R – squared

R squared, also known as the coefficient of determination, is a statistical measure that determines how well the regression line fits the values. In other words it explains the variability between independent and dependent variables. The basic idea of R square is to provide information about the relationship between independent and dependent variable. Therefore higher the value of R² greater the relationship between independent and dependent variables.

R squared can be calculated using either statsmodel libthe rary or Scikit learn library. Let us look at each of them

Output:

R-Squared : 0.9838496264009963

I,n the above code we have defined two numpy arrays. For statistical calculations we have imported statsmodel library. After that we specified a constant term and then used linear regression model to fit the independent values and the dependent values. The model analyses the values and establishes a relationship between the dependent and independent variable. Now to determine the strength of relationship we use squared to calculate the coefficient of determine action

Regression is a statistical technique used to establish a relationship between dependent and independent variables. It predicts a continuous set of values in a given range. The general equation of Regression is given by

• Here y is the dependent variable. It is the variable whose value changes when the independent values are changed
• x is the independent variable. Here y is dependent on x. It is to be noted that there can be more than one independent variable.
• m is the slope
• c is the y-intercept

There are different types of Regression: Linear Regression, Ridge Regression, Polynomial Regression, and Lasso Regression. Regression analysis involves the prediction of continuous values within a given range therefore we require evaluation metrics. Evaluation metrics help us to analyze the performance of the Machine Learning model. In Regression Analysis, we calculate how much the predicted values deviate from the actual values. There are different evaluation metrics for Regression Analysis like Mean Squared Error, Mean Absolute Error, R squared, etc.