文件列表:

.devcontainer (0, 2022-01-19)
.devcontainer\Dockerfile (1076, 2022-01-19)
.devcontainer\devcontainer.json (2144, 2022-01-19)
.devcontainer\docker-compose.yml (2027, 2022-01-19)
MANIFEST.in (25, 2022-01-19)
Makefile (225, 2022-01-19)
django-app (0, 2022-01-19)
django-app\Dockerfile (557, 2022-01-19)
django-app\Dockerfile.prod (1629, 2022-01-19)
django-app\data (0, 2022-01-19)
django-app\data\__init__.py (0, 2022-01-19)
django-app\data\admin.py (63, 2022-01-19)
django-app\data\apps.py (140, 2022-01-19)
django-app\data\migrations (0, 2022-01-19)
django-app\data\migrations\__init__.py (0, 2022-01-19)
django-app\data\models.py (57, 2022-01-19)
django-app\data\templates (0, 2022-01-19)
django-app\data\templates\news (0, 2022-01-19)
django-app\data\templates\news\index.html (508, 2022-01-19)
django-app\data\templates\news\layout.html (162, 2022-01-19)
django-app\data\tests.py (60, 2022-01-19)
django-app\data\urls.py (166, 2022-01-19)
django-app\data\views.py (1051, 2022-01-19)
django-app\django-app (0, 2022-01-19)
django-app\django-app\__init__.py (0, 2022-01-19)
django-app\django-app\asgi.py (383, 2022-01-19)
django-app\django-app\settings.py (3832, 2022-01-19)
django-app\django-app\urls.py (916, 2022-01-19)
django-app\django-app\wsgi.py (390, 2022-01-19)
django-app\entrypoint.prod.sh (195, 2022-01-19)
django-app\entrypoint.sh (280, 2022-01-19)
django-app\flink (0, 2022-01-19)
django-app\flink\__init__.py (0, 2022-01-19)
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# Stock prices prediction with RNN and Stock News Sentiment Analysis. The main purpose of this personal project it's to setup a entire infrastructure required for the ingestion, processing, and storage of data from third-party APIs. Programming language and technologies used: - Python, SQL, Pandas, Apache Spark, Airflow, Django, FastAPI, Streamlit, Bootstrap - AWS EC2 (Ubuntu), Docker, PostgreSQL, Kafka, ZooKeeper From the [project](https://github.com/FrCloers/stock_news_pred) carried out at the end of the Data science bootcamp at Le Wagon, I want to create a data repository and provide for users ready-to-use data (news, tweets and stock prices) for analysis, ML and RNN.

what is being done ? - Containerize the monolithic application (easier scaling, flexibility to work and create functionality, easier management, improved security by isolating applications) - Collect data from disparate sources in a postgreSQL database. - [Alpha vantage](https://www.alphavantage.co/documentation/) - [EOD Historical data](https://eodhistoricaldata.com/financial-apis/financial-news-api/) - [Twitter API](https://developer.twitter.com/en/docs/twitter-api) - Data processing and storage : 1. Created pipelines ETL for Data warehouse (Amazon Redshift or Snowflake). 2. Stream processing for real-time data access (newest) (Apache Spark) - User interface : - backend : Django - frontend : Bootstrap, streamlit (https://colorlib.com/polygon/elaadmin/) ## Infrastructure

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