Stock Prediction with Sentiment Analysis - README

Deployed Website:

https://alpha-stocks.netlify.app/

Project Overview:

Our project focuses on providing accurate stock predictions by integrating sentiment analysis. We offer a comprehensive solution through three primary components:

1. LSTM Model:

Utilizing LSTM models to ensure precise stock predictions.

2. Sentiment Analysis of News:

Analyzing current news sentiments to gauge their impact on stock prices.

3. Portfolio Guidance for Beginners:

Offering personalized portfolio suggestions based on sentiment analysis and stock progress.

Detailed Description:

1. LSTM Model:

Our project distinguishes itself by employing LSTM models, known for their accuracy in stock prediction. These models analyze historical data and market trends to forecast future stock prices.

2. Sentiment Analysis of News:

In addition to traditional stock prediction methods, we incorporate sentiment analysis of financial news articles. By assessing the sentiment of current news, we provide insights into how public sentiment influences stock prices.

3. Portfolio Guidance for Beginners:

We understand that navigating the stock market can be daunting, especially for beginners. Hence, we offer a feature that analyzes stock progress and suggests BUY/SELL options, simplifying portfolio management.

Why Our Solution?

• Accuracy: Our models, coupled with sentiment analysis, ensure precise stock predictions.
• Comprehensive Analysis: By considering both historical data and current news sentiment, we provide a holistic view of market trends.
• User-Friendly: Our platform offers intuitive portfolio guidance, catering to both novice and experienced investors.

Tech Stack:

• Models: LSTM for stock prediction, integrated with Python libraries like TensorFlow, Keras.
• Sentiment Analysis: Natural Language Processing (NLP) techniques using Python libraries like distilroberta-finetuned-financial-news-sentiment-analysis model.
• Development: Python for backend development, HTML/CSS/JavaScript for frontend, and Flask for web framework.

Contributing:

We welcome contributions from the community to enhance our project. Please fork the repository, make your changes, and submit a pull request.

Contact Us:

If you have any questions or suggestions, feel free to contact us at [email protected]. We value your feedback and support.

Dataset Used:

• Sentiment Analysis: Sentiment Analysis Dataset
• Stock Prediction Model: Stock Prediction Model Dataset