📈 Stock Price Prediction

An intelligent, ML-powered app built with Streamlit that predicts future stock prices using historical data and time-series forecasting models. Designed for learning, experimentation, and demonstrating AI-driven market forecasting techniques.

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🌐 Live Demo

👉 https://predictrade.streamlit.app

⚠️ This project is for educational and research purposes only. Not financial advice.

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🧠 Project Overview

This Stock Price Prediction app uses Streamlit for the frontend interface and Python for backend logic, combining simplicity and power in one codebase.

Users can:

• Input stock ticker symbols.
• View historical data charts.
• Generate AI-powered short-term predictions.

Built with yfinance, Pandas, Scikit-learn, and optional deep learning tools, it makes real-time stock prediction accessible to anyone.

Key Goals:

• Provide an interactive UI without separate frontend/backend setup.
• Fetch, visualize, and predict stock data in one streamlined Python app.
• Demonstrate multiple forecasting models (Linear Regression, Prophet, LSTM).

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✨ Key Features

• 🔍 Search any stock ticker for real-time & historical data.
• 🤖 ML-powered price predictions (configurable horizon).
• 📊 Interactive charts (prices vs predictions).
• 🌐 Works fully in the browser via Streamlit.
• ⚡ Minimal setup — run one Python file to start.

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🛠 Technology Used

Data & APIs: yfinance, Alpha Vantage API (optional)
ML/Analysis: Pandas, NumPy, Scikit-learn, Prophet, TensorFlow/Keras (optional for LSTM)
Charts: Matplotlib, Plotly
Hosting: Streamlit Cloud

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🔬 Models & How They Work

• Simple Moving Average (SMA) → Baseline rolling averages.
• Linear Regression → Uses lag features for predictions.
• Prophet → Captures trends & seasonality.
• LSTM → Deep learning model for time-series forecasting (optional).

Workflow:

1. Fetch historical adjusted close prices.
2. Preprocess data (cleaning, lag features, scaling).
3. Train & validate model.
4. Display predictions alongside historical charts in Streamlit UI.

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📈 Evaluation Metrics

• MAE (Mean Absolute Error)
• RMSE (Root Mean Squared Error)
• MAPE (Mean Absolute Percentage Error)

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⚠️ Limitations & Disclaimer

• Predictions rely only on historical prices — no external signals.
• Market events can cause unexpected price movements.
• Strictly for educational purposes only.

👨‍💻 Author

Made with ❤️ by Asim Husain www.asimhusain.dev

🚀 Quick Start (Local)

Requirements: Python 3.9+

# Clone the repo
git clone https://github.com/your-username/stock-price-prediction.git
cd stock-price-prediction

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run app.py