Since we are predicting continues value via ML, therefore it's belongs to a regression problem and to counter that we have used Support vector regression (SVR) a sub part of SVM. C the penalty term and gamma are two important parameters which needs to be optimize before fittings of SVR model. We have also tested our training data fixed in different kernel function of SVR like linear function, radial basis function (RBF) and polynomial function and then select the most optimized kernel considering the loss function as mean absolute percentage error (MAPE). As we have discussed before the large contribution of LIBs in overall dataset might result in a mimic of Li data, we divided our data set in three sets Na+K, Li, and Li+Na+K dataset and then assessed the performance of ML models. The training set folded in 10-fold and for each cross-validation test 9fold used for the training whether the remain 1-fold used for the assessment of the model performance in terms of MAPE as loss function. We have also calculated the standard deviation for each 10-fold cross validation set. By the cross validation test we have tried to be sampling our data in such a way so that machine can't overfit certain data which is the main reason behind a good train score but a very bad test score. The testing of different kernels are shown in Table .