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Author: Shimanto

Polynomial Regression with Python & R/Polynomial Regression with Python.png

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+Position,Level,Salary
+Business Analyst,1,45000
+Junior Consultant,2,50000
+Senior Consultant,3,60000
+Manager,4,80000
+Country Manager,5,110000
+Region Manager,6,150000
+Partner,7,200000
+Senior Partner,8,300000
+C-level,9,500000
+CEO,10,1000000

Polynomial Regression with Python & R/Polynomial regression with Python.ipynb

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+# Polynomial Regression
+
+# Importing the dataset
+dataset = read.csv('Position_Salaries.csv')
+dataset = dataset[2:3]
+
+# Splitting the dataset into the Training set and Test set
+# # install.packages('caTools')
+# library(caTools)
+# set.seed(123)
+# split = sample.split(dataset$Salary, SplitRatio = 2/3)
+# training_set = subset(dataset, split == TRUE)
+# test_set = subset(dataset, split == FALSE)
+
+# Feature Scaling
+# training_set = scale(training_set)
+# test_set = scale(test_set)
+
+# Fitting Linear Regression to the dataset
+lin_reg = lm(formula = Salary ~ .,
+             data = dataset)
+
+# Fitting Polynomial Regression to the dataset
+dataset$Level2 = dataset$Level^2
+dataset$Level3 = dataset$Level^3
+dataset$Level4 = dataset$Level^4
+poly_reg = lm(formula = Salary ~ .,
+              data = dataset)
+
+# Visualising the Linear Regression results
+# install.packages('ggplot2')
+library(ggplot2)
+ggplot() +
+  geom_point(aes(x = dataset$Level, y = dataset$Salary),
+             colour = 'red') +
+  geom_line(aes(x = dataset$Level, y = predict(lin_reg, newdata = dataset)),
+            colour = 'blue') +
+  ggtitle('Truth or Bluff (Linear Regression)') +
+  xlab('Level') +
+  ylab('Salary')
+
+# Visualising the Polynomial Regression results
+# install.packages('ggplot2')
+library(ggplot2)
+ggplot() +
+  geom_point(aes(x = dataset$Level, y = dataset$Salary),
+             colour = 'red') +
+  geom_line(aes(x = dataset$Level, y = predict(poly_reg, newdata = dataset)),
+            colour = 'blue') +
+  ggtitle('Truth or Bluff (Polynomial Regression)') +
+  xlab('Level') +
+  ylab('Salary')
+
+# Visualising the Regression Model results (for higher resolution and smoother curve)
+# install.packages('ggplot2')
+library(ggplot2)
+x_grid = seq(min(dataset$Level), max(dataset$Level), 0.1)
+ggplot() +
+  geom_point(aes(x = dataset$Level, y = dataset$Salary),
+             colour = 'red') +
+  geom_line(aes(x = x_grid, y = predict(poly_reg,
+                                        newdata = data.frame(Level = x_grid,
+                                                             Level2 = x_grid^2,
+                                                             Level3 = x_grid^3,
+                                                             Level4 = x_grid^4))),
+            colour = 'blue') +
+  ggtitle('Truth or Bluff (Polynomial Regression)') +
+  xlab('Level') +
+  ylab('Salary')
+
+# Predicting a new result with Linear Regression
+predict(lin_reg, data.frame(Level = 6.5))
+
+# Predicting a new result with Polynomial Regression
+predict(poly_reg, data.frame(Level = 6.5,
+                             Level2 = 6.5^2,
+                             Level3 = 6.5^3,
+                             Level4 = 6.5^4))

Polynomial Regression with Python & R/Position_Salaries.csv

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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.cross_validation 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)"""
+
+
+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))