def visualisingPolynomialRegressionInHighResolution(): X = readIndepentDataset() y = readDependentDataset() polynomialRegressionModel = readPolynomialRegressionModel() polynomialRegressionModelForVisualization = readPolynomialRegressionModelForVisualization( ) X_grid = np.arange(min(X), max(X), 0.1) X_grid = X_grid.reshape((len(X_grid), 1)) # Visualising the Polynomial Regression results plt.scatter(X, y, color='red') plt.plot( X_grid, polynomialRegressionModel.predict( polynomialRegressionModelForVisualization.fit_transform(X_grid)), color='blue') plt.title('Truth or Bluff (Polynomial Regression)') plt.xlabel('Position level') plt.ylabel('Salary') plt.savefig("polynomial_regression_trainingsetresult_high_resolution.png") plt.show()
def trainLinearRegressionModel(): X = readIndepentDataset() y = readDependentDataset() # Fitting Linear Regression to the dataset linearRegression = LinearRegression() linearRegression.fit(X, y) saveLinearRegressionModel(linearRegression)
def visualisingTrainingSetResult(): X = readIndepentDataset() y = readDependentDataset() linearRegressionModel = readLinearRegressionModel() # Visualising the Linear Regression results plt.scatter(X, y, color="red") plt.plot(X, linearRegressionModel.predict(X), color="blue") plt.title("Truth or Bluff (Linear Regression)") plt.xlabel("Position level") plt.ylabel("Salary") plt.savefig("linear_regression_trainingsetresult.png") plt.show()
def trainPolynomialRegressionModel(): X = readIndepentDataset() y = readDependentDataset() # Fitting Polynomial Regression to the dataset polynomialFeatures = PolynomialFeatures(degree=4) X_Polynomial = polynomialFeatures.fit_transform(X) polynomialFeatures.fit(X_Polynomial, y) savePolynomialRegressionModelForVisualization(polynomialFeatures) polynomialLinearRegression = LinearRegression() polynomialLinearRegression.fit(X_Polynomial, y) savePolynomialRegressionModel(polynomialLinearRegression)
def visualisingTrainingSetResultForPolynomialRegression(): X = readIndepentDataset() y = readDependentDataset() polynomialRegressionModel = readPolynomialRegressionModel() polynomialRegressionModelForVisualization = readPolynomialRegressionModelForVisualization( ) # Visualising the Polynomial Regression results plt.scatter(X, y, color='red') plt.plot(X, polynomialRegressionModel.predict( polynomialRegressionModelForVisualization.fit_transform(X)), color='blue') plt.title('Truth or Bluff (Polynomial Regression)') plt.xlabel('Position level') plt.ylabel('Salary') plt.savefig("polynomial_regression_trainingsetresult.png") plt.show()