p-MarketForecast
Welcome to p-MarketForecast, a Claremont Colleges P-ai Data Science Club project exploring the effectiveness of machine learning in predicting stock market performance.
Project Overview
p-MarketForecast employs machine learning techniques to predict significant swings in stock market trends. We utilize historical financial data (price, volume, technical indicators) to train and evaluate three powerful models:
- Random Forest: An ensemble method building multiple decision trees.
- LSTM Networks: RNNs specialized for learning long-term dependencies in time series.
- XGBoost: A gradient boosting algorithm building trees sequentially to correct errors.
Our project addresses challenges like data scarcity and high noise levels in financial forecasting. We are focused on optimizing model performance using structured financial data.
Technology Stack
Data Science: Python, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, yFinance
Frontend: Next.js, TypeScript, Tailwind CSS