Stock Prices Predictor

This is a stock prices predictor app build in python using recurrent neural network, yahoo finance api, streamlit, and other machine learning models.

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ABOUT THE PROJECT

This is a stock prices predictor app build in python using recurrent neural network, yahoo finance api, streamlit, pandas, etc. Data for the stocks is read into a Data frame from yahoo finance using the pandas_datareader module. The variety of the recurrent neural networks used for this project are LSTM(Long Short-Term Memory networks) which are capable of learning long-term dependencies in sequential prediction problems like the stock prices prediction. The choice of using LSTM instead of RNN came because according to GeeksforGeeks RNN fails to store information for a longer period of time.

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DISCLAIMER: This is not investing advice. I am not a proffesional who is qualified in giving any financial advice. This a preoject purely about Machine Learning using Financial data.

BUILT WITH

Here are the main modules and tool I used for the project

• Tensorflow
• Keras
• Sklearn
• Google Colab
• Streamlit

ROADMAP

1. Create a prediction model for a specific stock
2. Train a model that can be used for any stock
3. Add other prediction models like Facebook's FBProphet
4. Create a Streamlit web application
5. Deploy the web application

CONTRIBUTING

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are extremely appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

CONTACT

Tinotenda Rodney Alfaneti - @Linkedin

ACKNOWLEDGEMENTS

• NeuralNine