An AI-powered Streamlit app that predicts stock prices using LSTM, visualizes historical data with interactive charts, and analyzes top 50 US stocks with technical indicators like SMA and EMA. Built with Python, yfinance, Plotly, TensorFlow, and deployed on Streamlit Cloud. A fully interactive Streamlit web app that predicts future stock prices using machine learning, displays historical price data with technical indicators, and provides a user-friendly dashboard for visual analysis.
This application allows users to:
- 📊 Select from the Top 50 US stocks
- 🕒 View historical price data and interactive charts
- 📉 Analyze technical indicators (SMA, EMA)
- 🤖 See AI-based stock price forecasts using LSTM (deep learning)
- 📰 Monitor market trends with a section for financial news (Coming Soon)
Built entirely in Python and deployed on Streamlit Cloud for free access.
| Tool/Library | Purpose |
|---|---|
Python |
Core programming language |
Pandas |
Data manipulation |
yfinance |
Fetch historical stock data |
plotly |
Interactive data visualization |
Streamlit |
Web UI development |
scikit-learn |
Data preprocessing |
TensorFlow/Keras |
LSTM-based forecasting model |
matplotlib |
Technical indicator plotting |
joblib |
Model serialization |
✅ Top 50 Stock Selector – Pick from the most popular US stocks
✅ Historical Price Charts – Line charts with volume and date filters
✅ Technical Indicators – Includes SMA (Simple Moving Average) and EMA (Exponential Moving Average)
✅ LSTM-Based Forecasting – Predict the next 7 days of closing prices using AI
✅ Modern Dashboard UI – Built using Streamlit with interactive elements
✅ News Integration – (Coming Soon) live financial headlines section
👉 Click here to launch the app
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Clone this repository:
git clone https://github.com/YOUR-USERNAME/stock-price-predictor
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Navigate to the project directory:
cd stock-price-predictor -
Install required dependencies:
pip install -r requirements.txt
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Launch the app:
streamlit run app.py
Hashir Haffee
🔗 LinkedIn
🐙 GitHub
MIT License. This project is open-source and free to use, just give credit.