Some statistical methods of data analysis like box plot for outlier detection, joint plot to understand the distribution of descriptors, heatmap for the correlation metrics, principal component analysis to reduce the dimension of our dataset have been utilized. We have evaluated the performance of considered machine learning models by compairing the mean absolute percentage error between training set and validation set in each case. Further, adopting kernel ridge regression we have predicted the capacity of unknown electrode materials for K-ion battery (Table ). Using the value of specific capacity, the number of intercalated K ions in the formula unit of the nonintercalated electrode material compounds have been calculated. DFT calculations have been performed for sample electrode materials to verify that our ML model can give similar results. Due to large range in output specific capacity values, the ML techniques are still not able to provide very accurate results and further improvements are expected in future. Thus, implementing ML approach is much more faster compared to the computationally demanding quantum mechanecial methods for quick screening of electrode materials which will help to guide the experiments for devoloping electrode materials for metal ion batteries.