How to run an OLS regression with a Pandas DataFrame in Python

An Ordinary Least Squares (OLS) regression is a method for estimating the unknown parameters in a regression model by using a set of training and target values.

Solution for How to run an OLS regression with a Pandas DataFrame in Python : You can use sklearn.linear_model.LinearRegression() and LinearRegression.fit() to run an OLS regression Create a regression model by calling sklearn.linear_model.LinearRegression(). Use pandas indexing to define a set of training and target values, and call LinearRegression.fit(X, Y) with X as the training data and Y as the target values to run an OLS regression. Return the estimations for unknown parameters by accessing the coef_ attribute of the regression model.


how-to-run-an-ols-regression-with-a-pandas-dataframe-in-python