mlmodelcomparison
Comparison of ML Models for predicting Energy Efficiency
Science Score: 44.0%
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Low similarity (7.8%) to scientific vocabulary
Repository
Comparison of ML Models for predicting Energy Efficiency
Basic Info
- Host: GitHub
- Owner: mcemim
- Language: Jupyter Notebook
- Default Branch: main
- Size: 62.5 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
readme.md
Comparison of Machine Learning Models for Predicting Energy Efficiency in Metallurgical Heat Treatment Furnaces
This project aims to compare different machine learning approaches to predict the energy efficiency of a metallurgical heat treatment furnace. Input features include soaking time, total cycle time, and load weight in tons. The methods to be evaluated are Support Vector Machine, Gradient Boost Regressor, Stochastic Gradient Descent Regression, and Linear Regression, all implemented using the Scikit Learn library.
The context involves the pursuit of accurate predictive models that can optimize the energy efficiency of the heat treatment process, reducing costs and environmental impacts. The problem lies in selecting the most suitable machine learning model for this task, considering the complexity of the process and the limited availability of data.
The main objective is to identify the model that best fits the provided data and provides the most accurate predictions in terms of energy efficiency. The methodology will involve implementing and evaluating the different models using performance metrics such as RMSE and R².
Expected results include insights into the relative effectiveness of different models in predicting the energy efficiency of metallurgical heat treatment furnaces, providing useful guidance for industrial process optimization.
Owner
- Login: mcemim
- Kind: user
- Repositories: 1
- Profile: https://github.com/mcemim
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
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cff-version: 1.2.0
title: MLModelComparison
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Marcos
family-names: Cemim
email: mcemim@gmail.com
repository-code: 'https://github.com/mcemim/MLModelComparison'
keywords:
- Heat Treatment
- Energy Saving
- Energy Efficiency