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)
Yhat = lm.predict(X)