Simple Linear Regression

Reference Model

A prototype model that displays the most basic performance that becomes a reference for the prediction model

Types:
- Classification = Mode of the Target

Regression = Mean of the Target

Time-Series Regression = The value of the prior time-stamp

Regression Line/Shape

Residual = The difference between predicted value and observed value

Error = The difference between predicted value and true value (of population)

Line of Regression = Residual Sum of Squares (RSS) -- the line that minimizes RSS (also called Sum of Squared Errors)

Least Squared Method = Used to find the slope/intercept of the linear regression

Variables:

x = independent variable/feature

y = dependent variable/target

Linear Regression Model using Scikit-learn

from sklearn.linear_model import Linear Regression

model = LinearRegression()

feature = [x]
target = [y]
X_train = df[feature]
y_train = df[target]

model.fit(X_train, y_train)

X_test = [test] #example
y_pred = model.predict(X_test)

y_pred

Coefficients:

Coefficient of Slope: model.coef_

Coefficient of Intercept: model.intercept_