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In the previous tutorial, we have learned about Simple Linear Regression, where a single Independent/Predictor(X) variable is used to model the response variable (Y). But there may be various cases in which the response variable is affected by more than one predictor variable; for such cases, the Multiple Linear Regression algorithm is used. Moreover, Multiple Linear Regression is an extension of Simple Linear regression as it takes more than one predictor variable to predict the response variable. We can define it as: Multiple Linear Regression is one of the important regression algorithms which models the linear relationship between a single dependent continuous variable and more than one independent variable. Example: Prediction of CO2 emission based on engine size and number of cylinders in a car. Some key points about MLR: For MLR, the dependent or target variable(Y) must be the continuous/real, but the predictor or independent variable may be of continuous or categor...

Simple Linear Regression in Machine Learning Simple Linear Regression is a type of Regression algorithms that models the relationship between a dependent variable and a single independent variable. The relationship shown by a Simple Linear Regression model is linear or a sloped straight line, hence it is called Simple Linear Regression. The key point in Simple Linear Regression is that the dependent variable must be a continuous/real value. However, the independent variable can be measured on continuous or categorical values. Simple Linear Regression algorithm has mainly two objectives. Model the relationship between the two variables. Such as the relationship between Income and expenditure, experience and Salary, etc. Forecasting new observations.   Such as Weather forecasting according to temperature, Revenue of a company according to the investments in a year, etc. Simple Linear Regression Model: The Simple Linear Regression model can be represented using the be...