Simple Linear Regression

Simple linear regression is a type of supervised learning where the model learns to predict a continuous output variable based on a single input variable by fitting a straight line to the data.

Multiple Linear Regression

Multiple linear regression is a type of supervised learning where the model predicts a continuous output variable based on multiple input variables by fitting a linear equation to the data.

Polynomial Linear Regression

Polynomial linear regression is a type of supervised learning where the model predicts a continuous output variable based on a polynomial relationship between the input variable(s) and the output variable, extending beyond straight lines to capture more complex patterns in the data.

Decision Tree

Decision tree regression is a supervised learning method used for predicting a continuous output variable by partitioning the input space into regions and predicting the average target value of the training instances in each region.

Random Forest Intuition (RFR)

Random forest regression is a supervised learning algorithm that builds multiple decision trees and averages their predictions to make a more accurate and robust prediction of a continuous output variable.

Support Vector Machine (SVR)

Support Vector Regression (SVR) is a supervised learning algorithm that predicts a continuous output variable by finding the best-fitting line (or hyperplane in higher dimensions) that maximizes the margin around the training data points within a specified margin of error.