Welcome! In this lesson, we'll dive deep into Lasso Regression, a powerful technique for making predictions and reducing overfitting by adding a penalty to the regression model.

Regression models help us understand relationships between variables and make predictions. We'll talk about Lasso Regression and show how it works using simple Python code. By the end of the lesson, you'll know how to use it in your projects.

Imagine you're trying to predict the price of a house based on factors like area, number of bedrooms, and location. Including too many unnecessary factors can make predictions less accurate. Regularization helps by penalizing unnecessary factors.

Lasso Regression stands for "Least Absolute Shrinkage and Selection Operator." It adds a penalty for large coefficients, shrinking some to zero, selecting only the most important features.

We'll use numpy for numerical operations, LinearRegression and Lasso from sklearn.linear_model for our regression models, and load_diabetes to load a sample dataset. We'll also use train_test_split to divide the dataset into training and testing sets.

import numpy as np
from sklearn.linear_model import Lasso, LinearRegression
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

# Load and split dataset
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)