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2025
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| Online Access: | https://doi.org/10.5281/zenodo.15247669 |
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| _version_ | 1866901057247903744 |
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| author | Md Khasrur, Rahman |
| author_facet | Md Khasrur, Rahman |
| contents | <p>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.</p> <p>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.</p> <p>⚠️ 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.</p> <p>The full code, evaluation metrics, and visualizations (including RMSE, R², scatter plot comparisons, and feature importance) are available in the GitHub repository.</p> <p> GitHub: <a href="https://github.com/KhasrurRahman/Data-Stewardship-UE-2025S---Data-Management-part--2-">https://github.com/KhasrurRahman/Data-Stewardship-UE-2025S—Data-Management-part–2-</a></p> <p> Includes:</p> <ul> <li> <blockquote>Final trained Random Forest model</blockquote> </li> <li> <blockquote>Output prediction CSVs</blockquote> </li> <li> <blockquote>Feature importance visualization</blockquote> </li> <li> <blockquote>Requirements.txt file</blockquote> </li> <li> <blockquote>Data Management Plan (PDF)</blockquote> </li> </ul> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15247669 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | DMP: CO₂ Emissions Prediction Using Machine Learning Md Khasrur, Rahman <p>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.</p> <p>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.</p> <p>⚠️ 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.</p> <p>The full code, evaluation metrics, and visualizations (including RMSE, R², scatter plot comparisons, and feature importance) are available in the GitHub repository.</p> <p> GitHub: <a href="https://github.com/KhasrurRahman/Data-Stewardship-UE-2025S---Data-Management-part--2-">https://github.com/KhasrurRahman/Data-Stewardship-UE-2025S—Data-Management-part–2-</a></p> <p> Includes:</p> <ul> <li> <blockquote>Final trained Random Forest model</blockquote> </li> <li> <blockquote>Output prediction CSVs</blockquote> </li> <li> <blockquote>Feature importance visualization</blockquote> </li> <li> <blockquote>Requirements.txt file</blockquote> </li> <li> <blockquote>Data Management Plan (PDF)</blockquote> </li> </ul> <p> </p> |
| title | DMP: CO₂ Emissions Prediction Using Machine Learning |
| url | https://doi.org/10.5281/zenodo.15247669 |