https://github.com/erickarpovits/stock_prediction_model

#Hack-The-North-2021-Challenge
https://github.com/erickarpovits/stock_prediction_model

data-science machine-learning prediction python python3

Last synced: 8 months ago
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#Hack-The-North-2021-Challenge

• Host: GitHub
• URL: https://github.com/erickarpovits/stock_prediction_model
• Owner: EricKarpovits
• Created: 2021-12-16T02:03:29.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-12-16T02:05:40.000Z (almost 4 years ago)
• Last Synced: 2024-12-28T07:42:00.844Z (9 months ago)
• Topics: data-science, machine-learning, prediction, python, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 244 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Stock Prediction Tool - Predict - Hack The North 2021

![Screen Shot 2021-09-19 at 9 51 18 AM](https://user-images.githubusercontent.com/46465622/133930040-e763bc3f-85a7-484f-be11-51ea0300a466.png)

![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)

**Method & Project Description:** Our stock prediction model uses a technical analysis approach at predicting the daily price movement of a stock and uses this data to attempt to predict future price movements. Using machine learning and the random forest regression model, we analyzed different financial indicators, such as RSI (Relative Strength Index) that gave the model signals about the next price movement.

**Our Group:** [Khuong Tran](https://github.com/KVKTRAN), [Deniz Jasarbasic](https://github.com/Deniz-Jasa), [Eric Karpovits](https://github.com/EricKarpovits), [Raz Levi](https://github.com/razlevio)

**Our Topic of Research:** Effective Stock Prediction.

**Our Submission:** [DevPost](https://devpost.com/software/stock-predictor-tool?ref_content=my-projects-tab&ref_feature=my_projects)

**How to run the project?** 

Within the project directory,

    1. Python install all the required python libraries and packages found in "requirements.txt".

    2. Execute in the terminal: "Jupyter notebook rsi.ipynb" - To run the prediction model.

    3. Execute in the terminal: "streamlit run data_visualization.py" - To run the model and web app.

Visit https://hackthenorth.com/ for more information about the competition and challenge.