The electronic properties also play important role for electrode materials. To understand how the capacity of electrode materials change with the change in the electronic properties of those materials, we have calculated the average valence s, d, f electrons by taking the average of the valence electrons of the constituent atoms of electrode materials and plotted the contribution of valence electrons with capacity change. From the Figure , it is observed that though the average capacity falling in the range of higher s electron contribution, completely opposite trend observed in case of d orbital valence electron. So, in general there are large number of electrodes having high s orbital valence electrons and low d orbital valence electrons but there is no such trend is observed for the f orbital valence electron as the capacity range changes from low magnitude to high magnitude at constant f orbital valence electron. The reason may be most of the electrode consists of transition metals having high number of d electrons with filled s orbital whereas the f orbital remains almost empty and even though for some electrode materials there exist some f electrons since they are deeply seated might not be as effective as the s or d valence electrons. In the Figure , we have shown how the distribution of capacity changes with the different lattice parameters (a, b, c) and lattice angle (gamma) of electrode materials. From the plot it is observed, the effect of lattice parameter a and c is very prominent compared to the lattice parameter b. This may be because for most of the electrode materials in one direction remain fixed like b here and the other two direction a and c has changed for the intercalation of metal ions. Thus, we can say most of the electrode varies in two directions keeping the length in one direction constant. In case of gamma, though it changes rapidly but the capacity changes almost in the same range for low and high value of gamma. We have also plotted the box plot between Gravimetric capacity and the ionic radius of intercalated ions (Figure ) to get a visualization of average capacity range of Li, Na, and K ion battery. The mid-line in the box plot is the median, lower line outside the box is the minimum range and the upper line outside the box is the maximum range of our property of interest. Therefore, in the above plot we can see the average capacity for Li ion battery is higher followed by Na and K ion batteries and it is expected since we know with decrease in ionic radius the number of ions intercalated within an electrode material will be higher and so the capacity of that electrode for that particular ion. Since the target variable capacity varies rapidly with the slide change in the electrode materials so in order to understand the distribution of the electrode materials across the capacity, we have plotted the range of % electrodes across per 100 mAh/g intervals of capacity. The distribution of capacity has shown in Figure . From the Figure , we can observe, more than 44% electrodes are there having capacity 101 to 200 mAh/g, around 35% electrodes having capacity 1 to 100 mAh/g and 15% electrodes having capacity 200 to 299 mAh/g. % of electrodes having capacity greater than 299 mAh/g is very less compared to the first three group of electrodes materials. Therefore, the sampling of target variable is not homogeneous rather highly heterogeneous, and this may be cause in a misinterpretation of the nature data by machine as there is a high chance of overestimate or remembering capacity data having range from 1 to 299. To avoid this overestimation, we have fit the ML models in three different data set Na+K, Li, and Li+Na+K which has been discussed later. Further, the dimensionality reduction was done by the principal component analysis (PCA) on the selected descriptors to avoid the dimensionality curse. The PCA analysis shrink the dimension of the dataset by X %. We have assessed the performance of different ML models with PCA data and Without PCA data and it has been observed that the performance of ML models does not vary by large margin. However, since the dimension of the data getting reduced by the PCA method, we go ahead with the PCA data. The dimensionality reduction plot by PCA has been shown in Figure .