A simple Linear Regression code
Speaking about Linear Regression model about this Repository
This repository contains an implementation of a linear regression model. Linear regression is a widely used statistical method for predicting continuous values. It works by finding the best line of fit for a set of training data points, which can then be used to make predictions on unseen data points. The implementation in this repository includes the ability to train the model using ordinary least squares and gradient descent methods, as well as the ability to evaluate the performance of the model using various metrics such as mean squared error and R-squared.
Theory:
Linear regression is a statistical approach that models the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the input variables and the output variable. The goal of linear regression is to find the line of best fit that accurately represents the relationship between the input variables and the output variable. This line is represented by a linear equation, with coefficients that are estimated based on the training data. The line of best fit can be used to make predictions on new data points by plugging in the values of the input variables and solving for the corresponding output variable. Linear regression can be performed using ordinary least squares (OLS) or gradient descent methods.