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GitHub / timryang / Stock_Prediction_Analysis
Using machine learning to predict stock price performance. Various methods of analysis include Twitter NLP, historical price data, and technical indicators.
JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timryang%2FStock_Prediction_Analysis
Stars: 0
Forks: 0
Open Issues: 7
License: None
Language: HTML
Repo Size: 4.05 MB
Dependencies:
74
Created: about 4 years ago
Updated: over 1 year ago
Last pushed: over 1 year ago
Last synced: about 1 year ago
Files
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Readme
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Dependencies
- Flask ==1.1.2
- GetOldTweets3 ==0.0.11
- Jinja2 ==2.11.2
- MarkupSafe ==1.1.1
- Pillow ==7.1.2
- PyYAML ==5.3.1
- SQLAlchemy ==1.3.17
- Werkzeug ==1.0.1
- bokeh ==1.4.0
- click ==7.1.2
- cssselect ==1.1.0
- cycler ==0.10.0
- gunicorn ==20.0.4
- holidays ==0.10.5.2
- itsdangerous ==1.1.0
- joblib ==0.15.1
- kiwisolver ==1.2.0
- lxml ==4.5.1
- matplotlib ==3.2.1
- nltk ==3.5
- numpy ==1.18.4
- packaging ==20.4
- pandas ==1.0.3
- pyparsing ==2.4.7
- pyquery ==1.4.1
- python-dateutil ==2.8.1
- pytz ==2020.1
- regex ==2020.5.14
- scikit-learn ==0.23.1
- scipy ==1.4.1
- seaborn ==0.10.1
- six ==1.15.0
- sklearn ==0.0
- ta ==0.7.0
- threadpoolctl ==2.0.0
- tornado ==6.0.4
- tqdm ==4.46.0
requirements.txt
pypi
- Flask ==1.1.2
- GetOldTweets3 ==0.0.11
- Jinja2 ==2.11.2
- MarkupSafe ==1.1.1
- Pillow ==7.1.2
- PyYAML ==5.3.1
- SQLAlchemy ==1.3.17
- Werkzeug ==1.0.1
- bokeh ==1.4.0
- click ==7.1.2
- cssselect ==1.1.0
- cycler ==0.10.0
- gunicorn ==20.0.4
- holidays ==0.10.5.2
- itsdangerous ==1.1.0
- joblib ==0.15.1
- kiwisolver ==1.2.0
- lxml ==4.5.1
- matplotlib ==3.2.1
- nltk ==3.5
- numpy ==1.18.4
- packaging ==20.4
- pandas ==1.0.3
- pyparsing ==2.4.7
- pyquery ==1.4.1
- python-dateutil ==2.8.1
- pytz ==2020.1
- regex ==2020.5.14
- scikit-learn ==0.23.1
- scipy ==1.4.1
- seaborn ==0.10.1
- six ==1.15.0
- sklearn ==0.0
- ta ==0.7.0
- threadpoolctl ==2.0.0
- tornado ==6.0.4
- tqdm ==4.46.0