GitHub - SamH135/Recurrent-Neural-Network-from-Scratch: This repository implements a simple recurrent neural network (RNN) for stock price prediction using principal component analysis (PCA) for dimensionality reduction. The model is trained on the closing price of Infosys stock data and evaluated on its ability to predict future prices. Developed with https://github.com/Kash1r

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This repository implements a simple recurrent neural network (RNN) for stock price prediction using principal component analysis (PCA) for dimensionality reduction. The model is trained on the closing price of Infosys stock data and evaluated on its ability to predict future prices. Developed with https://github.com/Kash1r

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RNN_dataVisualizations		RNN_dataVisualizations
ReadMe		ReadMe
experiment_log		experiment_log
simpleRNN_stockAnalysis.py		simpleRNN_stockAnalysis.py

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RUN THE MODEL:

  Technique 1 - the easy way: 
  
      To run the programs, open a Google Colab Notebook and copy and paste the code over.
      Once that is done, run the code, and the results should appear on the screen.
  
  Technique 2: 
      If the user does not have access to Google Colab Notebook, copy and paste or open
      the codes in a Python IDE (like PyCharm by JetBrains) and run the code there and the results 
      will appear.

TROUBLESHOOTING:

    If the 2 options are not working, copy and paste or open the code in an IDE of your choice 
    and ensure Python is installed on the device. 

    After that, install all the libraries that are used in this program

    Command prompt:

        "pip install pandas numpy seaborn matplotlib scikit-learn"
        or by the following commands below:
        "pip install pandas"
        "pip install numpy"
        "pip install seaborn"
        "pip install matplotlib"
        "pip install scikit-learn"

    After all the libraries are installed, the program should run.