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- # Polynomial Regression
- # Importing the libraries
- import numpy as np
- import matplotlib.pyplot as plt
- import pandas as pd
- # Importing the dataset
- dataset = pd.read_csv('Position_Salaries.csv')
- X = dataset.iloc[:, 1:2].values
- y = dataset.iloc[:, 2].values
- # Splitting the dataset into the Training set and Test set
- """from sklearn.model_selection import train_test_split
- X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"""
- # Feature Scaling
- """from sklearn.preprocessing import StandardScaler
- sc_X = StandardScaler()
- X_train = sc_X.fit_transform(X_train)
- X_test = sc_X.transform(X_test)"""
- # Fitting Linear Regression to the dataset
- from sklearn.linear_model import LinearRegression
- lin_reg = LinearRegression()
- lin_reg.fit(X, y)
- # Fitting Polynomial Regression to the dataset
- from sklearn.preprocessing import PolynomialFeatures
- poly_reg = PolynomialFeatures(degree = 4)
- X_poly = poly_reg.fit_transform(X)
- poly_reg.fit(X_poly, y)
- lin_reg_2 = LinearRegression()
- lin_reg_2.fit(X_poly, y)
- # Visualising the Linear Regression results
- plt.scatter(X, y, color = 'red')
- plt.plot(X, lin_reg.predict(X), color = 'blue')
- plt.title('Truth or Bluff (Linear Regression)')
- plt.xlabel('Position level')
- plt.ylabel('Salary')
- plt.show()
- # Visualising the Polynomial Regression results
- plt.scatter(X, y, color = 'red')
- plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')
- plt.title('Truth or Bluff (Polynomial Regression)')
- plt.xlabel('Position level')
- plt.ylabel('Salary')
- plt.show()
- # Visualising the Polynomial Regression results (for higher resolution and smoother curve)
- X_grid = np.arange(min(X), max(X), 0.1)
- X_grid = X_grid.reshape((len(X_grid), 1))
- plt.scatter(X, y, color = 'red')
- plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')
- plt.title('Truth or Bluff (Polynomial Regression)')
- plt.xlabel('Position level')
- plt.ylabel('Salary')
- plt.show()
- # Predicting a new result with Linear Regression
- lin_reg.predict([[6.5]])
- # Predicting a new result with Polynomial Regression
- lin_reg_2.predict(poly_reg.fit_transform([[6.5]]))
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