Machine Learning

Decision Tree Regression is a machine learning technique used for predicting continuous numeric values. It works by partitioning the data into smaller subsets based on the features and recursively splitting those subsets to create a tree-like structure. In Python, you can implement Decision Tree Regression using Scikit-Learn. Here’s a step-by-step …

Support Vector Regression (SVR) is a regression technique that uses support vector machines to predict continuous numeric values. It’s particularly useful when dealing with non-linear and complex datasets. In Python, you can implement SVR using libraries such as Scikit-Learn. Below is a step-by-step guide on how to perform SVR in …

Polynomial linear regression is a variation of linear regression where the relationship between the independent variable(s) and the dependent variable is modeled as an nth-degree polynomial. In Python, you can perform polynomial linear regression using the scikit-learn library. Here’s how you can do it: Import Necessary Libraries: import numpy as …

Multiple linear regression is a statistical method used to model the relationship between multiple independent variables (predictors) and a dependent variable (response) by fitting a linear equation to the observed data. In Python, the scikit-learn library provides a straightforward way to perform multiple linear regression. Here’s an overview of how …

Understanding Linear Regression in Machine Learning using Python Language Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value. Example: Let’s start with an example — suppose we have a dataset with information about the area of a house (in square feet) …