Linear Regression
Using Python
#Import all necessary libraries like pandas,numpy etc.
from sklearn import linear_model
#Load Train and Test datasets
#Identify feature(s) and response variable(s) and values must be numeric and numpy arrays
X_train=input_variables_values_training_datasets
y_train=target_variables_values_training_datasets
x_test=input_variables_values_test_datasets
#Create linear regression object
linear = linear_model.LinearRegression ()
#Train the model using the training sets and check score
linear.fit(X_train, y_train)
linear.score(X_train, y_train)
#Equation coefficient and Intercept
print('Coefficient: In', linear.coef_)
print('Intercept: \n', linear.intercept_)
#Predict Output
predicted= linear.predict(x_test)
Using R
#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
X_train <- input_variables_values_training_datasets
y_train <- target_variables_values_training_datasets
x_test ‹- input variables values test datasets
x <-cbind(x_train,y_train)
#Train the model using the training sets and check score
linear <- lm(y_train ~ ., data = x)
summary (linear)
#Predict Output
predicted= predict (linear, x_test)
