https://github.com/sayamalt/steel-energy-consumption-prediction-using-pyspark
Successfully established a machine learning model using PySpark which can precisely predict the energy consumption of the steel industry, up to an r2 score of approximately 99.5%.
https://github.com/sayamalt/steel-energy-consumption-prediction-using-pyspark
apache-spark big-data-analytics big-data-processing cross-validation data-visualization exploratory-data-analysis hyperparameter-tuning machine-learning model-training-and-evaluation python regression spark sql
Last synced: 8 months ago
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Successfully established a machine learning model using PySpark which can precisely predict the energy consumption of the steel industry, up to an r2 score of approximately 99.5%.
- Host: GitHub
- URL: https://github.com/sayamalt/steel-energy-consumption-prediction-using-pyspark
- Owner: SayamAlt
- Created: 2024-08-09T08:55:30.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-09T19:58:49.000Z (about 1 year ago)
- Last Synced: 2025-03-06T17:15:36.326Z (8 months ago)
- Topics: apache-spark, big-data-analytics, big-data-processing, cross-validation, data-visualization, exploratory-data-analysis, hyperparameter-tuning, machine-learning, model-training-and-evaluation, python, regression, spark, sql
- Language: Jupyter Notebook
- Homepage:
- Size: 823 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Dataset Information
The data is collected from a smart small-scale steel industry in South Korea.
The information gathered is from the DAEWOO Steel Co. Ltd in Gwangyang, South Korea. It produces several types of coils, steel plates, and iron plates. The information on electricity consumption is held in a cloud-based system. The information on energy consumption of the industry is stored on the website of the Korea Electric Power Corporation (pccs.kepco.go.kr), and the perspectives on daily, monthly, and annual data are calculated and shown.