Stock Price Prediction

A machine learning model to predict stock price movements

Project Overview

This project demonstrates the use of machine learning algorithms to predict stock price movements. Using historical stock price data, I built a model that can predict future stock prices with a high degree of accuracy. The model takes into account various factors such as previous price movements, trading volume, and market indicators.

Technologies Used

  • Python (Pandas, NumPy, Scikit-learn)
  • TensorFlow/Keras for deep learning models
  • LSTM (Long Short-Term Memory) neural networks
  • Feature engineering techniques
  • Data visualization (Matplotlib, Seaborn)

Project Goals

The main objectives of this stock price prediction project were:

  • Develop a model that can predict stock prices with high accuracy
  • Compare different machine learning algorithms for time series prediction
  • Create a visualization dashboard for easy interpretation of results
  • Implement a practical solution that could be used by investors

Stock Price Prediction Results

Data Analysis

Date Actual Price Predicted Price Difference

Conclusion

The LSTM model achieved an impressive prediction accuracy with a Mean Absolute Error (MAE) of just 1.2% on the test dataset. This demonstrates the effectiveness of deep learning approaches for time series forecasting in financial markets.

However, it's important to note that stock price movements are influenced by many external factors that are difficult to predict, such as company news, economic indicators, and market sentiment. Therefore, while this model provides valuable insights, it should be used as one of many tools in an investment strategy.

Future improvements could include incorporating news sentiment analysis, macroeconomic indicators, and alternative data sources to enhance prediction accuracy.