Single-Stock Price Prediction (Educational Project)

This repository contains a simple machine learning project that predicts the next day's closing price of a single NASDAQ stock using historical price data and basic technical indicators.

The goal is to demonstrate an end-to-end workflow in a Jupyter Notebook: data loading → feature engineering → model training → evaluation → next-day prediction.

⚠️ Disclaimer: This project is for educational and experimental purposes only and does not constitute financial or investment advice.

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Features

• Uses symbols_valid_meta.csv as the universe of NASDAQ-listed symbols.
• Lets you search and choose a single stock (e.g., AAPL) from the metadata file.
• Downloads historical OHLCV data from Yahoo Finance using yfinance.
• Engineers simple technical features:
  • 10-day moving average (MA10)
  • 50-day moving average (MA50)
  • 1-day percentage return
• Builds a Random Forest Regressor to predict the next day's closing price.
• Visualizes historical prices and compares actual vs predicted prices on the test set.

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Project Structure

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├── stock_price_prediction.ipynb   # Main Jupyter notebook
├── symbols_valid_meta.csv         # NASDAQ symbols metadata
├── requirements.txt               # Python dependencies (optional)
└── README.md                      # Project documentation