The training data for these metal ion batteries has been retrieved from the materials project database using pymatgen (Python Materials Genomics. The data set consists of 69.54% of Li, 22.4% of Na and 8.06% of K ion battery data. So, from the amount of data, there is a high chance of getting the target variable that is capacity to mimic the electrodes used for the LIBs since the contribution in the overall data from LIBs is very high compared to other two metal ion batteries. The overall known dataset is divided in training set and validation set. The training set has been used to train the ML model whereas the validation set was used to validate the performance of our machine learning models. The validation set is composed of 20% of total data and rest of the data used for training. The training set remains unique for all the ML model used and same is true for the validation set. The distribution of the training set of metal ion batteries electrode materials has been shown in Figure . For each electrode materials we have generated 196 unique elemental descriptors depending on chemical formula of individual electrode materials using choice-based feature vectorization. Along with these descriptors other structural parameters like lattice parameters (a, b, c), lattice angles (  ), volume of void etc. have been also considered, so that these descriptors can represent each electrode materials uniquely. In order to specify the intercalated ion, we have also included some elemental properties of intercalated ions like ionic radius, ionization energy, heat of atomization, etc. After the generation of descriptors scaling has performed on each descriptor except on target variable using StandardScaler module of python package to bring down the all the descriptors in the same scale so that we can avoid the biasness of our data set based on the magnitude of each descriptor of electrode materials by machine. Further, whether all features are important or not for the desired target variable, we have performed Lasso Regression. With the help of Lasso regression, we have calculated the feature importance of each individual descriptors and based on the magnitude of the feature importance we have screened the features. We have only selected those features having features greater than zero and rest of the features have been eliminated for the fitting of ML models. The mathematical expression of LASSO Regression is given as