SIMPLE LINEAR REGRESSION (SLR) AND MULTIPLE LINEAR REGRESSION (MLR)
1. Simple Linear Regression (SLR) The simple linear regression refers one independent variable to make a prediction X : The predictor (independent) variable Y : The target (dependent) variable b0 - the intercept b1 - the slope Import linear model from scikit-learn from sklearn.linear_model import LinearRegression lm = LinearRegression( ) X = X = df[['horsepower-mpg']] Y = df[['price']] lm.fit(X,Y) Yhat = lm.predict(X) 2. Multiple Linear Regression (MLR) The multiple linear regression refers multiple independent variable to make a prediction X : The predictor (independent) variable Y : The target (dependent) variable b0 - the intercept b1, b2, b3... - the slope Import linear model from scikit-learn from sklearn.linear_model import LinearRegression lm = LinearRegression( ) X = df[[''horsepower-mpg', 'curb-weight', 'engine-size', highway-mpg']] Y = df[['price']] lm.fit(X,Y) ...