Simple Linear Regression is a linear regression model where we have one dependent and one independent variable.
We need to predict values for the dependent variable as a function of the independent variable.
Formula for Simple Linear Regression:
We need to predict values for the dependent variable as a function of the independent variable.
Formula for Simple Linear Regression:
where
y is the dependent Variable
is the independent Variable
is the coefficient (connector between dependent and Independent)
is the Constant
Code Snippet:
from sklearn.linear_model import LinearRegression
simple_regressor = LinearRegression()
simple_regressor.fit(X_train, y_train)
# Prediction
y_pred = simple_regressor.predict(X_test)
Plot
plt.scatter(X_test, y_test, color = 'red')
plt.plot(X_train, simple_regressor.predict(X_train), color = 'blue')
plt.show()
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