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--- |
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library_name: sklearn |
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tags: |
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- regression |
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- scikit-learn |
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- UCS |
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- cement |
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license: mit |
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pipeline_tag: tabular-regression |
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--- |
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# RandomForestRegressor for UCS Prediction |
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## Model Overview |
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This model is a `RandomForestRegressor` trained to predict the Unconfined Compressive Strength (UCS) of soil-cement mixtures based on the following features: |
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- **Curing Period (`curing_period`)**: Duration in days. |
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- **Compaction Rate (`compaction_rate`)**: Numerical value. |
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- **Cement Percentage (`cement_percent`)**: Percentage of cement in the mixture. |
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## Performance |
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The model achieved an R² score of **0.968** during cross-validation, indicating a high level of accuracy in predicting UCS values. |
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## Feature Ranges |
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- **Curing Period**: Min = 0.0 days, Max = 28.0 days, Mean = 11.06 days |
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- **Compaction Rate**: Min = 0.5, Max = 1.25, Mean = 0.989 |
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- **Cement Percentage**: Min = 0.0%, Max = 10.0%, Mean = 5.77% |
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## Usage |
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To utilize this model, load the `model.joblib` file and input data within the specified feature ranges to obtain UCS predictions. |
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## Limitations |
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The model is calibrated for predictions within the specified feature ranges. Using it outside these ranges may result in less accurate predictions. It is specifically designed for soil-cement mixtures and may not be applicable to other materials. |
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## Author |
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Bogdan TEODORU |
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Gheorghe Asachi Technical University of Iasi (TUIASI) |
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## Contact |
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For inquiries or suggestions, please contact [email protected]. |