https://github.com/devmuniz02/stock-prediction-lstm-sentiment-analysis-python

Project to predict stock prices of next day data using a LSTM model in python and sentiment analysis of news related to the stock market in general and the news of the stock to predict.
https://github.com/devmuniz02/stock-prediction-lstm-sentiment-analysis-python

Last synced: over 1 year ago
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Project to predict stock prices of next day data using a LSTM model in python and sentiment analysis of news related to the stock market in general and the news of the stock to predict.

• Host: GitHub
• URL: https://github.com/devmuniz02/stock-prediction-lstm-sentiment-analysis-python
• Owner: devMuniz02
• Created: 2023-10-31T15:11:24.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-01-22T00:10:49.000Z (over 1 year ago)
• Last Synced: 2025-01-29T13:14:05.482Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 646 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Stock Prediction with LSTM and Sentiment Analysis

This project aims to predict stock prices for the next day using a Long Short-Term Memory (LSTM) model in Python, supplemented by sentiment analysis of news. The approach combines:

- **Deep Learning**: Using LSTM for time-series stock price prediction.

- **Natural Language Processing**: Analyzing market-related news sentiment.

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## Project Overview

### Files in the Repository

1. **`LSTM.ipynb`**

   - Contains the main workflow for training and testing the LSTM model with stock price data.

   - Below are sample results from the notebook:

     **Full Data Prediction**  

     ![Full Data Prediction](https://github.com/user-attachments/assets/ca0f8a97-3fe3-4739-a173-67ce0b438451)

     **Test Predictions**  

     ![Test Predictions](https://github.com/user-attachments/assets/1127e88a-b1d8-405e-a88e-5a512d38faf2)

2. **`SaveSentiment.ipynb`**

   - Saves the sentiment data extracted from news articles using sentiment analysis techniques.

   - This notebook gathers news sentiments relevant to specific stocks or markets.

3. **`LSTMdef.ipynb`**

   - Uses the sentiment data saved by `SaveSentiment.ipynb` and integrates it with stock data to train and evaluate the LSTM model.

   - Combines stock data and sentiment data for improved predictive performance.

4. **`README.md`**

   - Provides an overview of the project, the files, and their roles in the workflow.

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## Features

- **Stock Price Prediction**: Time-series forecasting with LSTM for accurate trend analysis.

- **Sentiment Analysis**: Evaluates market sentiment from general news and stock-specific articles to inform predictions.

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## Getting Started

### Prerequisites

To run the project, ensure you have the following installed:

- Python 3.8 or later

- Required Python libraries: `tensorflow`, `pandas`, `numpy`, `sklearn`, `matplotlib`, `nltk`, `yfinance`, and `fredapi`.

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### Check Out My Other Projects

Explore more of my AI and ML work [here](https://github.com/devMuniz02/AI-ML-Code-and-projects/).

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## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.