Last Updated on July 24, 2023 by Editorial Team
Author(s): Ranganath Venkataraman
Originally published on Towards AI.
TL/DR: regression offers improved performance over classification on the UCI Energy Efficiency dataset. A neural network regressor was no more effective than ensemble techniques like Gradient Boosting or Random Forest.
Photo by Kevin Ku on Unsplash
My previous posts have used Python visualizations to explore oil refining and environmental emissions in the United States. Before applying machine learning and deep learning techniques to vast volumes of data in the energy industry, I will be practicing on the Energy Efficiency Data Set from the UCI Machine Learning Repository.
This article has samples of code and output, with all work available at this Github repo.
The key steps that you’ll see executed in this article are:
Step 1 = Make sure to understand the goal and big picture before diving into modeling.
Step 2 = load and… Read the full blog for free on Medium.
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Published via Towards AI