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()