https://github.com/radom12/ml-project

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/ml-project

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/ml-project
• Owner: Radom12
• Created: 2023-12-10T07:22:06.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-01-18T18:27:46.000Z (10 months ago)
• Last Synced: 2024-01-18T20:08:23.386Z (10 months ago)
• Language: Jupyter Notebook
• Size: 486 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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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

## Getting Started

    Clone the repository.

    Install the required dependencies by running pip install -r requirements.txt.

    Explore the Jupyter Notebooks to understand the analysis and model implementation.

    Run the provided scripts to train and evaluate the predictive models.

## 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.

## 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.