## 3 Experimental Database Representation
### Programming-Based Presentation of Datasets  
Anaconda-based python version (version 3.7) programming [46] has been adopted to depict the influence of various input parameters upon the mechanical strength of HSC. Compressive strength of concrete is influenced by the number of parameters used in experimental work. Thus, cement content (Type 1), water, superplasticizer (polycarboxylate), and fine and coarse aggregate (20 mm) were used in modeling of the compressive strength of HSC. The impact of these input parameters was visualized with the use of python which is done in Jupiter notebook [56] as shown in Figure 1.  
Figure 1: Hex contour graph of input parameters; (**a**) Cement; (**b**) Coarse aggregate; (**c**) Fine aggregate; (**d**) Super plasticizer; (**e**) Water; (**f**) Compressive strength. Figure 1 represents the quantities which have adamant influence on the mechanical properties of HSC. The darkish region shows the optimal/maximum concentration of variables as depicted in Figure 1. Python is an effective machine learning approach that enables users to have a deep understanding of the parameters that alter the functioning of the model. Python uses the seaborn command to plot the correlation among the desired parameters. The description of the data variables (see Table 2) used in the model consist of training set, validation set, and testing set as represented in Tables 3-5. The parameters that define and ensure that optimum results are achieved for all techniques. Identifying these parameters is of core importance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Cement**} & \multicolumn{2}{c}{**Fine/Coarse**} & \multirow{2}{*}{**Water**} & \multirow{2}{*}{**Superplasticizer**} \\  & & **aggregate** & & \\ \hline Mean & 387.04 & 0.92 & 172.18 & 1.98 \\ Standard Error & 12.46 & 0.02 & 1.34 & 0.33 \\ Median & 400 & 0.90 & 170 & 1 \\ Mode & 360 & 0.75 & 170 & 1 \\ Standard Deviation & 95.76 & 0.18 & 10.35 & 2.55 \\ Sample Variance & 9170.56 & 0.03 & 107.25 & 6.55 \\ Kurtosis & 0.22 & 6.82 & 0.18 & 4.75 \\ Skewness & 0.17 & 1.66 & 0.33 & 2.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical description of testing data points used in the model (Kg/m\({}^{3}\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Cement**} & \multicolumn{2}{c}{**Fine/Coarse**} & \multirow{2}{*}{**Water**} & \multirow{2}{*}{**Superplasticizer**} \\  & & **Aggregate** & & \\ \hline Mean & 384.34 & 0.96 & 173.56 & 2.34 \\ Standard Error & 4.92 & 0.01 & 0.82 & 0.14 \\ Median & 360 & 0.92 & 170 & 1.25 \\ Mode & 360 & 1.01 & 170 & 1 \\ Standard Deviation & 93.00 & 0.26 & 15.56 & 2.69 \\ Sample Variance & 8650.50 & 0.06 & 242.19 & 7.24 \\ Kurtosis & 0.36 & 6.45 & 15.59 & 2.88 \\ Skewness & 0.14 & 2.12 & 2.45 & 1.79 \\ Range & 440 & 1.86 & 170.08 & 12 \\ Minimum & 160 & 0.23 & 132 & 0 \\ Maximum & 600 & 2.1 & 302.08 & 837.61 \\ Count & 357 & 357 & 357 & 357 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical description of all data points used in model (Kg/m\({}^{3}\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Cement**} & \multicolumn{2}{c}{**Fine/Coarse**} & \multirow{2}{*}{**Water**} & \multirow{2}{*}{**Superplasticizer**} \\  & & **Aggregate** & & \\ \hline Mean & 384.34 & 0.96 & 173.56 & 2.34 \\ Standard Error & 4.92 & 0.01 & 0.82 & 0.14 \\ Median & 360 & 0.92 & 170 & 1.25 \\ Mode & 360 & 1.01 & 170 & 1 \\ Standard Deviation & 93.00 & 0.26 & 15.56 & 2.69 \\ Sample Variance & 8650.50 & 0.06 & 242.19 & 7.24 \\ Kurtosis & 0.36 & 6.45 & 15.59 & 2.88 \\ Skewness & 0.14 & 2.12 & 2.45 & 1.79 \\ Range & 440 & 1.86 & 170.08 & 12 \\ Minimum & 160 & 0.23 & 132 & 0 \\ Range & 420 & 1.86 & 170.08 & 12 \\ Minimum & 180 & 0.23 & 132 & 0 \\ Maximum & 600 & 2.1 & 302.08 & 12 \\ Sum & 95,823.1 & 242.79 & 43,431.75 & 606.43 \\ Count & 250 & 250 & 250 & 250 \\ \hline \hline \end{tabular}
\end{table}