In this Github repository, you'll find my machine learning model and data analysis code for predicting the signal of the stock market for Numerai's tournament. The model is trained using lightGBM, a gradient boosting framework that's particularly suited for high-dimensional datasets.
To build the model, I analyze a massive 11GB dataset of market data from the past decade using the pandas library in Python. This dataset is carefully anonymized to protect the privacy of individual traders and companies, while still providing enough information for accurate predictions.
Overall, this repository provides a detailed look at my approach to building machine learning models for financial data, and may be of interest to anyone interested in participating in Numerai's tournament or similar competitions.