Ensamble Voting for Financial Time Series
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Updated
May 2, 2023 - Jupyter Notebook
Ensamble Voting for Financial Time Series
Developed an ensemble voting model that included Random Forests, Linear Regression, Orthogonal Matching Pursuit, and Gradient Boosting Regressor to predict future solar power generated by a solar plant in India at 98.7% accuracy. Placed 1st at the Virginia Tech Computational Modeling & Data Analytics Fall 2022 Data Competition.
Orthogonal matching pursuit-based feature selection for motor-imagery EEG signal
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