Research Article
Machine Learning Modeling Integrating Experimental Analysis for Predicting Compressive Strength of Concrete Containing Different Industrial Byproducts
Table 2
The nature of the dataset.
| S no. | Features | The unused count | Percentage of the respective sample indicated with zeros |
| 1 | Cement | Compulsory | Compulsory (0%) | 2 | Limestone powder | 187 | 83.86% | 3 | Fly ash | 112 | 50.23% | 4 | GGBS | 199 | 89.23% | 5 | Silica fume | 179 | 80.27% | 6 | RHA | 211 | 94.61% | 7 | Marble powder | 205 | 91.93% | 8 | Brick powder | 205 | 91.93% | 9 | Coarse aggregate | 6 | 2.7% | 10 | Fine aggregate | Compulsory | Compulsory (0%) | 11 | Recycled coarse aggregate | 205 | 91.93% | 12 | Water | Compulsory | Compulsory | 13 | SP | 55 | 24.67% | 14 | VMA | 186 | 83.5% |
|
|