Lasso Regression Analysis
LASSO Regression is used to reduce the model overfitting. It increase the bias and reduce the variance in model.
Full form of LASSO is Least Absolute Shrinkage and Selection Operator. So the model itself is capable of feature selection. It shrinks the less important features and remove the features which are not important by making the value of features zero.
LASSO regression also know as L1 regularization. It takes the absolute value of variable and remove variables which don't much contribute to the model.
#Conclusion
We can clearly see the prediction accuracy is stable when we used both the dataset When we add more data the prediction error decreases. The R-square values of .74 and .70 indicate training and test model have variance of .74 and .70
