GitHub - JBFH-Dev/Stock-Headline-Sentiment-Accuracy-Classifier: A random forrest classifier used to predict if the sentiment of a stock analysis headline will be accurate to the stock's performance

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A random forrest classifier used to predict if the sentiment of a stock analysis headline will be accurate to the stock's performance

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HeadlinePerfModel.py		HeadlinePerfModel.py
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#               README
#   - This code requires the use of the following Python packages:
#       * Pandas
#       * Numpy
#       * Matplotlib
#       * Sklearn
#       * Stanza
#       * NLTK

#
#   - These packages must be the versions available as of 24/05/2021 or higher
#
#   - To install/update these packages type:
#       -$ pip3 install <PACKAGE NAME> -U
#
#   - CSV files are also required which can be found in the included folder 'NLPData'
#
#   RUNNING THE CODE
#   - Ensure that the file structure is as follows (Other csv files are included for convenience but not necessary):
#      | HeadlinePerfModel.py
#        NLPData/
#          | semantic_headlines.csv
#          | api_response_window_91.csv

#
#   - Navigate to the parent directory of HeadlinePerfModel.py
#
#   - Type the following into the terminal
#       -$ python3 HeadlinePerfModel.py
#