This project predicts stock prices based on historical data, market trends, and news sentiment analysis using machine learning. The model uses LSTM (Long Short-Term Memory) for time series prediction and NLP (Natural Language Processing) for sentiment analysis of financial news.

• 📈 Fetches real-time stock data using Yahoo Finance API.
• 📰 Scrapes and analyzes news sentiment related to the stock.
• 🤖 Uses LSTM, ARIMA, or XGBoost for stock price prediction.
• 📊 Provides a Streamlit dashboard for visualization.

• Programming Language: Python
• Libraries: Pandas, Scikit-learn, TensorFlow/Keras, Matplotlib, Seaborn, Plotly
• Data Sources: Yahoo Finance API, Web Scraping (BeautifulSoup)
• Frameworks: Streamlit (for Dashboard UI)

├── main.py          # Data fetching, training, and prediction
├── app.py           # Streamlit dashboard for visualization
├── requirements.txt # Required Python packages
├── README.md        # Project documentation

git clone https://github.com/ishusharma/stock-price-prediction.git
cd stock-price-prediction

python -m venv venv

Activate it:

• Windows: venv\Scripts\activate
• Mac/Linux: source venv/bin/activate

pip install -r requirements.txt

If you don’t have requirements.txt, run:

pip install numpy pandas scikit-learn tensorflow keras yfinance beautifulsoup4 requests nltk matplotlib seaborn plotly streamlit

python main.py

This will:

• Fetch historical stock data
• Perform sentiment analysis
• Train an LSTM-based prediction model

streamlit run app.py

This will launch an interactive web app to visualize stock predictions.

• Stock Price Prediction Graph 📉
• Sentiment Analysis Score 📰
• Real-time Data & Forecast 📊