DMP: CO₂ Emissions Prediction Using Machine Learning
Year: 2025
Type: article
Abstract: This upload contains the final model and output files developed as part of the second task (“Part 2 – Data Management”) of the Data Stewardship UE 2025S course at TU Wien. The goal of the project was to implement a machine learning pipeline to analyze and predict CO₂ emissions based on real-world socio-economic indicators. A Random Forest Regressor was used after data cleaning, preprocessing, and splitting the data into training, validation, and test subsets using the DBRepo API. ⚠️ Due to technical limitations, only partial subsets of the dataset were uploaded to DBRepo. This may have impacted the predictive performance of the trained model. The full code, evaluation metrics, and visualizations (including RMSE, R², scatter plot comparisons, and feature importance) are available in the GitHub repository. 🔗 GitHub: https://github.com/KhasrurRahman/Data-Stewardship-UE-2025S—Data-Management-part–2- 📁 Includes: Final trained Random Forest model Output prediction CSVs Feature importance visualization Requirements.txt file Data Management Plan (PDF)
Author: MD Khasrur, Rahman
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W6893344612
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