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LASSO regression is another sort of linear regression; it makes use of shrinkage. Its data values are reduce in measurement in the route of a valuable point like mean. The system encourages easy and sparse models; the acronym “LASSO” is for the Least Absolute Shrinkage and choice Operator [4, 5]. L1 regularization is done with the aid of LASSO regression; it gives a sanction, which is equal to the absolute fee of the coefficients significance. This form of regularization outcomes in sparse fashions with much less coefficients; many coefficients can emerge as zero and are eliminated from the model. Huge penalties result in values close by to zero, which produces less difficult fashions. On the opposite, L2 regularization (e.g., ridge regression) does not bring about the exception of the coefficients or sparse models. This makes the LASSO higher to elucidate than the ridge.