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%