The scikit-learn library in Python implements Linear Regression through the LinearRegression class. This class allows us to fit a linear model to a dataset, predict new values, and evaluate the model's performance. To use the LinearRegression class, we first need to import it from sklearn.linear_model module.

And here is the corresponding plot: Now the trend line looks perfectly straight and the linear model fits very well. Another very popular python library for linear regression is scikit-learn (usually abbreviated as sklearn).

To use the LinearRegression class, we first need to import it from sklearn.linear_model module. We can then create an instance of the class and call its fit method to train the model on a dataset. Finally, we can use the prediction method to generate predictions on new data.

Scikit-Learn has a plethora of model types we can easily import and train, LinearRegression being one of them: Now, we need to fit the line to our data, we will do that by using the .fit() method along with our X_train and y_train data: If no errors are thrown - the regressor found the best fitting line!