![]() ![]() We will partially differentiate the quantity □0 with respect to □0 and equate to zero, we will get □0. So we have to minimize the quantity with respect to □0, □1, □2, …. We should build an equation so that MSE is very low. The Error can be measured by the quantity MSE (Mean Squared Error). The whole instruments of linear regression is built around predicting the value or getting the values of □0, □1, □2, … □□ are variables that we used to predict □. Since we are using linear regression, the equation is a linear equation. It is a supervised learning task where output is a continuous value. So here, we are trying to use the relationship pattern to predict the value of a variable. ![]() If there is only one independent variable, it is a simple linear regression if there are multiple independent variables, it is a multiple linear regression. ![]() This technique is used to predict the value of an independent variable based on the value of other dependent variables. Before building a model, let us know a little about what is Linear Regression? So Linear regression is a statistical method used to explore the correlation between two continuous quantitative variables. With latest versions of Excel, it does not take more than a minute to build a model. Can we use excel to do the same? Yes we can use different excel Add-ins or tools to do this. In data science, we build regression models to see how well one variable can be predicted based on one or more variables. ![]()
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