1. What is linear Regression?   Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting.   Linear regression is a quiet and the simplest statistical regression method used for predictive analysis in machine learning. Linear regression shows the linear relationship between the independent(predictor) variable i.e. X-axis and the dependent(output) variable i.e. Y-axis, called linear regression .  If there is a single input variable  X (independent variable), such linear regression is called  simple T o calculate best-fit line linear regression uses a traditional slope-intercept form which is given below,                                     ...

In this blog I will be writing about Linear Regression, that is, what is linear regression, finding best fit regression line, checking goodness of fit etc. Introduction Models use machine learning algorithms, during which the machine learns from the data just like humans  learn from their experiences. Machine learning models can be broadly divided into two categories  based on the learning algorithm which can further be classified based on the task performed and  t he nature of the output. 1.  Supervised learning methods:  It contains past data with labels which are then used for building the model. Regression : The output variable to be predicted is  continuous  in nature, e.g. scores of a student, diam ond prices, etc. Classification : The output variable to be predicted is  categorical  in nature, e.g.classifying incoming emails as spam or ham, Yes or No, True or False, 0 or 1. 2.  Unsupervised learning methods:  It contains no p...