0431\u044b \u0442\u0430\u043a\u0436\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c Flask (\u0435\u0449\u0435 \u043e\u0434\u043d\u043e \u043f\u043e\u043f\u0443\u043b\u044f\u0440\u043d\u043e\u0435 \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u043d\u0430 Python) \u0438\u043b\u0438 \u043c\u043d\u043e\u0433\u0438\u0435 \u0434\u0440\u0443\u0433\u0438\u0435 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438. \u0414\u0435\u043b\u043e \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e, \u0435\u0441\u043b\u0438 \u0432\u044b \u043f\u0440\u043e\u0441\u0442\u043e \u0445\u043e\u0442\u0438\u0442\u0435, \u0447\u0442\u043e\u0431\u044b \u0432\u0430\u0448\u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u0440\u0430\u0431\u043e\u0442\u0430\u043b\u0430 \u043d\u0430 \u0444\u0440\u043e\u043d\u0442\u0435\u043d\u0434\u0435 \u0441\u0430\u0439\u0442\u0430, \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u0438 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\u043f\u043e\u043d\u0440\u0430\u0432\u0438\u043b\u0441\u044f \u044d\u0442\u043e\u0442 \u043f\u0440\u043e\u0435\u043a\u0442 \u0438 \u043f\u043e\u043d\u0440\u0430\u0432\u0438\u043b\u043e\u0441\u044c \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0435\u0433\u043e!  <\/p>\n<hr>\n<p>  <a href=\"https:\/\/skillfactory.ru\/?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_banner&amp;utm_term=regular&amp;utm_content=habr_banner\"><\/p>\n<div style=\"text-align:center;\"><img decoding=\"async\" src=\"https:\/\/habrastorage.org\/webt\/io\/qw\/4m\/ioqw4mpr6--zjzxym0zijkgkqu8.png\" alt=\"image\"><\/div>\n<p><\/a><\/p>\n<ul>\n<li><a href=\"https:\/\/skillfactory.ru\/dstpro?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_DSPR&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f Data Scientist<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/dataanalystpro?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_DAPR&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f Data Analyst<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/ml-programma-machine-learning-online?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_ML&amp;utm_term=regular&amp;utm_content=220121\">\u041a\u0443\u0440\u0441 \u043f\u043e Machine Learning<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/math_and_ml?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_MATML&amp;utm_term=regular&amp;utm_content=220121\">\u041a\u0443\u0440\u0441 \u00ab\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u043a\u0430 \u0438 Machine Learning \u0434\u043b\u044f Data Science\u00bb<\/a><\/li>\n<\/ul>\n<p>  <\/p>\n<div class=\"spoiler\" role=\"button\" tabindex=\"0\">                         <b class=\"spoiler_title\">\u0414\u0440\u0443\u0433\u0438\u0435 \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u0438 \u0438 \u043a\u0443\u0440\u0441\u044b<\/b>                         <\/p>\n<div class=\"spoiler_text\"><strong>\u041f\u0420\u041e\u0424\u0415\u0421\u0421\u0418\u0418<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/skillfactory.ru\/frontend?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_FR&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f Frontend-\u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/webdev?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_WEBDEV&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f \u0412\u0435\u0431-\u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/cybersecurity?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_HACKER&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f \u042d\u0442\u0438\u0447\u043d\u044b\u0439 \u0445\u0430\u043a\u0435\u0440<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/cplus?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_CPLUS&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f C++ \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/java?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_JAVA&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f Java-\u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/game-dev?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_GAMEDEV&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f \u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a \u0438\u0433\u0440 \u043d\u0430 Unity<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/iosdev?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_IOSDEV&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f iOS-\u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a \u0441 \u043d\u0443\u043b\u044f<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/android?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_ANDR&amp;utm_term=regular&amp;utm_content=220121\">\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044f Android-\u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a \u0441 \u043d\u0443\u043b\u044f<\/a><\/li>\n<\/ul>\n<p>  <\/p>\n<hr>\n<p>  <strong>\u041a\u0423\u0420\u0421\u042b<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/skillfactory.ru\/javascript?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_FJS&amp;utm_term=regular&amp;utm_content=220121\">\u041a\u0443\u0440\u0441 \u043f\u043e JavaScript<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/algo?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_algo&amp;utm_term=regular&amp;utm_content=220121\">\u041a\u0443\u0440\u0441 \u00ab\u0410\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u044b \u0438 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u0434\u0430\u043d\u043d\u044b\u0445\u00bb<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/python-for-web-developers?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_PWS&amp;utm_term=regular&amp;utm_content=220121\">\u041a\u0443\u0440\u0441 \u00abPython \u0434\u043b\u044f \u0432\u0435\u0431-\u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438\u00bb<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/analytics?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_SDA&amp;utm_term=regular&amp;utm_content=220121\">\u041a\u0443\u0440\u0441 \u043f\u043e \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0435 \u0434\u0430\u043d\u043d\u044b\u0445<\/a><\/li>\n<li><a href=\"https:\/\/skillfactory.ru\/devops?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_DEVOPS&amp;utm_term=regular&amp;utm_content=220121\">\u041a\u0443\u0440\u0441 \u043f\u043e DevOps<\/a><\/li>\n<\/ul>\n<p>  <\/div>\n<\/p><\/div>\n<\/div>\n<p> \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 <a href=\"https:\/\/habr.com\/ru\/company\/skillfactory\/blog\/538320\/\"> https:\/\/habr.com\/ru\/company\/skillfactory\/blog\/538320\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"\n<div class=\"post__text post__text-html post__text_v1\" id=\"post-content-body\">\u0412\u0437\u043b\u0451\u0442 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 \u043f\u0440\u0438\u0432\u0451\u043b \u043a \u043f\u043e\u043f\u0443\u043b\u044f\u0440\u043d\u043e\u0441\u0442\u0438 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f MLaaS. \u0415\u0441\u043b\u0438 \u0432\u0430\u0448\u0430 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f \u043d\u0435 \u0441\u043e\u0431\u0438\u0440\u0430\u0435\u0442\u0441\u044f \u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a \u0438 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\u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u043c\u0438, \u0447\u0442\u043e\u0431\u044b \u0431\u044b\u0441\u0442\u0440\u043e \u043f\u0440\u043e\u0442\u043e\u0442\u0438\u043f\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0438 \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438, \u0430 \u043f\u043e\u0437\u0436\u0435 \u0440\u0435\u0448\u0430\u044e\u0442, \u0431\u0443\u0434\u0443\u0442 \u043b\u0438 \u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0434\u0430\u043b\u044c\u0448\u0435.\u00a0<\/p>\n<p>  \u041d\u043e \u043d\u0435 \u0431\u043e\u0439\u0442\u0435\u0441\u044c \u0432\u0441\u0435\u0439 \u044d\u0442\u043e\u0439 \u0438\u043d\u0444\u0440\u0430\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b; \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u043d\u044f\u0442\u044c \u044d\u0442\u0443 \u0441\u0442\u0430\u0442\u044c\u044e, \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u043c\u0438\u043d\u0438\u043c\u0443\u043c\u0430 \u0437\u043d\u0430\u043d\u0438\u0439 \u044f\u0437\u044b\u043a\u0430 Python \u0438 \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 Django.\u00a0 \u0421\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u043a \u0441\u0442\u0430\u0440\u0442\u0443 \u043d\u043e\u0432\u043e\u0433\u043e \u043f\u043e\u0442\u043e\u043a\u0430 <a href=\"https:\/\/skillfactory.ru\/ml-programma-machine-learning-online?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_ML&amp;utm_term=regular&amp;utm_content=200121\">\u043a\u0443\u0440\u0441\u0430 \u043f\u043e \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u043c\u0443 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044e<\/a> \u0432 \u044d\u0442\u043e\u043c \u043f\u043e\u0441\u0442\u0435 \u043f\u043e\u043a\u0430\u0436\u0435\u043c, \u043a\u0430\u043a \u0431\u044b\u0441\u0442\u0440\u043e \u0441\u043e\u0437\u0434\u0430\u0442\u044c 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