Generate synthetic nonlinear data. Transform the input features to higher-degree polynomials. Apply Ridge Regression (L2 regularization) to prevent overfitting. Visualize training and testing sets ...

Linear regression is a basic regression technique that establishes a linear relationship between the dependent variable (profit) and independent variables (R&D Spend, Administration, and Marketing ...

A regression problem ... used for saving a trained model. Notice the name of the root scikit module is sklearn rather than scikit. import numpy as np from sklearn.kernel_ridge import KernelRidge ...

This research compares five regression models, namely Multiple Linear Regression, Ridge, Lasso, Random Forest, and Polynomial Regression, to predict house prices in West Nusa Tenggara Province. These ...

Our findings reveal significant differences in the accuracy and robustness of these models. Linear and polynomial regression provide a baseline for comparison, while regularization methods such as ...