https://github.com/radom12/stockpredictior

Stock Price Prediction Predict stock prices using machine learning and deep learning models. Analyze historical market data, implement state-of-the-art algorithms, and visualize predictions. Explore trends, evaluate accuracy, and contribute to enhance predictive capabilities. Educational and research-focused. 📈💡
https://github.com/radom12/stockpredictior

ml model nasdaq python stock-price-prediction training-data

Last synced: 2 days ago
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Stock Price Prediction Predict stock prices using machine learning and deep learning models. Analyze historical market data, implement state-of-the-art algorithms, and visualize predictions. Explore trends, evaluate accuracy, and contribute to enhance predictive capabilities. Educational and research-focused. 📈💡

• Host: GitHub
• URL: https://github.com/radom12/stockpredictior
• Owner: Radom12
• License: apache-2.0
• Created: 2023-12-10T07:03:24.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-08-11T07:29:49.000Z (3 months ago)
• Last Synced: 2024-08-11T08:35:52.746Z (3 months ago)
• Topics: ml, model, nasdaq, python, stock-price-prediction, training-data
• Language: Jupyter Notebook
• Homepage:
• Size: 17.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Stock Price Prediction with Machine Learning

## Overview

This project utilizes machine learning techniques to predict stock prices, providing insights into potential market trends. Leveraging deep learning and statistical analysis, we aim to build a robust model capable of making accurate predictions based on historical stock market data.

## Key Features

    Data Analysis: In-depth exploration and analysis of historical stock market data to identify patterns and trends.

    Machine Learning Models: Implementation of state-of-the-art machine learning models, including deep learning algorithms, for stock price prediction.

    Evaluation Metrics: Comprehensive evaluation using metrics such as Mean Squared Error (MSE) and accuracy to assess the performance of the predictive models.

    Interactive Visualization: Engaging visualizations to illustrate predicted vs. actual stock prices, aiding in result interpretation.

## Technologies Used

    Python

    Scikit-learn

    TensorFlow

    Pandas

    Matplotlib

    Jupyter Notebooks

## Contributions

Contributions are welcome! Feel free to open issues, submit pull requests, or provide feedback to enhance the capabilities and accuracy of stock price prediction.

License

This project is licensed under the Apache 2.0 License.

## Disclaimer

This project is for educational and research purposes only. Stock market predictions are inherently uncertain, and users should exercise caution when making financial decisions based on model predictions.