Lasso regression

Lasso Regression is a statistical technique used to analyze data and make predictions. It enhances standard regression by adding a penalty for including too many variables in the model. This helps to simplify the model by forcing some variables' coefficients to zero, effectively excluding them. As a result, Lasso focuses on the most important factors, making it easier to interpret the results and improving prediction accuracy. It's particularly useful when dealing with data that has many predictors, helping to prevent overfitting, where a model becomes too complex and performs poorly on new data.