# LASSO REGRESSION

LASSO stands for Least Absolute Selection Shrinkage Operator, and shrinkage is specified as a parameter constraint. The purpose of lasso regression is to find the collection of predictors with the lowest prediction error for a quantitative response variable. The approach works by placing a restriction on the model parameters, causing the regression coefficients for some variables to decrease toward zero.

Variables having a regression coefficient of 0 after shrinking are removed from the model. Variables with non-zero regression coefficients have the strongest relationship with the response variable. Explanatory variables might be quantitative, categorical, or a combination of the two. This lasso regression analysis is essentially a shrinkage and variable selection strategy that assists analysts in determining which predictors are most relevant.

# THE MAIN CODE OF LASSO REGRESSION

# Full Code Exercise of Lasso Regression

In this part, we will show how to apply the Lasso Regression technique. First, let's look at a typical regression dataset. We'll be using the data from technical spec of cars. The dataset is downloaded from UCI Machine Learning Repositor.

The dataset can be downloaded here.

`from sklearn.linear_model import Lasso`