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
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
