Machine Learning and Deep Learning — a Systematic Application
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.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Take our 90+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!
Towards AI has published Building LLMs for Production—our 470+ page guide to mastering LLMs with practical projects and expert insights!

Discover Your Dream AI Career at Towards AI Jobs
Towards AI has built a jobs board tailored specifically to Machine Learning and Data Science Jobs and Skills. Our software searches for live AI jobs each hour, labels and categorises them and makes them easily searchable. Explore over 40,000 live jobs today with Towards AI Jobs!
Note: Content contains the views of the contributing authors and not Towards AI.