A machine learning-based stock price predictor that forecasts S&P 500 movements using historical data and technical indicators.
This project implements a Random Forest classifier to predict daily price movements of the S&P 500 index. The system analyzes historical market data to identify patterns and generate trading signals with measurable precision.
- Python 3 with JupyterLab
- Machine Learning: Scikit-learn (Random Forest Classifier)
- Data Analysis: Pandas, NumPy
- Financial Data: yFinance API
- Visualization: Matplotlib
- Data Collection & Preprocessing
- Automated S&P 500 historical data retrieval (1990-present)
- Data cleaning and feature engineering
- Technical indicator calculation (moving averages, trends, ratios)
- Launch JupyterLab: jupyter lab
- Open stock_price_predictor.ipynb
- Run all cells to:
- Download latest S&P 500 data
- Train the machine learning model
- Generate predictions
- Evaluate performance metrics
This tool is for educational and research purposes only. Past performance does not guarantee future results.