Machine Learning Algorithms : Python Vs. R

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)