pep8 variable naming convention

model.py

@@ -14,34 +14,34 @@ def import_data_and_split():
  # Split the dataset into the training set and test set
  # We're splitting the data in 1/3, so out of 30 rows, 20 rows will go into the training set,
  # and 10 rows will go into the testing set.
- xTrain, xtest, y_Train, yTest = train_test_split(x, y, test_size =1 / 3, random_state = 0)
+ x_train, x_test, y_train, y_test = train_test_split(x, y, test_size =1 / 3, random_state = 0)
 
- return (xTrain, xtest, y_Train, yTest)
+ return (x_train, x_test, y_train, y_test)
 
 
 
 if __name__ == '__main__':
- xTrain, xtest, y_Train, yTest = import_data_and_split()
+ x_train, x_test, y_train, y_test = import_data_and_split()
 
  # Creating a LinearRegression object and fitting it
  # on our trainging set.
- linearRegressor = LinearRegression()
- linearRegressor.fit(xTrain, y_Train)
+ linear_regressor = LinearRegression()
+ linear_regressor.fit(x_train, y_train)
 
  # Predicting the test set results
- yPrediction = linearRegressor.predict(xtest)
+ y_prediction = linear_regressor.predict(x_test)
 
  # Visualising the training set results
- plot.scatter(xTrain, y_Train, color ='red')
- plot.plot(xTrain, linearRegressor.predict(xTrain), color = 'blue')
+ plot.scatter(x_train, y_train, color ='red')
+ plot.plot(x_train, linear_regressor.predict(x_train), color ='blue')
  plot.title('Salary vs Experience (Training set)')
  plot.xlabel('Years of Experience')
  plot.ylabel('Salary')
  plot.show()
 
  # Visualising the test set results
- plot.scatter(xtest, yTest, color ='red')
- plot.plot(xtest, linearRegressor.predict(xtest), color ='blue')
+ plot.scatter(x_test, y_test, color ='red')
+ plot.plot(x_test, linear_regressor.predict(x_test), color ='blue')
  plot.title('Salary vs Experience (Test set)')
  plot.xlabel('Years of Experience')
  plot.ylabel('Salary')