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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.

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eshan-kaul/Regression-Voting-Ensemble-for-Solar-Power-Prediction

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Regression-Voting-Ensemble-for-Solar-Power-Prediction

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.

To view the project as a notebook click on the .ipynb file as shown below.

Screen Shot 2023-01-22 at 5 07 17 PM

Screen Shot 2023-01-22 at 5 08 22 PM

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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.

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