📈 AI in Python – Stock Price Prediction

Machine learning exploration of stock price prediction using classical ML models and LSTM-based deep learning on historical market data.

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Project Overview

This project was developed as part of the AI in Python coursework at The University of Texas at Tyler and focuses on applying machine learning techniques to financial time-series data.

The workflow includes:

• Data preprocessing and feature scaling
• Model experimentation and comparison
• Performance evaluation on historical stock market data

The project benchmarks multiple approaches — including traditional machine learning models and deep learning architectures — against S&P 500 historical data sourced from Yahoo Finance to assess predictive behavior under real-world market conditions.

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🛠 Technologies & Techniques

Data Processing

• pandas
• NumPy
• MinMaxScaler

Visualization

• matplotlib

Machine Learning

• scikit-learn
  • Support Vector Machines (SVM)
  • Random Forest Classifier
  • Train/Test Split
  • F1-score evaluation

Deep Learning

• TensorFlow / Keras
  • Sequential models
  • Dense layers
  • LSTM
  • Dropout

Data Source

• Yahoo Finance (S&P 500 historical market data)